Ethereum

Mastering Ethereum: Building Smart Contracts and DApps
by Andreas M. Antonopoulos and Gavin Wood Ph. D.
Published 23 Dec 2018

Building Geth from source code To build Geth, change to the directory where the source code was downloaded and use the make command: $ cd go-ethereum $ make geth If all goes well, you will see the Go compiler building each component until it produces the geth executable: build/env.sh go run build/ci.go install ./cmd/geth >>> /usr/local/go/bin/go install -ldflags -X main.gitCommit=58a1e13e6dd7f52a1d... github.com/ethereum/go-ethereum/common/hexutil github.com/ethereum/go-ethereum/common/math github.com/ethereum/go-ethereum/crypto/sha3 github.com/ethereum/go-ethereum/rlp github.com/ethereum/go-ethereum/crypto/secp256k1 github.com/ethereum/go-ethereum/common [...] github.com/ethereum/go-ethereum/cmd/utils github.com/ethereum/go-ethereum/cmd/geth Done building. Run "build/bin/geth" to launch geth. $ Let’s make sure geth works without actually starting it running: $ ./build/bin/geth version Geth Version: 1.6.6-unstable Git Commit: 58a1e13e6dd7f52a1d5e67bee47d23fd6cfdee5c Architecture: amd64 Protocol Versions: [63 62] Network Id: 1 Go Version: go1.8.3 Operating System: linux [...]

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standards, Ethereum Improvement Proposals (EIPs) Turing completeness and, Ethereum and Turing Completeness wallet choices, Choosing an Ethereum Wallet web3 and, The Third Age of the Internet Ethereum Classic (ETC)Emerald Wallet and, Choosing an Ethereum Wallet Ethereum compared to, Ethereum and Ethereum Classic origins, Ethereum Classic (ETC), The DAO Hard Fork Ethereum Improvement Proposals (see EIP entries) Ethereum Modification (EMOD), Other Notable Ethereum Forks Ethereum Name Service (see ENS) Ethereum Virtual Machine (see EVM) EthereumFog (ETF), Other Notable Ethereum Forks EthereumJS, Raw Transaction Creation and Signing, EthereumJS EthereumJS helpeth, Inter Exchange Client Address Protocol, EthereumJS helpeth: A Command-Line Utility EtherInc (ETI), Other Notable Ethereum Forks EtherJar, EtherJar Etherpot smart contract lottery, Real-World Example: Etherpot and King of the Ether ethers.js, ethers.js EtherZero (ETZ), Other Notable Ethereum Forks ethpm project, Contract Libraries Ethstick contract, Real-World Example: Ethstick ETI (EtherInc), Other Notable Ethereum Forks eventscatching, Catching events defined, Quick Glossary Solidity, Events-Catching events EVM (Ethereum Virtual Machine), The Ethereum Virtual Machine-Conclusionsabout, What Is the EVM?

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-What This Book Will Teach You and EVM, Introducing the World Computer as general-purpose blockchain, Ethereum: A General-Purpose Blockchain basics, Ethereum Basics-Conclusions birth of, The Birth of Ethereum Bitcoin compared to, Compared to Bitcoin blockchain components, Components of a Blockchain, Ethereum’s Components clients (see clients, Ethereum) control and responsibility, Control and Responsibility-Control and Responsibility currency units, Ether Currency Units DApps and, From General-Purpose Blockchains to Decentralized Applications (DApps) development culture, Ethereum’s Development Culture EIPs, Ethereum Improvement Proposals (EIPs) EOAs and contracts, Externally Owned Accounts (EOAs) and Contracts Ethereum Classic compared to, Ethereum and Ethereum Classic fork history, Ethereum Fork History-Other Notable Ethereum Forks four stages of development, Ethereum’s Four Stages of Development MetaMask basics, Getting Started with MetaMask-Exploring the Transaction History of an Address purpose of, Compared to Bitcoin reasons to learn, Why Learn Ethereum? standards, Ethereum Improvement Proposals (EIPs) Turing completeness and, Ethereum and Turing Completeness wallet choices, Choosing an Ethereum Wallet web3 and, The Third Age of the Internet Ethereum Classic (ETC)Emerald Wallet and, Choosing an Ethereum Wallet Ethereum compared to, Ethereum and Ethereum Classic origins, Ethereum Classic (ETC), The DAO Hard Fork Ethereum Improvement Proposals (see EIP entries) Ethereum Modification (EMOD), Other Notable Ethereum Forks Ethereum Name Service (see ENS) Ethereum Virtual Machine (see EVM) EthereumFog (ETF), Other Notable Ethereum Forks EthereumJS, Raw Transaction Creation and Signing, EthereumJS EthereumJS helpeth, Inter Exchange Client Address Protocol, EthereumJS helpeth: A Command-Line Utility EtherInc (ETI), Other Notable Ethereum Forks EtherJar, EtherJar Etherpot smart contract lottery, Real-World Example: Etherpot and King of the Ether ethers.js, ethers.js EtherZero (ETZ), Other Notable Ethereum Forks ethpm project, Contract Libraries Ethstick contract, Real-World Example: Ethstick ETI (EtherInc), Other Notable Ethereum Forks eventscatching, Catching events defined, Quick Glossary Solidity, Events-Catching events EVM (Ethereum Virtual Machine), The Ethereum Virtual Machine-Conclusionsabout, What Is the EVM?

The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze
by Laura Shin
Published 22 Feb 2022

Yoichi Hirai (@pirapira), “Remove the modulo 2^{256} effect in the memory size computation #185,” GitHub, September 9, 2016, https://github.com/ethereum/yellowpaper/pull/185, “Fix mistakes in DELEGATECALL semantics #187,” September 29, 2016, GitHub, https://github.com/ethereum/yellowpaper/pull/187, and “Nitpicking equation (100) #188,” GitHub, September 29, 2016, https://github.com/ethereum/yellowpaper/pull/188. 17. Vitalik Buterin, “Olympic: Frontier Pre-Release,” Ethereum Foundation Blog, May 9, 2015, https://blog.ethereum.org/2015/05/09/olympic-frontier-pre-release. 18. Gavin Wood, “Another Ethereum ÐEV Update,” Ethereum Foundation Blog via Wayback Machine, June 15, 2015, https://web.archive.org/web/20150629033357/https://blog.ethereum.org/2015/06/15/another-ethereum-dξv-update. 19. “What Is Ether?,” Ethereum.org via Wayback Machine, August 7, 2015, https://web.archive.org/web/20150807141640/https://ethereum.org/ether; “Ethereum Frontier Release,” Ethereum.org via Wayback Machine, August 2, 2015, https://web.archive.org/web/20150802035735/https://www.ethereum.org. 20.

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Vitalik Buterin, “Ethereum Foundation Internal Update,” Ethereum Foundation Blog, January 7, 2016, https://blog.ethereum.org/2016/01/07/2394. 35. “About the Ethereum Foundation,” Ethereum.org/foundation via Wayback Machine, September 6, 2015, https://web.archive.org/web/20150906200827/https://www.ethereum.org/foundation; “About the Ethereum Foundation,” Ethereum.org/foundation via Wayback Machine, March 4, 2016, https://web.archive.org/web/20160304212822/https://www.ethereum.org/foundation. 36. June (@JUN_SYNQA), “Omise is now official Special advisor (Thomas) for Ethereum. #omise #blockchain #ethereum https://ethereum.org/foundation,” Twitter, March 8, 2016, https://twitter.com/jun_omise/status/707168442449661952. 37.

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Jeffrey Wilcke, “The Ethereum network is currently undergoing a DoS attack,” Ethereum Foundation Blog, September 22, 2016, https://blog.ethereum.org/2016/09/22/ethereum-network-currently-undergoing-dos-attack; Vitalik Buterin, “Transaction spam attack: Next Steps,” Ethereum Foundation Blog, September 22, 2016, https://blog.ethereum.org/2016/09/22/transaction-spam-attack-next-steps. 19. Wilcke, “The Ethereum network is currently undergoing a DoS attack.” 20. Others did as well: @TommyEconomis, “Why did Gavin leave the Ethereum team?,” Reddit, November 12, 2016, https://www.reddit.com/r/ethereum/comments/5clffg/why_did_gavin_leave_the_ethereum_team. 21.

The Infinite Machine: How an Army of Crypto-Hackers Is Building the Next Internet With Ethereum
by Camila Russo
Published 13 Jul 2020

“so damn smooth”: Hasu, “Ethereum Presale Dynamics Revisited,” Medium, April 27, 2018, https://medium.com/@hasufly/ethereum-presale-dynamics-revisited-c1b70ac38448. 16: Takeoff 1. “Paul,” he said: Paul is a pseudonym for the investor’s real name. 2. bugs or setbacks: Vitalik Buterin, “Olympic: Frontier Pre-Release,” Ethereum Foundation Blog, May 9, 2015, https://blog.ethereum.org/2015/05/09/olympic-frontier-pre-release/. 3. 4:26 p.m. in Berlin: Etherscan, Block #1, https://etherscan.io/block/1. 4. someone else wrote: Ethereum/go-Ethereum chat archives, Gitter, 2015, https://gitter.im/ethereum/go-ethereum/archives/2015/07/30. 17: The Shrinking Runway 1.

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“anyhow,” Vitalik wrote: “We just went from $530 to sub $500 in under a minute,” Reddit thread, 2014, https://www.reddit.com/r/Bitcoin/comments/2diqyy/we_just_went_from_530_to_sub_500_in_under_a/cjpxgln/. 2. “a little under two”: Vitalik Buterin, “The Evolution of Ethereum,” Ethereum Foundation Blog, September 28, 2015, https://blog.ethereum.org/2015/09/28/the-evolution-of-ethereum/. 3. Web 3 dream a reality: Gavin Wood, “The Last Blog Post,” Ethereum Foundation Blog, January 11, 2016, https://blog.ethereum.org/2016/01/11/last-blog-post/. 18: The First Dapps 1. Ethereum smart contracts: Jack Peterson, Joseph Krug, Micah Zoltu, Austin K. Williams, and Stephanie Alexander, “Augur: A Decentralized Oracle and Prediction Market Platform (v2.0),” November 1, 2019, https://www.augur.net/whitepaper.pdf. 2. he wrote on GitHub: Vitalik Buterin, “Standardized_Contract_APIs,” GitHub, June 23, 2015, https://github.com/ethereum/wiki/wiki/Standardized_Contract_APIs/499c882f3ec123537fc2fccd57eaa29e6032fe4a. 3. with their thoughts: Alex Van de Sande, “Let's talk about the coin standard,” Reddit, 2015, https://www.reddit.com/r/ethereum/comments/3n8fkn/lets_talk_about_the_coin_standard/. 4. issue being discussed: Fabian Vogelsteller, “ERC: Token standard #20,” GitHub, November 19, 2015, https://github.com/ethereum/EIPs/issues/20. 5. ended up implementing: Rune Christensen, “Introducing eDollar, the ultimate stablecoin built on Ethereum,” Reddit, 2015, https://www.reddit.com/r/ethereum/comments/30f98i/introducing_edollar_the_ultimate_stablecoin_built/. 6. companies to build on: Jeff Wilcke, “Homestead Release,” Ethereum Foundation Blog, February 29, 2016, https://blog.ethereum.org/2016/02/29/homestead-release/. 7. world had Ethereum: Gavin Andresen, “Bit-thereum,” GavinTech (blog), June 9, 2014, http://gavintech.blogspot.com/2014/06/bit-thereum.html. 19: The Magic Lock 1.

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lang=en. 21: The Fork 1. called “DAO Wars”: Peter Szilagyi, “DAO Wars: Your Voice on the Soft-Fork Dilemma,” Ethereum Foundation Blog, June 24, 2016, https://blog.ethereum.org/2016/06/24/dao-wars-youre-voice-soft-fork-dilemma/. 2. jam up the network: Tjaden Hess, River Keefer, and Emin Gun Sirer, “Ethereum’s DAO Wars Soft Fork Is a Potential DoS Vector,” Hacking, Distributed, June 28, 2016, http://hackingdistributed.com/2016/06/28/ethereum-soft-fork-dos-vector/. 3. running the show: Peter Szilagyi, “The Network Strikes Back (1.4.9),” GitHub, June 29, 2016, https://github.com/ethereum/go-ethereum/releases/tag/v1.4.9. 4. the software upgrade: Andreas M. Antonopoulos, “Ethereum Fork History,” Ethereum Book, GitHub, 2016, https://github.com/ethereumbook/ethereumbook/blob/3a21b603899e27427ca64fc5d3fd57af96a3cbd8/forks-history.asciidoc. 5. worked without a glitch: Alex Van de Sande (@avsa), “Watching the successful hard fork.

Mastering Blockchain: Unlocking the Power of Cryptocurrencies and Smart Contracts
by Lorne Lantz and Daniel Cawrey
Published 8 Dec 2020

different token types, Different Token Types, Different Token Types many different token types, Fungible and Nonfungible Tokens Ethereum Classic (ETC), Forking Ethereum and the creation of Ethereum Classic Ethereum Foundation, The Ethereum Foundation Ethereum Improvement Proposals (EIPs), Understanding Ethereum Requests for Comment Ethereum Naming Service, Naming Services Ethereum Requests for Comment (ERCs), Understanding Ethereum Requests for Comment-ERC-1155ERC-1155, ERC-1155 ERC-20, ERC-20 ERC-721, ERC-721-ERC-777 ERC-777, ERC-777 viewing all ERC standards online, Decentralized Exchange Contracts Ethereum Virtual Machine (EVM), The Ethereum Virtual Machine-Gas and Pricingauthoring a smart contract, Authoring a smart contract deploying a smart contract, Deploying a smart contract-Deploying a smart contract executing a smart contract, Executing a smart contract interacting with a smart contract, Interacting with a smart contract reading a smart contract, Reading a smart contract writing a smart contract, Writing a smart contract Etherscan.io, Block explorers exchange traded funds (ETFs), Derivatives exchange traded notes (ETNs), Derivatives exchanges, Exchanges, Evolution of the Price of Bitcoin, The Role of Exchanges-The Role of ExchangesAPIs and trading bots, Exchange APIs and Trading Bots-Market Aggregators as custodial wallets, Wallet Types: Custodial Versus Noncustodial basic types of, The Role of Exchanges Bitcoin addresses, The Evolution of Crypto Laundering custody over customer funds, Counterparty Risk custody setup, how it might work, Counterparty Risk decentralized, Decentralized Exchanges decentralized exchange contracts, Decentralized Exchange Contracts-Summary decentralized exchange on Omni Layer, How Omni Layer works decentralized versus centralized, Decentralized Versus Centralized Exchanges-Scalability hacking attacks on, Exchange Hacks-NiceHashMt.

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Gox-Bitfinex jurisdiction over cryptocurrency exchanges, Jurisdiction order types in cryptocurrency exchanges, The Role of Exchanges risks of, in cryptocurrency trading, Exchange Risk types of cryptocurrency exchanges, Jurisdiction externally owned account (EOA) wallets, Multisignature Contracts F Fabric (Hyperledger), Hyperledger FacebookLibra Association, The Libra Association Novi wallet, Novi false stake attacks, Proof-of-Stake faucets (Ethereum testnets), Authoring a smart contract Federal Reserve (see US Federal Reserve) federated sidechains, Sidechains fiat currencies, Electronic Systems and Trustblockchain-based assets pegged to, Stablecoins mint-based model, The Whitepaper file storage in web applications, Web 3.0 Financial Action Task Force (FATF), Travel Rule, The FATF and the Travel Rule Financial Crimes Enforcement Network (FinCEN), FinCEN Guidance and the Beginning of Regulation financial crisis of 2008, Electronic Systems and Trust, The 2008 Financial Crisis financial transactions, reliance on trust, Electronic Systems and Trust flash loans, Flash Loans-The Fulcrum Exploitcreating a smart contract for, Creating a Flash Loan Contract-Deploying the Contract deploying the smart contract, Deploying the Contract executing, Executing a Flash Loan-Executing a Flash Loan floatconfiguration 1, Float Configuration 1 configuration 2, Float Configuration 2 configuration 3, Float Configuration 3 timing and managing, Timing and Managing Float Force, Carl, Skirting the Laws forks, Understanding Forks-Replay attacks, Altcoins(see also altcoins) contentious hard forks, Contentious Hard Forks-Replay attacksfork of Bitcoin Cash into Bitcoin SV, The Bitcoin Cash Fork replay attacks vulnerability, Replay attacks different types of, Understanding Forks Ethereum Classic, The Ethereum Classic Fork, Forking Ethereum and the creation of Ethereum Classic fork choice rule in Ethereum 2.0, Ethereum Scaling other Ethereum forks, Other Ethereum forks in proof-of-stake networks, Proof-of-Stake fraud risk as seen by banking audits, Banking Risk Fulcrum attack, The Fulcrum Exploit full nodes (Libra), How the Libra Protocol Works funding amount, Lightning funding transactions, Funding transactions fungible tokens, Fungible and Nonfungible TokensERC-20 standard for, ERC-20 ERC-777 proposed standard for, ERC-777 futures, Derivatives G gambling, on Web 3.0, Web 3.0 gamingpermissioned ledger uses of blockchain, Gaming tracking virtual goods in games, ERC-1155 Garza, Homero Joshua, Skirting the Laws gas, Ether and GasETH Gas Station, Gas and Pricing list of gas prices by opcode, Gas and Pricing GAW Miners, Skirting the Laws GeistGeld, Altcoins Gemini, arbitrage trading on, Arbitrage Trading-Exchange APIs and Trading BotsAPI example, BTC/USD ticker call, Exchange APIs and Trading Bots Genesis block (Bitcoin), Achieving Consensus Gitcoin, Web 3.0 Gnosis, Tokenize Everything government-backed currencies (see fiat currencies) graphics processing units (GPUs), Mining Is About Incentives Grin, Mimblewimble, Beam, and Grin H halting problem, Ether and Gas hard forks, Understanding Forks hardware wallets, Wallet Type Variations, Wallets hash algorithms, Proof-of-Work hash power, Block discovery, How Omni Layer works hash rates, Proof-of-Work Hashcash, Hashcash hashes, Hashcash, Hashes-Custody: Who Holds the KeysBitcoin hash function, double SHA-256, The Merkle Root block, Storing Data in a Chain of Blocks, Block Hashes-Custody: Who Holds the Keys of information generated by transactions in Bitcoin, Introducing the Timestamp Server MD5 password hashes, Zero-Knowledge Proof Merkle root, The Merkle Root-The Merkle Root in proof-of-work cryptocurrency mining, Proof-of-Work public key hash on Bitcoin, Public and Private Keys in Cryptocurrency Systems in Satoshi Nakamoto's whitepaper, The Whitepaper health care, permissioned ledger implementations of blockchain, Health Care height number (block), Storing Data in a Chain of Blocks hex value arguments to smart contract calls, Custody and counterparty risk Honest validator framework, Ethereum Scaling Hong Kong, regulatory arbitrage, Hong Kong hot or cold storage wallets, Counterparty Risk hot wallets, Wallet Type Variations HotStuff algorithm, Borrowing from Existing Blockchains Hyperledger, Hyperledger I IBMIoT interaction by Watson and data storage in Blockchain Platform, Internet of Things toolset offering support for Hyperledger Fabric, Blockchain as a Service identifyverification of, Security Fundamentals identityand dangers of hacking, Identity and the Dangers of Hacking associating with Bitcoin addresses, The Evolution of Crypto Laundering identification services, Private Keys IDEX decentralized exchange, Decentralized Exchange Contracts illiquidity, signs of, Counterparty Risk infinite recursion, Forking Ethereum and the creation of Ethereum Classic information on blockchain industry, Information Infura, Interacting with Code initial coin offerings (ICOs), Mastercoin and Smart Contracts, Tokenize Everything, Initial Coin Offerings-Whitepaperas example of regulatory arbitrage, Initial Coin Offerings DAOs and, Decentralized Autonomous Organizations Ethereum, Tokenize Everything founder intentions, Founder Intentions funds collected into multisignature wallets, Multisignature Contracts illegal activities in, Skirting the Laws legal, regulatory, and other problems with, Tokenize Everything Mastercoin, Tokenize Everything motivations for founders versus venture-funding startups, Whitepaper other terms for, Initial Coin Offerings spectrum of ICO viability, Initial Coin Offerings token economics, Token Economics use of Ethereum platform, Use Cases: ICOs whitepaper, Whitepaper intermediary trust, Electronic Systems and Trust internetdata exchange protocols, evolution of, The More Things Change dot-com crash, Tulip Mania or the internet?

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The Ethereum project understands the need to experiment, and when upgrades that are deemed important for the entire community become apparent, forking is seen as a better alternative than maintaining the concept of immutability. The Ethereum ecosystem has no qualms about forking its blockchain and gathering enough momentum for such changes to be successful. This attitude stands in stark contrast to other chains, like Bitcoin, where immutability is sacrosanct. Key Organizations in the Ethereum Ecosystem In the Ethereum ecosystem, multiple stakeholders and organizations support the vision that Ethereum is building, and each organization supports it from its own angle. The Ethereum Foundation As a leader in developing the roadmap and implementing further changes to the Ethereum platform, the Ethereum Foundation wields significant influence in the community.

Mastering Blockchain, Second Edition
by Imran Bashir
Published 28 Mar 2018

Topics such as history, the definition of smart contracts, Ricardian contracts, Oracles, and the theoretical aspects of smart contracts are presented in this chapter. Chapter 10, Ethereum 101, introduces the design and architecture of the Ethereum blockchain in detail. It covers various technical concepts related to the Ethereum blockchain that explains the underlying principles, features, and components of this platform in depth. Chapter 11, Further Ethereum, continues the introduction of Ethereum from pervious chapter and covers topics related to Ethereum Virtual Machine, mining and supporting protocols for Ethereum. Chapter 12, Ethereum Development Environment, covers the topics related to setting up private networks for Ethereum smart contract development and programming.

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The following table shows the list of Ethereum network with their network IDs. These network IDs are used to identify the network by Ethereum clients. Network name Network ID / Chain ID Ethereum mainnet 1 Morden 2 Ropsten 3 Rinkeby 4 Kovan 42 Ethereum Classic mainnet 61 More discussion on how to connect to testnet and how to set up private nets will be discussed in Chapter 12, Ethereum Development Environment. Components of the Ethereum ecosystem The Ethereum blockchain stack consists of various components. At the core, there is the Ethereum blockchain running on the peer-to-peer Ethereum network.

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Formal specification of Ethereum has been described in the yellow paper which can be used to develop Ethereum implementations. We briefly touch on this subject here. The yellow paper The Ethereum yellow paper available at https://ethereum.github.io/yellowpaper/paper.pdf has been written by Dr. Gavin Wood, Founder, Ethereum & Parity (http://gavwood.com) and serves as a formal definition of the Ethereum protocol. Anyone can implement an Ethereum client by following the protocol specifications defined in the paper. While this paper can be somewhat challenging to read, especially for those who do not have a background in algebra or mathematics and are not familiar with mathematical notations, it contains a complete formal specification of Ethereum.

DeFi and the Future of Finance
by Campbell R. Harvey , Ashwin Ramachandran , Joey Santoro , Vitalik Buterin and Fred Ehrsam
Published 23 Aug 2021

Technically, a transaction sent to an EOA can also send data, but the data have no Ethereum-specific functionality. 2. Fabian Fobelsteller and Vitalik Buterin, “EIP-20: ERC-20 Token Standard,” Ethereum Improvement Proposals, no. 20, November 2015 [Online serial], https://eips.ethereum.org/EIPS/eip-20. 3. William Entriken et al., “EIP-721: ERC-721 Non-Fungible Token Standard,” Ethereum Improvement Proposals, no. 721, January 2018 [Online serial], https://eips.ethereum.org/EIPS/eip-721. 4. Witek Radomski et al., “EIP-1155: ERC-1155 Multi Token Standard,” Ethereum Improvement Proposals, no. 1155, June 2018 [Online serial], https://eips.ethereum.org/EIPS/eip-1155. 5.

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We will discuss blockchain and cryptocurrency in greater depth later on but for now will focus on a particular cryptocurrency with special relevance to DeFi. ETHEREUM AND DeFi Ethereum (ETH) is currently the second largest cryptocurrency by market cap ($260b). Vitalik Buterin introduced the idea in 2014, and Ethereum mined its first block in 2015. Ethereum is in some sense a logical extension of the applications of Bitcoin because it allows for smart contracts – which are code that lives on a blockchain, can control assets and data, and define interactions between the assets, data, and network participants. The capacity for smart contracts defines Ethereum as a smart contract platform. Ethereum and other smart contract platforms specifically gave rise to the decentralized application, or dApp.

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Ethereum Request for Comments (ERC) related to defining a multitoken model, in which a contract can hold balances of a number of tokens, either fungible or non-fungible. Ethereum (ETH). In existence since 2015, second largest cryptocurrency or blockchain. Its native cryptocurrency is known as ether (ETH). Ethereum’s blockchain has the capability of running computer programs known as smart contracts. It is considered a distributed computational platform and sometimes referred to as the Ethereum Virtual Machine. Ethereum 2.0. A proposed improvement on the Ethereum blockchain that uses horizontal scaling, proof-of-stake consensus and other enhancements. Externally owned account (EOA). An Ethereum account controlled by a specific user.

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond
by Chris Burniske and Jack Tatar
Published 19 Oct 2017

Dmitry Buterin is also very much involved in the cryptoasset world as cofounder of Blockgeeks and other influential startups. 3. http://fortune.com/ethereum-blockchain-vitalik-buterin/. 4. http://www.ioi2012.org/competition/results-2/. 5. https://backchannel.com/the-uncanny-mind-that-built-ethereum-9b448dc9d14f#.4yr8yhfp8. 6. https://blog.ethereum.org/2014/01/23/ethereum-now-going-public/. 7. http://counterparty.io/platform/. 8. https://steemit.com/ethereum/@najoh/beyond-bitcoin-and-crypto-currency-ethereum. 9. https://blog.ethereum.org/2014/01/23/ethereum-now-going-public/. 10. https://github.com/ethereum/wiki/wiki/white-paper. 11. Turing complete refers to a system that is effectively capable of the full functionality of a general purpose computer.

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Bitcoin was intentionally constructed not to be Turing complete to constrain complexity and prioritize security. 12. https://ethereum.org/ether. 13. Nathaniel Popper, Digital Gold: Bitcoin and the Inside Story of the Misfits and Millionaires Trying to Reinvent Monday, Harper, 2015. 14. http://www.coindesk.com/peter-thiel-fellowship-ethereum-vitalik-buterin/. 15. http://www.wtn.net/summit-2014/2014-world-technology-awards-winners. 16. http://ether.fund/market. 17. https://www.ethereum.org/foundation. 18. https://blog.ethereum.org/2015/03/14/ethereum-the-first-year/. 19. http://ethdocs.org/en/latest/introduction/history-of-ethereum.html. 20. http://ether.fund/market. 21. http://ethdocs.org/en/latest/introduction/history-of-ethereum.html. 22.

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For example, the largest cryptocommodity, Ethereum, is a decentralized world computer upon which globally accessible and uncensored applications can be built. It’s easy to appreciate the value of using such a computer, and therefore Ethereum provides a digitally tangible resource. Paying to use Ethereum’s world computer—also known as the Ethereum Virtual Machine (EVM)—is reminiscent of when schools and libraries had shared computers that students could use. One person could sit down and use a computer for a while before moving on, and then another person would come and use it. The Ethereum Virtual Machine operates somewhat similarly to a shared computer, except it is global in scale and more than one user can operate it at a time.

How to DeFi
by Coingecko , Darren Lau , Sze Jin Teh , Kristian Kho , Erina Azmi , Tm Lee and Bobby Ong
Published 22 Mar 2020

Like Argent, Metamask is a non-custodial wallet and it acts as both a wallet and an interaction bridge for the Ethereum network. You can store your Ethereum and ERC20 tokens on Metamask. Acting as an interaction bridge, Metamask enables you to use all Decentralized Applications (Dapps) that are hosted on the Ethereum Network. Without the use of an interaction bridge like MetaMask, your browser would not be able to access the Ethereum blockchain unless you were running a full Ethereum node and have the entire Ethereum blockchain of over 400GB downloaded on your computer. On a technical level, MetaMask does this by injecting a javascript library known as web3.js written by the core Ethereum developers into your browser’s page to enable you to easily interact with the Ethereum network.

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Retrieved February 20, 2020, from https://cointelegraph.com/news/most-significant-hacks-of-2019-new-record-of-twelve-in-one-year ~ Chapter 3: The Decentralized Layer: Ethereum What is Ethereum? (2020, February 11). Retrieved from https://ethereum.org/what-is-ethereum/ Rosic, A. (2018). What is Ethereum Gas? [The Most Comprehensive Step-By-Step Guide!]. Retrieved from https://blockgeeks.com/guides/ethereum-gas/ Rosic, A. (2017). What Are Smart Contracts? [Ultimate Beginner's Guide to Smart Contracts]. Retrieved from https://blockgeeks.com/guides/smart-contracts/ ~ Chapter 4: Ethereum Wallets Lee, I. (2018, June 22). A Complete Beginner's Guide to Using MetaMask. Retrieved from https://www.coingecko.com/buzz/complete-beginners-guide-to-metamask Lesuisse, I. (2018, December 22).

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It is a useful tool to visualize and track where your assets are across the different DeFi protocols. E Ethereum Ethereum is an open-source, programmable, decentralized platform built on blockchain technology. Compared to Bitcoin, Ethereum allows for scripting languages which has allowed for application development. Ether Ether is the cryptocurrency that powers the Ethereum blockchain. It is the fuel for the apps on the decentralized Ethereum network ERC-20 ERC is an abbreviation for Ethereum Request for Comment and 20 is the proposal identifier. It is an official protocol for proposing improvements to the Ethereum network. ERC-20 refers to the commonly adopted standard used to create tokens on Ethereum.

Confessions of a Crypto Millionaire: My Unlikely Escape From Corporate America
by Dan Conway
Published 8 Sep 2019

While walking the dog, I listened to podcasts about Ethereum. When stealing any free time at work, I read about Ethereum. I rejiggered my Twitter feed to follow mostly Ethereum-related contacts. At my usual Friday night Twelve Step meeting, I pulled aside a shaken-looking newcomer. He also happened to be a software engineer. I didn’t ask him about his recovery. I asked him whether he’d heard of Ethereum. At night I played Ethereum-focused YouTube videos on the big screen. My favorites were from DevCon 1, which took place in London in November 2015—the first congregation of the embryonic Ethereum community. The event had a Woodstock feel.

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What I thought would happen with Ethereum might just be happening. Ironically, the catalyst for this price rise was a sudden interest in Ethereum from corporations, governments, and other centralized institutions. Back in New York, they’d announced a new organization, spearheaded by Ethereum veterans, called the Enterprise Ethereum Alliance. There were thirty-five initial members, including some of the largest corporations in the world: Microsoft, J.P. Morgan, and British Petroleum. Over a day-long webcast conference at the J.P. Morgan office, they expressed their belief in Ethereum and sketched out working groups to aid development and interoperability with private chains.

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In crypto, this was usually a sign that something batshit crazy was about to happen. In Taipei, on November 25, Vitalik gave a detailed lecture about the future of Ethereum. He laid out the road map for what he dubbed Ethereum 2.0 and explained how Ethereum would implement a theoretical scalability breakthrough called sharding and a new smart contract programming language called Viper. He also spoke at length about the eventual upgrade from Ethereum’s proof of work algorithm, which most blockchains, including Bitcoin, utilized. Ethereum was moving to proof of stake which required much less energy. It would enable real decentralized apps for the first time.

Kings of Crypto: One Startup's Quest to Take Cryptocurrency Out of Silicon Valley and Onto Wall Street
by Jeff John Roberts
Published 15 Dec 2020

Spurning the hard fork, the splinter group continued to build on the original blockchain, calling it—and the digital currency associated with it—Ethereum Classic. Today, Ethereum and Ethereum Classic operate as separate realms, two versions of what was once one reality. Both are going strong. While the former is forty times more valuable—Ethereum was worth more than $45 billion in mid-2020—both are adding new blocks to their respective chains every fifteen seconds or so. The DAO debacle briefly damaged Ethereum’s credibility, but did little to halt its steady rise as the first serious challenger to bitcoin. The buzz over Vitalik’s creation came from the power of smart contracts, but Ethereum had a currency of its own called ether, which was mined and traded just like bitcoin.

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This meant it was not just speculators investing in Ethereum, but many software developers who had to pay for it as part of their day-to-day business operations. Ethereum had become akin to a hot piece of real estate where anyone who wanted to run a store had to pay a small tax. The price began to shoot up like crazy. At the outset of 2016, Ethereum sold for 95 cents and by June the price hit $18. If bitcoin was digital gold, Ethereum was digital silver. Meanwhile, venture capitalists, including Coinbase board member Chris Dixon, had begun to take notice and rave about the potential of Ethereum to change the world. It was like the original 2013 bitcoin mania all over again but this time it was about something much bigger than digital money—Ethereum was a way to change business, the internet, and society itself.

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Other cryptocurrencies had been hacked and hijacked. Ethereum wasn’t only hacked, but its ledger was tampered with on purpose. What’s more, buying and selling bitcoin had always been Coinbase’s bread and butter—straying from the company’s core mission to deal in a still-unproven alternative could bite them in the ass. Brian’s partner Fred Ehrsam didn’t see it that way. A trip to Shang-hai had convinced him that Ethereum and smart contracts were the future. Ethereum had momentum. It had technology that bitcoin lacked. And unlike bitcoin, Ethereum insiders weren’t consumed by civil war. “Ethereum’s core development team is healthy while bitcoin’s is dysfunctional,” he would write in a blog post, contrasting Vitalik’s take-charge ability to the leaderless and toxic state of bitcoin in the wake of the block-size debate.

The Truth Machine: The Blockchain and the Future of Everything
by Paul Vigna and Michael J. Casey
Published 27 Feb 2018

Barry Silbert came up with the SegWit2x compromise: “Bitcoin Scaling Agreement at Consensus 2017,” Digital Currency Group, Medium, May 23, 2017, https://medium.com/@DCGco/bitcoin-scaling-agreement-at-consensus-2017-133521fe9a77. When Buterin released his white paper in December 2013: Vitalik Buterin, “Ethereum White Paper: A Next Generation Smart Contract & Decentralized Application Platform,” http://www.the-blockchain.com/docs/Ethereum_white_paper-a_next_generation_smart_contract_and_decentralized_application_platform-vitalik-buterin.pdf. “You’re just as likely to find a web developer…”: “An Ode to the Ethereum Community,” Steemit, October 2016, https://steemit.com/ethereum/@owaisted/an-ode-to-the-ethereum-community. After launching Ethereum at the North American Bitcoin Conference: Interview with Michael J. Casey, Miami, January 26, 2014.

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And since Ethereum is more of a community of software engineers than of cryptocurrency investors, it was less contentious than Bitcoin’s struggle over hard-fork proposals. What’s more, it turned out that disgruntled Ethereum participants weren’t entirely powerless, either. A group decided to continue mining and trading the older, non-forked version of ether, with The DAO attacker’s coins still there in the transaction history. They called it Ethereum Classic, adopted the code ETC for its currency, and traded it alongside the forked Ethereum’s ETH ether. You now had two versions of Ethereum. This created much confusion and some interesting arbitrage opportunities—as well as some lessons for bitcoin traders when their own currency split two years later—but it can also be viewed as the actions of a dissenting group non-violently exercising their right to secede.

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Dubai Dunbar, Robin Dunbar number Dyn eBay Eckblaw, Ariel Economic Space Agency (ECSA) Edge, John Eich, Brendan Elliptic energy sector Enigma code EOS Equifax ERC20 Eris Ltd. Estonia ether (ETH) Ethereum adChain ConsenSys and decentralization ether (native currency) history of initial coin offering (ICO) MedRec and open-source innovation as permissionless system Plasma and scalability and security workforce Ethereum Classic (ETC) Ethereum Enterprise Alliance Ethereum Foundation Ethereum Meetup Fabric Factom “fake news” Fibonacci Filecoin financial crisis of 2008 financial inclusion financial sector and central bank fiat digital currency and Hyperledger and permissionless systems and private blockchains and reform See also monetary and banking systems Forde, Brian forks Fourth Industrial Revolution and energy sector and Internet of Things and supply chains and trusted computing Foxconn France Francis, Pope Freakonomics (Levitt and Dubner) freemium model Friedman, Thomas Furst, Raif Gage, John Galt, Juan (pseudonym) Gamecredits Gem Gendal-Brown, Richard General Data Protection Regulation (GDPR) Georgia, Republic of Germany Ghana Global Blockchain Business Council Global Synchronization Log Gnosis GoBitcoin.io God Protocol Goldman Sachs Golem Google Google, Amazon, Facebook, Apple (GAFA) governance and Bitcoin and blockchain technology and citizenship and Ethereum and ICANN and re-decentralization of the Internet and regulation and trust Grid Singularity Grigg, Ian Gün Sirer, Emin Harari, Yuval Noah Hardin, Garrett Hardjono, Thomas Harple, Dan hashes health care sector Health Insurance Portability and Accountability Act Hearn, Mike Hessel, Andrew Hong Kong Howey Test Human Genomics Hyperledger IBM ICANN (Internet Corporation for Assigned Names and Numbers) ICO.

Stake Hodler Capitalism: Blockchain and DeFi
by Amr Hazem Wahba Metwaly
Published 21 Mar 2021

You have to use whatever resource you are given because you cannot code independently of these resources. Ethereum Ethereum is a known blockchain platform that is the most advanced for coding and processing smart contracts. You are given the liberty to code whatever you desire but would have to pay for computing power with “ETH” tokens. Smart contracts have no end to the number of industries they can influence from healthcare, real estate, and even the law. The list of its potentials goes on and on. Chapter 4: Ethereum and DeFi Let’s start by specifying that presently, most if not pretty much all of the DeFi projects are built on Ethereum. Ethereum is a decentralized blockchain that allows other decentralized blockchain applications (dApps) to be built on smart contracts, and some of these apps can have their exchangeable tokens.

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However, the gas cost of using the contract is much higher. Ethereum provides a coder-friendly language for writing smart contracts Called “Solidity.” ● Solidity Solidity is an object-oriented coding language for writing smart contracts. The great thing about Ethereum is that smart contracts can be programmed in a relatively developer-friendly language. If you have experience with Python or JavaScript, you can find a smart contract framework to use with a familiar syntax. Smart contracts regulate the behavior of accounts in the Ethereum platform. Solidity is inspired by C++, Python, and JavaScript and is structured for the Ethereum Virtual Machines.

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It is mainly used to perform smart contracts on various blockchain platforms such as Ethereum. It was developed by Christian Reitwissner, Alex Beregsazi, and several former Ethereum key contributors to enter smart contracts on blockchain platforms. The Solidity programming language is primarily intended for code development and implementation of the Ethereum virtual machine. Furthermore, smart contracts establish the rules and penalties surrounding an agreement in the same way that a conventional contract does, but it also enforces those obligations automatically. ● Vyper Vyper allows you to program on Ethereum, a blockchain-based virtual machine that permits smart contracts' design and execution without the need for centralized or trusted intermediaries.

Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum & Smart Contracts
by David Gerard
Published 23 Jul 2017

– the community sponsored sending a physical Dogecoin on an Astrobotic commercial moon shot.293 It came out in May 2017 that the operator of the Dogecoin tipping bot on Reddit had stolen all the deposited Dogecoins two years earlier.294 Much sorry, many loss. Ethereum Ethereum was proposed by Vitalik Buterin (an early Bitcoiner and a co-founder of Bitcoin Magazine) and developed by Buterin, Gavin Wood, Jeffrey Wilcke and others. Its key innovation is that you can run smart contracts on a blockchain: programs that are triggered to run automatically in a given circumstance. If Bitcoin is like an Excel spreadsheet, then Ethereum is like a spreadsheet with macros. This new idea was interesting enough to quickly make Ethereum the second most popular cryptocurrency. Transactions and smart contract programs (which they call “dapps,” short for “distributed applications”) require gas (a certain amount of the currency token, ether, abbreviated ETH), which is paid to the miner whose computer runs the transaction or smart contract.

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As at mid-2017 it’s running about 2-3 TPS, having rapidly risen over 2017;301 popular dapps already fill the blocks and clog the system for hours at a time, such as the Bancor and Status ICOs. The Ethereum community seems to have faith in the Ethereum Foundation, so a fix is more likely to be accepted without a Bitcoin-style community civil war; and backward-compatibility-breaking changes in Ethereum are a regular occurrence and are mostly managed without controversy. The developers have always stated that Ethereum is explicitly experimental and unfinished (and never mind the hundreds of millions of dollars in ether swilling around in it), and that the promised fancy functionality will need years of work.302 They occasionally boggle at people treating it as much more of a finished product than they do.303 Ethereum advocates talk up corporate adoption by Microsoft and other companies – it’s a popular choice of platform for business blockchain trials, and its smart contract functionality is reused by a lot of other blockchain software – but this is adoption of the software to run separate in-house blockchains, not adoption of the public Ethereum chain and currency.

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In computer science terms, this approach could only have worked by first solving the halting problem: you would need to be able to determine the outcome of any possible Ethereum program without actually running it and observing the result.361) The DAO was shut down soon after, and on 20 July the Ethereum Foundation – several of whose principals were curators of The DAO362 and/or heavily invested in it – changed how the actual code of Ethereum interpreted their blockchain (the “immutable” ledger) so as to wind back the hack and take back their money. The blockchain was “immutable,” so they changed how it was interpreted. The “impossible” bailout had happened. This illustrated the final major problem with smart contracts: CODE IS LAW until the whales are in danger of losing money. Ethereum promptly split into two separate blockchains, each with its own currency – Ethereum (ETH), the wound-back version, supported by the Ethereum Foundation, and Ethereum Classic (ETC), the original code and blockchain – because all this was too greedy even for crypto fans to put up with.

Blockchain: Blueprint for a New Economy
by Melanie Swan
Published 22 Jan 2014

Rather than being a blockchain, or a protocol running over a blockchain, or a metaprotocol running over a protocol like other projects, Ethereum is a fundamental underlying infrastructure platform that can run all blockchains and protocols, rather like a unified universal development platform. Each full node in the Ethereum network runs the Ethereum Virtual Machine for seamless distributed program (smart contract) execution. Ethereum is the underlying blockchain-agnostic, protocol-agnostic platform for application development to write smart contracts that can call multiple other blockchains, protocols, and cryptocurrencies. Ethereum has its own distributed ecosystem, which is envisioned to include file serving, messaging, and reputation vouching. The first component is Swarm (“Ethereum-Swarm,” not to be confused with the crowdfunding site Swarm) as a decentralized file-serving method.

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(no publishing or posting data available) http://static.benet.ai/t/ipfs.pdf. 61 Atkin, A. “TrustDavis on Ethereum.” Slideshare, June 19, 2014. http://www.slideshare.net/aatkin1971/trustdavis-on-ethereum. 62 Galt, J. “Crypto Swartz Will Get You Paid for Your Great Content.” The CoinFront, June 23, 2014. http://thecoinfront.com/crypto-swartz-will-get-you-paid-for-your-great-content/. 63 Prisco, G. “Counterparty Recreates Ethereum on Bitcoin.” CryptoCoins News, updated November 12, 2014. https://www.cryptocoinsnews.com/counterparty-recreates-ethereum-bitcoin/. See also: “Counterparty Recreates Ethereum’s Smart Contract Platform on Bitcoin.” Counterparty Press Release. http://counterparty.io/news/counterparty-recreates-ethereums-smart-contract-platform-on-bitcoin/. 64 Swan, M.

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Counterparty Press Release. http://counterparty.io/news/counterparty-recreates-ethereums-smart-contract-platform-on-bitcoin/. 64 Swan, M. “Counterparty/Ethereum: Why Bitcoin Topped $450 Today (Was Under $350 Last Week).” Broader Perspective blog, November 12, 2014. http://futurememes.blogspot.com/2014/11/counterpartyethereum-why-bitcoin-topped.html. 65 “DEV PLAN,” Ethereum, accessed 2014, https://www.ethereum.org/pdfs/Ethereum-Dev-Plan-preview.pdf. 66 Finley, K. “Out in the Open: An NSA-Proof Twitter, Built with Code from Bitcoin and BitTorrent.” Wired, January 13, 2014. http://www.wired.com/2014/01/twister/. 67 Johnston, D. et al.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

In 2017, it survived a possibly life-threatening crisis when one of the projects using its blockchain, the Distributed Autonomous Corporation, was hacked for some $150 million worth of ether. (Two breakdowns involving Ethereum-related “wallets” followed). Under Buterin’s sure-footed leadership, much of the damage was contained, but at the cost entering the chain forcibly and reversing the offending transactions, resulting in a “hard fork” and the rise of a rival chain called Ethereum Classic, led by the former Ethereum coder Hoskinson. This incident was not the result of flaws in the Ethereum blockchain itself, but that didn’t matter. As the ruler of the chain, Buterin could not escape the need to intervene. Proponents of Ethereum Classic contend that this arbitrary action undermined the immutability of the database and the principle of decentralization that is the heart of the blockchain.

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But his grandiosity and apparent lack of focus defied all the rules of successful enterprise. They decided to support him anyway. In November 2013, Buterin wrote the Ethereum white paper, and on June 5, 2014, Peter Thiel announced a new group of twenty Thiel Fellows, which included Buterin. A year later, Ethereum went live, with the announcement, “What bitcoin does for payments, Ethereum does for anything that can be programmed.” It was another step in the decentralization of the Internet. Just as Ethereum was entering the larger world, in July 2015, Strachman and Gibson were leaving the Thiel Fellowship to start a new but related project, the 1517 Fund, which would invest in Thiel Fellows and other high school and college-aged company founders.

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Proponents of Ethereum Classic contend that this arbitrary action undermined the immutability of the database and the principle of decentralization that is the heart of the blockchain. Ethereum Classic has not yet had much influence. Hoskinson has gone on to form Cardano, attempting a blockchain that redresses all the flaws of bitcoin through rigorous functional software. Saifedean Ammous, the author of The Bitcoin Standard, contends: “The fact that Ethereum could be rolled back means that all blockchains smaller than Bitcoin’s are essentially centralized databases under the control of their operators.”5 The key difference between the bitcoin and Ethereum blockchains is that bitcoin focuses on security and simplicity while Ethereum focuses on capability and functionality.

The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology
by William Mougayar
Published 25 Apr 2016

Blockchains do not impose restrictions on us. To the opposite, they grant us new levels of freedom, and let us program our world on top of them, any way we would like. Blockchains will be the best new tool of the decade. SELECTED BIBLIOGRAPHY Buterin, Vitalik. “Ethereum and Oracles.” Ethereum Blog. 2014. https://blog.ethereum.org/2014/07/22/ethereum-and-oracles/. Chaum, David, Debajyoti Das, Aniket Kate, Farid Javani, Alan T. Sherman, Anna Krasnova, and Joeri de Ruiter. “cMix: Anonymization by High-Performance Scalable Mixing.” Cryptology ePrint Archive. 2016. http://eprint.iacr.org/2016/008.pdf. Chaum, David.

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That architecture drawing was later iterated upon, and appeared in one of Vitalik's blog posts, titled “On Silos.”2 Over the next several months, and up to this day, we became reverse mentors. He taught me a lot about blockchains, and I advised him on business matters and growing Ethereum. I may never comprehend a fraction of Vitalik's blockchain dreams on a given night, but one thing I am certain about, is that Vitalik Buterin is emerging as a savvy business person, following the ranks of other bright technologists, while continuing to lead the Ethereum core technology and its Foundation. I proceeded to write 50 blog posts on Bitcoin, blockchains, and Ethereum, and immersed myself with global creators, innovators, pioneers, leaders, entrepreneurs, startups, enterprise executives and practitioners who were at the leading edges of blockchain technology and its implementation.

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I hope that readers will find The Business Blockchain as useful as I found it exhilarating to write. William Mougayar Toronto, Ontario wmougayar@gmail.com MARCH 2016 NOTES 1.“A Next-Generation Smart Contract and Decentralized Application Platform,” https://github.com/ethereum/wiki/wiki/White-Paper#ethereum. 2.“On Silos,” https://blog.ethereum.org/2014/12/31/silos/. INTRODUCTION IF THE BLOCKCHAIN has not shocked you yet, I guarantee it will shake you soon. I have not seen anything like this since the start of the Internet, in terms of capturing the imagination of people, a small number first, but then spreading rapidly.

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World
by Don Tapscott and Alex Tapscott
Published 9 May 2016

CHAPTER 4 RE-ARCHITECTING THE FIRM: THE CORE AND THE EDGES BUILDING CONSENSYS July 30, 2015, was a big day for a global group of coders, investors, entrepreneurs, and corporate strategists who think that Ethereum is the next big thing—not just for business, but possibly for civilization. Ethereum, the blockchain platform eighteen months in the making, went live. We witnessed the launch firsthand in the Brooklyn office of Consensus Systems (ConsenSys), one of the first Ethereum software development companies. Around 11:45 a.m., there were high fives all around as the Ethereum network created its “genesis block,” after which a frenzy of miners raced to win the first block of ether, Ethereum’s currency. The day was eerily suspenseful. A massive thunderstorm broke over the East River, triggering loud and random emergency flood warnings on everyone’s smart phones.

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The Silver Stallion, chapter 26; www.cadaeic.net/cabell.htm, accessed October 2, 2015. 63. Interview with Yochai Benkler, August 26, 2015. Chapter 11: Leadership for the Next Era 1. Stephan Tual, “Announcing the New Foundation Board and Executive Director,” Ethereum blog, Ethereum Foundation, July 30, 2015; https://blog.ethereum.org/2015/07/30/announcing-new-foundation-board-executive-director/, accessed December 1, 2015. 2. Ethereum: The World Computer, produced by Ethereum, YouTube, July 30, 2015; www.youtube.com/watch?v=j23HnORQXvs, accessed December 1, 2015. 3. Interview with Vitalik Buterin, September 30, 2015. 4. Ibid. 5. Ibid. 6. Ibid. 7.

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A massive thunderstorm broke over the East River, triggering loud and random emergency flood warnings on everyone’s smart phones. According to its Web site, Ethereum is a platform that runs decentralized applications, namely smart contracts, “exactly as programmed without any possibility of downtime, censorship, fraud, or third party interference.” Ethereum is like bitcoin in that its ether motivates a network of peers to validate transactions, secure the network, and achieve consensus about what exists and what has occurred. But unlike bitcoin it contains some powerful tools to help developers and others create software services ranging from decentralized games to stock exchanges. Ethereum was conceived in 2013 by then-nineteen-year-old Vitalik Buterin, a Canadian of Russian descent.

Radical Technologies: The Design of Everyday Life
by Adam Greenfield
Published 29 May 2017

If Deleuze and Guattari held that the fundamental task of a philosopher is the creation of new concepts, indeed, it’s not entirely ridiculous to think of Buterin’s work in this way.3 However we may evaluate them practically or ethically, the explosion of post-Satoshi development has populated the world with new and challenging objects of thought, and the community around Ethereum has been especially fruitful in this regard. Ethereum does offer its own token of value—a cryptocurrency called Ether4—and people do mine and trade in Ether directly, just as they might Bitcoin. But what the network does with it is quite a bit subtler and more interesting than the mere transfer of value between human beings. Buterin had conceived Ethereum from the outset as a single, massively distributed computing engine sprawled across the global network, in which all processing is paid for in increments of Ether.

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But however much one or another of these propositions might appeal to some pie-slice of its target audience, none of them is quite as compelling to the community gathered around Ethereum as its power to act conditionally. This ability to trigger events should certain contingencies arise is something the Bitcoin blockchain could not do, and Buterin had designed it into his creation from the start. You can store data of any nature on the Ethereum blockchain, and it will be validated, agreed upon and permanently accessible to anyone who asks for it, just as records of Bitcoin transactions are accessible to anyone querying the Bitcoin blockchain. But you can also perform logical operations with Ethereum—any operation that a general-purpose computer would be capable of handling.

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abstract_id=2580664. 24.Ethereum Project, “How to Build a Democracy on the Blockchain: Distributed Autonomous Organization,” undated, ethereum.org/dao. 25.Stephan Tual, “On DAO Contractors and Curators,” Slock.it blog, April 10, 2016, blog.slock.it/on-contractors-and-curators-2fb9238b2553#.3jnmvy1jm. 26.Elinor Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action, Cambridge UK: Cambridge University Press, 1990. 27.Michael Cox, Gwen Arnold and Sergio Villamayor Tomás, “A review of design principles for community-based natural resource management,” Ecology and Society, Volume 15, Number 4: 38, 2010; see also Ostrom’s own work The Challenge of Common-Pool Resources. 28.Stavros Stavrides, Common Space, Zed Books, London, 2016. 29.Though, in fairness, Ethereum’s DAO tutorial later points out steps that can be taken to limit an Owner’s power, enacting these measures requires that Owner’s consent. 30.Michael Del Castillo, “The DAO: Or How A Leaderless Ethereum Project Raised $50 Million,” CoinDesk, May 12, 2016. 31.Tom Simonite, “The ‘Autonomous Corporation’ Called The DAO Is Not a Good Way To Spend $130 Million,” MIT Technology Review, May 17, 2016; Jon Evans, “All The Cool Kids Are Doing Ethereum Now,” TechCrunch, May 22, 2016; Emin Gün Sirer, “Caution: The DAO Can Turn Into A Naturally Arising Ponzi,” Hacking, Distributed, June 13, 2016; Kyle Torpey, Tweet, May 11, 2016. twitter.com/kyletorpey/status/730535910949916672 32.Dino Mark, Vlad Zamfir and Emin Gün Sirer.

Cloudmoney: Cash, Cards, Crypto, and the War for Our Wallets
by Brett Scott
Published 4 Jul 2022

The Ethereum system’s key innovation was to allow people to code and deploy the equivalent of armoured digital vending machines on their network, and to give those machines their own addresses so that they could act as agents doing business with humans on the system. In the Ethereum system, these go by the confusing name of ‘smart contracts’, a term first used in 1994 by the cryptographer Nick Szabo, who also used this metaphor of a vending machine to describe the concept. If a normal vending machine is made from mechanical parts, a digital vending machine is written out in code. Ethereum has a token called ether, which can be used to activate those digital machines. To picture this, think of a theme park that only accepts the tokens issued by the park management. The Ethereum network is like a digital theme park with installations calibrated to accept the native ether token.

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A month later I saw this physics break down. Standing behind a prominent Ethereum developer in a Brussels restaurant, I watched him sketch a diagram on a piece of scrap paper. He was detailing an emergency plan to change Ethereum’s code in order to fix a major hack that had just occurred. A rogue hacker had worked out how to confuse the finance vending machine into giving them tens of millions of dollars’ worth of ether tokens out of it. If the offender got away with this, they would control a large chunk of stolen tokens, but if the Ethereum developers tried to intervene, it would shatter the illusion of their system being governed purely by unstoppable code.

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In the final analysis, the apparently unstoppable code gave way to the real world of immovable human politics. The Ethereum team released an update to the system designed to eliminate a slice out of the Ethereum history, to make it as if the hack had never happened. To ‘go back in time’ like this required convincing the techno-clerks to accept the change. It was an exercise in decentralised cyber-statecraft, but it also created a band of rebels who forked off to create a new version called Ethereum Classic in which the hack still happened and in which the unstoppable forces of code still prevail. Proponents of the two now have sectarian fights, much like those seen between the different forks of Bitcoin.

The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency
by Ian Demartino
Published 2 Feb 2016

What has really made headlines, however, is that major technology companies, banks and financial institutions around the world have announced they are working on projects using Ethereum. Details are thin at this point, but UBS and Barclays have both announced an interest in Ethereum, and Microsoft is offering Ethereum in some capacity on its cloud computing service, making it easy for developers to create Ethereum apps. This support from the outside world is unheard of for cryptocurrencies not named Bitcoin and that, more than anything, is the biggest sign that Ethereum will have some success going forward. It has a long way to go before it becomes the next Bitcoin, which is still light-years ahead in support from both inside and outside the community.

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It has a long road ahead of it, because as it turns out, even useless scam coins die hard. Ethereum Algorithm: Ethash Mining Type: Proof-of-Work with plans to switch to new proof-of-stake method Block Time: Around 20 seconds Block Reward: 5 Ether per block Maximum Blocksize: 1MB Total Number: No limit I conclude with perhaps the most exciting cryptocurrency since Bitcoin. Ethereum is more than a coin, describing itself thus: “Ethereum is a decentralized platform that runs smart contracts: applications that run exactly as programmed without any possibility of downtime, censorship, fraud or third party interference. Ethereum is how the Internet was supposed to work.”

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If no one is using an asset exchange or if only scammers are using it, it is not very useful. The support for Ethereum from the outside world is, to me, a sign that the altcoin world is finally solidifying. Most altcoins will fail; in fact, most already have. There are simply too many of them. But a few might find niches and one or two might even be a part of our mainstream economy one day. I would suggest you don’t invest anything more than pocket change into an altcoin with the possible exception of Ethereum. But this doesn’t mean altcoins aren’t worth paying attention to. Anonymous coins, asset-creating Bitcoin 2.0 coins, and intriguing technologies such as Ethereum will all play some role in our future economy. 1 Farivar, Cyrus.

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance
by Eswar S. Prasad
Published 27 Sep 2021

Proof of Stake versus Proof of Work There are variants of Proof of Work that operate slightly differently. For more comprehensive (and technical) overviews of these and alternative consensus protocols, see Bano et al. (2019) and Ismail and Materwala (2019). Descriptions of the technical aspects of Ethereum can be found at https://ethereum.org/learn/#improving-ethereums-scalability. The first building block for the Ethereum upgrade, the Beacon Chain, went live in December 2020. See https://ethereum.org/en/eth2. Some back-of-the-envelope calculations (by a company whose blockchain uses Proof of Stake) comparing energy consumption under the Proof of Work and Proof of Stake consensus protocols are reported in ODIN Blockchain, “Going Green: Energy Consumption Evaluation Part 2: Proof of Stake Consensus Algorithms,” Medium, November 12, 2019, https://medium.com/@odinblockchain/going-green-energy-consumption-evaluation-part-2-proof-of-stake-consensus-algorithms-8ce613f1179b.

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Ethereum’s ICO and the regulatory scrutiny it subsequently faced are reported in Kate Rooney, “Ethereum Falls on Report That the Second-Biggest Cryptocurrency Is under Regulatory Scrutiny,” CNBC, May 1, 2018, https://www.cnbc.com/2018/05/01/ethereum-falls-on-report-second-biggest-cryptocurrency-is-under-regulatory-scrutiny.html. Ethereum price and market capitalization data are from https://www.coindesk.com/price/ethereum. The top four ICOs mentioned in the text are all private and do not include the Petro, an official cryptocurrency issued by the Venezuelan government, which will be discussed in Chapter 7. See Chris Grundy, “The 10 Biggest ICOs and Where They Are Today,” Coin Offering, April 30, 2019, https://thecoinoffering.com/learn/the-10-biggest-icos/.

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Smart Contracts Over time, other virtual currencies added significant new features that updated the blockchain concept so it could handle a wider variety of information. The most valuable virtual currency other than Bitcoin is Ether, which runs on the Ethereum blockchain. In addition to recording virtual currency transactions, the Ethereum blockchain can record and execute automated programs. It is possible, for example, to create a program on the Ethereum blockchain that will move Ether between wallets only after a specific event. This functionality has turned out to have useful applications. Smart contracts are self-executing computer programs that perform predefined tasks based on a predetermined set of criteria or conditions.

Everything for Everyone: The Radical Tradition That Is Shaping the Next Economy
by Nathan Schneider
Published 10 Sep 2018

(June 10, 2015), coindesk.com/research/who-really-uses-bitcoin; Olga Kharif, “The Bitcoin Whales: 1,000 People Who Own 40 Percent of the Market,” Bloomberg Businessweek (December 8, 2017); Coin Dance, “Bitcoin Community Engagement by Gender Summary,” coin.dance/stats/gender (94.73 percent male in March 2018). 7. Buterin’s original white paper can be found at github.com/ethereum/wiki/wiki/White-Paper. 8. Perhaps the first scholarly presentation to take Ethereum seriously was Primavera De Filippi, “Ethereum: Freenet or Skynet?” luncheon at Berkman Klein Center for Internet and Society at Harvard University (April 15, 2014), cyber.harvard.edu/events/luncheon/2014/04/difilippi; see also the widely circulated video “Vitalik Buterin Reveals Ethereum at Bitcoin Miami 2014,” youtube.com/watch?v=l9dpjN3Mwps; a compelling early analysis of Buterin’s worldview is Sam Frank, “Come With Us If You Want to Live,” Harper’s Magazine (January 2015); for a technical perspective, see Ethereum Foundation, “How to Build a Democracy on the Blockchain,” ethereum.org/dao. 9.

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v=l9dpjN3Mwps; a compelling early analysis of Buterin’s worldview is Sam Frank, “Come With Us If You Want to Live,” Harper’s Magazine (January 2015); for a technical perspective, see Ethereum Foundation, “How to Build a Democracy on the Blockchain,” ethereum.org/dao. 9. Vitalik Buterin, comment on Reddit thread (April 6, 2014), reddit.com/r/ethereum/comments/22av9m/code_your_own_utopia. 10. Follow the ongoing contest of currencies at coinmarketcap.com; for CU Ledger, see culedger.com. 11. Joon Ian Wong and Ian Kar, “Everything You Need to Know About the Ethereum ‘Hard Fork,’” Quartz (July 18, 2016). 12. Duran served as a significant informant, referred to as “Pau,” in Jeffrey S.

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He later interrupted the discussions on technical feasibility and implementation with stories of real-life good deeds done through his lodge, offline. Ethereum went live in 2015. Its underlying currency, ether, soon became second only to bitcoin on the crypto-markets, with a total value in the billions of dollars. Walmart is using Ethereum to manage supply chains, and J. P. Morgan is writing smart contracts to automate transactions. A coalition of US credit unions is building a “CU Ledger” to manage member identities. Some people are trying to craft the perfect co-ops or other sorts of egalitarian DAOs, but they’re not making money like the ones concocting blockchain ledgers for big, old banks.10 A year into Ethereum’s life, the system hit trouble.

The Metaverse: And How It Will Revolutionize Everything
by Matthew Ball
Published 18 Jul 2022

However, the community governs these changes and must therefore be convinced that any revisions are to their collective benefit.* Developers and users need not worry that, as an example, “Ethereum Corp” might suddenly increase Ethereum transaction fees or impose new ones, deny an emerging technology or standard, launch a first party service that competes with the most successful dapps, and so on. Ethereum’s trust­less and permissionless programming actually encourages developers to “compete” with its core functionality. Ethereum has its detractors, who level three primary criticisms: its processing fees are too high, its processing times are too long, and its programming language is too difficult.

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Not long after Bitcoin emerged (its creator remains anonymous), two early users, Vitalik Buterin and Gavin Wood, began developing a new blockchain, Ethereum, which they described as a “decentralised mining network and software development platform rolled into one.”1 Like Bitcoin, Ethereum pays those operating its network through its own cryptocurrency, Ether. However, Buterin and Wood also established a programming language (Solidity) that enabled developers to build their own permissionless and trustless applications (called “dapps,” for decentralized apps), which could also issue their own cryptocurrency-like tokens to contributors. Ethereum, then, is a decentralized network that is programmed to automatically compensate its operators.

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[DAOs have] the potential to change the way people buy things, build companies, share resources and run nonprofits.”11 At the same time, ConstitutionDAO also illuminated many of the problems with the Ethereum blockchain. For example, an estimated $1 million to $1.2 million was spent processing transactions to fund the DAO. Though this represented 2.1% of contributions—within the average range for traditional payment rails—the median contribution was estimated at $217, with nearly $50 spent in “gas.” In addition, the Ethereum blockchain cannot “waive” fees for reversing or refunding a transaction. As a result, these fees were effectively doubled as a result of the auction, as most contributors reclaimed their donations.

Bitcoin: The Future of Money?
by Dominic Frisby
Published 1 Nov 2014

And the app catalogue is totally decentralized just like the network. So even we don’t control it – so we can’t take apps down or anything and the government can’t take apps down or something like that. So, that’s what we’re trying to do: mainstream this dis-intermediation technology’. The implications of Ethereum – if it takes off – are clearly enormous. For more information about Ethereum, visit ethereum.org. 10 Should You Buy In? You can’t stop things like Bitcoin. It’s like trying to stop gunpowder. It will be everywhere and the world will have to readjust. John McAfee, computer scientist, founder of McAfee Inc In the 1830s and 1840s a mania gripped the UK.

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‘That is what we’re hoping to do.’ How Bitcoin is just the start of something much, much bigger Ethereum is probably the most talked-about development in cryptography at present. Some call it Bitcoin 2.0. It combines the decentralized mining system central to Bitcoin with a software development platform. Its founders say the potential applications are unlimited: from peer-to-peer betting, to financial derivatives, to identity and reputation systems, to insurance and legal contracts. Some say Satoshi Nakamoto may now even be working for Ethereum. Its former CEO is Charles Hoskinson. A bespectacled, bearded, extremely bright, friendly and fast-talking mathematician.

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This is a system that would work just as well in Africa as it works in North America or Europe, which is a huge step forward for ecommerce and banking the unbanked. And that’s what gets me really excited about the technology and why I came into this space.’ So, what exactly is Ethereum? Everybody’s talking about it, but few seem to understand it. ‘Ethereum is really a continuation of what Satoshi Nakamoto was working on. He wanted to study two things when he released Bitcoin. He’d been working on it for quite some time. The first was this idea of a decentralized database secured by a proof-of-work consensus system, and the second thing was a transaction system – tokens.

The Pay Off: How Changing the Way We Pay Changes Everything
by Gottfried Leibbrandt and Natasha de Teran
Published 14 Jul 2021

In this case, it led to a ‘hard fork’ in the code. The purists branched off into Ethereum Classic, while the pragmatists adapted the code and recovered the funds. The recovered DAO did not live up to its expectations, however, and by the end of 2016 most major crypto exchanges had delisted its tokens from trading. Ethereum Classic still exists, but now ranks #41 on the list of cryptocurrencies, worth only 1 per cent of the value of Ethereum. While Ethereum’s coding language is specifically geared towards smart contracts, few smart contracts have been entered into thus far. But the Ethereum code seems to be quite handy for launching new crypto tokens, as many of the 5,500 cryptocurrencies (mostly ICOs) are built and executed on the Ethereum distributed computing platform.

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Still, there are two important drawbacks that will have to be overcome if the technology is to go mainstream: it is expensive and it is a dream for crooks. Bitcoin payments are not free – in fact, compared with most other forms of payment, they can cost a lot. Each transaction involves a fee for the sender and mining costs. Ethereum/Ether With 11 per cent of the crypto market’s total value, Ethereum’s Ether is the next largest cryptocurrency after Bitcoin, and the prime example of a utility coin. Ethereum is a ‘distributed computing platform’ and a very different animal from Bitcoin. Its currency, Ether, is not used to pay for goods or services. Instead, it is used to buy computer time from participants. In exchange, these participants run code that you give them.

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The crooks, perhaps sensitive to the fact that their business model rests on their reputation for responding to ransom payments, reportedly unlocked the systems. In Ethereum the contracts are submitted as code and automatically executed or enforced. This takes the thinking behind Bitcoin even further. Bitcoin aims to replace money and much of banking: there is no need to put our trust in institutions like (central) banks when we can trust code and cryptography instead. Ethereum would apply this not only to money and banks but to all contracts and intermediaries. Contracts and data are put on shared ledgers, where their integrity can be inspected and verified by a community of users or miners.

The Currency Cold War: Cash and Cryptography, Hash Rates and Hegemony
by David G. W. Birch
Published 14 Apr 2020

Tokens took off with the development of the ERC-20 standard back in 2015. ERC-20 defined a way of creating a standard form of token using ‘smart contracts’ on the Ethereum blockchain. Please note, once again, that smart contracts are not contracts at all because there is no possibility of uncertainty in their execution, and thus no compliance; strictly speaking, they are just automaticity created by the consensus-forming process. The inventor of Ethereum, Vitalik Buterin, says as much: ‘I now regret calling the objects in Ethereum “contracts” as you’re meant to think of them as arbitrary programs and not smart contracts specifically’ (DuPont and Maurer 2015).

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There are many ways that this will be beneficial to society as a whole, not least because any Libra transactions will be placed on an immutable shared ledger. * * * 40 Hint: it is not transaction fees. 41 In Ethereum’s decentralized autonomous organization (DAO) case study, it was possible to write and deploy a smart contract that turned out to be exploitable by attackers. This is because Ethereum is a permissionless platform with no intrinsically defined ‘control layer’. 42 Note that, given my 5Cs taxonomy of the future of money in table 4, I would classify Libra as a community currency rather than a corporate currency.

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DeFi start-ups are trying to build an interlocking financial system denominated in cryptocurrencies. They offer a wide array of lending and derivatives products, available globally, peer-to-peer and without any middlemen, but they have their own systemic risks. Their initiatives all rely on the protocols on which they are built, such as Ethereum, which has faced its own problems with scaling (remember CryptoKitties?). They are also tied to the cryptocurrencies that ‘fuel’ these protocols. If the underlying cryptocurrency layer does not work, the apps on it will also stop working. It is as simple as that. As we will see in part 3, DeFi could be a building block in a parallel financial system that some countries might find attractive.

Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson
Published 26 Jun 2017

The Nakamoto Institute’s withering assessment was that Ethereum was “doomed.” Its combination of poor programming and terms of use that essentially made this lousy programming legally binding spelled disaster. Believers in the dream of decentralizing all the things, however, weren’t yet ready to give up. In July of 2016, Vitalik Buterin, one of Ethereum’s cofounders and the author (at nineteen years old) of the influential 2013 “Ethereum White Paper,” announced a “hard fork” in the cryptocurrency and its blockchain. If a majority of participants in The DAO accepted this fork (which was embodied in a new version of the Ethereum software), all previous transactions that occurred within the decentralized autonomous organization would be essentially forgotten, and all involved ethers would be returned to their original owners.

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GMT: The DAO, “Introduction to the DAO,” last modified June 29, 2016, https://daowiki.atlassian.net/wiki/display/DAO/Introduction+to+the+DAO. 302 “The DAO”: Will Dunn, “The Rise and Fall of The DAO, the First Code-Based Company,” NS Tech, July 22, 2016, http://tech.newstatesman.com/feature/dao-code-based-company. 303 “paradigm shift” that could “offer new opportunities”: Seth Bannon, “The Tao of ‘The DAO’ or: How the Autonomous Corporation Is Already Here,” TechCrunch, May 16, 2016, https://techcrunch.com/2016/05/16/the-tao-of-the-dao-or-how-the-autonomous-corporation-is-already-here. 303 “entrepreneurs of the future”: Joanna Belbey, “How to Invest in the Institutional Revolution of Blockchain,” Forbes, January 18, 2017, http://www.forbes.com/sites/joannabelbey/2017/01/18/how-to-invest-in-the-institutional-revolution-of-blockchain/2/#5807c7603890. 303 Real money: Giulio Prisco, “The DAO Raises More than $117 Million in World’s Largest Crowdfunding to Date,” Bitcoin Magazine, May 16, 2016, https://bitcoinmagazine.com/articles/the-dao-raises-more-than-million-in-world-s-largest-crowdfunding-to-date-1463422191. 303 $162 million: The DAO, “Introduction.” 303 Shortly before The DAO’s funding window closed: Nathaniel Popper, “Paper Points Up Flaws in Venture Fund Based on Virtual Money,” New York Times, May 27, 2016, https://www.nytimes.com/2016/05/28/business/dealbook/paper-points-up-flaws-in-venture-fund-based-on-virtual-money.html. 303 “We discuss these attacks”: Dino Mark, Vlad Zamfir, and Emin Gün Sirer, “A Call for a Temporary Moratorium on ‘The DAO,’ ” Draft (v0.3.2), last modified May 30, 2016, https://docs.google.com/document/d/10kTyCmGPhvZy94F7VWyS-dQ4lsBacR2dUgGTtV98C40. 304 The anonymous hacker: Nathaniel Popper, “A Hacking of More than $50 Million Dashes Hopes in the World of Virtual Currency,” New York Times, June 17, 2016, https://www.nytimes.com/2016/06/18/business/dealbook/hacker-may-have-removed-more-than-50-million-from-experimental-cybercurrency-project.html. 304 Daniel Krawisz: Daniel Krawisz, LinkedIn profile, accessed February 7, 2017, https://www.linkedin.com/in/daniel-krawisz-323bb121. 304 The Nakamoto Institute’s withering assessment: Daniel Krawisz, “Ethereum Is Doomed,” Satoshi Nakamoto Institute, June 20, 2016, http://nakamotoinstitute.org/mempool/ethereum-is-doomed. 304 “hard fork”: E. J. Spode, “The Great Cryptocurrency Heist,” Aeon, February 14, 2017, https://aeon.co/essays/trust-the-inside-story-of-the-rise-and-fall-of-ethereum. 305 “In [minority members’] view”: Ibid. 305 “Ethereum Classic”: Ibid. 306 “The Resolution of the Bitcoin Experiment”: Mike Hearn, “The Resolution of the Bitcoin Experiment,” Mike’s blog, January 14, 2016, https://blog.plan99.net/the-resolution-of-the-bitcoin-experiment-dabb30201f7#.rvh0ditgj. 306 “It has failed because the community has failed”: Ibid. 306 the performance of the Bitcoin system suffered: Daniel Palmer, “Scalability Debate Continues as Bitcoin XT Proposal Stalls,” CoinDesk, January 11, 2016, http://www.coindesk.com/scalability-debate-bitcoin-xt-proposal-stalls. 306 Chinese exchanges accounted for 42%: Nathaniel Popper, “How China Took Center Stage in Bitcoin’s Civil War,” New York Times, June 29, 2016, https://www.nytimes.com/2016/07/03/business/dealbook/bitcoin-china.html. 306 an estimated 70% of all Bitcoin-mining gear: Danny Vincent, “We Looked inside a Secret Chinese Bitcoin Mine,” BBC News, May 4, 2016, http://www.bbc.com/future/story/20160504-we-looked-inside-a-secret-chinese-bitcoin-mine. 308 “a kid in Africa with a smartphone”: Brandon Griggs, “Futurist: We’ll Someday Accept Computers as Human,” CNN, March 12, 2012, http://www.cnn.com/2012/03/12/tech/innovation/ray-kurzweil-sxsw. 309 “The Nature of the Firm”: R.

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The hard fork, many felt, did something much worse. It didn’t arbitrarily change the value of ethers; it actually changed who owned them. Some participants in the original DAO refused to go along with the hard fork, continued to use the original version of the distributed software, and named their system “Ethereum Classic.” As we write this in early 2017, Ethereum and Ethereum Classic continue to exist in parallel. Bitcoin’s Bitter End? Despite ample worldwide enthusiasm for them, Bitcoin and the blockchain have also experienced trouble. In January of 2016 Mike Hearn, who had been a prolific and respected contributor to programming for the blockchain, and who had believed in its promise so deeply that he had quit his job at Google to devote himself full-time to it, sold all his Bitcoins and walked away from the project.

Number Go Up: Inside Crypto's Wild Rise and Staggering Fall
by Zeke Faux
Published 11 Sep 2023

As a teenager, Stone was encouraged to play the stock market by his grandpa. One of his picks was Apple. That trade eventually netted him more than six figures. It was Ethereum—the blockchain that enabled the ICO boom—that got him interested in crypto. He did some Ethereum mining on his laptop around 2016, abandoned it when it was too hard to sell the tokens for real money, and then kicked himself when a friend told him at a poker game that their price had increased tenfold. Stone took his money out of stocks and went all-in on Ethereum, eventually starting Battlestar, which was supposed to help investors earn a return on their crypto holdings through what it called “institutional grade Staking-as-a-Service.”

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What a Bored Ape buyer pays hundreds of thousands of dollars for is not a digital ape cartoon—it’s the ability to prove they are the one who paid hundreds of thousands of dollars for a digital ape cartoon. Think back to that giant Excel spreadsheet in the cloud—the blockchain. What if, in addition to keeping track of how many Bitcoins or Ethereum tokens each person owns, it could also track who owns which ape picture? NFTs did that by adding an additional column: ETHEREUM COINS BORED APE ZEKE 103 #2,735 The blockchain doesn’t even hold the actual image file. It just contains a pointer to the image, which is stored elsewhere on the internet.

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Scum with my fear that I would be hacked. These were some of the worst hours of my life. That evening, I realized the website hadn’t been working properly and someone had already paid me $19,896.20 worth of Ethereum. Then I had to repeat the excruciating process of sending those coins to Coinbase and converting them back to dollars without losing my money due to typos or hacks. At one point, Coinbase’s access to the Ethereum network went down. My money was in limbo for hours. Had I been trading apes in U.S. dollars, I would have lost about $800. But in crypto, there’s a fee associated with every transaction.

The Age of Cryptocurrency: How Bitcoin and Digital Money Are Challenging the Global Economic Order
by Paul Vigna and Michael J. Casey
Published 27 Jan 2015

In mid-2013, journalist Vitalik Buterin also got: Vitalik Buterin, interviewed by Michael J. Casey, January 26, 2014. Buterin first laid out his vision in a white paper: Vitalik Buterin, “Ethereum White Paper,” January 2014, https://www.ethereum.org/pdfs/EthereumWhitePaper.pdf. The team also planned a fund-raiser: Michael J. Casey, “BitBeat: Ethereum Presale Hits $12.7 Million Tally,” Wall Street Journal, MoneyBeat blog, http://blogs.wsj.com/moneybeat/2014/08/05/bitbeat-ethereum-presale-hits-12-7-million-tally/. Here, once again at the vanguard: “The Ripple Protocol: Executive Summary for Financial Institutions,” Ripple.com, https://ripple.com/files/ripple-FIs.pdf.

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Sounding every bit the MBA-qualified financial engineer, Buterin rattles off concepts for apps that could run on Ethereum and help reinvent Wall Street: digital-currency-denominated derivative contracts through which traditional currencies and commodities trade as digital IOU tokens; Ethereum-based security offerings that function without a need for the underwriting and book-running services of an investment bank; decentralized algorithms to challenge the sinister “dark pool” investment vehicles and high-frequency trading machines with which hedge funds, investment banks, and Wall Street high rollers get an edge on the market. But he admits he’s just tossing out ideas. For now, Ethereum is an unproven project.

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The solution he came up with quickly took the cryptocurrency world by storm: a completely redesigned, fully versatile, decentralized blockchain that could function as an open platform on which all manner of contracts and decentralized applications could be installed. He called it Ethereum. “We are hoping to be like the Android of cryptocurrency,” Buterin says, referring to the Google-designed mobile operating system that’s used by multiple models of smartphones and which had by 2014 inspired more than a million apps. “On Android you can install Google Maps, you can install Gmail, you can install whatever you want. That’s where we want cryptocurrency to go. Ethereum provides the base layer, and if you want to install a wallet, there’s an app for that; if you want to install a block explorer, you can design one; or a merchant payments solution or whatever.”

Easy Money: Cryptocurrency, Casino Capitalism, and the Golden Age of Fraud
by Ben McKenzie and Jacob Silverman
Published 17 Jul 2023

Kim’s Instagram post from June 2021 read as follows: ARE YOU GUYS INTO CRYPTO???? THIS IS NOT FINANCIAL ADVICE BUT SHARING WHAT MY FRIENDS JUST TOLD ME ABOUT THE ETHEREUM MAX TOKEN! A FEW MINUTES AGO ETHEREUM MAX BURNED 400 TRILLION TOKENS—LITERALLY 50% OF THEIR ADMIN WALLET, GIVING BACK TO THE ENTIRE E-MAX COMMUNITY. SWIPE UP TO JOIN THE E-MAX COMMUNITY. To borrow from the industry’s charming parlance, EthereumMax was a shitcoin. The hastily assembled crypto project seemed purposely designed to be confused with the second biggest cryptocurrency, Ethereum, even though the two were not related. Kim’s post, sent out to her then 251 million (!) followers, was an enormous publicity success for the obscure token.

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Over the past year, also using leverage, Ahmed had ridden the crypto wave up, turning an initial stake of 1,250 Ethereum tokens into 3,300 that were eventually worth more than $13 million. (He said he started trading in 2017 with about $25,000.) Ahmed was betting that the crypto market would continue its overall rise, though he said he planned to cash out if the price of Ethereum reached $4,100. Like Kim, Ahmed expected some volatility along the way, but it was only on May 19, when Ethereum plunged dramatically alongside Bitcoin and other currencies, that Ahmed realized the gravity of his situation.

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If you want to get somewhere else, you must run at least twice as fast as that!” That was the basic technological and philosophical framework for Bitcoin, the original cryptocurrency, from which all others sprang. Ethereum, the second largest cryptocurrency as of this writing, was launched in 2015. It offered an alternative open-source blockchain and became notable for offering what are called smart contracts: small computer programs that execute functions automatically on the Ethereum blockchain. A simple example might be to use smart contracts to replicate the escrow process. You could program a transaction that would go through only if two of the three parties said it should, like a buyer, a seller, and a trusted referee.

On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

The through line runs through Vitalik Buterin, a waifish, Russian Canadian computer programmer who created the Ethereum blockchain.[*11] The Ethereum blockchain has a native digital currency, Ether (ETH)—pronounced as “eth” with a long “e” as in the name Ethan. But like an appliance on one of those late-night infomercials—it slices, it dices, it makes julienne fries!—the Ethereum blockchain can do a whole lot more than just record ETH transactions. In particular, it allows for the creation of so-called smart contracts. Ethereum contains a “built-in fully fledged Turing-complete programming language,” so the possibilities are almost unlimited.

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Still, the Ethereum blockchain clearly has more potential for commercial, technological, and creative applications than the Bitcoin blockchain ever did. And yet, from Ethereum’s inception, Buterin encountered intense resistance from Bitcoin enthusiasts. “You know, it does all these amazing things,” he said. “And, like, we’re all part of Team Cryptocurrency and we’re in this together. But I was very surprised when the response was a lot of maximalism and hostility.” Some of this is because of the origins of the respective blockchains and the cultural baggage they carry with them: Ethereum was a quasi–Silicon Valley startup, while Bitcoin was a cyberlibertarian alternative to fiat currency.

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You could, for example, write an options contract—I have a right to buy one hundred ETH from you if the price rises above $2,500—and the blockchain would work to execute the contract automatically. In some ways, the origin story of Ethereum resembled that of a typical Silicon Valley startup, springing to life from a bright young immigrant founder willing to make a long-shot bet. Unlike Nakamoto, Buterin hadn’t had an ideological objection to the central banking system. Instead, he is more of a polymath—an award-winning programmer who was also the cofounder of Bitcoin Magazine, and who was just nineteen when he began laying the foundations for Ethereum. “Up until like maybe 2014 or even 2016, I was convinced that this whole space only has maybe a ten or twenty percent chance of turning out to be anything interesting,” Buterin told me.

The Bitcoin Standard: The Decentralized Alternative to Central Banking
by Saifedean Ammous
Published 23 Mar 2018

While this would be great for a new start‐up, it is anathema to a project that wants to demonstrate credible commitment to a fixed monetary policy. Should the teams behind any particular altcoin decide to change its monetary policy, it would be a relatively straightforward thing to achieve. Ethereum, for instance, does not yet have a clear vision of what it wants its monetary policy to be in the future, leaving the matter up to community discussion. While this may work wonders for the community spirit of Ethereum, it is no way to build a global hard money, which, to be fair, Ethereum does not claim to do. Whether it is because they are aware of this point, or to avoid run‐ins with political authority, or as a marketing gimmick, most altcoins do not market themselves as competitors to Bitcoin, but as performing tasks different to Bitcoin.

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In the aftermath of the DAO hack, Ethereum developers created a new version of Ethereum where this inconvenient mistake never occurred. This re‐injection of subjective human management is at odds with the objective of making code into law, and questions the entire rationale of smart contracts. Ethereum is the second largest blockchain after Bitcoin in terms of its processing power, and while the Bitcoin blockchain cannot effectively be rolled back, that Ethereum can be rolled back means that all blockchains smaller than Bitcoin's are effectively centralized databases under the control of their operators. It turns out code is not really law, because the operators of these contracts can override what the contract executes.

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This problem is more pronounced for digital currencies that begin with an Initial Coin Offering, which creates a highly visible group of developers communicating publicly with investors, making the entire project effectively a centralized project. The trials and tribulations of Ethereum, the largest coin in terms of market value after Bitcoin, illustrate this point vividly. The Decentralized Autonomous Organization (DAO) was the first implementation of smart contracts on the Ethereum network. After more than $150 million was invested in this smart contract, an attacker was able to execute the code in a way that diverted around one‐third of all the DAO's assets to his own account.

The Politics of Bitcoin: Software as Right-Wing Extremism
by David Golumbia
Published 25 Sep 2016

“Inside the Fight over Bitcoin’s Future.” New Yorker (August 25). http://www.newyorker.com/. Burdekin, Richard C., and Pierre L. Siklos, eds. 2004. Deflation: Current and Historical Perspectives. New York: Cambridge University Press. Buterin, Vitalik. 2014. “DAOs, DACs, Das, and More: An Incomplete Terminology Guide.” Ethereum (May 6). http://blog.ethereum.org/. Carrico, Dale. 2005. “Pancryptics: Technocultural Transformations of the Subject of Privacy.” Ph.D. diss., University of California, Berkeley. —. 2009. “Condensed Critique of Transhumanism.” Amor Mundi (January 25). http://amormundi.blogspot.com/. —. 2013a. “Futurological Discourse and Posthuman Terrains.”

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One of the main proponents of DAOs and DACs is Vitalik Buterin, author of the passage about “smart contracts” above, “a Canadian college dropout and Bitcoin enthusiast” (Schneider 2014), cofounder of Bitcoin Magazine, and a recipient of one of the US$100,000 Thiel Fellowships funded by the eponymous right-wing technology entrepreneur and PayPal founder Peter Thiel (Rizzo 2014a)—fellowships that specifically promote the rejection of higher education, in a manner harmonious with the rejection by Thiel and others on the right wing of public goods (Lind 2014). Buterin is a cofounder of Ethereum, the best-known project to generalize blockchain technology into applications that go beyond currency-like systems. Buterin (2014) describes DAOs “and their subclass, DACs,” as the “holy grail” of decentralized applications. A DAO “is an entity that lives on the internet and exists autonomously, but also heavily relies on hiring individuals to perform certain tasks that the automaton itself cannot do.”

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“Central Banks’ Failed Policies Are Strengthening Bitcoin.” Bitcoin.com (January 12). https://news.bitcoin.com/. Richardson, Tim. 2001. “Beenz Denies It’s about to Be Canned: Global ‘Net’ Currency Devalued Big-Time.” The Register (May 16). http://www.theregister.co.uk/. Rizzo, Pete. 2014a. “$100k Peter Thiel Fellowship Awarded to Ethereum’s Vitalik Buterin.” CoinDesk (June 5). http://www.coindesk.com/. —. 2014b. “Tokyo Police Launch Investigation into Missing Mt. Gox Bitcoin.” CoinDesk (July 30). http://www.coindesk.com/. Robinson, Jeffrey. 2014. Bitcon: The Naked Truth about Bitcoin. Seattle: Amazon Digital Services. “Ron Paul Sites Are Obsessed with Jews, Zionists, and Israel.” 2011.

The Code of Capital: How the Law Creates Wealth and Inequality
by Katharina Pistor
Published 27 May 2019

The decision rules of The DAO required a majority vote, and the pragmatists won the day; they altered the code, unwound The DAO, and returned the ether to the majority of the token holders. The minority, however, held on to the original code, which they now labeled “Ethereum Classic” (ETC), a new digital asset, which competes with the adulterated original Ethereum (ETH).38 The DAO is a cautionary tale of coded determinism operating in an unpredictable world. It also is a good illustration of how codes evolve, whether they are legal or digital. The pragmatists among The DAO’s token holders decided to leave the final say about its fate in the hands of humans.

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For a survey of the effects of formalizing property rights in the developing world in recent years, see Klaus Deininger, Land Policies for Growth and Poverty Reduction, World Bank Policy Research Reports (Washington, DC: World Bank, 2003). 29. The concept of the decentralized autonomous organization (DAO) is illustrated on the Ethereum website: https://www.ethereum.org/dao. For a useful account of The DAO’s brief existence, see Muhammed Izhar Mehar et al., “Under-standing a Revolutionary and Flawed Grand Experiment in Blockchain: The DAO Attack,” available online at ssrn.com/abstract=3014782 (2017). 30. See the SEC’s press release of July 25, 2017, available online at https://www.sec.gov/news/press-release/2017-131, about its investigative report that concluded that ICOs of the kind The DAO had issued were securities and as such subject to regulations and supervision. 31.

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In the wake of a frenzy in the offerings of tokens or coins in digital ventures to the public, also dubbed “initial coin offerings,” or ICOs after the legacy practice of “initial public offerings” of shares or bonds, the US Securities and Exchange Commission (SEC) intervened. It affirmed that ICOs qualify as “securities” that are subject to standard registration requirements, a decision that reached The DAO only posthumously.30 The DAO was a venture capital fund that was built on the Ethereum blockchain. The DAO was designed to operate without a board of directors or any human officers. Instead, the firms’ investors received voting rights that allowed them to participate directly in developing investment strategies by proposing new investment opportunities to the firm. If agreed by the majority of investors, they would be implemented by the code, an open-source software that was available for everyone to see, but not for everyone to change.

The Internet of Money
by Andreas M. Antonopoulos
Published 28 Aug 2016

v=jw28y81s7Wo This is going to be a bit more of a philosophical talk about the future of cryptocurrencies and what I’ve learned here at this event. This event is called the Bitcoin Expo 2014. It might have been called the Bitcoin and Ethereum Expo 2014. I don’t know if you noticed, but Ethereum had a pretty big presence here. An interesting question comes up, actually quite a few people have asked me: "Does Ethereum threaten the future of bitcoin? Does it steal some of its thunder?" Those are questions I’ve heard several times, and I’ve heard people refer to that issue in trying to understand altcoins - wondering whether altcoins essentially threaten the dominance of bitcoin, if they make bitcoin weaker, if they distribute the value of the network too broadly. 7.1.

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"Currency as a means of expression, currency as a tool of language, is no longer up to the issuer. It is up to us as individuals making a choice to use that currency, and we give it value through our use." We’re going to have currencies for different uses. Already, you have bitcoin that provides a very specific monetary policy. You have Ethereum that can provide a contract platform. There’s Namecoin for distributed naming conventions. There are many others, and there will be many others that will solve other problems: protein folding, the search for extraterrestrial life. Maybe we’ll have currencies that are better for microtransactions and micropayments with very fast resolution.

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We’re going to start treating currency as an application, and in order to do that we’re going to need interfaces that allow us a unified currency experience, that allow us to have a single wallet with perhaps 150 different currencies in it. Because of inventions like sidechains, decentralized exchanges, fluid liquid systems and the complete absence of monopoly, of lock-in, of hostage situations around the currency, we will be able to instantaneously and at very low cost convert from bitcoin to Namecoin to Dogecoin to Ethereum. If we can do that, then it doesn’t matter because we won’t do that; our unified wallet interface will do that, by trying to see what we’re trying to achieve with our currency. If I’m buying a house, it might express my transactional will in the modality of bitcoin because that is the most suitable currency.

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory
by Kariappa Bheemaiah
Published 26 Feb 2017

A simple, secure swap then takes place, allowing Transferwise to execute the transfer up to 89% cheaper than with a bank ( http://www.telegraph.co.uk/money/transferwise/how-does-it-work-and-is-it-safe/ ). 23On the 30th of April 2016, the founders of SLOCK.IT launched the DAO (Decentralized Autonomous Organization) on the Ethereum platform. 24Gavin Wood is widely known in the Blockchain community as the Co-founder and CTO of Ethereum and the Co-founder of Grid Singularity (a company that uses the Blockchain for decentralized energy data management) 25BRACKETS: Blockchain-based Release of funds, that Are Conditionally Key-signed, and Triggered by Signals. 26EDD: Enhanced Due Diligence. 27Know your business. 28In the UK, there are currently eight such providers approved by the government, including PayPal, the Post Office and Experian. 29A PKC is an identity with some other information (such as an expiry date) that is put together and digitally signed by a third party.

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Retrieved from International Business Times: http://www.ibtimes.com/next-financial-crisis-could-be-predicted-smarter-economic-model-experts-say-2313345 Palley, T. I. (2007). Financialization: What It Is and Why It Matters. Washington, D.C.: The Levy Economics Institute and Economics for Democratic and Open Societies. Palmer, D. (2016, Feburary 24). 7 Cool Decentralized Apps Being Built on Ethereum . Retrieved from CoinDesk: http://www.coindesk.com/7-cool-decentralized-apps-built-ethereum/ Partnoy, F. (2009). Infectious Greed: How Deceit and Risk Corrupted the Financial Markets . New York City: PublicAffairs. Plotkin, A. J. (2016). Small groups and long memories promote cooperation. Nature , Scientific Reports 6, Article number: 26889, doi: 10.​1038/​srep26889 .

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An App functions in very simple way: it is a piece of code that reacts to a certain input to provide the user with a certain output. Press this button on the screen and you are taken to a website or you can call an Uber to come pick you up where you stand. Apps use the information that is being exchanged on the protocol on which they run to perform these activities and deliver these outputs. A smart contract , according to Ethereum’s founder, Vitalik Buterin, “is a computer program that directly controls some digital asset.” Smart Contracts are essentially the same as Apps, except they perform a different kind of automation. While the traditional Apps available on a Google Play Store or Apple App Store are useful for certain operations, Smart Contracts function as Apps that perform value exchange operations when they receive a certain input.

Orwell Versus the Terrorists: A Digital Short
by Jamie Bartlett
Published 12 Feb 2015

Bitcoin creates an immutable, unchangeable public copy of every transaction ever made by its users, which is hosted and verified by every computer that downloads the software. This public copy is called the ‘blockchain’. Pretty soon, enthusiasts figured out that the blockchain system could be used for anything. Armed with 30,000 Bitcoins (around $12 million) of crowdfunded support, the Ethereum project is dedicated to creating a new, blockchain-operated internet. Ethereum’s developers hope the system will herald a revolution in the way we use the net – allowing us to do everything online directly with each other, not through the big companies that currently mediate our online interaction and whom we have little choice but to trust with our data.

The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism
by Arun Sundararajan
Published 12 May 2016

Melanie Swan, Blockchain: Blueprint for a New Economy (Sebastopol, CA: O’Reilly Media, Inc., 2015). 12. Primavera De Fillipi, “Ethereum: Freenet or Skynet?,” Talk presented at the Berkman Center for Internet & Society, Harvard University, Cambridge, MA, April 15, 2014. 13. Lawrence Lessig, Code and Other Laws of Cyberspace (New York: Basic Books, 1999). 14. Vitalik Buterin, “Decentralized Protocol Monetization and Forks,” Ethereum Blog, April 30, 2014. https://blog.ethereum.org/2014/04/30/decentralized-protocol-monetization-and-forks. 15. It is likely that architecting it “optimally” is impossible, based on a set of results from a branch of economics called mechanism design. 16.

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Once the level of adoption in any neighborhood is sufficiently high, the ridesharing portion of the app gets activated. You can then use your accumulated zooz to buy rides, much like you’d use currency to buy an Uber or Lyft ride. As Vitalik Buterin, an influential writer about decentralized peer-to-peer systems and the founder of Ethereum, a decentralized platform that runs smart contracts, noted in a blog post, “the idea of releasing a new currency as a mechanism for funding protocol development is perhaps one of the most interesting economic innovations to come out of the cryptocurrency space.”14 But a critical determinant of success is architecting this distribution of value right.15 It also seems important for the manifestation of this value, the “coin,” to be of continuing value as the platform grows and matures.

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Otherwise, what begins as a decentralized system may simply evolve into a traditional third-party platform that merely uses a glorified blockchain database. Perhaps some of the greatest opportunity from decentralized peer-to-peer systems will come from the emerging decentralized autonomous organizations (DAOs) and decentralized collaborative organizations (DCOs) such as those that are being architected by Buterik’s Ethereum and by Field and DeFillipi’s Backfeed. Such organizations posit a holistic model for organizing economic activity in a decentralized manner. Perhaps these will allow the kind of decentralized, distributed ownership and control of the “platform cooperatives” that I discuss in chapter 8. One might wonder what realistic possibilities exist for organizations that seemingly exist embedded only in computer code.

The Evolution of Everything: How New Ideas Emerge
by Matt Ridley

Consent of the Networked. Basic Books. On blockchains, Frisby, Dominic 2014. Bitcoin: The Future of Money?. Unbound. On Nick Szabo’s ‘shelling out’, nakamotoinstitute.org/shelling-out/. On Ethereum’s white paper, A Next-Generation Smart Contract and Decentralized Application Platform. https://github.com/ethereum. On private money, Dowd, K. 2014. New Private Monies. IEA. On smart contracts, De Filippi, P. 2014. Ethereum: freenet or skynet?. At cyber.law.harvard.edu/events 14 April 2014. On digital politics, Carswell, Douglas 2014. iDemocracy will change Westminster for the Better. Govknow.com 20 April 2014.

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With Twister, that will not be possible. Then there is Namecoin, which aims to issue internet names in a decentralised, peer-to-peer fashion; Storj, which plans to allow cloud storage of files hidden inside blockchains; and Ethereum, which is a decentralised peer-to-peer network ‘designed to replace absolutely anything that can be described in code’, as Matthew Sparkes puts it. The digital expert Primavera De Filippi sees Ethereum and its ilk coming up with smart contracts, allowing ‘distributed autonomous organisations’ that, once they have been deployed on the blockchain, ‘no longer need (nor heed) their creators’. In other words, not just driverless cars, but ownerless firms.

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Bitcoin was designed to maintain its value without any precious-metal backing, without any centralised issuer, and without any intrinsic value. Satoshi invited users to ‘escape the arbitrary inflation risk of centrally managed currencies!’ It is hard to get your head around how bitcoin works. One of the pithiest explanations I have come across is in a recent launch by Ethereum, a business built to follow up on bitcoin: ‘The innovation provided by Satoshi is the idea of combining a very simple decentralised consensus protocol, based on nodes combining transactions into a “block” every ten minutes, creating an ever-growing blockchain, with proof of work as a mechanism through which nodes gain the right to participate in the system.’

Before Babylon, Beyond Bitcoin: From Money That We Understand to Money That Understands Us (Perspectives)
by David Birch
Published 14 Jun 2017

Since you can’t go back and edit a blockchain (which is sort of the point of it), this is achieved by ‘forking’ to create a new blockchain. This was done but a significant minority of miners felt that this was the wrong decision so they continued with the original blockchain as Ethereum Classic. At the time of writing, the ‘market cap’ of Ethereum is significantly higher than that of Ethereum Classic. Ripple After Bitcoin and Ethereum, the third biggest cryptocurrency is Ripple, which unlike those first two has its roots in local exchange trading systems (Peck 2013). It is a protocol for value exchange that uses a shared ledger but it does not use a Bitcoin-like blockchain, preferring another kind of what is known as a ‘Byzantine fault-tolerant consensus-forming process’.

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Crypto-alternatives Not only will there be cryptocurrencies beyond Bitcoin and not only will they be better – more powerful and more efficient – there will also be a great many of them as the cost of launching a digital currency falls. Without launching into a treatise on cryptocurrencies, I think it would be useful to take a quick look at a couple of the newer cryptocurrencies on the block (pun intended) to give a sense of the spectrum of possibilities. Ethereum Ethereum is comparable to Bitcoin, in that it uses a blockchain, but it was designed to provide a better platform for shared ledger applications. One of the most interesting users of such applications was the Distributed Autonomous Organization (DAO). The general concept of a distributed autonomous organization goes back a few years, having roots in organizational decentralization theories that have been turbocharged (as William Mougayar puts it) by cryptocurrency technologies and trust-based automation (Mougayar 2016), but the DAO in question was created as a new kind of business: an investor-directed investment fund.

Trading at the Speed of Light: How Ultrafast Algorithms Are Transforming Financial Markets
by Donald MacKenzie
Published 24 May 2021

There is no abbot of bitcoin who, like the abbot of St Albans, monopolizes bitcoin ASICs—although there is one dominant designer of them, the Chinese company Bitmain, which has some 90 percent of the market (Liu and McMorrow 2019), and the miners who use Bitmain’s ASICs do tend to be organized in very large pools—but there has been deep unhappiness in the world of cryptocurrencies about the shift to ASICs. Indeed, bitcoin’s main rival, ethereum, was designed to be, in the terminology of the field, ASIC resistant. The hashing algorithm for ethereum was designed to make it hard to develop ASICs that are much more efficient than ordinary computers in ethereum’s equivalent of bitcoin mining. Trying to make a cryptocurrency ASIC resistant is quintessential material political economy, reminiscent (as I’ve said) of the defense of hand-milling, even if in the case of ethereum the effort was less than fully successful. Efficient ethereum ASICs have been developed, although they haven’t yet swept the board as fully as their bitcoin equivalents have.

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I think it is more generally applicable, and want to end this book by briefly discussing one sphere in which that is most definitely the case, and another in which I think the approach may be applicable (but in which further research is needed to know exactly how). The sphere to which material political economy unequivocally applies is decentralized cryptocurrencies such as bitcoin and ethereum. (The issues are different for cryptocurrencies such as Facebook’s proposed Libra, which, if it is launched, will be run, at least initially, in a centralized fashion; I don’t propose to discuss those currencies here.) The issue that most clearly makes material political economy applicable is how to motivate at least a subset of the users of a cryptocurrency to check the validity of each transaction (including checking the validity of each other’s checking) and take part in adding it irreversibly to the blockchain, the record of every transaction that has taken place.

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INDEX 50 Broad Street, 1, 2f Abbott, Andrew, 16 Abolafia, Mitchel, 19 actor-network theory, 14 advertising, online, 236–37 Aldrich, Eric, 229 Aldrich, Eric, and Seung Lee, 248nn23 and 27 algorithms, 4, 172–205, 213–17; defined, 12–13; volume-participation, 230–31 Amazon, 236 Angel, James, 225 Anova, 155, 156, 159–60, 253n12 AOptics, 159–60 application-specific integrated circuits (ASICs), 254n23 Aquilina, Matteo, 183–84, 223, 256n13 Aquis, 225 arb, 43 arbitrage, 175; statistical, 243n5 Archipelago, 91, 96, 255n7 asterisk, battle of, 219–20, 222 Aurora, 50, 52 Automated Trading Desk (ATD), 28, 29, 66–69, 77–80, 82–84, 90, 101–4, 176, 210–11; staff roles, 84f banks, 5, 103 Barclays Bank, 197 BATS (Better Alternative Trading System), 96, 97 Biais, Bruno, and Richard Green, 110 bigs and littles, 54–55 bilateral relationships, 227–28 Birch, Kean, 246n32 bird droppings, 160 bitcoin, 234–35 Bloomberg FIT (Fixed-Income Trading), 106–7, 110 Borch, Christian, 10–11, 231, 241 Brogaard, Jonathan, 240 broker groups, 46–47 BrokerTec, 105–6, 110, 113, 114, 115t, 165 Budish, Eric, 23, 223–24, 225–26, 240 C++, 167 cabling, 139–47, 165–66 Callon, Michel, 243n17 Cantor Fitzgerald, 111–13 Carlson, Ryan, 60, 247nn5 and 6 Cermak, 135–37, 136f Chicago Board of Trade, 33, 35f, 36f, 37, 59–60 Chicago Board Options Exchange (CBOE), 203–4 Chicago Mercantile Exchange (CME), 29, 32, 33, 37, 63–64, 232–33 Chi-X, 99–101, 240, 256n21 Christie, William, and Paul Schultz, 94 circuit breakers, 261n26 Citadel, 4, 104, 233, 260n16 Citigroup, 103–4 Citron, Jeffrey, 85, 250n17 Clackatron, 3f, 128 clearing and settlement, 209 CLOBs (consolidated limit order books), 71–72, 97, 178, 218, 219 clock synchronization, 11, 187 coils, 258n34 Commodity Exchange Authority, 39 Commodity Futures Trading Commission (CFTC), 41–42, 133 cookies, 261n36 Coombs, Nathan, 242 Copenhagen Business School, 231, 241 coronavirus, 10 Cowan, Ruth Schwartz, 14 cryptocurrencies, 234–35 Cummings, Dave, 3–4, 29, 92 dark pools, 19–20, 116, 251n24 datacenters, 6–8, 135–39, 138f, 162–71 Datek, 85 dealers, 105–8, 110f, 119 decimalization, 101, 198, 199, 252n12 Deutsche Terminbörse, 57, 58 digital economy, 235–37 Direct Match, 114–16, 209 Dodd-Frank Act 2010, 221, 259n16 Dourish, Paul, 243n18 Einstein, Albert, 11 Electronic Broking Services (EBS), 126–28, 198, 199, 200–201 E-Mini, 51f, 54, 55, 56 equities triangle, 7f, 8 ES, 55, 56, 61, 183, 247n27 eSpeed, 112, 113, 114 ethereum, 234, 235 Eurex, 59, 62, 164, 165, 168–69, 244n13, 254n22 EuroMTS, 120 exchange-traded funds (ETFs), 61 Exegy, 9 Facebook, 235, 237 fees, 20–21, 223 fiber-optic cable, 253n9 fiber tail, 152–53, 158, 208 field theory, 14–15, 16, 222 fill messages, 163–65, 209 FINRA (Financial Industry Regulatory Authority), 259nn8 and 15 Fixed Income Clearing Corporation (FICC), 116 Flash Boys, 142, 239 flash crashes, 228–30 foreign exchange trading, 123–31, 134, 196–201, 213, 214 Foucault, Michel, 217 FPGAs (field programmable gate arrays), 30, 169–71, 170f, 233 fragmentation, 97t, 132–34, 154, 211 futures, 37–38, 40–43, 69, 131, 209, 210; defined, 8, 32–33 futures lead, 43, 61–65, 92, 97t, 132–33, 211, 249n30 Galison, Peter, 11 geodesics, 9 Getco (Global Electronic Trading Co.), 55, 232 Ginsey, 247n8 Globex, 49–53, 55, 56, 61 Godechot, Oliver, 17 gold line, 140–41, 209, 252n6 Google, 235–36, 237 governmentality, 217–18 Guardian, 236 Gutterman, Burt, 50, 52 Hackers, 87–88 Harris, Lawrence, and Venkatesh Panchapagesan, 19 Hawkes, James, 67–68 Hendershott, Terrence, 262n2 high-frequency trading, 23–29; defined, 4 hinges, 16, 20–21, 93–98, 210, 224–25; in Europe, 99–101 Hobson, John, and Leonard Seabrooke, 245n23 Hotspot, 129, 130 IEX, 202–3, 202f, 204, 258n36 information, politics of, 210–13 Instinet, 67, 76–77, 78–79, 96 Intercontinental Exchange (ICE), 62, 257n30 Intermarket Sweep Orders (ISOs), 179–81, 215–16, 217, 255n10 Intermarket Trading System (ITS), 72–73, 91 interviewees, 24–28, 25t, 246n34 inverted exchanges, 257n32 Island, 1–3, 4, 29, 56, 85–93, 96, 201, 243n10, 257n31 Itch, 89 jitter, 232–33, 248n25 Johnson, Neil, 230 journalism, 236 Jump Trading, 55, 153 Knight Capital, 232 Lange, Ann-Christina, 6 lasers, 159–60 last look, 196–97 Latour, Bruno, 202, 243n17 Latour Trading LLC, 255n11 Laughlin, Gregory, 20, 244nn12 and 14 Laumonier, Alexandre, 147, 242 Law, John, and Annemarie Mol, 14 Lehmann Brothers, 3f, 128 Lenglet, Marc, 242 leverage, 63 Levine, Josh, 85, 87, 89, 250nn16, 17, and 18 Levy, Stephen, 87–88 Lewis, Michael, 142, 239 LIFFE (London International Financial Futures Exchange), 48–49, 57, 58, 59, 62, 248n14 light, speed of, 4, 11–12 liquidity, stickiness of, 27, 63, 249n28 Liquidity Edge, 117–18 liquidity-taking, 30.

Blockchain Basics: A Non-Technical Introduction in 25 Steps
by Daniel Drescher
Published 16 Mar 2017

About the Technical Reviewer Laurence Kirk who after a successful career writing low latency financial applications for the City of London, was captivated by the potential of distributed ledger technology. He moved to Oxford to study for his master’s degree and set up Extropy.io, a consultancy working with start- ups to develop applications on the Ethereum platform. Passionate about distributed technol- ogy, he now works as a developer, evangelist, and educator about Ethereum. Introduction This introduction answers the most important question that every author has to answer: Why should anyone read this book? Or more specifically: Why should anyone read another book about the blockchain? Continue reading and you will learn why this book was written, what you can expect from this book, what you cannot expect from this book, for whom the book was writ- ten, and how the book is structured.

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Instead they typically utilize a dynamic difficulty level based on the speed at which new blocks are added. 3 This ensures that the time needed to solve the hash puzzle stays at a level that prevents nodes from manipulating the history of transaction data while the actual computational effort may increase. 3Okupski, Krzysztof. Bitcoin developer reference. Working paper. 2014; Wood, Gavin. Ethereum: A secure decentralized generalized transaction ledger. 2014. http://gavwood. com/paper.pdf. Blockchain Basics 143 Outlook This step explained that the blockchain prevents the history of transaction data from being manipulated or forged by turning the blockchain-data-structure into an immutable append-only data store.

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How It Works The idea of selecting a transaction history based on the computational effort that was spent for creating it has led to the following two criteria: • The longest-chain-criterion2 • The heaviest-chain-criterion3 The Longest-Chain-Criterion The longest-chain-criterion is based on the idea that the blockchain-data- structure that comprises the most blocks represents the most aggregated computational effort. In order to study this criterion, let’s consider an initial situation were all the nodes of a distributed system maintain and agree on 2Nakamoto, Satoshi. Bitcoin: A peer-to-peer electronic cash system. 2008. https://bitcoin. org/bitcoin.pdf. 3Wood, Gavin. Ethereum: A secure decentralized generalized transaction ledger. 2014. http://gavwood.com/paper.pdf; Okupski, Krzysztof. Bitcoin developer reference. Working paper. 2014. Blockchain Basics 169 the identical version of the blockchain-data-structure, as depicted in Figure 19-1, which presents a schematic blockchain-data-structure that omits many details for simplicity.

Magic Internet Money: A Book About Bitcoin
by Jesse Berger
Published 14 Sep 2020

Saifedean Ammous, The Bitcoin Standard If blockchain is best understood as a tool for public collaboration, and if block rewards are the method by which participants are enticed to discover, expand, and utilize the network’s value, and if that value can be freely transferred, secured, and accounted for in a trust-minimized environment, then a well-designed blockchain invariably exhibits the attributes and functions of money. Since there is no other embodiment of value that can be both fully functional and fully verifiable as a purely digital entity, money necessarily rises to the fore as blockchain’s most compelling use. Many public blockchains such as Litecoin, Zcash, and Ethereum (more to come on these in Chapter 8) are attempting to funnel trust and value into their networks through the creative adaptation of blockchain principles, effectively testing the limits of its functionality, but none are more proficient than Bitcoin. Its value has grown faster than any other asset since its inception,10 and it is the most liquid of all blockchains.

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Some of the more well-known examples include Litecoin,15 which aspired to be the silver to Bitcoin’s digital gold, and sometimes moonlights as a testnet for Bitcoin. There is Zcash,16 an enigmatic, privacy-focused currency that broadcasted its launch ceremony live on the Internet, and allots 20% of coin issuance to its founders. There is also Ethereum,17 which initially offered its coins as digital gas for fueling a world computer, and presently fuels a complex economy of digital tokens. Within the broader crypto landscape, some projects are clever and well intentioned, some are outright scams, and some are just electronic Rube Goldberg machines18 – overly touted and complicated systems that provide little real value.

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The Litecoin network went live on October 13, 2011. 16Zcash (ZEC) was developed by Electric Coin Company (zcashd) and Zcash Foundation (zebra) on open source. It was initially released on October 28, 2016. 17Ethereum (ETH, also known as Ether) was proposed in late 2013 by Vitalik Buterin, a cryptocurrency researcher and programmer. With its original release on July 30, 2015, Ethereum has an unusually long list of founders. 18Named after American cartoonist Rube Goldberg, this is a machine intentionally designed to perform a simple task in an indirect and overly complicated way. 19Amanda Russo, “Central Banks ‘Waking Up’ to Digital Currency, Create New Framework for CBDC Deployment with World Economic Forum” World Economic Forum (January 22, 2020). 20Nathaniel Popper and Mike Isaac, “Facebook and Telegram Are Hoping to Succeed Where Bitcoin Failed” The New York Times (February 28, 2019). 21Alun John, “China’s digital currency will kick off ‘horse race’: central bank official” Reuters (November 6, 2019).

Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist
by Kate Raworth
Published 22 Mar 2017

Such decentralised networks – ranging from a neighbourhood block to a whole city – build community resilience against blackouts and cut long-distance energy transmission losses at the same time. What’s more, the information embedded in every Ethereum transaction allows network members to put their values into action in the microgrid market, for example by opting to buy electricity from the nearest or greenest suppliers, or only from those that are community-owned or not-for-profit.59 And this is just one example of its potential. ‘Ethereum is a currency for the modern age,’ says the cryptocurrency expert David Seaman. ‘It’s a platform that could be really important to society down the road in ways that we can’t even predict yet.’60 These very different examples illustrate a few of the myriad possibilities of monetary redesign, involving the market, the state and the commons.

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Strassheim, I. (2014) ‘Zeit statt Geld fürs Alter sparen’, Migros-Magazin, 1 September 2014. www.zeitvorsorge.ch/#!/DE/24/Medien.htm 59. DEVCON1 (2016) Transactive Grid: a decentralized energy management system. Presentation at Ethereum Developer Conference, 9–13 November 2015, London, available at: https://www.youtube.com/watch?v=kq8RPbFz5UU 60. Seaman, D. (2015) ‘Bitcoin vs. Ethereum explained for NOOBZ’, published 30 November 2015, available at: https://www.youtube.com/watch?v=rEJKLFH8q5c 61. Trades Union Congress (2012) The Great Wages Grab. London: TUC. https://www.tuc.org.uk/sites/default/files/tucfiles/TheGreatWagesGrab.pdf 62.

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And since that record is stored on every computer in the network, it acts as a public ledger that cannot be altered, corrupted or deleted, making it a highly secure digital backbone for the future of e-commerce and transparent governance. One fast-rising digital currency that uses blockchain technology is Ethereum, which, among its many possible applications, is enabling electricity microgrids to set up peer-to-peer trading in renewable energy. These microgrids allow every nearby home, office or institution with a smart meter, Internet connection, and solar panel on its roof to hook in and sell or buy surplus electrons as they are generated, all automatically recorded in units of the digital currency.

The Knowledge Illusion
by Steven Sloman
Published 10 Feb 2017

Platforms to support this kind of decentralized collaborative activity are just coming into being with futuristic names like Ethereum, Sensorica, and Colony. Ethereum is inspired by the success of Bitcoin, an Internet currency that is decentralized, not administered by any single entity. The information about who owns how much Bitcoin is stored in a public ledger of transactions called a block chain. A block chain is a sophisticated technology for maintaining a record of all transactions that is updated and stored across the network of Bitcoin users. Distributing the ledger of transactions across the network is a good way to prevent mistakes and cheating. Ethereum uses a block chain method to allow collaboration via decentralized agreement of everyone involved in a project.

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See chaos theory consequences vs. values arguments, 182–87 contribution of individuals example of group thinking, 122 Copernicus, Nicolaus, 198–99 counterfactual thought, 64–65 Galileo’s experiments with dropping different weights, 65–66 imagining scenarios to figure out likely outcomes, 66 crowdsourcing expertise, 146–50 ox’s weight example, 148 Pallokerho-35 Finnish soccer club example, 148 prediction market, 149 user ratings, 148 crows ability to reason diagnostically, 62 CRT (Cognitive Reflection Test), 80–84 bat and ball problem, 81 lily pad problem, 81–82 machines and widgets problem, 82 crystallized intelligence, 202 cult communities, 260 cultural values and cognition, 160–63 reconciling conflicting beliefs, 161–62 “Science Mike” (Mike McHargue), 160–62 cumulative culture, 117–18 curse of knowledge, 128, 244 curving bullets example of physics, 69–70 Dalio, Ray, 253 Damasio, Antonio, 103 decentralized collaborative activity, 149–50 Bitcoin, 150 block chain technology, 150 Ethereum, 150 decision-making, 103–05, 240, 241, 248–49, 250–53 deficit model of science attitudes, 157–60 Dehghani, Morteza, 185–86 Descartes, René, 87 de Soto, Hernando, 244–45 DeVito, Danny, 45–46 Dewey, John, 216 diagnostic reasoning, 58–62 crow example, 62 lethargy example, 59–61 diSessa, Andrea, 71 disgust, feelings of, 104–05 division of cognitive labor, 14, 109–11, 120–21, 128–29 area of expertise example, 120 car analogy example, 207–08 in the field of science, 222–23 household finances, 247 wine expert example, 120 dogs Cassie example, 49–50 Pavlovian conditioning, 50–51 doorway example of optic flow, 99–100 driving ability example of ignorance, 257–58 Dunbar, Robin, 113 Dunning, David, 257–58 Dunning-Kruger effect, 258 Eastwood, Clint, 172 economics of science, 227–28 education application of classroom learning, 216–17 becoming a car mechanic example, 219–20 expressing desire to learn that which is unknown, 221 financial issues, 240–41 history of Spain example, 220 Ignorance course, 221 illusion of comprehension, 217–18 just-in-time, 251–52 learning to accept what you don’t know, 220–21 mathematical abilities of Brazilian children, 215–16 peer, 230–31 purpose of, 219–21 teaching science, 222, 225–32 Einstein, Albert, 199 embodied intelligence, 91–93 embodiment, 102 emotional responses that influence decision-making, 103–05, 240 engagement as a human concept, 117 environment, knowledge of your personal, 94–96 Ethereum, 150 expertise and crowdsourcing, 146–50 in scientific matters, 226–27 to understand community issues, 188–89 explanation foes and fiends, 237–39 advertising, 239–40, 241–42 Band-Aids example, 237–38 skin care example, 239–40 vesting service letter example, 243–44 explorers’ self-confidence, 263 eyesight.

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See chaos theory consequences vs. values arguments, 182–87 contribution of individuals example of group thinking, 122 Copernicus, Nicolaus, 198–99 counterfactual thought, 64–65 Galileo’s experiments with dropping different weights, 65–66 imagining scenarios to figure out likely outcomes, 66 crowdsourcing expertise, 146–50 ox’s weight example, 148 Pallokerho-35 Finnish soccer club example, 148 prediction market, 149 user ratings, 148 crows ability to reason diagnostically, 62 CRT (Cognitive Reflection Test), 80–84 bat and ball problem, 81 lily pad problem, 81–82 machines and widgets problem, 82 crystallized intelligence, 202 cult communities, 260 cultural values and cognition, 160–63 reconciling conflicting beliefs, 161–62 “Science Mike” (Mike McHargue), 160–62 cumulative culture, 117–18 curse of knowledge, 128, 244 curving bullets example of physics, 69–70 Dalio, Ray, 253 Damasio, Antonio, 103 decentralized collaborative activity, 149–50 Bitcoin, 150 block chain technology, 150 Ethereum, 150 decision-making, 103–05, 240, 241, 248–49, 250–53 deficit model of science attitudes, 157–60 Dehghani, Morteza, 185–86 Descartes, René, 87 de Soto, Hernando, 244–45 DeVito, Danny, 45–46 Dewey, John, 216 diagnostic reasoning, 58–62 crow example, 62 lethargy example, 59–61 diSessa, Andrea, 71 disgust, feelings of, 104–05 division of cognitive labor, 14, 109–11, 120–21, 128–29 area of expertise example, 120 car analogy example, 207–08 in the field of science, 222–23 household finances, 247 wine expert example, 120 dogs Cassie example, 49–50 Pavlovian conditioning, 50–51 doorway example of optic flow, 99–100 driving ability example of ignorance, 257–58 Dunbar, Robin, 113 Dunning, David, 257–58 Dunning-Kruger effect, 258 Eastwood, Clint, 172 economics of science, 227–28 education application of classroom learning, 216–17 becoming a car mechanic example, 219–20 expressing desire to learn that which is unknown, 221 financial issues, 240–41 history of Spain example, 220 Ignorance course, 221 illusion of comprehension, 217–18 just-in-time, 251–52 learning to accept what you don’t know, 220–21 mathematical abilities of Brazilian children, 215–16 peer, 230–31 purpose of, 219–21 teaching science, 222, 225–32 Einstein, Albert, 199 embodied intelligence, 91–93 embodiment, 102 emotional responses that influence decision-making, 103–05, 240 engagement as a human concept, 117 environment, knowledge of your personal, 94–96 Ethereum, 150 expertise and crowdsourcing, 146–50 in scientific matters, 226–27 to understand community issues, 188–89 explanation foes and fiends, 237–39 advertising, 239–40, 241–42 Band-Aids example, 237–38 skin care example, 239–40 vesting service letter example, 243–44 explorers’ self-confidence, 263 eyesight.

Ours to Hack and to Own: The Rise of Platform Cooperativism, a New Vision for the Future of Work and a Fairer Internet
by Trebor Scholz and Nathan Schneider
Published 14 Aug 2017

However, to deliver the AI-powered features that near-future users will demand, applications will need to draw upon sophisticated industrial-strength AI software services and harness powerful clusters of data-mining server farms. This stuff doesn’t come cheap. Free, open, and radically decentralized AI isn’t a thing yet, but blockchain-based platforms like Ethereum and Backfeed could offer decentralized alternatives to the corporate cloud. More libre but not gratis, as you’ll pay for decentralization with cryptocurrency. In its infancy, Ethereum is far more expensive than the Amazon cloud but with laughable performance and capability by comparison. Can you afford to wait for the decentralized solution or do you accept that a corporate cloud is presently your only viable high-performance and affordable option?

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Put differently, cooperatives tended to focus too much on how the value would be shared rather than on a compelling offer to create the value in the first place. Perhaps part of the solution will come from the possibility, created by blockchain technologies, of “distributed collaborative organizations,” or DCOs—new decentralized collectives that, in the eyes of pioneers like Matan Field of Backfeed and Vitalik Buterin of Ethereum, can use rules embedded in computer code to align the incentives of different contributors, of financial capital, of expertise, of labor, and of participation. These DCOs are connected intellectually to a variety of related decentralized ownership models. They range from the FairShare Model of Karl Sjogren, which proposes a structure of different classes of ownership shares for different contributors—for founders, people with a continuous working role, for users, and for investors—to the Swarm approach to “crypto-equity” crowdfunding developed by Joel Dietz.

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We started with an approximately $1 million crowdfunding campaign around our own blockchain-issued asset, the Swarm coin. After much of the funds were exhausted in the process of our own legal research and the coin’s price fluctuations, we financed development through corporate partnerships. At this point we are re-engineering our initial proof of concepts (developed on Bitcoin) to release ready concepts on Ethereum. We are currently building a global ambassador network through training events in various cities across the globe. We are also currently exploring other projects that might bridge to a mainstream audience and serve as a proof of concepts for both the future of governance and community abundance.

Personal Finance with Python
by Max Humber

—Chief Keef A couple of weeks ago my grandma asked me if she should put some money into Bitcoin. I didn’t know what to tell her. But I knew that in a book about finance I would have to at least give Bitcoin and cryptocurrencies at least a little bit of lip service. For the uninitiated, cryptocurrencies like Bitcoin (and Ethereum, Dogecoin, and Zcash) are digital assets that are designed to function as a medium of exchange and that use cryptography to secure transactions, to control the creation of new money, and to verify asset transfer. Because I think it’s hilarious, I’m going to use Dogecoin1 as the glue for the rest of this chapter.

…

print(c.convert(3000, 'CAD', 'USD')) print(c.convert(5000, 'USD', 'CAD')) 2302.35 6515.08 show_alternative The Open Exchange Rates API is incredibly robust, and it actually includes access points for alternative cryptocurrencies. This means that it’s totally legit to instantiate a new CurrencyConverter with ETH (Ethereum), BTC (Bitcoin), and DOGE (Dogecoin) on top of CAD and USD. c = CurrencyConverter(['CAD', 'USD', 'DOGE', 'ETH', 'BTC'], API_KEY) With all the currencies stored inside of a dictionary attached to the CurrencyConverter object: c.rates_ {'BTC': 0.00013350885, 'CAD': 1.303016, 'DOGE': 289.975486957, 'ETH': 0.0017451855, 'USD': 1} we can, again, run the .convert method and find out that $3,000 CAD is equal to the following: c.convert(3000, 'CAD', 'DOGE') 667625.31 .apply The whole point of this chapter was to figure out what the values from previous chapter were in USD instead of CAD.

Tribe of Mentors: Short Life Advice From the Best in the World
by Timothy Ferriss
Published 14 Jun 2017

Vitalik Buterin TW: @VitalikButerin Reddit: /u/vbuterin VITALIK BUTERIN is the creator of Ethereum. He first discovered blockchain and cryptocurrency technologies through Bitcoin in 2011, and was immediately excited by the technology and its potential. He co-founded Bitcoin magazine in September 2011, and after two and a half years looking at what the existing blockchain technology and applications had to offer, wrote the Ethereum white paper in November 2013. He now leads Ethereum’s research team, working on future versions of the Ethereum protocol. In 2014, Vitalik was a recipient of the two-year Thiel Fellowship, tech billionaire Peter Thiel’s project that awards $100,000 to 20 promising innovators under 20 so they can pursue their inventions in lieu of a post-secondary institution

…

It’s the kind of thing you see in Japan: a notebook turned into a high art. Maybe next year. . . . “No one is qualified to tell you how you experience the world.” Vlad Zamfir TW: @VladZamfir Medium: @vlad_zamfir vladzamfir.com VLAD ZAMFIR is a blockchain architect and researcher at Ethereum, working on blockchain efficiency and scaling. Vlad is interested in governance and privacy solutions, and he was also the first person to introduce me to absurdism. He is a frequent contributor on Medium and lives in Antarctica (or so he wants us to believe). What is the book (or books) you’ve given most as a gift, and why?

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See also Food(s); Nutrition at Aoki Bootcamp, 522 cutting out sugar, 405, 434 improving live through, 118, 434, 448 lactose intolerance, 406 low-carb, 480–81 meat industry, 295–96 misinformation on, 488 no-carb, 508 slow-carb, 448 Whole30, 295 DigiCash, 507 Dillard, Annie, 375 Diller, Barry, 206 Dim Mak Collection, 519 Dim Mak Records, 519, 520 Disney, Walt, 93 Disraeli, Benjamin, 210 Disruptive technology, 222–23, 295, 346 Doctor, Ken, 437 Dogspotting, 101–2 Douglas, Michael, 328 Douglass, Frederick, 210 Dropbox, 456 Drucker, Peter, 140, 205, 458 Duffin, Chris, 317 Duke, Annie, 171–74 Duncan, Graham, 56–63 Duolingo, 250 Duterimbere, 324 Dyson, Esther, 222, 243–45 E East Rock Capital, 56 eBay, 92 Ebroji, 79 Echelon Front, 536 Education Networks of America, Inc., 289 EDventure Holdings, 243 Effective Altruism, 300 Efferding, Mark, 318 Efferding, Stan, 318 Egg boxing, 516 Einstein, Albert, 51, 232, 356, 375, 515 Ek, Daniel, 286–88 Eligible, 243 Elizabeth Arden, 87 Ellison, Larry, 446 El Rey Network, 541, 544 Emerson, Ralph Waldo, 21, 123, 178, 253, 528–29 Eminem, 239 Emotional intelligence, 557–58 Endeavor Global, 349–51 Enlightenment Intensive, 343 Enneagram, 456–57 Environmental Institute for Golf, 283 Epicurus, 418 Epinions.com, 31 Epitaph Test, 47, 49 Erwin, Brian, 221 Ethereum, 153, 501 Évora, Cesária, 12 Exercise, 493, 522. See also specific forms, e.g.: CrossFit for basic fitness, 422, 465–66 to improve life, 217, 222, 290, 490 investing in, 497 as investing in yourself, 212–13 outdoors, 98 to refocus, 535 for stress relief, 530, 531 sweating during, 393 F Facebook, 82 Fallon, Jimmy, 175–77 Fame, 149–50 farWord Inc., 79 Fasting, 448 Fatalism, 119–20 Fear, 21, 547–48, 552–53 Fenway Strategies, 353 Ferdowsi, Arash, 456 Festool, 381 Fidgitz, 405 FINIS swim paddles, 59 First Aid Only H5041-AMP Ammonia Inhalant Ampoules, 387 First Look Media, 141 Fischer, Bobby, 186 Fisher, Adam, 428–30 Fitness, 290, 422, 465 The Five-Minute Journal, 306 Five whys, 67–68 Flexibility training, 388 Floodgate, 64, 65, 200 Florenz, Gabriel, 51 Food(s).

The Curse of Cash
by Kenneth S Rogoff
Published 29 Aug 2016

Because the technology is evolving so rapidly, I am hesitant to go into much more detail, beyond saying that phasing out paper currency does not really move the needle much on society’s vulnerability to cybercrime. Some of the present-day obstacles to improving security are really more political than economic. Some innovations in security, such as the potentially disruptive distributed-ledger technology embodied in cryptocurrencies like Bitcoin or Ethereum, may eventually lead to major improvements in financial security, at least at the core of the payment system, as discussed further in chapter 14. It is particularly hard to see in any of these arguments why large-denomination notes are important. Probably they would be looked on askance after a power outage, earthquake, or other kind of catastrophe.

…

And some applications of distributed-ledger technology offer the promise of cutting out intermediaries in transactions between, say, two banks. This would substantially reduce costs, particularly in international transactions. The approach can also be used to save on legal contracting costs. Some of Bitcoin’s competitors, notably the newer Ethereum platform, aim to offer the possibility of creating secure exchanges for transactions of almost any type. People sometimes ask whether the cryptocurrency Bitcoin could be a currency (supposing that the government does not interfere). The answer is certainly yes, Bitcoin (or perhaps one of its present or future competitors) can fulfill many of the basic functions of currency, including unit of account and medium of exchange, with or without government adherence.3 In fact, digital currencies in some ways offer the capacity for much more complex kinds of transactions and contracts than traditional paper currency offers, precisely because the former embed so much information, including the history of transactions.

…

The answer is certainly yes, Bitcoin (or perhaps one of its present or future competitors) can fulfill many of the basic functions of currency, including unit of account and medium of exchange, with or without government adherence.3 In fact, digital currencies in some ways offer the capacity for much more complex kinds of transactions and contracts than traditional paper currency offers, precisely because the former embed so much information, including the history of transactions. Already, markets are forming to exploit this capacity, for example, in applications surrounding Ethereum.4 That distributed-ledger technology could in theory someday produce a superior currency, however, hardly means that the world is already there in practice. One problem is that the value of Bitcoin 1.0 fluctuates wildly (figure 14.1), so it hardly fulfills the function of a stable store of value.

Radical Markets: Uprooting Capitalism and Democracy for a Just Society
by Eric Posner and E. Weyl
Published 14 May 2018

Liran Einav, Chiara Farronato, & Jonathan Levin, Peer-to-Peer Markets, 8 Annual Review of Economics 615 (2016). 47. Chris Nosko & Steven Tadelis, The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment (National Bureau of Economic Research, Working Paper No. 20830, 2015). 48. Andrew Quentson, Can Ethereum-Based Akasha Revolutionize Social Networks? Cryptocoins News, January 29, 2017, https://www.cryptocoinsnews.com/can-ethereum-based-akasha-revolutionize-social-networks/. 49. See Eric A. Posner & E. Glen Weyl, Quadratic Voting as Efficient Corporate Governance, 81 University of Chicago Law Review 251 (2014), for more on the application to corporate governance. 50.

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Such a system combines the best of both tipping and rating, creating a real cost to expressing enthusiasm, but also discouraging free-riding and allowing other participants to benefit from the feedback. A version of this system is being implemented by a social network called Akasha, based on the increasingly prominent Ethereum cryptocurrency.48 QV fits with the framework of cryptocurrencies, which require formal governance rules to allow for the decentralized management they rely on, so using it also for social aggregation in such a context is natural. However, the exact implementation is unclear at the time of this writing, and not available to the public; much in the world of cryptocurrencies is secretive.

…

INDEX Italic page numbers indicate figures and tables abortion, 27, 112–13, 116 Acemoglu, Daron, 240, 316n4 activism, 3, 124, 140, 176–77, 188, 193, 211, 232 Adachi, Kentaro, 80–81, 105–8 Africa, 136, 138 African Americans, 24, 89, 209–10 Airbnb, 70, 117 airlines, 171, 183, 189–91, 194 Akerlof, George, 66–67 algorithms, 208, 214, 219, 221, 281–82, 289–93, 307n7 Allen, Robert C., 240 Amazon, 112, 230–31, 234, 239, 248, 288, 290–91 American Constitution, 86–87 American Federation of Musicians, 210 American Tobacco Company, 174 America OnLine (AOL), 210 Anderson, Chris, 212 antitrust: Clayton Act and, 176–77, 197, 311n25; landlords and, 201–2; monopolies and, 23, 48, 174–77, 180, 184–86, 191, 197–203, 242, 255, 262, 286; resale price maintenance and, 200–201; social media and, 202 Apple, 117, 239, 289 Arginoussai Islands, 83 aristocracy, 16–17, 22–23, 36–38, 84–85, 87, 90, 135–36 Aristotle, 172 Arrow, Kenneth, 92, 303n17 Articles of Confederation, 88 artificial intelligence (AI), 202, 257, 287; Alexa and, 248; algorithms and, 208, 214, 219, 221, 281–82, 289–93; automated video editing and, 208; Cortana and, 219; data capacities and, 236; Deep Blue and, 213; democratization of, 219; diminishing returns and, 229–30; facial recognition and, 208, 216–19; factories for thinking machines and, 213–20; Google Assistant and, 219; human-produced data for, 208–9; marginal value and, 224–28, 247; Microsoft and, 219; neural networks and, 214–19; payment systems for, 224–30; recommendation systems and, 289–90; siren servers and, 220–24, 230–41, 243; Siri and, 219, 248; technofeudalism and, 230–33; techno-optimists and, 254–55, 316n2; techno-pessimists and, 254–55, 316n2; worker replacement and, 223 Athens, 55, 83–84, 131 Atwood, Margaret, 18–19 auctions, xv–xxi, 49–51, 70–71, 97, 99, 147–49, 156–57, 300n34 au pair program, 154–55, 161 Australia, 10, 12, 13, 159, 162 Austrian school, 2 Autor, David, 240 Azar, José, 185, 189, 310n24 Bahrain, 158 banking industry, 182–84, 183, 190 Bank of America, 183, 184 Becker, Gary, 147 Beckford, William, 95 behavioral finance, 180–81 Bénabou, Roland, 236–37 Bentham, Jeremy, 4, 35, 95–96, 98, 132 Berle, Adolf, 177–78, 183, 193–94 Berlin Wall, 1, 140 Berners-Lee, Tim, 210 big data, 213, 226, 293 Bing, xxi BlackRock, 171, 181–84, 183, 187, 191 Brazil, xiii–xvii, 105, 135 Brin, Sergey, 211 broadcast spectrum, xxi, 50–51, 71 Bush, George W., 78 Cabral, Luís, 202 Cadappster app, 31 Caesar, Julius, 84 Canada, 10, 13, 159, 182 capitalism, xvi; basic structure of, 24–25; competition and, 17 (see also competition); corporate planning and, 39–40; cultural consequences of, 270, 273; Engels on, 239–40; freedom and, 34–39; George on, 36–37; growth and, 3 (see also growth, economic); industrial revolution, 36, 255; inequality and, 3 (see also inequality); labor and, 136–37, 143, 159, 165, 211, 224, 231, 239–40, 316n4; laissez-faire, 45; liberalism and, 3, 17, 22–27; markets and, 278, 288, 304n36; Marx on, 239–40; monopolies and, 22–23, 34–39, 44, 46–49, 132, 136, 173, 177, 179, 199, 258, 262; monopsony and, 190, 199–201, 223, 234, 238–41, 255; ownership and, 34–36, 39, 45–49, 75, 78–79; property and, 34–36, 39, 45–49, 75, 78–79; Radical Markets and, 169, 180–85, 203, 273; regulations and, 262; Schumpeter on, 47; shareholders and, 118, 170, 178–84, 189, 193–95; technology and, 34, 203, 316n4; wealth and, 45, 75, 78–79, 136, 143, 239, 273 Capitalism and Freedom (Friedman), xiii Capitalism for the People, A (Luigi), 203 Capra, Frank, 17 Carroll, Lewis, 176 central planning: computers and, 277–85, 288–93; consumers and, 19; democracy and, 89; governance and, 19–20, 39–42, 46–48, 62, 89, 277–85, 288–90, 293; healthcare and, 290–91; liberalism and, 19–20; markets and, 277–85, 288–93; property and, 39–42, 46–48, 62; recommendation systems and, 289–90; socialism and, 39–42, 47, 277, 281 Chetty, Raj, 11 Chiang Kai-shek, 46 China, 15, 46, 56, 133–34, 138 Christensen, Clayton, 202 Chrysler, 193 Citigroup, 183, 184, 191 Clarke, Edward, 99, 102, 105 Clayton Act, 176–77, 197, 311n25 Clemens, Michael, 162 Coase, Ronald, 40, 48–51, 299n26 Cold War, xix, 25, 288 collective bargaining, 240–41 collective decisions: democracy and, 97–105, 110–11, 118–20, 122, 124, 273, 303n17, 304n36; manipulation of, 99; markets for, 97–105; public goods and, 98; Quadratic Voting (QV) and, 110–11, 118–20, 122, 124, 273, 303n17, 304n36; Vickrey and, 99, 102, 105 colonialism, 8, 131 Coming of the Third Reich, The (Evans), 93 common ownership self-assessed tax (COST): broader application of, 273–76; cybersquatters and, 72; education and, 258–59; efficiency and, 256, 261; equality and, 258; globalization and, 269–70; growth and, 73, 256; human capital and, 258–61; immigrants and, 261, 269, 273; inequality and, 256–59; international trade and, 270; investment and, 258–59, 270; legal issues and, 275; markets and, 286; methodology of, 63–66; monopolies and, 256–61, 270, 300n43; objections to, 300n43; optimality and, 61, 73, 75–79, 317n18; personal possessions and, 301n47, 317n18; political effects of, 261–64; predatory outsiders and, 300n43; prices and, 62–63, 67–77, 256, 258, 263, 275, 300n43, 317n18; property and, 31, 61–79, 271–74, 300n43, 301n47; public goods and, 256; public leases and, 69–72; Quadratic Voting (QV) and, 123–25, 194, 261–63, 273, 275, 286; Radical Markets and, 79, 123–26, 257–58, 271–72, 286; taxes and, 61–69, 73–76, 258–61, 275, 317n18; technology and, 71–72, 257–59; true market economy and, 72–75; voting and, 263; wealth and, 256–57, 261–64, 269–70, 275, 286 communism, 19–20, 46–47, 93–94, 125, 278 competition: antitrust policies and, 23, 48, 174–77, 180, 184–86, 191, 197–203, 242, 255, 262, 286; auctions and, xv–xix, 49–51, 70–71, 97, 99, 147–49, 156–57; bargaining and, 240–41, 299n26; democracy and, 109, 119–20; by design, 49–55; elitism and, 25–28; equilibrium and, 305n40; eternal vigilance and, 204; horizontal concentration and, 175; imperfect, 304n36; indexing and, 185–91, 302n63; innovation and, 202–3; investment and, 196–97; labor and, 145, 158, 162–63, 220, 234, 236, 239, 243, 245, 256, 266; laissez-faire and, 253; liberalism and, 6, 17, 20–28; lobbyists and, 262; monopolies and, 174; monopsony and, 190, 199–201, 223, 234, 238–41, 255; ownership and, 20–21, 41, 49–55, 79; perfect, 6, 25–28, 109; prices and, 20–22, 25, 173, 175, 180, 185–90, 193, 200–201, 204, 244; property and, 41, 49–55, 79; Quadratic Voting (QV) and, 304n36; regulations and, 262; resale price maintenance and, 200–201; restoring, 191–92; Section 7 and, 196–97, 311n25; selfishness and, 109, 270–71; Smith on, 17; tragedy of the commons and, 44 complexity, 218–20, 226–28, 274–75, 279, 281, 284, 287, 313n15 “Computer and the Market, The” (Lange), 277 computers: algorithms and, 208, 214, 219, 221, 281–82, 289–93; automation of labor and, 222–23, 251, 254; central planning and, 277–85, 288–93; data and, 213–14, 218, 222, 233, 244, 260; Deep Blue, 213; distributed computing and, 282–86, 293; growth in poor countries and, 255; as intermediaries, 274; machine learning (ML) and, 214 (see also machine learning [ML]); markets and, 277, 280–93; Mises and, 281; Moore’s Law and, 286–87; Open-Trac and, 31–32; parallel processing and, 282–86; prices of, 21; recommendation systems and, 289–90 Condorcet, Marquis de, 4, 90–93, 303n15, 306n51 conspicuous consumption, 78 Consumer Reports magazine, 291 consumers: antitrust suits and, 175, 197–98; central planning and, 19; data from, 47, 220, 238, 242–44, 248, 289; drone delivery to, 220; as entrepreneurs, 256; goods and services for, 27, 92, 123, 130, 175, 280, 292; institutional investment and, 190–91; international culture for, 270; lobbyists and, 262; machine learning (ML) and, 238; monopolies and, 175, 186, 197–98; preferences of, 280, 288–93; prices and, 172 (see also prices); recommendation systems and, 289–90; robots and, 287; sharing economy and, 117; Soviet collapse and, 289; technology and, 287 cooperatives, 118, 126, 261, 267, 299n24 Corbyn, Jeremy, 12, 13 corruption, 3, 23, 27, 57, 93, 122, 126, 157, 262 Cortana, 219 cost-benefit analysis, 2, 244 “Counterspeculation, Auctions and Competitive Sealed Tenders” (Vickrey), xx–xxi Cramton, Peter, 52, 54–55, 57 crowdsourcing, 235 crytocurrencies, 117–18 cybersquatters, 72 data: algorithms and, 208, 214, 219, 221, 281–82, 289–93; big, 213, 226, 293; computers and, 213–14, 218, 222, 233, 244, 260; consumer, 47, 220, 238, 242–44, 248, 289; diamond-water paradox and, 224–25; diminishing returns and, 226, 229–30; distribution of complexity and, 228; as entertainment, 233–39, 248–49; Facebook and, 28, 205–9, 212–13, 220–21, 231–48; feedback and, 114, 117, 233, 238, 245; free, 209, 211, 220, 224, 231–35, 239; Google and, 28, 202, 207–13, 219–20, 224, 231–36, 241–42, 246; investment in, 212, 224, 232, 244; labeled, 217–21, 227, 228, 230, 232, 234, 237; labor movement for, 241–43; Lanier and, 208, 220–24, 233, 237, 313n2, 315n48; marginal value and, 224–28, 247; network effects and, 211, 236, 238, 243; neural networks and, 214–19; online services and, 211, 235; overfitting and, 217–18; payment systems for, 210–13, 224–30; photographs and, 64, 214–15, 217, 219–21, 227–28, 291; programmers and, 163, 208–9, 214, 217, 219, 224; Radical Markets for, 246–49; reCAPTCHA and, 235–36; recommendation systems and, 289–90; rise of data work and, 209–13; sample complexity and, 217–18; siren servers and, 220–24, 230–41, 243; social networks and, 202, 212, 231, 233–36; technofeudalism and, 230–33; under-employment and, 256; value of, 243–45; venture capital and, 211, 224; virtual reality and, 206, 208, 229, 251, 253; women’s work and, 209, 313n4 Declaration of Independence, 86 Deep Blue, 213 DeFoe, Daniel, 132 Demanding Work (Gray and Suri), 233 democracy: 1p1v system and, 82–84, 94, 109, 119, 122–24, 304n36, 306n51; artificial intelligence (AI) and, 219; Athenians and, 55, 83–84, 131; auctions and, 97, 99; basic structure of, 24–25; central planning and, 89; check and balance systems and, 23, 25, 87, 92; collective decisions and, 97–105, 110–11, 118–20, 122, 124, 273, 303n17, 304n36; collective mediocrity and, 96; competition and, 109, 119–20; Declaration of Independence and, 86; efficiency and, 92, 110, 126; elections and, 22, 80, 93, 100, 115, 119–21, 124, 217–18, 296n20; elitism and, 89–91, 96, 124; Enlightenment and, 86, 95; Europe and, 90–96; France and, 90–95; governance and, 84, 117; gridlock and, 84, 88, 122–24, 261, 267; Hitler and, 93–94; House of Commons and, 84–85; House of Lords and, 85; impossibility theorem and, 92; inequality and, 123; Jury Theorem and, 90–92; liberalism and, 3–4, 25, 80, 86, 90; limits of, 85–86; majority rule and, 27, 83–89, 92–97, 100–101, 121, 306n51; markets and, 97–105, 262, 276; minorities and, 85–90, 93–97, 101, 106, 110; mixed constitution and, 84–85; multi-candidate, single-winner elections and, 119–20; origins of, 83–85; ownership and, 81–82, 89, 101, 105, 118, 124; public goods and, 28, 97–100, 107, 110, 120, 123, 126; Quadratic Voting (QV) and, 105–22; Radical Markets and, 82, 106, 123–26, 203; supermajorities and, 84–85, 88, 92; tyrannies and, 23, 25, 88, 96–100, 106, 108; United Kingdom and, 95–96; United States and, 86–90, 93, 95; voting and, 80–82, 85–93, 96, 99, 105, 108, 115–16, 119–20, 123–24, 303n14, 303n17, 303n20, 304n36, 305n39; wealth and, 83–84, 87, 95, 116 Demosthenes, 55 Denmark, 182 Department of Justice (DOJ), 176, 186, 191 deregulation, 3, 9, 24 Desmond, Matthew, 201–2 Dewey, John, 43 Dickens, Charles, 36 digital economy: data producers and, 208–9, 230–31; diamond-water paradox and, 224–25; as entertainment, 233–39; facial recognition and, 208, 216, 218–19; free access and, 211; Lanier and, 208, 220–24, 233, 237, 313n2, 315n48; machine learning (ML) and, 208–9, 213–14, 217–21, 226–31, 234–35, 238, 247, 289, 291, 315n48; payment systems for, 210–13, 221–30, 243–45; programmers and, 163, 208–9, 214, 217, 219, 224; rise of data work and, 209–13; siren servers and, 220–24, 230–41, 243; spam and, 210, 245; technofeudalism and, 230–33; virtual reality and, 206, 208, 229, 251, 253 diversification, 171–72, 180–81, 185, 191–92, 194–96, 310n22, 310n24 dot-com bubble, 211 double taxation, 65 Dupuit, Jules, 173 Durkheim, Émile, 297n23 Dworkin, Ronald, 305n40 dystopia, 18, 191, 273, 293 education, 114; common ownership self-assessed tax (COST) and, 258; data and, 229, 232, 248; elitism and, 260; equality in, 89; financing, 276; free compulsory, 23; immigrants and, 14, 143–44, 148; labor and, 140, 143–44, 148, 150, 158, 170–71, 232, 248, 258–60; Mill on, 96; populist movements and, 14; Stolper-Samuelson Theorem and, 143 efficient capital markets hypothesis, 180 elections, 80; data and, 217–18; democracy and, 22, 93, 100, 115, 119–21, 124, 217–18, 296n20; gridlock and, 124; Hitler and, 93; multi-candidate, single-winner, 119–20; polls and, 13, 111; Quadratic Voting (QV) and, 115, 119–21, 268, 306n52; U.S. 2016, 93, 296n20 Elhauge, Einer, 176, 197 elitism: aristocracy and, 16–17, 22–23, 36–38, 84–85, 87, 90, 135–36; bourgeoisie and, 36; bureaucrats and, 267; democracy and, 89–91, 96, 124; education and, 260; feudalism and, 16, 34–35, 37, 41, 61, 68, 136, 230–33, 239; financial deregulation and, 3; immigrants and, 146, 166; liberalism and, 3, 15–16, 25–28; minorities and, 12, 14–15, 19, 23–27, 85–90, 93–97, 101, 106, 110, 181, 194, 273, 303n14, 304n36; monarchies and, 85–86, 91, 95, 160 Emergency Economic Stabilization Act, 121 eminent domain, 33, 62, 89 Empire State Building, 45 Engels, Friedrich, 78, 240 Enlightenment, 86, 95 entrepreneurs, xiv; immigrants and, 144–45, 159, 256; labor and, 129, 144–45, 159, 173, 177, 203, 209–12, 224, 226, 256; ownership and, 35, 39 equality: common ownership self-assessed tax (COST) and, 258; education and, 89; immigrants and, 257; labor and, 147, 166, 239, 257; liberalism and, 4, 8, 24, 29; living standards and, 3, 11, 13, 133, 135, 148, 153, 254, 257; Quadratic Voting (QV) and, 264; Radical Markets and, 262, 276; trickle down theories and, 9, 12 Espinosa, Alejandro, 30–32 Ethereum, 117 Europe, 177, 201; democracy and, 88, 90–95; European Union and, 15; fiefdoms in, 34; government utilities and, 48; income patterns in, 5; instability in, 88; labor and, 11, 130–31, 136–47, 165, 245; social democrats and, 24; unemployment rates in, 11 Evans, Richard, 93 Evicted (Desmond), 201–2 Ex Machina (film), 208 Facebook, xxi; advertising and, 50, 202; data and, 28, 205–9, 212–13, 220–21, 231–48; monetization by, 28; news service of, 289; Vickrey Commons and, 50 facial recognition, 208, 216–19 family reunification programs, 150, 152 farms, 17, 34–35, 37–38, 61, 72, 135, 142, 179, 283–85 Federal Communications Commission (FCC), 50, 71 Federal Trade Commission (FTC), 176, 186 feedback, 114, 117, 233, 238, 245 feudalism, 16, 34–35, 37, 41, 61, 68, 136, 230–33, 239 Fidelity, 171, 181–82, 184 financial crisis of 2008, 3, 121 Fitzgerald, F.

Decoding the World: A Roadmap for the Questioner
by Po Bronson
Published 14 Jul 2020

He had been on 4chan, the dark web, looking for vintage porn to buy and sell for profit. He went on Silk Road and saw people using Bitcoin, and he thought it was more of a PayPal-type thing. But as he learned more, he started buying it, a little every month. One night, here at IndieBio, Tall Joe hosted the first-ever Ethereum Meetup Group for San Francisco. Thirteen people showed up, but one of them was the CTO for the Ethereum Foundation. Ethereum was almost out of money, and at the time nobody knew if Ether would survive. But Joe bought a bunch because he thought smart contracts made sense. Joe took a coding class in San Jose and started buying into ICOs he thought would have a real market.

…

Let me revise that; they’re not agreeing to pay your debt long-term, it’s more like your parents would be your escrow company, putting up their own money only for the length of time it takes for the transaction to go through. This method would end the arms race, but making this paradigm shift would be too radical for Bitcoin to go first. So other crypto assets, like Ethereum, will do it first. The Bitcoin community is pretty convinced they need to find a new solution. But the community is very divided over what the new solution should be. So let’s summarize a little bit. 1. Bitcoin has no intrinsic value. 2. But big money needed an alternative to the dollar. 3.

How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs
by Tim Draper
Published 18 Dec 2017

In effect, people found that they could create their own currencies using Bitcoin as a model. These would be known as decentralized autonomous organizations (DAOs). The first of these, DAO Maker, had an inauspicious beginning. The company used Ethereum (a decentralized currency built using a protocol similar to the Bitcoin blockchain) as its platform. A hacker figured out that when money was moving from one entity to another, they could siphon off the Ethereum currency, called Ether, collected from the sale of the tokens. The hacker managed to steal 3.6 million Ether (at the time valued around $72 million) and the price of Ether dropped from $20 to about $13.

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Industries transformed by the Internet: Information (Google), Shopping (Amazon), Communications (Skype), Entertainment (Netflix), Media (iTunes), Gaming (Minecraft), Community (Facebook) Industries transformed by the marketplace: Transportation (Uber), Hotels (Airbnb), Startups (AngelList), Workforce (Thumbtack), Lawyers (LawTrades), PR (PRx), Brokerage (Robinhood), Interior Decorating (Laurel and Wolf), Stock Market (Equidate, EquityZen), Cap Tables (eShares, Capshare), Gaming (Twitch) Industries transformed by Bitcoin: Currencies (Bitcoin), Government (Tezos), Contracts (Ethereum), Banking (Ripple), Real Estate (BenBen), Insurance (Augur), Finance (Bancor) Industries transformed by AI: Automotive (Cruise Automation), Identity (Neurala) In addition to the industries and transformations listed above, VR/AR promise to challenge education, drones will likely challenge surveillance, and Tesla will challenge utilities Understanding Trends It can also be a good exercise to study trends in and around your industry.

The Mutant Project: Inside the Global Race to Genetically Modify Humans
by Eben Kirksey
Published 10 Nov 2020

The doctor who ran this clinic, Eric Scott Sills, was later arrested and charged with murdering his wife and business partner.4 * * * When Ascendance Biomedical launched their new website, http://ascendance.io, in December 2017, they invited the public to “Help Us Choose Our Next Experiments.” Tristan Roberts designed the front end of the website, plus a back-end cryptocurrency infrastructure that would enable anyone to invest in the project, undetected by the government. Rather than Bitcoin, the best-known cryptocurrency, the group was banking on Ethereum, a newer currency that was growing exponentially in value. One coin (known as an “ether”) was valued at $299 in early November 2017, and the price skyrocketed to $821 just before Christmas. Ascendance offered a diverse menu of options on their website. An estrogen/testosterone experiment topped the list of possible treatments.

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Imagining himself as the next Jeff Bezos or Mark Zuckerberg, Aaron planned to bring a disruptive technology directly to consumers, government regulators and societal concerns be damned. * * * Aaron Traywick’s dreams for enhancing humanity seemed to be getting closer to reality in January 2018. The price of an Ethereum ether peaked at $1,432 as Ascendance Biomedical was about to go public with new findings and research initiatives. Aaron started messaging me on Facebook, saying, “We have confirmation of the first known gene therapy to transfect N6 successfully into human cells and bind to HIV.” He claimed that Ascendance had made “a pretty major breakthrough that no one has been able to do.”

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See also CRISPR; zinc fingers Doudna, Jennifer (biochemist) CCR5 research discovery of CRISPR gene editing encounters with Jiankui He Down syndrome drug pricing for AZT for HIV medicine by Novartis by Sangamo Therapeutics Editas Medicine Edwards, Robert (physiologist) enhancement He, Jiankui, on See also eugenics; genes; genotype Epstein, Steven (sociologist) Ethereum (cryptocurrency) ethics animal welfare and art and China and Christian values commercial enterprises and cyborg disability and environmental genetic engineering and of He, Jiankui human dignity and human enhancement and human subjects and imagination and justice and misconduct mutant queer regulation of relational reproductive medicine and shock art and technology and See also ableism; activists; conflicts of interest; informed consent eugenics (good genes) Cold Spring Harbor Laboratory disabled people and genetic testing and HIV and immigrants and military and Nazis and sterilization and Watson, James and yousheng (“high-quality birth”) See also disability; queer; race Eugenics Record Office euthanasia experimental volunteers Anne (pseudonym, Dr.

The Money Tree: A Story About Finding the Fortune in Your Own Backyard
by Chris Guillebeau
Published 6 Apr 2020

More than 250,000 users have allowed the service to access their personal information, including email and financial accounts. There is no word yet as to whether those accounts are at risk. This is a developing story and will be updated. * * * — From: KQuanChangi97@robomail.com To: lolzhackbuzz@hushmail.com What is Ethereum? And why would I pay you $100,000? Who are you? * * * — But What Could Possibly Go Wrong? By Katlyn Everett Valley News Pop quiz time again! Who plays with fire and acts surprised when their house burns down? Ding ding! You guessed it . . . Buzzard Co., that’s who.

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There’s a good reason why messages that delete themselves upon being viewed are so popular. Stay tuned for updates on the house burning. I’m running out to get popcorn. * * * — From: KQuanChangi97@robomail.com To: Finance How do I transfer a large amount of money? And do you know what Ethereum is?? * * * — UPDATED: Decision-Making Service Resolves Attempted Hacking Reuters Newswire — The concern that hackers had gained control of a startup’s full set of user accounts turned into a false alarm. Users breathed a sigh of relief as experts learned that only the service’s home page and founder’s email account had been compromised.

Working in Public: The Making and Maintenance of Open Source Software
by Nadia Eghbal
Published 3 Aug 2020

But JavaScript, including Node.js, is designed to be modular, where each maintainer has a limited ability to affect other components of the ecosystem, so JavaScript developers are more likely to prioritize moving fast and accepting contributions. In cryptocurrency, these philosophies play out as visible differences in how the Bitcoin and Ethereum projects are managed. Bitcoin’s community, like Clojure’s, prioritizes stability and security, preferring to move slowly and with care, even if it means including fewer features and contributors. Ethereum is more like Node.js: it’s a platform for others to develop on, flinging itself far and wide. It resembles a sprawling city like Los Angeles, comprised of many neighborhoods and subcultures. Despite impassioned rants to the contrary (if there’s one thing I’ve learned, it’s that developers have opinions), there’s no one right way of doing things, just different communities that each have their own cultural norms.

5 Day Weekend: Freedom to Make Your Life and Work Rich With Purpose
by Nik Halik and Garrett B. Gunderson
Published 5 Mar 2018

Cryptocurrencies make it easier to transfer funds between two parties in a transaction, and with minimal processing fees compared to the steep fees charged by most financial institutions. The adoption rate of cryptocurrencies is increasing daily with banks, corporations, and governments recognizing its mainstream popularity. The current leading cryptocurrencies are Bitcoin, Ethereum, Litecoin, Monero, Dash, and Ripple. A popular way to buy and sell cryptocurrencies and create your own digital currency “wallet” is to use a platform like Coinbase.com or Bittrex.com. Speculative investors should be aware there are risks involved in the investment and use of cryptocurrencies, such as fraud and security of the platforms.

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Harv Elder, Larry email, and productivity rituals emergency preparedness, as protective expense Emerson, Ralph Waldo emotional attachment, and Momentum investments and real estate investments emotional energy energy, amplifying entertainment, as tax deduction entrepreneurship, and academic systems and active income analyzing income opportunities and cash flow and continuous improvement direct sales/network marketing opportunities domain trading opportunities e-commerce opportunities and embracing failure and experimentation fix and flip opportunities and freedom and Growth investments and hiring employees and income growth lack of resources for and letting go and leverage online opportunities and opportunities presented by technology and passion and perfectionism personal service opportunities and quick adjustments to feedback and quitting your job and risk and scalability of businesses and self-employment sharing economy opportunities starting small and strong mindset three levels of and value creation equity growth, and real estate investments estate planning, as protective expense Ethereum Evans, Richard Paul excellence, replacing perfectionism with exercise, and energy amplification exit strategies, for Growth investments for real estate investments expense ratios expenses, and Active Income Ratio cutting and Passive Income Ratio experimentation F Facebook failure, and productivity fear, and building your inner circle and economic cycles and entrepreneurship and opportunity and procrastination and purpose and real estate investments and the Rockefeller Formula and security and strengthening your mindset of taxes federal government assistance, and loans for real estate investments Federal Housing Administration (FHA) feelings of entrapment, and weekend/workweek structure financial capital, and entrepreneurship financial independence, and Passive Income Ratio financial wealth, and Passive Income Ratio 5 Day Weekend, changing your mindset toward work contract for creating a vision for free-time activities five steps of importance of following correct sequence and “mailbox money” myth manifesto and passive vs. active income streams and security vs. freedom and thinking outside the box universal availability of weekend/workweek paradigm 5DayWeekend.com, and Passport codes 5-Second Rule, for impulse buying Fiverr.com fix and flip opportunities fix-up costs, for real estate investments Fon Ford, Henry foreclosure, and tax lien certificates Forleo, Marie foundation step (keep more money) Francis of Assisi (saint) Frank, Ben Frank, Joyce Franklin, Benjamin fraud, and cryptocurrencies freedom, and active vs. passive income creation and boredom and entrepreneurship and generosity and learning when to say no and lifestyle and peace and perfectionism and purpose sacrificing for security and simplicity Freedom Lifestyle freelancing, as entrepreneurial opportunity frequency of work requirements, business ownership (not managing) business ownership (working and managing) royalties and overrides sales subscriptions wage or salary employment Frost, Robert fulfillment, and purpose Fuller, Buckminster G Gardner, Chris generosity Gerber, Michael Gibbs, Marshall goal setting, and Passive Income Ratio and purpose Godin, Seth gold, as Momentum investment opportunity Golightly, Craig Google government social welfare programs, and retirement Grameen Bank gratitude Graybiel, Ann Gretzky, Wayne Groupon Growth investments, and active vs. passive income streams aggressive and conservative strategies for Bank Strategy and cash flow description of and economic cycles for funding Momentum investments minimum criteria for opportunities for Sharelord Strategy storage units tax lien certificates Guitar Institute of Technology (GIT) Gunderson, Garrett H habits, fortifying Halik, Nik happiness, and generosity and materialism hard money lenders, and loans for real estate investments health, and habits health savings accounts (HSA) “HELL YEAH” philosophy Hendrix, Jimi Herodotus Hicks, Bill hidden fees Hill, Napoleon hiring employees hiring your children, as tax deduction hobbies Holland, Danny home expenses, as tax deduction home office deductions homeowner’s insurance Hopper, Grace Hori, Jim hugedomains.com Hulu HyreCar.com I Idea Optimizer impulse buying income, and Active Income Ratio increasing with entrepreneurship and Passive Income Ratio See also cash flow income growth step (make more money) Income Opportunity Score Sheet incorporation Industrial Revolution, vs.

Brave New Work: Are You Ready to Reinvent Your Organization?
by Aaron Dignan
Published 1 Feb 2019

Moreover, as you put money into that machine, you and its other users have a say in what snacks it will order and how often it should be cleaned. It has no managers, all of those processes were pre-written into code.” Developers, leveraging what they have learned in creating cryptocurrencies such as Bitcoin and Ethereum, are pioneering a new generation of decentralized applications that allow organizations to operate like that magical vending machine. Through a series of rules called smart contracts, founders can create, fund, and operate an entire organization independent of hierarchical management. Everything, from paying contributors for their work to making decisions about investment, is managed in a distributed way.

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one study of cooperatives: Virginie Pérotin, “What Do We Really Know About Worker Co-operatives?” Co-operatives UK, no date, www.uk.coop/resources/what-do-we-really-know-about-worker-co-operatives. “pre-written into code”: “What Is DAO,” Cointelegraph, accessed July 31,2016, https://cointelegraph.com/ethereum-for-beginners/what-is-dao#how-daos-work. becomes valuable when it moves: Charles Eisenstein, Sacred Economics: Money, Gift, and Society in the Age of Transition (Berkeley, CA: North Atlantic Books, 2011). producer/philanthropist Jeff Skoll: “A New Global Impact Fund,” The Rise Fund, accessed September 1, 2018, http://therisefund.com

MegaThreats: Ten Dangerous Trends That Imperil Our Future, and How to Survive Them
by Nouriel Roubini
Published 17 Oct 2022

Using BaconCoin, LoanSnap aims to revolutionize home buying by sharing mortgages via the blockchain that can etch every transaction from day one.20 The first such mortgages changed hands in late 2021. Once inscribed on a blockchain visible to every user, new transactions update the status quo but, in principle, no one can alter or hack what occurred in the past. The rush to DeFi is premature and ultimately misguided. The rapid rise of Bitcoin, Ethereum, Dogecoin, and thousands of fledgling cryptocurrencies since 2010 exposes our collective wilting faith in the ability of governments to back the money they issue. But elevating crypto to the status of legitimate currencies endorses a dangerous precedent that is most likely to fail miserably. Fabio Panetta, member of the European Central Bank executive board, gave a sober update in December 2021.

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Were someone to write a mortgage with principal and interest in bitcoin, a spike in the value of bitcoin would cause the real value of the mortgage to skyrocket. If default then likely occurs, the lender loses money, and the borrower loses her house. A true currency should also be a scalable and widely used means of payment. Bitcoin and Ethereum can handle fewer than a dozen transactions per second, due to the enormous computing resources involved. The Visa network, by contrast, handles fifty thousand transactions per second. The proof-of-work etched into every blockchain transaction may improve its reliability, but it does so at a snail’s pace.

You've Been Played: How Corporations, Governments, and Schools Use Games to Control Us All
by Adrian Hon
Published 14 Sep 2022

“Wikipedia is not a role-playing game,” in “Wikipedia: Wikipedia is in the real world,” Wikipedia, updated July 21, 2021, https://en.wikipedia.org/wiki/Wikipedia:Wikipedia_is_in_the_real_world#Wikipedia_is_not_a_role-playing_game. 92. Hannah Miller, “Solana’s Bid to Take On Ethereum,” The Information, June 15, 2021, www.theinformation.com/articles/solana-s-bid-to-take-on-ethereum; Austin Federa, “Solana Labs Completes a $314.15M Private Token Sale Led by Andreesse Horowitz and Polychain Capital,” Solana, June 9, 2021, https://solana.com/news/solana-labs-completes-a-314-15m-private-token-sale-led-by-andreessen-horowitz-and-polychain-capital.

Radicals Chasing Utopia: Inside the Rogue Movements Trying to Change the World
by Jamie Bartlett
Published 12 Jun 2017

* Bitcoins can be divided into eight decimal places. The smallest non-divisible unit is known as a ‘Satoshi’. * In 2015 a company called the DAO (decentralised autonomous organisation) was founded as an investor-directed capital fund, which exists only virtually, as a series of public smart contracts. Investors can buy shares in the DAO using Ethereum’s (another blockchain) currency, which gives votes on investments. Anyone anywhere in the world can invest, it’s all transparent, there is no board or employees at all, and shareholders receive any profits directly. One month after it was launched to great fanfare, hackers and/or investors managed to exploit a vulnerability by inserting some code that redirected shares into their personal wallets, allowing them to walk off with millions of dollars of investors’ money.

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives
by Peter H. Diamandis and Steven Kotler
Published 28 Jan 2020

Artificial intelligence does the rest. The entire experience is mobile, simple, and fast. Ninety seconds to get insured, three minutes to get a claim paid, and zero paperwork. Adding more technology to this arrangement, companies like the Swiss firm Etherisc sell “bespoke insurance products” on the Ethereum blockchain. Because smart contracts remove the need for employees, paperwork, and all the rest, all sorts of new insurance products are being created. Etherisc’s first offering is something not covered by traditional insurers: flight delays and cancellations. Individuals sign up via credit card, and if their plane is more than forty-five minutes late, they’re paid instantly, automatically, and without the need for any paperwork.

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Eric, 63, 231 drones: disaster relief and, 48 increasing demand for, 10 package delivery and, 47–48, 107 reforestation and, 224, 227 drought, 242 drug development: AI and, 165–67 quantum computing and, 30, 167 Duplex (AI assistant), 35 DxtER, 157 dynamic risk, 187–89 Eagleman, David, 134 Easter Island (Rapa Nui), 174 Echo, 35, 101, 132 e-commerce revolution, 98–100 economy: new business models for, 83–87, 111–13 paradigm shifts in, 97–98 technological unemployment and, 227–30 ecosystems, ecosystem services, collapse of, 223–24 Edison, Thomas, 61 educational system: computer-aided self-teaching and, 144–47 customization of, 150 future of, 143–50 impact of exponential technologies on, 23 multisensory learning in, 148 outmoded models for, 143–44 standardized testing in, 144 teacher shortages in, 143 VR and, 51–52, 147–49, 248 e-governance, 234–35 Ekocenters, 214 electric cars, 10, 16–17, 221–23 electroencephalogram (EEG) sensors, 141 Elevian, 90, 178 emotional intelligence: AI and, 135–38 computers and, 103 robots and, 107 empathy, VR and, 148 endorphins, 247 energy: demonetization and, 78 new sources of, and economic paradigm shifts, 98 renewable, 78, 214, 215–18; see also specific technologies storage of, 218–20 Engines of Creation (Drexler), 63–64 Enlightenment, 82 entertainment: affective computing and, 136–38 content in, see content, entertainment future of, 125–42 streaming and, 126–27 entrepreneurship, immigrants and, 239–40 environment, as global and exponential, 12, 22–24 environmental threats, 211, 240 biodiversity crisis and, 48, 207, 212, 223–27 convergence and, 226–27 deforestation and, 48, 206, 207, 223, 224, 226 extreme weather and, 212, 223, 226 pollution and, 212, 226 water scarcity and, 212–15 see also climate change EOS token, 76 epigenetic alterations, 170 Essex, University of, 203 Estonia, e-governance in, 234–35 Ethereum, 187 Etherisc, 187 Ethiopia, Negroponte’s self-teaching experiment in, 144–46 evolution, trajectory of, 258–59 eVTOLs (electric vertical take-off and landing vehicles), 6, 9–10, 11 see also flying cars Exceptional People (Goldin and Cameron), 237–38 existential risks, 230–36, 240 Exo Imaging, 157 experience economy, 86, 111–13 exponential technologies, xi, 31–33, 39, 200, 215 abundance and, 261–63 brains as poorly adapted to, 12 convergence of, see convergence existential risks and, 230–36 healthcare and, 155, 156–67 Moore’s Law and, 7–8 Netflix and, 126 eXp Realty, 196–97 extreme weather, 212, 223, 226, 241–42 FAA, 6 Facebook, 51, 81, 256 advertising revenue of, 117–18 facial recognition, 120 fakes, fakery, digital, 121–23, 131–32 Federal Judicial Center, 49 Feynman, Richard, 63 Filecoin, 76 Final Frontier Medical Devices, 157 finance industry, 181–82 AI and, 194–96 blockchain and, 193, 194 convergence and, 189–96 Fintech, 194 Fitbit, 41–42 5G networks, 39–40, 119, 149 floating cities, 199–200 Floating Island Project, 200 FLOPS (floating operations per second), 28 Florida, Richard, 244 flow, flow states, 81, 257–58 VR and, 247–48 flow batteries, 219–20 Flow Research Collective, xii, 265 flying cars, 3–7, 26 convergence and, 9–12 healthcare and, 154 prime real estate redefined by, 199 ridesharing and, 4, 19 fMRI studies, 21–22 food chain, 202–3 food industry, 181, 201–8 Forbes, 35, 129 Ford, Henry, 12–13 Ford Motor Company, 221 foreign currency exchange, 194 Forest (quantum developer’s kit), 30, 32 Form Energy, 220 fossil fuels, 215–16 Foster, Richard, 23 Fox News, 247 free/data economy, 84–85 Freestyle Foundation Beverage Dispenser, 214 Friis, Janus, 106 Fukushima Daiichi nuclear plant disaster, 45 future, thinking about, 21–22 Gagarin, Yuri, 73 Garcetti, Eric, 20 Gates, Bill, 203, 214, 220 GDF11 (growth differentiation factor 11), 90, 178–79 Gelsinger, Jesse, 65–66 General Motors (GM), 14, 226 generative adversarial networks (GANs), 165–66, 167 gene sequencing, 78 gene therapy, 65–66, 67, 68 genetic diseases, 65–68 genetics, longevity and, 172–73 genius, nurturing of, 79–82 genome, 66–67 editing of, 67–68, 160 epigenetic alterations to, 170 instability in, 170 Genome Project-Write, 159 genomics, personalized, 158–60 Georgia Institute of Technology, 130 Germany, electric cars in, 221 Germany, Nazi, 238–39 germline engineering, 67–68 Giegel, Josh, 17–18 Gigafactory, 219, 222 GitHub, 146 Glaxo, 152 Global Learning XPRIZE, 146 Global Risks Report (World Economic Forum), 212 global warming, see climate change Gmail, Smart Compose feature of, 35 GM Cruise, 14 Go, 36 Goddard, Robert, 17 Goldin, Ian, 237–38 Goldman Sachs, 230 Good Money, 190–91 Google, 8, 36, 51, 71, 89, 100, 128, 146, 156 advertising revenue of, 117–18 Talk to Books program of, 35 see also Alphabet Google Duplex, 101–2 Google Home, 35 Google Lens, 120 Google Now, 100 governance, existential risks and, 234–36 GPS, 43 graphics processing units (GPUs), 34 Great Recession of 2008, 196, 228 greenhouse gases, 206, 207, 215–16, 221, 226 Gross, Neil, 42–43 group flow, 257–58 Groupon, 4 Guardian, 242, 246 Hagler, Brett, 55–56 Haiti, 58 2010 earthquake in, 55 Hanyecz, Laszlo, 57 haptic sensation, 25, 26, 134–35 Hardy, G.

This Is Not a Drill: An Extinction Rebellion Handbook
by Extinction Rebellion
Published 12 Jun 2019

But instead of being wired with a microphone or taken to a stage, I just sat there at a plain round table as my audience was brought to me: five super-wealthy guys – yes, all men – from the upper echelon of the hedge-fund world. After a bit of small talk, I realized they had no interest in the information I had prepared about the future of technology. They had come with questions of their own. They started out innocuously enough. Ethereum or bitcoin? Is quantum computing a real thing? Slowly but surely, however, they edged into their real topics of concern. Which region will be less impacted by the coming climate crisis: New Zealand or Alaska? Is Google really building Ray Kurzweil a home for his brain, and will his consciousness live through the transition, or will it die and be reborn as a whole new one?

Tracers in the Dark: The Global Hunt for the Crime Lords of Cryptocurrency
by Andy Greenberg
Published 15 Nov 2022

Aside from the money that had paid for it, the machine seemed to have nothing to do with AlphaBay. The laptop, on the other hand, was a gold mine of evidence—quite literally. Aside from being logged in to AlphaBay and containing that incriminating net worth file, the computer had keys for all of Cazes’s various wallets, containing not only Bitcoin but also other, newer cryptocurrencies: Ethereum, Monero, Zcash. Rabenn remembers watching the two FBI analysts, Ali and Erin, in the war room as they siphoned that money into wallets under FBI control, announcing every time they had transferred another multimillion-dollar stash. “It was the coolest thing I have ever seen,” Rabenn says. On the evening after the arrest, Rabenn and Hemesath met with Cazes for the first time.

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.): founding of, 126 Joint Criminal Opioid and Darknet Enforcement group and, 235 Manchin, Joe, and, 34 Operation Bayonet and, 204 Silk Road case and, 144 in Thailand, 162 DuckDuckGo, 322 Dutch National High Tech Crime Unit, 188, 195 Dutch National Police: Dark Scandals case and, 281 Gambaryan, Tigran, and, 238 Hansa case and, 186–7, 233–4 Hansa operation by, 230–2, 234–6 Hansa takeover by, 187–9, 191–2, 195–8, 228–30 Operation Bayonet and, 189–91 E Eastwood, Clint, 56 EBX Technologies, 159 eCash, 45–7, 298 Ekeland, Tor, 291 Electronic Frontier Foundation, 308 Electrum, 317 Elliptic, 138 “Ensuring Responsible Development of Digital Assets” (2022 U.S. executive order), 308 Erin (pseud.; FBI analyst): Cazes, Alexandre, case and, 171–7, 193, 215, 218 Escobar, Pablo, 164 Esposito, Calogero. See Bridges, Shaun Ethereum (cryptocurrency), 218 European Organization for Nuclear Research (CERN), 96 Europol: conference on virtual currency investigations at, 199–201 Hansa case and, 186, 198, 229, 235 Operation Bayonet meeting at, 190 Operation Onymous and, 123 ransomware investigations by, 302 Evolution (dark web market), 123–4 Excygent, 246 exit scams, 161, 171–2, 228 F Falder, Matthew, 243–4 Falkvinge, Rick, 97 Farivar, Cyrus, 331, 336, 340 Faruqui, Zia: Gambaryan, Tigran, and, 125–6 North Korean cryptocurrency theft cases and, 288 Tamsi, Thomas, and, 256–7 virtual currency strike force of, 125 Welcome to Video case and, 246–7, 254–6, 263–6, 269–71, 280 Federal Bureau of Investigation (U.S.), 17 AlphaBay case and, 160 Bitcoin report by, 73–4 Cazes, Alexandre, arrest and, 204 child sexual abuse materials cases and, 247 Colonial Pipeline ransomware attack and, 301 Dutch National Police and, 186 Gambaryan, Tigran, and, 238 Joint Criminal Opioid and Darknet Enforcement group and, 235 NetWalker case and, 300 New York field office of, 64 North Korean cryptocurrency theft cases and, 288 Operation Onymous and, 123 ransomware cases and, 301–2 Sacramento field office of, 160, 167, 194 Silk Road takedown by, 64–5, 84 Ulbricht, Ross, case and, 12–13, 63–5 Federal Security Service (FSB; Russia), 303 fentanyl, 166–7, 221, 236, 314 Figueroa Agosto, José, 164 Financial Crimes Enforcement Network (FinCEN), 13, 326.

Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It
by Kashmir Hill
Published 19 Sep 2023

The search for face bounty hunters sometimes took him into the darker zones of the internet, places where people wouldn’t share their real names. One guy said he had scraped Meetup, AngelList, and Couchsurfing and offered to sell Ton-That the photos. But he wanted to be paid in the cryptocurrency Ethereum. “I had to swap my Bitcoin to buy it,” Ton-That said. “And it was good. I wanted to hire him. He said no, but he introduced me to some of his friends.” Ton-That would eventually hire about a dozen contractors from all over the world to hunt faces on the internet for him. He had no idea who some of those people actually were, only how to pay them.

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See also Kodak Edelson, 205 Eidelman, Vera Abrams and, 207 Abrams case and, 204, 205, 208–209, 211–212 with ACLU, 202 background of, 203 eigenfaces, 46–47, 60, 63 Eisenhower, Dwight D., 42 Electronic Frontier Foundation, 306n206 Ellsberg, Daniel, 207 Ellwood, Charles, 23 embodied internet, 145–146 Energy Department, 247 Epstein, Jeffrey, 270n43 Ethereum, 81 eugenics, 20, 25–26 Everyone, 9 Expando, 6 Exposing.ai, 242 F “Face Facts” workshop, 121–122, 123–127 Face Recognition Vendor Test, 99 Face Unlock, 109, 179 Facebook AR glasses and, 243, 244 automatic tagging on, viii–ix, 142, 144 BIPA and, 151–152, 204, 205 Cambridge Analytica scandal and, 92 Clearview AI and, vii, 96, 158, 165 “Face Facts” workshop and, 121–122 Franken and, 141, 143–144 FTC and, 123 identification technology and, 144–148 Luckey and, 57 monetization and, 6–7 neural networks technology and, 74 photo experiment and, 106–108, 126 pornography and, 198–200 public profile photos and, 142–143 racial bias and, 178–179 Saverin and, 94 scraping and, 58, 117, 144, 195, 242 Smartcheckr and, 53 State Privacy and Security Coalition and, 150 Thiel and, xi, 13 third-party apps for, 4–6 Ton-That and, 5 turning off facial recognition, 244 use of contact lists by, 7 Face.com, 143, 146 Facewatch, 220 facial recognition.

Billionaires' Row: Tycoons, High Rollers, and the Epic Race to Build the World's Most Exclusive Skyscrapers
by Katherine Clarke
Published 13 Jun 2023

At 111 West 57th Street, there was a contest between two wealthy buyers for one of the building’s most expensive units, a 7,130-square-foot aerie on the 72nd floor asking $66 million. The developers had already accepted an offer on the unit when they got a second offer from Gavin Wood, one of the founders of Ethereum, the blockchain-based computer network. (The unit went to the first, unidentified buyer.) Tim Gong, an executive whose firm owned a major stake in the parent company of social media giant TikTok, bought two units at the building for $34 million. At Central Park Tower, new buyers included Nicole Mendelsohn, the vice president of the global business group at Meta, Facebook’s parent company, which was then in the midst of doubling down on the digital realm known as the metaverse.

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See also 432 Park Avenue Children’s Investment Fund and, 80–81 CIM and, 73–78 Citigroup Inc.’s private bank and, 79 CMZ Ventures and, 39–41, 43–46 Macklowes and, 13–14, 34, 35, 37–38, 44–45 Dubai, 49, 346 Dyson, James, 266 E East 60th Street between Park and Madison avenues, 190 EB-5 visa program, 89, 270 Ecclestone, Petra, 263, 264 Edward Durell Stone & Associates, 3 Ehrsam, Fred, 336 Eichner, Ian Bruce, 37, 314 80/20 program, 152 800 Park Avenue, 21–22 834 Fifth Avenue, 20 11 Madison Avenue, 78 Emaar Properties, 38 Emery Roth & Sons, 3, 101 Empire State Building, xxvii, xxviii–xxx, 48 Ethereum, 336 Extell Management and Investment Company basic facts about, 56 Calvary Baptist Church and, 65 Central Park Tower and, 271–72 Andrew Cuomo and, 151–52, 153, 154–55 financial status of, 83, 335 hundredth birthday of Iris Apfel, 334–35 One57 and, 100–101, 138–40, 142, 150, 152–53 Vornado and, 134, 136–37 Zuckerman and, 154 F façades Carnegie Hall, 24, 30 53 West 53rd Street, 190 One57, 83, 143 111 West 57th Street, xxi, 212 Steinway Hall, 30 fair housing laws, 205, 251 Fascitelli, Mike, 129 Fayed, Ali, 35, 75 Fegel, Gary, 290 Feldman, Ziel, 57, 62, 170 Fertitta, Frank, 266 Field, Nikki, 65, 101–3, 260 15 Central Park West Blackmon and, 88–89 design, 142 history of, 31–32 marketing of, 94, 117 projected sales of units at, 210 purchase prices, 242, 252 residents, 32, 86–87, 88–89, 90–91, 209, 242 residents moving to 220 Central Park South from, 208–9 restaurant at, 31, 110 Sukenik and, 130–31 53 West 53rd Street, 147–48, 190, 266 financial crisis of 2008 American housing market and, 52 Barnett’s plans for One57 and, 82–83 Extell and, 83 Macklowe and, 3, 5 traditional commercial banks and, 42 Financial District, high-rise condos in, 262 Finkelstein-LeBow, Jacqueline, 322 Firtash, Dmytro, 43–44, 45–46 Fiscal Policy Institute, 307 520 Park Avenue marketing of, 237 residents and prices, 266 sales of units at, 301 550 Madison Avenue, 191 Flaunt, 259 floor area ratios (FARs), 59, 60–61 Florida income sources of wealthy residents in, 205 mansion listings, 240, 263 Mink allegations against Stern and JDS in, 333 supertalls in, 346 ultra-wealthy in, 233, 315 Floyd, George, 314 Forbes, 89 Forrell & Thomas, 62 Fortress Investment Group, 343 Foster, Norman, 296 Foucault, Michel, xiii 421-A tax abatements, 150–55 432 Park Avenue.

Coders: The Making of a New Tribe and the Remaking of the World
by Clive Thompson
Published 26 Mar 2019

Nonetheless, the libertarian protestations of a certain set of coders continues apace. In recent years, blockchain technology has been the latest site of tech’s anti-government fervor. That ranges from Bitcoin—a currency specifically designed to create money that couldn’t be controlled by dough-printing central banks—to Ethereum, a way of creating “smart contracts” that, its adherents hope, would allow commerce so frictionless and decentralized that even lawyers wouldn’t be necessary: The instant someone performed the service you’d contracted them to do for you, the digital cash would arrive in their digital wallet. One survey of people in the cryptocurrency community found that fully 27 percent called themselves libertarian, more than double the rate Pew Research Center found in the general population.

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S., ref1 Ellis, Kelly, ref1, ref2 encasement strategy, ref1 encryption Clipper Chip, ref1 criminal/terrorist use of, cypherpunk views on, ref1 entertainment and copyright law and, ref1 munitions law, ref1, ref2 public/private key crypto and, ref1 Zimmermann’s creation of Pretty Good Privacy, ref1 See also cypherpunks engagement (compulsive use) advertising and, ref1 Like button (Facebook) and, ref1 psychological lures to encourage, ref1 English Electric, ref1 ENIAC computer, ref1, ref2 ENIAC Girls, ref1 Ensmenger, Nathan, ref1, ref2 entertainment industry, ref1 decoding software and, ref1 digital rights management (DRM) software, ref1, ref2, ref3 Erickson, Carolina, ref1 Ethereum, ref1 Everingham, James, ref1, ref2, ref3, ref4 expert systems, ref1 “Exploratory Experimental Studies Comparing Online and Offline Programming Performance” (Sackman et al.), ref1 Facebook, ref1, ref2, ref3, ref4, ref5, ref6 ad tech, civic impacts of, ref1 Cambridge Analytica scandal, ref1 content moderation, ref1, ref2 deep-learning model at, ref1 free-to-use model of, ref1 Like button, ref1 News Feed feature of (See News Feed [Facebook]) purchases Instagram, ref1 Sanghvi hired at, ref1 scale and, ref1 tracking of user activities by, ref1 women and minority coders at, percentage of, ref1 work atmosphere at, ref1 Fancy Bear, ref1 Fan Hui, ref1, ref2 Fast Company, ref1 feature creep, ref1 Ferrucci, Dave, ref1, ref2 file-sharing tools, ref1 Firefox, ref1 Fisher, Allan, ref1, ref2, ref3, ref4 Fitzpatrick, Brad, ref1, ref2 Flatiron School, ref1, ref2, ref3 Flickr, ref1 Flombaum, Avi, ref1 FLOW-MATIC computer language, ref1 flow state, ref1 Foer, Franklin, ref1 Fogg, B.

Radical Uncertainty: Decision-Making for an Unknowable Future
by Mervyn King and John Kay
Published 5 Mar 2020

And what is meant by ‘money’ is temporally and geographically specific. Money is dollars in the US, and euros in Europe. Not so long ago, money was gold and silver. For the inhabitants of Yap in the Caroline Islands, money was Rai, heavy circles of limestone with a hole in the middle. Some people think that crypto-currencies such as Bitcoin and Ethereum are ‘money’. Numbers are essential to economic analysis. But economic data and economic models are never descriptive of ‘the world as it really is’. Economic interpretation is always the product of a social context or theory. Expressing uncertainty When we are wondering whether the man in the compound is bin Laden or what happened to the Mary Celeste , whether the second Smith child is a girl or whether Joyce met Lenin, probabilities are unhelpful.

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Y., 110 Edison, Thomas, 431 Edmond de Belamy (computer created portrait), 176 education system, 409 efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 Egypt, ancient, 142 Ehrlich, Paul, The Population Bomb (1968), 359 , 362 Einstein, Albert, 19–20 , 259 Eisenhower, Dwight D., 279 , 282 , 292 , 293–4 Eliot, George, Middlemarch , 220 Elizabeth II, Queen, 382–3 , 393 Ellsberg, Daniel, 135 , 136 , 282 emerging economies, 315–16 engineering, 23–4 , 33 , 383 , 384 , 390–1 , 399 Enlightenment, eighteenth-century, 163 , 187 , 387 entrepreneurship, 15 , 49 , 74 , 170 , 258 , 275–6 , 337 , 405 , 430–2 environmentalism, 220 , 361 , 362 Equitable Life Assurance Society, 56 , 328 Ethereum, 96 eugenics, 158 European Central Bank, 350 European Monetary System, 319 European Monetary Union, 45 , 316 European Union, 369–70 , 372 eusociality, 172–3 , 274 evolutionary science: adaptation, 401 ; application to economics, 158 ; and behavioural economics, 154–5 ; co-evolution, 163–4 , 429 , 430–1 ; coping strategies for uncertainty, 47 , 155 ; and decision making, 47 , 171–2 , 177 , 272 , 401 ; discovery and development of, 156 , 157–8 ; an essentially continuous process, 407 , 428–9 , 430–1 ; evolutionary psychology, 416–17 ; and extinctions, 32 ; false association with far-right causes, 158 , 161 ; and intentionality, 431 ; language and communication, 159 , 161–2 , 272 ; and learning of complex skills, 268 , 274–5 , 408 ; nature and nurture, 164–5 ; non-scientific mechanisms of, 158–9 ; optimism and confidence, 167–70 , 330 , 427–8 ; parable of the scorpion and the frog, 164 ; predispositions influencing behaviour, 163–5 ; and rationality, 16–17 , 47 , 152–3 , 155 , 157 , 162 , 171–3 , 272 , 401 ; and risk, 129 , 160–1 , 162 , 166 , 170 , 171 ; the ‘selfish gene’, 156 ; social and cultural practices, 156–65 , 408 ; and survival, 165–7 , 401 ; and trust, 162–3 , 165 ; uncertainty as essential, 428–9 , 431 executive pay, xiv , 409 expected value , concept of, 60 , 106–9 , 114–16 , 124–5 ‘expert’ forecasters, 21–2 , 221–2 Falklands War (1982), 291 , 295 falsificationism, 259–60 Fama, Eugene, 252 , 318 , 320 Fauchard, Pierre, 387 Federal Reserve, US, 103 , 317–18 Ferguson, Adam, 163 , 343 Ferguson, Sir Alex, 273 Fermat, Pierre de, 53 , 56 , 57 , 59–60 , 106 Fermi, Enrico, 84 , 129 Feynman, Richard, 373 , 374 fiction, works of, 92–3 , 212–13 , 219 , 220 , 224–6 , 344 , 397 finance theory: beta coefficients, 332–3 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 , 334 ; covariances , 332–3 , 366–7 ; definition of risk, 420–1 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; limits of, 318–21 ; quest for large-world model, 392 ; risk as volatility, 124–5 , 310 , 333 , 336 ; three pillars of, 309–10 , 320 , 332 financial crisis (2007–08): and Bank of England ‘fan charts’, 105 ; bankers attribute to chance, 266–7 ; and evolutionary theory, 158–9 ; failure of economic models, xv , 6–7 , 260 , 311–12 , 319 , 339 , 349–50 , 357 , 367–8 , 399 , 407 , 423–4 ; financial sector output after, 95 ; Goldman Sachs risk models, 6–7 , 9 , 68 , 202 , 246–7 ; greedy bankers as risk averse, 127–8 ; and historical narratives, 356–7 ; as intellectual failure, 12 , 319 , 320–1 ; and narrative reasoning, 5–6 ; as not unpredictable/unavoidable, 402–3 ; and pernicious narrative, 410–11 ; prevailing narrative changed by, 351 ; the Queen on, 382–3 , 393 ; and ‘real business cycle’ models, 348 ; recession after, 338–9 ; and ‘thick description’, 193–4 ; volatility and risk, 422–3 financial instruments, 6 , 351 , 366–7 , 401 financial sector: and assumptions of stationarity, 333 , 339 , 340–1 , 349 , 350 , 366–7 ; Basel regulations, 310 , 311 ; broad asset categories, 333–4 ; broad portfolio diversification, 333–5 ; ‘call’ and ‘put’ options, 422–3 ; correlations based on historic data sets, 333 , 366–7 , 390 , 406 ; dominance of narratives, 229–30 , 314–16 , 410–11 ; Dr Evil strategy, 229 , 255 ; executive pay, xiv , 409 ; and expected utility theory, 127 ; failure of finance theory, 318–21 ; government index-linked bonds, 330–1 ; inadequacy of forecasting models, 347–50 , 353–4 , 403–4 ; LIBOR scandal, 192 ; need for strong regulation, 313–14 ; as non-stationary, 16 , 202–3 , 268–9 , 320–1 , 331 , 333 , 339 , 366–8 , 402–3 , 406 ; and normal distribution, 233 ; and power laws, 238–9 ; rescue from collapse (2008), 95 , 311 ; risk and volatility, 124–5 , 310 , 333 , 335–7 , 421–3 ; securitisation, 311 , 316 – 18 , 366–7 , 401 ; securitised mortgages, 311 , 317 , 367 , 372 , 390 , 422–3 ; stock market crashes, 238 , 331 , 354 ; US stock market crash (19 October 1987), 238 ; US subprime mortgage market, 317 , 367 , 390 , 422–3 ; Value at risk models (VaR), 366–8 , 405 , 424 ; see also securities trading Financial Times , xiii Finetti, Bruno de, 73 , 80 , 135 First World War, 357 , 361 Fisher, R.

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside
by Xiaowei Wang
Published 12 Oct 2020

The first block on the Bitcoin blockchain was created along with the text “THE TIMES 03/JAN/2009 Chancellor on brink of second bailout for banks”—the anti-centralization message of Bitcoin coming through loud and clear. And since 2008, the cryptocurrency and blockchain space has blossomed beyond Bitcoin into other currencies and other blockchains, currencies like Ethereum and EOS, all with slightly different consensus algorithms—ways of ensuring that individual computers, or nodes, have records that agree with each other. Hardin’s original essay in 1968 used the example of the medieval commons, a place where peasants grazed their cows. According to Hardin, the ungoverned nature of the commons led to overgrazing, which is why the commons had to eventually be enclosed and privatized.

Survival of the Richest: Escape Fantasies of the Tech Billionaires
by Douglas Rushkoff
Published 7 Sep 2022

They sat around the table and introduced themselves: five super-wealthy guys—yes, all men—from the upper echelon of the tech investing and hedge fund world. At least two of them were billionaires. After a bit of small talk, I realized they had no interest in the talk I had prepared about the future of technology. They had come to ask questions. They started out innocuously and predictably enough. Bitcoin or Ethereum? Virtual reality or augmented reality? Who will get quantum computing first, China or Google? But they didn’t seem to be taking it in. No sooner would I begin to explain the merits of proof-of-stake versus proof-of-work blockchains than they would move to the next question. I started to feel like they were testing me—not my knowledge so much as my scruples.

Future Politics: Living Together in a World Transformed by Tech
by Jamie Susskind
Published 3 Sep 2018

A ‘smart contract’, for instance, is a piece of blockchain software that executes itself automatically under pre-agreed circumstances— like a purchase agreement which automatically transfers the ownership title of a car to a customer once all loan payments have been made.27 There are early ‘Decentralised Autonomous Organisations’ (DAOs) that seek to solve problems of collective action without a centralized power structure.28 Imagine services like Uber or Airbnb, but without any formal organization at the centre pulling the strings.29 The developers of the Ethereum blockchain, among ­others, have said they want to use DAOs to replace the state altogether. Blockchain still presents serious challenges of scale, governance, and even security, which are yet to be overcome.30 Yet for a youthful technology it is already delivering some interesting results. The governments of Honduras, Georgia, and Sweden are trialling the use of blockchain to handle land titles,31 and the government of Estonia is using it to record more than 1 million patient health records.32 In the UK, the Department for Work and Pensions is piloting a blockchain solution for the payment of welfare benefits.33 In the US, the Defense Advanced Research Projects Agency (DARPA) is looking into using blockchain technology to protect its military networks and communications.34 Increasingly connective technology is not just about people connecting with other people.

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S. 57 Elster, Jon 304, 425 EMC 386, 387 Emerging Technology from the arXiv 422 emotions affective computing 52–3 perception-control 148–9 Enchassi, Nadia Judith 370, 420 encryption 182–4 Engels, Friedrich 310, 326–7, 362, 426, 429, 436 England Levellers 215–16 Luddites 13 Revolution 77, 167–8 Entous, Adam 433 entrenchment, rule-based injustice 284 Epicenter 51 epidermal electronics 44 Epstein, Robert 398 equality 10 Data Democracy 247 Deliberative Democracy 234 democracy 223, 225–6 Direct Democracy 240 distributive justice 262–3 human nature 364–5 and justice, difference between 259 of opportunity 260, 261, 263, 270, 294 Esteva, Andre 372 Estonia 47, 220 Ethereum 47 ethics Data Deal 339 human enhancement 363 see also morality European Convention on Human Rights 326 European Court of Human Rights 31, 109 European Court of Justice 138 European Parliament 184 European Union (EU) Brexit 4, 233, 239 competition law 357 General Data Protection Regulation 138, 433 Google fine 357 personal data laws 138 right to explanation 354 taxation 328 everyware see smart devices Executive Office of the President 421, 422 exploitation 273 Eyefluence 319 Facebook acceptable/unacceptable posts 190 acquisitions 318, 319–20 artificial intelligence 116 ban on far-right groups 236 commercial value 66 concentration of tech industry 318, 319, 320, 321 democracy 359 embarrassing photographs 137 fake news 230 fragmented reality 230 friends 284 job applicants 267 Kurds 236 ‘Likes’ 132, 149, 221, 248 machine learning 35 Messenger 318 new media 77 news platform 147 Newsroom 406 Oculus Rift 59 personal data about users 64–5 photograph upload numbers 63 power 350 rules 116 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Index user emotions 148–9 user-generated content 315 user numbers 45, 65 users as unpaid workforce 338 US presidential election (2016) 354 Faception 173 facial analysis 52 facial recognition 30, 51 data-based injustice 282 machine learning 36 rule-based injustice 285–6 totalitarianism 178 fact-checking 234 Fairfield, Joshua A.

The Price of Time: The Real Story of Interest
by Edward Chancellor
Published 15 Aug 2022

Paris Hilton tweeted support for Lydian Coin and Floyd ‘Crypto’ Mayweather, as the retired boxer now styled himself, endorsed several issues over social networks (for which he was later fined by the Securities and Exchange Commission). Like the ‘bubble companies’ of 1720 that promised to extract silver from lead or to build an air pump for the brain, several of the new cryptos were spoofs. But that didn’t stop the Useless Ethereum Token, whose logo showed the middle finger and whose website advised ‘don’t buy these tokens’, rising five times faster than Bitcoin in the giddy last months of 2017.25 Just as internet companies were spun off from their parents during the Dotcom bubble, so ‘forks’ in the blockchain created yet more coins.

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D., 163–4 Röpke, Wilhelm, 97, 100, 299 Rothbard, Murray, 30 Rothermere, Lord, 93 Roubini, Nouriel, 207, 254 Rousseff, Dilma, 258 Rucellai, Giovanni, 21 Rueff, Jacques, 85, 91, 115‡, 251 Ruskin, John, 180–81 Sainsbury’s (British grocery chain), 160 Saint-Simon, Louis de Rouvroy, Duke of, 50–51, 52, 57 Samuelson, Paul, 246–7 Sarkozy, Nicolas, 292 Savills (property consultants), 174 saving: bonus of compound interest, 190; China’s savings glut, 268–9; as deferred gratification, 29, 188–90; and interest, xxiv, 44, 77, 188–93, 194–9, 205–6; interest as ‘wages of abstinence’, xxiv, xxv, 188–91; savings glut hypothesis, 115–16, 117, 126, 128–9, 132, 191, 252; Terborgh on, 125* savings & loan crisis, US, 111, 145 Say, Jean-Baptiste, 99 Sbrancia, Maria Belen, 290 Scandinavian banking crisis (early 1990s), 136 Schacht, Hjalmar, 82, 92, 312 Schäuble, Wolfgang, 299 Scheidel, Walter, 204 Schumpeter, Joseph, 16, 32, 46, 95, 218; Capitalism, Socialism and Democracy (1942), 126, 140, 296–7; ‘creative destruction’ idea, xx, 140–43, 153, 296–7; on deflation, 100; History of Economic Analysis, xviii; view of intellectuals, 297 Schwartz, Anna, 98, 99, 105, 116 Schwarzman, Steven, 207 Sears (department store), 169–70 secular stagnation, 77, 124–8, 131, 132–9, 151, 205–6 Sée, Henri Eugene, Modern Capitalism (1928), 28* Seneca the Younger, 20–21 Senior, Nassau, 188, 191 Senn, Martin, 193 shadow banks: in Canada, 174–5; in China, 266, 270, 282*, 283–5, 286; collapse in subprime crisis, 221, 283; illiquid products, 226–7; re-emergence after 2008 crisis, 221, 227, 231, 233; structured finance products, 116, 227, 283–5; Trust companies as precursors of, 84*; types of, 221; ‘Ultra-short’ bond exchange-traded funds (ETFs), 227 Shaftesbury, Anthony Ashley Cooper, Earl of, 27 ‘shareholder value’ philosophy, 163–6, 167, 170–71 Shaw, Edward, 286 Shaw, Leslie, 83, 83* Shiba Inu (cryptocurrency), 308 Shin, Hyun Song, 254, 263 Shiyan, Hubei province, 275 Silicon Valley, 148, 151, 173, 176, 204 Silver, Morris, 7, 11 Singer, Paul, 185, 246 Smith, Adam, 14, 174; on monopolies, 162, 298; view of interest, 27, 27*, 31, 183; on wealth, 181; The Wealth of Nations (1776), xxii, 27–8, 27*, 31 Smithers, Andrew, Productivity and the Bonus Culture (2019), 152* Smoot–Hawley Act (1930), 261 socialism, 188, 297, 298 Soddy, Frederick, 181, 242 Solon the ‘Lawgiver’, 9, 18 Solow, Bob, 128 Somary, Felix, 94–5, 308 Sombart, Werner, Modern Capitalism, 22* Soros, George, 148*, 273, 283 South Africa, 258 South America: loans/securities from, 77, 79–80; precious metals from, 49, 168; speculation in bonds from, 64, 65–6, 91; trade during Napoleonic Wars, 70 South Korea, 267 South Sea Bubble (1720), 62, 65*, 68, 69, 307 Soviet Union, 278 Spain, 144–5, 147, 168, 213, 253, 279; mortgage bonds (cédulas), 117 Special Purpose Acquisition Companies (SPACs), 307 speculative manias, xxiii; Borio on, 135; and cryptocurrencies, 177–9; ‘hyperbolic discounting’ during, 176–7; in period from 1630s to 1840s, 64–6, 67–72, 73, 74, 75–6, 77–8, 79–80; technology companies in post-crisis years, 176–9; before Wall Street Crash (1929), 91 see also Mississippi bubble Spencer, Grant, 177 Sraffa, Piero, 42 St Ambrose, 18 St Augustine, 18–19, 202 St Bonaventure, 19 Stable Money League/Association, 87, 96 Standard Oil, 157 state capitalism, 280, 284, 292–5, 297, 298 Stefanel (Italian clothing company), 147 Stein, Jeremy, 231, 233 Steuart, Sir James, 53, 273 ‘sticky prices’ theory, 87* Strong, Benjamin, 82–3, 86–8, 90*, 92, 93, 98, 112 Stuckey’s Bank, 63, 66–7 subprime mortgage crisis, xxii, 114, 116, 117–18, 131, 211, 292; produces ‘dash for cash’, 227; unwinding of carry trades during, 221, 227 Suetonius, The Twelve Caesars, 12 Suez Canal, 78 Sumerian civilization, 4, 6, 8, 15 Summers, Larry, 124–5, 127, 129, 185, 230, 230*, 235, 302 Sumner, William Graham, ‘Forgotten Man’, xx, xxii, 198 Susa, Henry of, 25 Svensson, Lars, 247 Sweden, 174, 241, 242, 244, 245, 247, 294 Sweezy, Paul, 156 Swiss National Bank, 172–3, 293–4 Switzerland, 172, 174, 226, 233, 241, 244, 245 Sydney (Australia), 175 Sylla, Richard, 4, 11, 68, 109 Tacitus, 20–21 Tasker, Peter, 271 Tawney, R.H., 201 tax structures, 164; offshore tax havens, 210 Taylor, John, 116–17, 129, 252 Tencent, 283 Tencin, Claudine Alexandrine Guérin, Madame de, 51 Terborgh, George, 125–6, 127 Tesla, 176–7 Theranos, 149 Thiel, Peter, 263 Third Avenue (investment company), 227–8 Thornton, Daniel, 192 Thornton, Henry, 41–2, 66*, 70, 75 Thornton, Henry Sykes, 66* Tiberius, Roman Emperor, 12 time, concept of, xviii; and act of saving, 188–90; canonical ‘hours’, 21; and Lewis Carroll, 309; in era of ultra-low interest rates, 59, 177; Franklin on, xviii, 22, 28; and Hayek, 32; interest as ‘time value of money’, xxiv, xxv–xxvi, 10, 14–15, 16, 20, 22, 26–7, 28–32; Lord King’s ‘paradox of policy’, 194, 230*; the Marshmallow Test, 29, 189; and medieval scholars, 19–20; Renaissance writings on, 21; secularization of, 21–2; speculators’ misunderstanding of, 59; and thought in ancient world, 20–21; time as individual’s possession, 20, 21, 25; ‘time in production’, xxiv, 14–15, 16, 22, 95, 95†, 141; ‘time preference’ theory, xxiv*, 28–32, 42, 95, 188–9; Thomas Wilson’s ideas, 26–7, 28, 30 Time-Warner, 167 Tooke, Thomas, 69 Toporowski, Jan, 167 Torrens, Robert, 66 Toys ‘R’ Us, 169 trade and commerce: in ancient world, 6, 7–8, 12, 14, 15; Atlantic trade, 59; business partnerships (commenda, societas), 26; commercial classes/interests, 35, 36–7, 38–40, 41, 43, 44, 66–7; commercial importance of time, xviii, 15–16, 21, 22; emergence of modern trade cycle, 62–4; expansion of in Middle Ages, 19, 21–3, 25–6; international trade, 6, 15, 23, 24, 59, 252–3, 261–2; and Italian Renaissance, 21; in medieval Italy, 21–3; mercantile/shipping loans, 6, 12, 14, 22–3, 26, 219 TransAmerica Life Insurance, 199* Trichet, Jean-Claude, 239 Trollope, Anthony, The Way We Live Now, 73 Truman, Harry, 84 The Truman Show (Peter Weir film, 1998), 185–7 Trump, Donald, 185, 261, 262, 291–2, 299, 304, 310 trusts/monopolies: in early twentieth century Europe, 159; Lenin on, 159–60; merger ‘tsunami’ after 2008 crisis, 160–63, 161*, 168–70, 182–3, 237, 298; ‘platform companies’, 161; Adam Smith on, 162, 298; in US robber baron era, 156, 157–9, 203 tulip mania (1630s), 68 Tunisia, 255 Turgot, Anne-Robert Jacques, 15, 28–9, 30, 218 Turkey, xxiii, 252, 258–60, 263 Turkmenistan, 262 Turner, Adair, 292 TXU (energy company), 162 Uber, 149, 150 ‘unicorn’ start-up companies, 148–50, 153, 155, 173, 176–7 Union Pacific Railroad, 157, 158 United States: as bubble economy, 184–7; credit expansion of 1920s, 87–91, 92–4, 96–8, 112, 203; Democrats’ Green New Deal policy, 302; economic expansion (1929–41), 143; economy in Bretton Woods era, 291, 302; financial crisis (1873), 157; foreign securities/loans in 1920s, 91; inflation in 1970s, 108–9; Knickerbocker Panic (1907), 83–4; large-scale immigration into, 78; loan of farm animals in, 4; long-term interest rates (1945–2021), 134; loss of manufacturing jobs to China, 261*, 261; low economic vitality in post-crisis decade, 124, 150–53, 191; monetary policy in 1900s, 83–4, 83*; post-Second World War recovery, 126; public debt today, 291–2, 291*; recessions of early 1980s, 109–10, 151; reversal of global capital flows (late-1920s), 93; robber baron era, 156–9, 203; shift from manufacturing towards services, 167–8, 182; and zombification, 146, 152–3, 155 see also Federal Reserve, US United States Steel Corporation, 157–8 Universities Superannuation Scheme, UK, 196 Useless Ethereum Token, 178 usury: attacked from left and right, 17; attitudes to in ancient world, 17–18, 19, 20–21, 219; in Britain, 24, 26–7, 34, 40, 42, 65‡, 65; Church law forbids, 18–19, 23–4; definitions in Elizabethan era, 26–7; etymology of word, 5; Galiani on, 218–19, 220, 221; and Jews, 18; Marx on, 16, 200–201; medieval Church acknowledges risk, 25–6; Old Testament restrictions on, 17; Proudhon-Bastiat debate on, xvii–xix, xxi, xxii, xxv, 9; in Renaissance world, 22–3; scholastic attack on, 18–20, 23–4, 25 Valeant Pharmaceuticals, 161, 168–9 Vancouver, 175 Veblen, Thorstein, Theory of Business Enterprise (1904), 158, 159, 166 Velde, François, 58*, 59 Venice, 22, 23 Vinci, Leonardo da, Salvator Mundi, 208–9 VIX index, 228–9, 254 La Voix du Peuple, xvii–xix volatility, 153, 228–30, 233, 234, 254, 304, 305 Volcker, Paul, 108–9, 121, 145, 184, 240 Voltaire, 57 Wainwright, Oliver, 209 Waldman, Steve, 206 Waldorf Astoria, New York, 285–6 Wall Street Crash (October 1929): Fed’s response to, 98, 100, 101, 108; Fisher and Keynes fail to foresee, 94–5; Hayek’s interpretation of, 101, 105; low real rates in 1920s USA, 87–91, 89, 92–4, 96–8, 203; low/stable inflation at time of, 134; monetarist view of, 98–9, 101, 105, 108; predictions/warnings of, 93–5, 96, 101, 105, 308; reversal of international capital flows (late-1920s), 93, 93*, 261 WallStreetBets, 307, 309 Walpole, Horace, 62–3 Warburg, Paul, 94 Warsh, Kevin, 228 wealth: ‘Buddenbrooks effect’, 216; conspicuous consumption by mega-rich, 54–5, 208–10, 212; definitions of, 179–82, 216; elite displays as signs of inequality, 209–10, 212; virtual wealth bubbles, 179, 180, 181–2, 185, 193–5, 206, 215, 216–17, 217†, 229–30, 237; wealth illusion, 193–5, 198 Welch, Jack, 170, 171 Wells, H.

Bitcoin for the Befuddled
by Conrad Barski
Published 13 Nov 2014

Named after John Maynard Keynes, the economist who is most strongly identified with this particular approach to monetary policy 14. One well-articulated version of this argument is given by Paul Krugman in the article “Bitcoin Is Evil,” New York Times, December 28, 2013, http://krugman.blogs.nytimes.com/2013/12/28/bitcoin-is-evil/. 15. http://ethereum.org/ 16. http://bitmessage.org/ Chapter 7: The Cryptography Behind Bitcoin 1. This term is used in addition to the many others for Bitcoin, such as digital currency, math-based currency, and among those who don’t think bitcoins are real money, virtual currency. 2. An integer is a number that can be written without a fractional or decimal component (e.g., −4, 2, or 17 but not 4.23 or 1.5). 3.

The New Silk Roads: The Present and Future of the World
by Peter Frankopan
Published 14 Jun 2018

* The rapid development of new technologies is also a significant difficulty to address, in terms of trying to predict the impact these will have in the coming years – and working out how to prepare accordingly for a world where artificial intelligence (AI), robotics, machine learning, Blockchain, Ethereum and more will change the way we live, love, work and communicate. Then there are cryptocurrencies like Bitcoin, which, while exciting for digital pioneers, seem most obviously of interest to those who seek to keep their transactions secure and away from prying eyes – including those who deal in illicit substances or goods, or who prefer to keep potentially taxable revenue away from the authorities.

The Great Wave: The Era of Radical Disruption and the Rise of the Outsider
by Michiko Kakutani
Published 20 Feb 2024

Blockchain is a kind of digital ledger where transaction data is stored not in one central location but on servers and hard drives around the world, making that data difficult to alter or hack and circumventing centralized authorities like governments and banks. In 2014, Gavin Wood, a co-founder of the platform Ethereum, argued that “post-Snowden” it had become clear that it was dangerous to entrust our information to “large organizations and governments” that “routinely attempt to stretch and overstep their authority.” Bitcoin was released as an open-source program in early 2009 by the mysterious Satoshi Nakamoto (possibly a pseudonym for an individual or group), who said that in the wake of the 2008 crash, he wanted to create a secure currency that would be impervious to manipulation by bankers, politicians, and national monetary policies.

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)
by Salim Ismail and Yuri van Geest
Published 17 Oct 2014

In the same way that Internet communications have seen costs drop to near zero, we expect to see internal organizational and transactions costs also fall to near zero as we increasingly information-enable and distribute our organizational structures. Ultimately, in the face of such low transaction costs, we anticipate what we’re calling a Cambrian Explosion in organizational design—everything from community-based structures to virtual organizations (see Ethereum) that will be small, nimble and extensible. It is also becoming increasingly clear that, like the Internet, the ExO paradigm is not just for business. It can just as easily be applied to all sorts of enterprises and organizations, from academia to non-profits to government. In short, it is not just a system of commerce, but also a philosophy of action.

Lab Rats: How Silicon Valley Made Work Miserable for the Rest of Us
by Dan Lyons
Published 22 Oct 2018

They’ve seen other big old companies get killed off by Silicon Valley, and they would rather not have this happen to them. They seem to believe that some magic elixir exists here, some recipe for innovation that floats in the air and can be absorbed if you drive around with your windows open, smelling the eucalyptus trees. They see people getting rich on things they don’t even understand. Blockchain? Ethereum? Initial coin offerings? So they fly out and have drinks at the Rosewood Hotel on Sand Hill Road in Menlo Park, where venture capitalists hang around, as do expensive “companions,” many with Eastern European accents. They eat lunch at the Battery, a members-only private club for social-climbing parvenus in San Francisco.

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity
by Douglas Rushkoff
Published 1 Mar 2016

Michael Carney, “Bitcoin Has a Dark Side: Its Carbon Footprint,” pando.com, December 16, 2013. 40. Lawler, “Bitcoin Miners Are Racking Up $150,000 a Day.” 41. Jon Evans, “Enter the Blockchain: How Bitcoin Can Turn the Cloud Inside Out,” techcrunch.com, March 22, 2014. 42. Vitalik Buterin, “DAOs, DACs, DAs and More: An Incomplete Terminology Guide,” blog.ethereum.org, May 6, 2014. 43. David Johnston, Sam Onat Yilmaz, Jeremy Kandah, Nikos Bentenitis, Farzad Hashemi, Ron Gross, Shawn Wilkinson, and Steven Mason, “The General Theory of Decentralized Applications, Dapps,” github.com, June 9, 2014. 44. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System.” 45.

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism
by Robin Chase
Published 14 May 2015

Much more content can be embedded in the transaction “Payer X wants to send Y Bitcoins to payee Z.” See the Bitcoin transaction as a subject-verb-object command. All kinds of important data relating to the subject and object (name, address, ratings, contingencies), and all types of actions (rent, buy, barter, rideshare, publish) could be substituted in. Budding companies—LaZooz, Swarm, and Ethereum, to name three—are fleshing out this idea. Any kind of business or personal transaction needing standard contracts, for standard actions, with standard rules could all be sent, appended, and amended using the block chain. Every transaction can have its own special combination built using standard building blocks.

Going Infinite: The Rise and Fall of a New Tycoon
by Michael Lewis
Published 2 Oct 2023

Modelbot was programmed to trade roughly five hundred different crypto coins on thirty or so different crypto exchanges, most of them in Asia, all of them basically unregulated. The tulip-­bulb-­like explosion in crypto over the previous year had encouraged the creation of hundreds of new coins. Modelbot made no distinction between the better-­known coins with deep markets, like bitcoin and ether, the Ethereum blockchain’s token, and the so-­called shitcoins that hardly traded at all, like Sexcoin and PUTinCoin and Hot Potato Coin.¶ Modelbot just hunted for any coin it could buy at one price in one place and sell in another at a higher price. Modelbot was maybe the biggest point of disagreement between Sam and his management team.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 17 Apr 2017

A transaction log can be made tamper-proof by periodically signing it with a hardware security module, but that does not guarantee that the right transactions went into the log in the first place. It would be interesting to use cryptographic tools to prove the integrity of a system in a way that is robust to a wide range of hardware and software issues, and even poten‐ tially malicious actions. Cryptocurrencies, blockchains, and distributed ledger tech‐ nologies such as Bitcoin, Ethereum, Ripple, Stellar, and various others [71, 72, 73] have sprung up to explore this area. I am not qualified to comment on the merits of these technologies as currencies or mechanisms for agreeing contracts. However, from a data systems point of view they contain some interesting ideas. Essentially, they are distributed databases, with a data model and transaction mechanism, in which different replicas can be hosted by mutually untrusting organizations.

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The opposite of bounded. 558 | Glossary Index A aborts (transactions), 222, 224 in two-phase commit, 356 performance of optimistic concurrency con‐ trol, 266 retrying aborted transactions, 231 abstraction, 21, 27, 222, 266, 321 access path (in network model), 37, 60 accidental complexity, removing, 21 accountability, 535 ACID properties (transactions), 90, 223 atomicity, 223, 228 consistency, 224, 529 durability, 226 isolation, 225, 228 acknowledgements (messaging), 445 active/active replication (see multi-leader repli‐ cation) active/passive replication (see leader-based rep‐ lication) ActiveMQ (messaging), 137, 444 distributed transaction support, 361 ActiveRecord (object-relational mapper), 30, 232 actor model, 138 (see also message-passing) comparison to Pregel model, 425 comparison to stream processing, 468 Advanced Message Queuing Protocol (see AMQP) aerospace systems, 6, 10, 305, 372 aggregation data cubes and materialized views, 101 in batch processes, 406 in stream processes, 466 aggregation pipeline query language, 48 Agile, 22 minimizing irreversibility, 414, 497 moving faster with confidence, 532 Unix philosophy, 394 agreement, 365 (see also consensus) Airflow (workflow scheduler), 402 Ajax, 131 Akka (actor framework), 139 algorithms algorithm correctness, 308 B-trees, 79-83 for distributed systems, 306 hash indexes, 72-75 mergesort, 76, 402, 405 red-black trees, 78 SSTables and LSM-trees, 76-79 all-to-all replication topologies, 175 AllegroGraph (database), 50 ALTER TABLE statement (SQL), 40, 111 Amazon Dynamo (database), 177 Amazon Web Services (AWS), 8 Kinesis Streams (messaging), 448 network reliability, 279 postmortems, 9 RedShift (database), 93 S3 (object storage), 398 checking data integrity, 530 amplification of bias, 534 of failures, 364, 495 Index | 559 of tail latency, 16, 207 write amplification, 84 AMQP (Advanced Message Queuing Protocol), 444 (see also messaging systems) comparison to log-based messaging, 448, 451 message ordering, 446 analytics, 90 comparison to transaction processing, 91 data warehousing (see data warehousing) parallel query execution in MPP databases, 415 predictive (see predictive analytics) relation to batch processing, 411 schemas for, 93-95 snapshot isolation for queries, 238 stream analytics, 466 using MapReduce, analysis of user activity events (example), 404 anti-caching (in-memory databases), 89 anti-entropy, 178 Apache ActiveMQ (see ActiveMQ) Apache Avro (see Avro) Apache Beam (see Beam) Apache BookKeeper (see BookKeeper) Apache Cassandra (see Cassandra) Apache CouchDB (see CouchDB) Apache Curator (see Curator) Apache Drill (see Drill) Apache Flink (see Flink) Apache Giraph (see Giraph) Apache Hadoop (see Hadoop) Apache HAWQ (see HAWQ) Apache HBase (see HBase) Apache Helix (see Helix) Apache Hive (see Hive) Apache Impala (see Impala) Apache Jena (see Jena) Apache Kafka (see Kafka) Apache Lucene (see Lucene) Apache MADlib (see MADlib) Apache Mahout (see Mahout) Apache Oozie (see Oozie) Apache Parquet (see Parquet) Apache Qpid (see Qpid) Apache Samza (see Samza) Apache Solr (see Solr) Apache Spark (see Spark) 560 | Index Apache Storm (see Storm) Apache Tajo (see Tajo) Apache Tez (see Tez) Apache Thrift (see Thrift) Apache ZooKeeper (see ZooKeeper) Apama (stream analytics), 466 append-only B-trees, 82, 242 append-only files (see logs) Application Programming Interfaces (APIs), 5, 27 for batch processing, 403 for change streams, 456 for distributed transactions, 361 for graph processing, 425 for services, 131-136 (see also services) evolvability, 136 RESTful, 133 SOAP, 133 application state (see state) approximate search (see similarity search) archival storage, data from databases, 131 arcs (see edges) arithmetic mean, 14 ASCII text, 119, 395 ASN.1 (schema language), 127 asynchronous networks, 278, 553 comparison to synchronous networks, 284 formal model, 307 asynchronous replication, 154, 553 conflict detection, 172 data loss on failover, 157 reads from asynchronous follower, 162 Asynchronous Transfer Mode (ATM), 285 atomic broadcast (see total order broadcast) atomic clocks (caesium clocks), 294, 295 (see also clocks) atomicity (concurrency), 553 atomic increment-and-get, 351 compare-and-set, 245, 327 (see also compare-and-set operations) replicated operations, 246 write operations, 243 atomicity (transactions), 223, 228, 553 atomic commit, 353 avoiding, 523, 528 blocking and nonblocking, 359 in stream processing, 360, 477 maintaining derived data, 453 for multi-object transactions, 229 for single-object writes, 230 auditability, 528-533 designing for, 531 self-auditing systems, 530 through immutability, 460 tools for auditable data systems, 532 availability, 8 (see also fault tolerance) in CAP theorem, 337 in service level agreements (SLAs), 15 Avro (data format), 122-127 code generation, 127 dynamically generated schemas, 126 object container files, 125, 131, 414 reader determining writer’s schema, 125 schema evolution, 123 use in Hadoop, 414 awk (Unix tool), 391 AWS (see Amazon Web Services) Azure (see Microsoft) B B-trees (indexes), 79-83 append-only/copy-on-write variants, 82, 242 branching factor, 81 comparison to LSM-trees, 83-85 crash recovery, 82 growing by splitting a page, 81 optimizations, 82 similarity to dynamic partitioning, 212 backpressure, 441, 553 in TCP, 282 backups database snapshot for replication, 156 integrity of, 530 snapshot isolation for, 238 use for ETL processes, 405 backward compatibility, 112 BASE, contrast to ACID, 223 bash shell (Unix), 70, 395, 503 batch processing, 28, 389-431, 553 combining with stream processing lambda architecture, 497 unifying technologies, 498 comparison to MPP databases, 414-418 comparison to stream processing, 464 comparison to Unix, 413-414 dataflow engines, 421-423 fault tolerance, 406, 414, 422, 442 for data integration, 494-498 graphs and iterative processing, 424-426 high-level APIs and languages, 403, 426-429 log-based messaging and, 451 maintaining derived state, 495 MapReduce and distributed filesystems, 397-413 (see also MapReduce) measuring performance, 13, 390 outputs, 411-413 key-value stores, 412 search indexes, 411 using Unix tools (example), 391-394 Bayou (database), 522 Beam (dataflow library), 498 bias, 534 big ball of mud, 20 Bigtable data model, 41, 99 binary data encodings, 115-128 Avro, 122-127 MessagePack, 116-117 Thrift and Protocol Buffers, 117-121 binary encoding based on schemas, 127 by network drivers, 128 binary strings, lack of support in JSON and XML, 114 BinaryProtocol encoding (Thrift), 118 Bitcask (storage engine), 72 crash recovery, 74 Bitcoin (cryptocurrency), 532 Byzantine fault tolerance, 305 concurrency bugs in exchanges, 233 bitmap indexes, 97 blockchains, 532 Byzantine fault tolerance, 305 blocking atomic commit, 359 Bloom (programming language), 504 Bloom filter (algorithm), 79, 466 BookKeeper (replicated log), 372 Bottled Water (change data capture), 455 bounded datasets, 430, 439, 553 (see also batch processing) bounded delays, 553 in networks, 285 process pauses, 298 broadcast hash joins, 409 Index | 561 brokerless messaging, 442 Brubeck (metrics aggregator), 442 BTM (transaction coordinator), 356 bulk synchronous parallel (BSP) model, 425 bursty network traffic patterns, 285 business data processing, 28, 90, 390 byte sequence, encoding data in, 112 Byzantine faults, 304-306, 307, 553 Byzantine fault-tolerant systems, 305, 532 Byzantine Generals Problem, 304 consensus algorithms and, 366 C caches, 89, 553 and materialized views, 101 as derived data, 386, 499-504 database as cache of transaction log, 460 in CPUs, 99, 338, 428 invalidation and maintenance, 452, 467 linearizability, 324 CAP theorem, 336-338, 554 Cascading (batch processing), 419, 427 hash joins, 409 workflows, 403 cascading failures, 9, 214, 281 Cascalog (batch processing), 60 Cassandra (database) column-family data model, 41, 99 compaction strategy, 79 compound primary key, 204 gossip protocol, 216 hash partitioning, 203-205 last-write-wins conflict resolution, 186, 292 leaderless replication, 177 linearizability, lack of, 335 log-structured storage, 78 multi-datacenter support, 184 partitioning scheme, 213 secondary indexes, 207 sloppy quorums, 184 cat (Unix tool), 391 causal context, 191 (see also causal dependencies) causal dependencies, 186-191 capturing, 191, 342, 494, 514 by total ordering, 493 causal ordering, 339 in transactions, 262 sending message to friends (example), 494 562 | Index causality, 554 causal ordering, 339-343 linearizability and, 342 total order consistent with, 344, 345 consistency with, 344-347 consistent snapshots, 340 happens-before relationship, 186 in serializable transactions, 262-265 mismatch with clocks, 292 ordering events to capture, 493 violations of, 165, 176, 292, 340 with synchronized clocks, 294 CEP (see complex event processing) certificate transparency, 532 chain replication, 155 linearizable reads, 351 change data capture, 160, 454 API support for change streams, 456 comparison to event sourcing, 457 implementing, 454 initial snapshot, 455 log compaction, 456 changelogs, 460 change data capture, 454 for operator state, 479 generating with triggers, 455 in stream joins, 474 log compaction, 456 maintaining derived state, 452 Chaos Monkey, 7, 280 checkpointing in batch processors, 422, 426 in high-performance computing, 275 in stream processors, 477, 523 chronicle data model, 458 circuit-switched networks, 284 circular buffers, 450 circular replication topologies, 175 clickstream data, analysis of, 404 clients calling services, 131 pushing state changes to, 512 request routing, 214 stateful and offline-capable, 170, 511 clocks, 287-299 atomic (caesium) clocks, 294, 295 confidence interval, 293-295 for global snapshots, 294 logical (see logical clocks) skew, 291-294, 334 slewing, 289 synchronization and accuracy, 289-291 synchronization using GPS, 287, 290, 294, 295 time-of-day versus monotonic clocks, 288 timestamping events, 471 cloud computing, 146, 275 need for service discovery, 372 network glitches, 279 shared resources, 284 single-machine reliability, 8 Cloudera Impala (see Impala) clustered indexes, 86 CODASYL model, 36 (see also network model) code generation with Avro, 127 with Thrift and Protocol Buffers, 118 with WSDL, 133 collaborative editing multi-leader replication and, 170 column families (Bigtable), 41, 99 column-oriented storage, 95-101 column compression, 97 distinction between column families and, 99 in batch processors, 428 Parquet, 96, 131, 414 sort order in, 99-100 vectorized processing, 99, 428 writing to, 101 comma-separated values (see CSV) command query responsibility segregation (CQRS), 462 commands (event sourcing), 459 commits (transactions), 222 atomic commit, 354-355 (see also atomicity; transactions) read committed isolation, 234 three-phase commit (3PC), 359 two-phase commit (2PC), 355-359 commutative operations, 246 compaction of changelogs, 456 (see also log compaction) for stream operator state, 479 of log-structured storage, 73 issues with, 84 size-tiered and leveled approaches, 79 CompactProtocol encoding (Thrift), 119 compare-and-set operations, 245, 327 implementing locks, 370 implementing uniqueness constraints, 331 implementing with total order broadcast, 350 relation to consensus, 335, 350, 352, 374 relation to transactions, 230 compatibility, 112, 128 calling services, 136 properties of encoding formats, 139 using databases, 129-131 using message-passing, 138 compensating transactions, 355, 461, 526 complex event processing (CEP), 465 complexity distilling in theoretical models, 310 hiding using abstraction, 27 of software systems, managing, 20 composing data systems (see unbundling data‐ bases) compute-intensive applications, 3, 275 concatenated indexes, 87 in Cassandra, 204 Concord (stream processor), 466 concurrency actor programming model, 138, 468 (see also message-passing) bugs from weak transaction isolation, 233 conflict resolution, 171, 174 detecting concurrent writes, 184-191 dual writes, problems with, 453 happens-before relationship, 186 in replicated systems, 161-191, 324-338 lost updates, 243 multi-version concurrency control (MVCC), 239 optimistic concurrency control, 261 ordering of operations, 326, 341 reducing, through event logs, 351, 462, 507 time and relativity, 187 transaction isolation, 225 write skew (transaction isolation), 246-251 conflict-free replicated datatypes (CRDTs), 174 conflicts conflict detection, 172 causal dependencies, 186, 342 in consensus algorithms, 368 in leaderless replication, 184 Index | 563 in log-based systems, 351, 521 in nonlinearizable systems, 343 in serializable snapshot isolation (SSI), 264 in two-phase commit, 357, 364 conflict resolution automatic conflict resolution, 174 by aborting transactions, 261 by apologizing, 527 convergence, 172-174 in leaderless systems, 190 last write wins (LWW), 186, 292 using atomic operations, 246 using custom logic, 173 determining what is a conflict, 174, 522 in multi-leader replication, 171-175 avoiding conflicts, 172 lost updates, 242-246 materializing, 251 relation to operation ordering, 339 write skew (transaction isolation), 246-251 congestion (networks) avoidance, 282 limiting accuracy of clocks, 293 queueing delays, 282 consensus, 321, 364-375, 554 algorithms, 366-368 preventing split brain, 367 safety and liveness properties, 365 using linearizable operations, 351 cost of, 369 distributed transactions, 352-375 in practice, 360-364 two-phase commit, 354-359 XA transactions, 361-364 impossibility of, 353 membership and coordination services, 370-373 relation to compare-and-set, 335, 350, 352, 374 relation to replication, 155, 349 relation to uniqueness constraints, 521 consistency, 224, 524 across different databases, 157, 452, 462, 492 causal, 339-348, 493 consistent prefix reads, 165-167 consistent snapshots, 156, 237-242, 294, 455, 500 (see also snapshots) 564 | Index crash recovery, 82 enforcing constraints (see constraints) eventual, 162, 322 (see also eventual consistency) in ACID transactions, 224, 529 in CAP theorem, 337 linearizability, 324-338 meanings of, 224 monotonic reads, 164-165 of secondary indexes, 231, 241, 354, 491, 500 ordering guarantees, 339-352 read-after-write, 162-164 sequential, 351 strong (see linearizability) timeliness and integrity, 524 using quorums, 181, 334 consistent hashing, 204 consistent prefix reads, 165 constraints (databases), 225, 248 asynchronously checked, 526 coordination avoidance, 527 ensuring idempotence, 519 in log-based systems, 521-524 across multiple partitions, 522 in two-phase commit, 355, 357 relation to consensus, 374, 521 relation to event ordering, 347 requiring linearizability, 330 Consul (service discovery), 372 consumers (message streams), 137, 440 backpressure, 441 consumer offsets in logs, 449 failures, 445, 449 fan-out, 11, 445, 448 load balancing, 444, 448 not keeping up with producers, 441, 450, 502 context switches, 14, 297 convergence (conflict resolution), 172-174, 322 coordination avoidance, 527 cross-datacenter, 168, 493 cross-partition ordering, 256, 294, 348, 523 services, 330, 370-373 coordinator (in 2PC), 356 failure, 358 in XA transactions, 361-364 recovery, 363 copy-on-write (B-trees), 82, 242 CORBA (Common Object Request Broker Architecture), 134 correctness, 6 auditability, 528-533 Byzantine fault tolerance, 305, 532 dealing with partial failures, 274 in log-based systems, 521-524 of algorithm within system model, 308 of compensating transactions, 355 of consensus, 368 of derived data, 497, 531 of immutable data, 461 of personal data, 535, 540 of time, 176, 289-295 of transactions, 225, 515, 529 timeliness and integrity, 524-528 corruption of data detecting, 519, 530-533 due to pathological memory access, 529 due to radiation, 305 due to split brain, 158, 302 due to weak transaction isolation, 233 formalization in consensus, 366 integrity as absence of, 524 network packets, 306 on disks, 227 preventing using write-ahead logs, 82 recovering from, 414, 460 Couchbase (database) durability, 89 hash partitioning, 203-204, 211 rebalancing, 213 request routing, 216 CouchDB (database) B-tree storage, 242 change feed, 456 document data model, 31 join support, 34 MapReduce support, 46, 400 replication, 170, 173 covering indexes, 86 CPUs cache coherence and memory barriers, 338 caching and pipelining, 99, 428 increasing parallelism, 43 CRDTs (see conflict-free replicated datatypes) CREATE INDEX statement (SQL), 85, 500 credit rating agencies, 535 Crunch (batch processing), 419, 427 hash joins, 409 sharded joins, 408 workflows, 403 cryptography defense against attackers, 306 end-to-end encryption and authentication, 519, 543 proving integrity of data, 532 CSS (Cascading Style Sheets), 44 CSV (comma-separated values), 70, 114, 396 Curator (ZooKeeper recipes), 330, 371 curl (Unix tool), 135, 397 cursor stability, 243 Cypher (query language), 52 comparison to SPARQL, 59 D data corruption (see corruption of data) data cubes, 102 data formats (see encoding) data integration, 490-498, 543 batch and stream processing, 494-498 lambda architecture, 497 maintaining derived state, 495 reprocessing data, 496 unifying, 498 by unbundling databases, 499-515 comparison to federated databases, 501 combining tools by deriving data, 490-494 derived data versus distributed transac‐ tions, 492 limits of total ordering, 493 ordering events to capture causality, 493 reasoning about dataflows, 491 need for, 385 data lakes, 415 data locality (see locality) data models, 27-64 graph-like models, 49-63 Datalog language, 60-63 property graphs, 50 RDF and triple-stores, 55-59 query languages, 42-48 relational model versus document model, 28-42 data protection regulations, 542 data systems, 3 about, 4 Index | 565 concerns when designing, 5 future of, 489-544 correctness, constraints, and integrity, 515-533 data integration, 490-498 unbundling databases, 499-515 heterogeneous, keeping in sync, 452 maintainability, 18-22 possible faults in, 221 reliability, 6-10 hardware faults, 7 human errors, 9 importance of, 10 software errors, 8 scalability, 10-18 unreliable clocks, 287-299 data warehousing, 91-95, 554 comparison to data lakes, 415 ETL (extract-transform-load), 92, 416, 452 keeping data systems in sync, 452 schema design, 93 slowly changing dimension (SCD), 476 data-intensive applications, 3 database triggers (see triggers) database-internal distributed transactions, 360, 364, 477 databases archival storage, 131 comparison of message brokers to, 443 dataflow through, 129 end-to-end argument for, 519-520 checking integrity, 531 inside-out, 504 (see also unbundling databases) output from batch workflows, 412 relation to event streams, 451-464 (see also changelogs) API support for change streams, 456, 506 change data capture, 454-457 event sourcing, 457-459 keeping systems in sync, 452-453 philosophy of immutable events, 459-464 unbundling, 499-515 composing data storage technologies, 499-504 designing applications around dataflow, 504-509 566 | Index observing derived state, 509-515 datacenters geographically distributed, 145, 164, 278, 493 multi-tenancy and shared resources, 284 network architecture, 276 network faults, 279 replication across multiple, 169 leaderless replication, 184 multi-leader replication, 168, 335 dataflow, 128-139, 504-509 correctness of dataflow systems, 525 differential, 504 message-passing, 136-139 reasoning about, 491 through databases, 129 through services, 131-136 dataflow engines, 421-423 comparison to stream processing, 464 directed acyclic graphs (DAG), 424 partitioning, approach to, 429 support for declarative queries, 427 Datalog (query language), 60-63 datatypes binary strings in XML and JSON, 114 conflict-free, 174 in Avro encodings, 122 in Thrift and Protocol Buffers, 121 numbers in XML and JSON, 114 Datomic (database) B-tree storage, 242 data model, 50, 57 Datalog query language, 60 excision (deleting data), 463 languages for transactions, 255 serial execution of transactions, 253 deadlocks detection, in two-phase commit (2PC), 364 in two-phase locking (2PL), 258 Debezium (change data capture), 455 declarative languages, 42, 554 Bloom, 504 CSS and XSL, 44 Cypher, 52 Datalog, 60 for batch processing, 427 recursive SQL queries, 53 relational algebra and SQL, 42 SPARQL, 59 delays bounded network delays, 285 bounded process pauses, 298 unbounded network delays, 282 unbounded process pauses, 296 deleting data, 463 denormalization (data representation), 34, 554 costs, 39 in derived data systems, 386 materialized views, 101 updating derived data, 228, 231, 490 versus normalization, 462 derived data, 386, 439, 554 from change data capture, 454 in event sourcing, 458-458 maintaining derived state through logs, 452-457, 459-463 observing, by subscribing to streams, 512 outputs of batch and stream processing, 495 through application code, 505 versus distributed transactions, 492 deterministic operations, 255, 274, 554 accidental nondeterminism, 423 and fault tolerance, 423, 426 and idempotence, 478, 492 computing derived data, 495, 526, 531 in state machine replication, 349, 452, 458 joins, 476 DevOps, 394 differential dataflow, 504 dimension tables, 94 dimensional modeling (see star schemas) directed acyclic graphs (DAGs), 424 dirty reads (transaction isolation), 234 dirty writes (transaction isolation), 235 discrimination, 534 disks (see hard disks) distributed actor frameworks, 138 distributed filesystems, 398-399 decoupling from query engines, 417 indiscriminately dumping data into, 415 use by MapReduce, 402 distributed systems, 273-312, 554 Byzantine faults, 304-306 cloud versus supercomputing, 275 detecting network faults, 280 faults and partial failures, 274-277 formalization of consensus, 365 impossibility results, 338, 353 issues with failover, 157 limitations of distributed transactions, 363 multi-datacenter, 169, 335 network problems, 277-286 quorums, relying on, 301 reasons for using, 145, 151 synchronized clocks, relying on, 291-295 system models, 306-310 use of clocks and time, 287 distributed transactions (see transactions) Django (web framework), 232 DNS (Domain Name System), 216, 372 Docker (container manager), 506 document data model, 30-42 comparison to relational model, 38-42 document references, 38, 403 document-oriented databases, 31 many-to-many relationships and joins, 36 multi-object transactions, need for, 231 versus relational model convergence of models, 41 data locality, 41 document-partitioned indexes, 206, 217, 411 domain-driven design (DDD), 457 DRBD (Distributed Replicated Block Device), 153 drift (clocks), 289 Drill (query engine), 93 Druid (database), 461 Dryad (dataflow engine), 421 dual writes, problems with, 452, 507 duplicates, suppression of, 517 (see also idempotence) using a unique ID, 518, 522 durability (transactions), 226, 554 duration (time), 287 measurement with monotonic clocks, 288 dynamic partitioning, 212 dynamically typed languages analogy to schema-on-read, 40 code generation and, 127 Dynamo-style databases (see leaderless replica‐ tion) E edges (in graphs), 49, 403 property graph model, 50 edit distance (full-text search), 88 effectively-once semantics, 476, 516 Index | 567 (see also exactly-once semantics) preservation of integrity, 525 elastic systems, 17 Elasticsearch (search server) document-partitioned indexes, 207 partition rebalancing, 211 percolator (stream search), 467 usage example, 4 use of Lucene, 79 ElephantDB (database), 413 Elm (programming language), 504, 512 encodings (data formats), 111-128 Avro, 122-127 binary variants of JSON and XML, 115 compatibility, 112 calling services, 136 using databases, 129-131 using message-passing, 138 defined, 113 JSON, XML, and CSV, 114 language-specific formats, 113 merits of schemas, 127 representations of data, 112 Thrift and Protocol Buffers, 117-121 end-to-end argument, 277, 519-520 checking integrity, 531 publish/subscribe streams, 512 enrichment (stream), 473 Enterprise JavaBeans (EJB), 134 entities (see vertices) epoch (consensus algorithms), 368 epoch (Unix timestamps), 288 equi-joins, 403 erasure coding (error correction), 398 Erlang OTP (actor framework), 139 error handling for network faults, 280 in transactions, 231 error-correcting codes, 277, 398 Esper (CEP engine), 466 etcd (coordination service), 370-373 linearizable operations, 333 locks and leader election, 330 quorum reads, 351 service discovery, 372 use of Raft algorithm, 349, 353 Ethereum (blockchain), 532 Ethernet (networks), 276, 278, 285 packet checksums, 306, 519 568 | Index Etherpad (collaborative editor), 170 ethics, 533-543 code of ethics and professional practice, 533 legislation and self-regulation, 542 predictive analytics, 533-536 amplifying bias, 534 feedback loops, 536 privacy and tracking, 536-543 consent and freedom of choice, 538 data as assets and power, 540 meaning of privacy, 539 surveillance, 537 respect, dignity, and agency, 543, 544 unintended consequences, 533, 536 ETL (extract-transform-load), 92, 405, 452, 554 use of Hadoop for, 416 event sourcing, 457-459 commands and events, 459 comparison to change data capture, 457 comparison to lambda architecture, 497 deriving current state from event log, 458 immutability and auditability, 459, 531 large, reliable data systems, 519, 526 Event Store (database), 458 event streams (see streams) events, 440 deciding on total order of, 493 deriving views from event log, 461 difference to commands, 459 event time versus processing time, 469, 477, 498 immutable, advantages of, 460, 531 ordering to capture causality, 493 reads as, 513 stragglers, 470, 498 timestamp of, in stream processing, 471 EventSource (browser API), 512 eventual consistency, 152, 162, 308, 322 (see also conflicts) and perpetual inconsistency, 525 evolvability, 21, 111 calling services, 136 graph-structured data, 52 of databases, 40, 129-131, 461, 497 of message-passing, 138 reprocessing data, 496, 498 schema evolution in Avro, 123 schema evolution in Thrift and Protocol Buffers, 120 schema-on-read, 39, 111, 128 exactly-once semantics, 360, 476, 516 parity with batch processors, 498 preservation of integrity, 525 exclusive mode (locks), 258 eXtended Architecture transactions (see XA transactions) extract-transform-load (see ETL) F Facebook Presto (query engine), 93 React, Flux, and Redux (user interface libra‐ ries), 512 social graphs, 49 Wormhole (change data capture), 455 fact tables, 93 failover, 157, 554 (see also leader-based replication) in leaderless replication, absence of, 178 leader election, 301, 348, 352 potential problems, 157 failures amplification by distributed transactions, 364, 495 failure detection, 280 automatic rebalancing causing cascading failures, 214 perfect failure detectors, 359 timeouts and unbounded delays, 282, 284 using ZooKeeper, 371 faults versus, 7 partial failures in distributed systems, 275-277, 310 fan-out (messaging systems), 11, 445 fault tolerance, 6-10, 555 abstractions for, 321 formalization in consensus, 365-369 use of replication, 367 human fault tolerance, 414 in batch processing, 406, 414, 422, 425 in log-based systems, 520, 524-526 in stream processing, 476-479 atomic commit, 477 idempotence, 478 maintaining derived state, 495 microbatching and checkpointing, 477 rebuilding state after a failure, 478 of distributed transactions, 362-364 transaction atomicity, 223, 354-361 faults, 6 Byzantine faults, 304-306 failures versus, 7 handled by transactions, 221 handling in supercomputers and cloud computing, 275 hardware, 7 in batch processing versus distributed data‐ bases, 417 in distributed systems, 274-277 introducing deliberately, 7, 280 network faults, 279-281 asymmetric faults, 300 detecting, 280 tolerance of, in multi-leader replication, 169 software errors, 8 tolerating (see fault tolerance) federated databases, 501 fence (CPU instruction), 338 fencing (preventing split brain), 158, 302-304 generating fencing tokens, 349, 370 properties of fencing tokens, 308 stream processors writing to databases, 478, 517 Fibre Channel (networks), 398 field tags (Thrift and Protocol Buffers), 119-121 file descriptors (Unix), 395 financial data, 460 Firebase (database), 456 Flink (processing framework), 421-423 dataflow APIs, 427 fault tolerance, 422, 477, 479 Gelly API (graph processing), 425 integration of batch and stream processing, 495, 498 machine learning, 428 query optimizer, 427 stream processing, 466 flow control, 282, 441, 555 FLP result (on consensus), 353 FlumeJava (dataflow library), 403, 427 followers, 152, 555 (see also leader-based replication) foreign keys, 38, 403 forward compatibility, 112 forward decay (algorithm), 16 Index | 569 Fossil (version control system), 463 shunning (deleting data), 463 FoundationDB (database) serializable transactions, 261, 265, 364 fractal trees, 83 full table scans, 403 full-text search, 555 and fuzzy indexes, 88 building search indexes, 411 Lucene storage engine, 79 functional reactive programming (FRP), 504 functional requirements, 22 futures (asynchronous operations), 135 fuzzy search (see similarity search) G garbage collection immutability and, 463 process pauses for, 14, 296-299, 301 (see also process pauses) genome analysis, 63, 429 geographically distributed datacenters, 145, 164, 278, 493 geospatial indexes, 87 Giraph (graph processing), 425 Git (version control system), 174, 342, 463 GitHub, postmortems, 157, 158, 309 global indexes (see term-partitioned indexes) GlusterFS (distributed filesystem), 398 GNU Coreutils (Linux), 394 GoldenGate (change data capture), 161, 170, 455 (see also Oracle) Google Bigtable (database) data model (see Bigtable data model) partitioning scheme, 199, 202 storage layout, 78 Chubby (lock service), 370 Cloud Dataflow (stream processor), 466, 477, 498 (see also Beam) Cloud Pub/Sub (messaging), 444, 448 Docs (collaborative editor), 170 Dremel (query engine), 93, 96 FlumeJava (dataflow library), 403, 427 GFS (distributed file system), 398 gRPC (RPC framework), 135 MapReduce (batch processing), 390 570 | Index (see also MapReduce) building search indexes, 411 task preemption, 418 Pregel (graph processing), 425 Spanner (see Spanner) TrueTime (clock API), 294 gossip protocol, 216 government use of data, 541 GPS (Global Positioning System) use for clock synchronization, 287, 290, 294, 295 GraphChi (graph processing), 426 graphs, 555 as data models, 49-63 example of graph-structured data, 49 property graphs, 50 RDF and triple-stores, 55-59 versus the network model, 60 processing and analysis, 424-426 fault tolerance, 425 Pregel processing model, 425 query languages Cypher, 52 Datalog, 60-63 recursive SQL queries, 53 SPARQL, 59-59 Gremlin (graph query language), 50 grep (Unix tool), 392 GROUP BY clause (SQL), 406 grouping records in MapReduce, 406 handling skew, 407 H Hadoop (data infrastructure) comparison to distributed databases, 390 comparison to MPP databases, 414-418 comparison to Unix, 413-414, 499 diverse processing models in ecosystem, 417 HDFS distributed filesystem (see HDFS) higher-level tools, 403 join algorithms, 403-410 (see also MapReduce) MapReduce (see MapReduce) YARN (see YARN) happens-before relationship, 340 capturing, 187 concurrency and, 186 hard disks access patterns, 84 detecting corruption, 519, 530 faults in, 7, 227 sequential write throughput, 75, 450 hardware faults, 7 hash indexes, 72-75 broadcast hash joins, 409 partitioned hash joins, 409 hash partitioning, 203-205, 217 consistent hashing, 204 problems with hash mod N, 210 range queries, 204 suitable hash functions, 203 with fixed number of partitions, 210 HAWQ (database), 428 HBase (database) bug due to lack of fencing, 302 bulk loading, 413 column-family data model, 41, 99 dynamic partitioning, 212 key-range partitioning, 202 log-structured storage, 78 request routing, 216 size-tiered compaction, 79 use of HDFS, 417 use of ZooKeeper, 370 HDFS (Hadoop Distributed File System), 398-399 (see also distributed filesystems) checking data integrity, 530 decoupling from query engines, 417 indiscriminately dumping data into, 415 metadata about datasets, 410 NameNode, 398 use by Flink, 479 use by HBase, 212 use by MapReduce, 402 HdrHistogram (numerical library), 16 head (Unix tool), 392 head vertex (property graphs), 51 head-of-line blocking, 15 heap files (databases), 86 Helix (cluster manager), 216 heterogeneous distributed transactions, 360, 364 heuristic decisions (in 2PC), 363 Hibernate (object-relational mapper), 30 hierarchical model, 36 high availability (see fault tolerance) high-frequency trading, 290, 299 high-performance computing (HPC), 275 hinted handoff, 183 histograms, 16 Hive (query engine), 419, 427 for data warehouses, 93 HCatalog and metastore, 410 map-side joins, 409 query optimizer, 427 skewed joins, 408 workflows, 403 Hollerith machines, 390 hopping windows (stream processing), 472 (see also windows) horizontal scaling (see scaling out) HornetQ (messaging), 137, 444 distributed transaction support, 361 hot spots, 201 due to celebrities, 205 for time-series data, 203 in batch processing, 407 relieving, 205 hot standbys (see leader-based replication) HTTP, use in APIs (see services) human errors, 9, 279, 414 HyperDex (database), 88 HyperLogLog (algorithm), 466 I I/O operations, waiting for, 297 IBM DB2 (database) distributed transaction support, 361 recursive query support, 54 serializable isolation, 242, 257 XML and JSON support, 30, 42 electromechanical card-sorting machines, 390 IMS (database), 36 imperative query APIs, 46 InfoSphere Streams (CEP engine), 466 MQ (messaging), 444 distributed transaction support, 361 System R (database), 222 WebSphere (messaging), 137 idempotence, 134, 478, 555 by giving operations unique IDs, 518, 522 idempotent operations, 517 immutability advantages of, 460, 531 Index | 571 deriving state from event log, 459-464 for crash recovery, 75 in B-trees, 82, 242 in event sourcing, 457 inputs to Unix commands, 397 limitations of, 463 Impala (query engine) for data warehouses, 93 hash joins, 409 native code generation, 428 use of HDFS, 417 impedance mismatch, 29 imperative languages, 42 setting element styles (example), 45 in doubt (transaction status), 358 holding locks, 362 orphaned transactions, 363 in-memory databases, 88 durability, 227 serial transaction execution, 253 incidents cascading failures, 9 crashes due to leap seconds, 290 data corruption and financial losses due to concurrency bugs, 233 data corruption on hard disks, 227 data loss due to last-write-wins, 173, 292 data on disks unreadable, 309 deleted items reappearing, 174 disclosure of sensitive data due to primary key reuse, 157 errors in transaction serializability, 529 gigabit network interface with 1 Kb/s throughput, 311 network faults, 279 network interface dropping only inbound packets, 279 network partitions and whole-datacenter failures, 275 poor handling of network faults, 280 sending message to ex-partner, 494 sharks biting undersea cables, 279 split brain due to 1-minute packet delay, 158, 279 vibrations in server rack, 14 violation of uniqueness constraint, 529 indexes, 71, 555 and snapshot isolation, 241 as derived data, 386, 499-504 572 | Index B-trees, 79-83 building in batch processes, 411 clustered, 86 comparison of B-trees and LSM-trees, 83-85 concatenated, 87 covering (with included columns), 86 creating, 500 full-text search, 88 geospatial, 87 hash, 72-75 index-range locking, 260 multi-column, 87 partitioning and secondary indexes, 206-209, 217 secondary, 85 (see also secondary indexes) problems with dual writes, 452, 491 SSTables and LSM-trees, 76-79 updating when data changes, 452, 467 Industrial Revolution, 541 InfiniBand (networks), 285 InfiniteGraph (database), 50 InnoDB (storage engine) clustered index on primary key, 86 not preventing lost updates, 245 preventing write skew, 248, 257 serializable isolation, 257 snapshot isolation support, 239 inside-out databases, 504 (see also unbundling databases) integrating different data systems (see data integration) integrity, 524 coordination-avoiding data systems, 528 correctness of dataflow systems, 525 in consensus formalization, 365 integrity checks, 530 (see also auditing) end-to-end, 519, 531 use of snapshot isolation, 238 maintaining despite software bugs, 529 Interface Definition Language (IDL), 117, 122 intermediate state, materialization of, 420-423 internet services, systems for implementing, 275 invariants, 225 (see also constraints) inversion of control, 396 IP (Internet Protocol) unreliability of, 277 ISDN (Integrated Services Digital Network), 284 isolation (in transactions), 225, 228, 555 correctness and, 515 for single-object writes, 230 serializability, 251-266 actual serial execution, 252-256 serializable snapshot isolation (SSI), 261-266 two-phase locking (2PL), 257-261 violating, 228 weak isolation levels, 233-251 preventing lost updates, 242-246 read committed, 234-237 snapshot isolation, 237-242 iterative processing, 424-426 J Java Database Connectivity (JDBC) distributed transaction support, 361 network drivers, 128 Java Enterprise Edition (EE), 134, 356, 361 Java Message Service (JMS), 444 (see also messaging systems) comparison to log-based messaging, 448, 451 distributed transaction support, 361 message ordering, 446 Java Transaction API (JTA), 355, 361 Java Virtual Machine (JVM) bytecode generation, 428 garbage collection pauses, 296 process reuse in batch processors, 422 JavaScript in MapReduce querying, 46 setting element styles (example), 45 use in advanced queries, 48 Jena (RDF framework), 57 Jepsen (fault tolerance testing), 515 jitter (network delay), 284 joins, 555 by index lookup, 403 expressing as relational operators, 427 in relational and document databases, 34 MapReduce map-side joins, 408-410 broadcast hash joins, 409 merge joins, 410 partitioned hash joins, 409 MapReduce reduce-side joins, 403-408 handling skew, 407 sort-merge joins, 405 parallel execution of, 415 secondary indexes and, 85 stream joins, 472-476 stream-stream join, 473 stream-table join, 473 table-table join, 474 time-dependence of, 475 support in document databases, 42 JOTM (transaction coordinator), 356 JSON Avro schema representation, 122 binary variants, 115 for application data, issues with, 114 in relational databases, 30, 42 representing a résumé (example), 31 Juttle (query language), 504 K k-nearest neighbors, 429 Kafka (messaging), 137, 448 Kafka Connect (database integration), 457, 461 Kafka Streams (stream processor), 466, 467 fault tolerance, 479 leader-based replication, 153 log compaction, 456, 467 message offsets, 447, 478 request routing, 216 transaction support, 477 usage example, 4 Ketama (partitioning library), 213 key-value stores, 70 as batch process output, 412 hash indexes, 72-75 in-memory, 89 partitioning, 201-205 by hash of key, 203, 217 by key range, 202, 217 dynamic partitioning, 212 skew and hot spots, 205 Kryo (Java), 113 Kubernetes (cluster manager), 418, 506 L lambda architecture, 497 Lamport timestamps, 345 Index | 573 Large Hadron Collider (LHC), 64 last write wins (LWW), 173, 334 discarding concurrent writes, 186 problems with, 292 prone to lost updates, 246 late binding, 396 latency instability under two-phase locking, 259 network latency and resource utilization, 286 response time versus, 14 tail latency, 15, 207 leader-based replication, 152-161 (see also replication) failover, 157, 301 handling node outages, 156 implementation of replication logs change data capture, 454-457 (see also changelogs) statement-based, 158 trigger-based replication, 161 write-ahead log (WAL) shipping, 159 linearizability of operations, 333 locking and leader election, 330 log sequence number, 156, 449 read-scaling architecture, 161 relation to consensus, 367 setting up new followers, 155 synchronous versus asynchronous, 153-155 leaderless replication, 177-191 (see also replication) detecting concurrent writes, 184-191 capturing happens-before relationship, 187 happens-before relationship and concur‐ rency, 186 last write wins, 186 merging concurrently written values, 190 version vectors, 191 multi-datacenter, 184 quorums, 179-182 consistency limitations, 181-183, 334 sloppy quorums and hinted handoff, 183 read repair and anti-entropy, 178 leap seconds, 8, 290 in time-of-day clocks, 288 leases, 295 implementation with ZooKeeper, 370 574 | Index need for fencing, 302 ledgers, 460 distributed ledger technologies, 532 legacy systems, maintenance of, 18 less (Unix tool), 397 LevelDB (storage engine), 78 leveled compaction, 79 Levenshtein automata, 88 limping (partial failure), 311 linearizability, 324-338, 555 cost of, 335-338 CAP theorem, 336 memory on multi-core CPUs, 338 definition, 325-329 implementing with total order broadcast, 350 in ZooKeeper, 370 of derived data systems, 492, 524 avoiding coordination, 527 of different replication methods, 332-335 using quorums, 334 relying on, 330-332 constraints and uniqueness, 330 cross-channel timing dependencies, 331 locking and leader election, 330 stronger than causal consistency, 342 using to implement total order broadcast, 351 versus serializability, 329 LinkedIn Azkaban (workflow scheduler), 402 Databus (change data capture), 161, 455 Espresso (database), 31, 126, 130, 153, 216 Helix (cluster manager) (see Helix) profile (example), 30 reference to company entity (example), 34 Rest.li (RPC framework), 135 Voldemort (database) (see Voldemort) Linux, leap second bug, 8, 290 liveness properties, 308 LMDB (storage engine), 82, 242 load approaches to coping with, 17 describing, 11 load testing, 16 load balancing (messaging), 444 local indexes (see document-partitioned indexes) locality (data access), 32, 41, 555 in batch processing, 400, 405, 421 in stateful clients, 170, 511 in stream processing, 474, 478, 508, 522 location transparency, 134 in the actor model, 138 locks, 556 deadlock, 258 distributed locking, 301-304, 330 fencing tokens, 303 implementation with ZooKeeper, 370 relation to consensus, 374 for transaction isolation in snapshot isolation, 239 in two-phase locking (2PL), 257-261 making operations atomic, 243 performance, 258 preventing dirty writes, 236 preventing phantoms with index-range locks, 260, 265 read locks (shared mode), 236, 258 shared mode and exclusive mode, 258 in two-phase commit (2PC) deadlock detection, 364 in-doubt transactions holding locks, 362 materializing conflicts with, 251 preventing lost updates by explicit locking, 244 log sequence number, 156, 449 logic programming languages, 504 logical clocks, 293, 343, 494 for read-after-write consistency, 164 logical logs, 160 logs (data structure), 71, 556 advantages of immutability, 460 compaction, 73, 79, 456, 460 for stream operator state, 479 creating using total order broadcast, 349 implementing uniqueness constraints, 522 log-based messaging, 446-451 comparison to traditional messaging, 448, 451 consumer offsets, 449 disk space usage, 450 replaying old messages, 451, 496, 498 slow consumers, 450 using logs for message storage, 447 log-structured storage, 71-79 log-structured merge tree (see LSMtrees) replication, 152, 158-161 change data capture, 454-457 (see also changelogs) coordination with snapshot, 156 logical (row-based) replication, 160 statement-based replication, 158 trigger-based replication, 161 write-ahead log (WAL) shipping, 159 scalability limits, 493 loose coupling, 396, 419, 502 lost updates (see updates) LSM-trees (indexes), 78-79 comparison to B-trees, 83-85 Lucene (storage engine), 79 building indexes in batch processes, 411 similarity search, 88 Luigi (workflow scheduler), 402 LWW (see last write wins) M machine learning ethical considerations, 534 (see also ethics) iterative processing, 424 models derived from training data, 505 statistical and numerical algorithms, 428 MADlib (machine learning toolkit), 428 magic scaling sauce, 18 Mahout (machine learning toolkit), 428 maintainability, 18-22, 489 defined, 23 design principles for software systems, 19 evolvability (see evolvability) operability, 19 simplicity and managing complexity, 20 many-to-many relationships in document model versus relational model, 39 modeling as graphs, 49 many-to-one and many-to-many relationships, 33-36 many-to-one relationships, 34 MapReduce (batch processing), 390, 399-400 accessing external services within job, 404, 412 comparison to distributed databases designing for frequent faults, 417 diversity of processing models, 416 diversity of storage, 415 Index | 575 comparison to stream processing, 464 comparison to Unix, 413-414 disadvantages and limitations of, 419 fault tolerance, 406, 414, 422 higher-level tools, 403, 426 implementation in Hadoop, 400-403 the shuffle, 402 implementation in MongoDB, 46-48 machine learning, 428 map-side processing, 408-410 broadcast hash joins, 409 merge joins, 410 partitioned hash joins, 409 mapper and reducer functions, 399 materialization of intermediate state, 419-423 output of batch workflows, 411-413 building search indexes, 411 key-value stores, 412 reduce-side processing, 403-408 analysis of user activity events (exam‐ ple), 404 grouping records by same key, 406 handling skew, 407 sort-merge joins, 405 workflows, 402 marshalling (see encoding) massively parallel processing (MPP), 216 comparison to composing storage technolo‐ gies, 502 comparison to Hadoop, 414-418, 428 master-master replication (see multi-leader replication) master-slave replication (see leader-based repli‐ cation) materialization, 556 aggregate values, 101 conflicts, 251 intermediate state (batch processing), 420-423 materialized views, 101 as derived data, 386, 499-504 maintaining, using stream processing, 467, 475 Maven (Java build tool), 428 Maxwell (change data capture), 455 mean, 14 media monitoring, 467 median, 14 576 | Index meeting room booking (example), 249, 259, 521 membership services, 372 Memcached (caching server), 4, 89 memory in-memory databases, 88 durability, 227 serial transaction execution, 253 in-memory representation of data, 112 random bit-flips in, 529 use by indexes, 72, 77 memory barrier (CPU instruction), 338 MemSQL (database) in-memory storage, 89 read committed isolation, 236 memtable (in LSM-trees), 78 Mercurial (version control system), 463 merge joins, MapReduce map-side, 410 mergeable persistent data structures, 174 merging sorted files, 76, 402, 405 Merkle trees, 532 Mesos (cluster manager), 418, 506 message brokers (see messaging systems) message-passing, 136-139 advantages over direct RPC, 137 distributed actor frameworks, 138 evolvability, 138 MessagePack (encoding format), 116 messages exactly-once semantics, 360, 476 loss of, 442 using total order broadcast, 348 messaging systems, 440-451 (see also streams) backpressure, buffering, or dropping mes‐ sages, 441 brokerless messaging, 442 event logs, 446-451 comparison to traditional messaging, 448, 451 consumer offsets, 449 replaying old messages, 451, 496, 498 slow consumers, 450 message brokers, 443-446 acknowledgements and redelivery, 445 comparison to event logs, 448, 451 multiple consumers of same topic, 444 reliability, 442 uniqueness in log-based messaging, 522 Meteor (web framework), 456 microbatching, 477, 495 microservices, 132 (see also services) causal dependencies across services, 493 loose coupling, 502 relation to batch/stream processors, 389, 508 Microsoft Azure Service Bus (messaging), 444 Azure Storage, 155, 398 Azure Stream Analytics, 466 DCOM (Distributed Component Object Model), 134 MSDTC (transaction coordinator), 356 Orleans (see Orleans) SQL Server (see SQL Server) migrating (rewriting) data, 40, 130, 461, 497 modulus operator (%), 210 MongoDB (database) aggregation pipeline, 48 atomic operations, 243 BSON, 41 document data model, 31 hash partitioning (sharding), 203-204 key-range partitioning, 202 lack of join support, 34, 42 leader-based replication, 153 MapReduce support, 46, 400 oplog parsing, 455, 456 partition splitting, 212 request routing, 216 secondary indexes, 207 Mongoriver (change data capture), 455 monitoring, 10, 19 monotonic clocks, 288 monotonic reads, 164 MPP (see massively parallel processing) MSMQ (messaging), 361 multi-column indexes, 87 multi-leader replication, 168-177 (see also replication) handling write conflicts, 171 conflict avoidance, 172 converging toward a consistent state, 172 custom conflict resolution logic, 173 determining what is a conflict, 174 linearizability, lack of, 333 replication topologies, 175-177 use cases, 168 clients with offline operation, 170 collaborative editing, 170 multi-datacenter replication, 168, 335 multi-object transactions, 228 need for, 231 Multi-Paxos (total order broadcast), 367 multi-table index cluster tables (Oracle), 41 multi-tenancy, 284 multi-version concurrency control (MVCC), 239, 266 detecting stale MVCC reads, 263 indexes and snapshot isolation, 241 mutual exclusion, 261 (see also locks) MySQL (database) binlog coordinates, 156 binlog parsing for change data capture, 455 circular replication topology, 175 consistent snapshots, 156 distributed transaction support, 361 InnoDB storage engine (see InnoDB) JSON support, 30, 42 leader-based replication, 153 performance of XA transactions, 360 row-based replication, 160 schema changes in, 40 snapshot isolation support, 242 (see also InnoDB) statement-based replication, 159 Tungsten Replicator (multi-leader replica‐ tion), 170 conflict detection, 177 N nanomsg (messaging library), 442 Narayana (transaction coordinator), 356 NATS (messaging), 137 near-real-time (nearline) processing, 390 (see also stream processing) Neo4j (database) Cypher query language, 52 graph data model, 50 Nephele (dataflow engine), 421 netcat (Unix tool), 397 Netflix Chaos Monkey, 7, 280 Network Attached Storage (NAS), 146, 398 network model, 36 Index | 577 graph databases versus, 60 imperative query APIs, 46 Network Time Protocol (see NTP) networks congestion and queueing, 282 datacenter network topologies, 276 faults (see faults) linearizability and network delays, 338 network partitions, 279, 337 timeouts and unbounded delays, 281 next-key locking, 260 nodes (in graphs) (see vertices) nodes (processes), 556 handling outages in leader-based replica‐ tion, 156 system models for failure, 307 noisy neighbors, 284 nonblocking atomic commit, 359 nondeterministic operations accidental nondeterminism, 423 partial failures in distributed systems, 275 nonfunctional requirements, 22 nonrepeatable reads, 238 (see also read skew) normalization (data representation), 33, 556 executing joins, 39, 42, 403 foreign key references, 231 in systems of record, 386 versus denormalization, 462 NoSQL, 29, 499 transactions and, 223 Notation3 (N3), 56 npm (package manager), 428 NTP (Network Time Protocol), 287 accuracy, 289, 293 adjustments to monotonic clocks, 289 multiple server addresses, 306 numbers, in XML and JSON encodings, 114 O object-relational mapping (ORM) frameworks, 30 error handling and aborted transactions, 232 unsafe read-modify-write cycle code, 244 object-relational mismatch, 29 observer pattern, 506 offline systems, 390 (see also batch processing) 578 | Index stateful, offline-capable clients, 170, 511 offline-first applications, 511 offsets consumer offsets in partitioned logs, 449 messages in partitioned logs, 447 OLAP (online analytic processing), 91, 556 data cubes, 102 OLTP (online transaction processing), 90, 556 analytics queries versus, 411 workload characteristics, 253 one-to-many relationships, 30 JSON representation, 32 online systems, 389 (see also services) Oozie (workflow scheduler), 402 OpenAPI (service definition format), 133 OpenStack Nova (cloud infrastructure) use of ZooKeeper, 370 Swift (object storage), 398 operability, 19 operating systems versus databases, 499 operation identifiers, 518, 522 operational transformation, 174 operators, 421 flow of data between, 424 in stream processing, 464 optimistic concurrency control, 261 Oracle (database) distributed transaction support, 361 GoldenGate (change data capture), 161, 170, 455 lack of serializability, 226 leader-based replication, 153 multi-table index cluster tables, 41 not preventing write skew, 248 partitioned indexes, 209 PL/SQL language, 255 preventing lost updates, 245 read committed isolation, 236 Real Application Clusters (RAC), 330 recursive query support, 54 snapshot isolation support, 239, 242 TimesTen (in-memory database), 89 WAL-based replication, 160 XML support, 30 ordering, 339-352 by sequence numbers, 343-348 causal ordering, 339-343 partial order, 341 limits of total ordering, 493 total order broadcast, 348-352 Orleans (actor framework), 139 outliers (response time), 14 Oz (programming language), 504 P package managers, 428, 505 packet switching, 285 packets corruption of, 306 sending via UDP, 442 PageRank (algorithm), 49, 424 paging (see virtual memory) ParAccel (database), 93 parallel databases (see massively parallel pro‐ cessing) parallel execution of graph analysis algorithms, 426 queries in MPP databases, 216 Parquet (data format), 96, 131 (see also column-oriented storage) use in Hadoop, 414 partial failures, 275, 310 limping, 311 partial order, 341 partitioning, 199-218, 556 and replication, 200 in batch processing, 429 multi-partition operations, 514 enforcing constraints, 522 secondary index maintenance, 495 of key-value data, 201-205 by key range, 202 skew and hot spots, 205 rebalancing partitions, 209-214 automatic or manual rebalancing, 213 problems with hash mod N, 210 using dynamic partitioning, 212 using fixed number of partitions, 210 using N partitions per node, 212 replication and, 147 request routing, 214-216 secondary indexes, 206-209 document-based partitioning, 206 term-based partitioning, 208 serial execution of transactions and, 255 Paxos (consensus algorithm), 366 ballot number, 368 Multi-Paxos (total order broadcast), 367 percentiles, 14, 556 calculating efficiently, 16 importance of high percentiles, 16 use in service level agreements (SLAs), 15 Percona XtraBackup (MySQL tool), 156 performance describing, 13 of distributed transactions, 360 of in-memory databases, 89 of linearizability, 338 of multi-leader replication, 169 perpetual inconsistency, 525 pessimistic concurrency control, 261 phantoms (transaction isolation), 250 materializing conflicts, 251 preventing, in serializability, 259 physical clocks (see clocks) pickle (Python), 113 Pig (dataflow language), 419, 427 replicated joins, 409 skewed joins, 407 workflows, 403 Pinball (workflow scheduler), 402 pipelined execution, 423 in Unix, 394 point in time, 287 polyglot persistence, 29 polystores, 501 PostgreSQL (database) BDR (multi-leader replication), 170 causal ordering of writes, 177 Bottled Water (change data capture), 455 Bucardo (trigger-based replication), 161, 173 distributed transaction support, 361 foreign data wrappers, 501 full text search support, 490 leader-based replication, 153 log sequence number, 156 MVCC implementation, 239, 241 PL/pgSQL language, 255 PostGIS geospatial indexes, 87 preventing lost updates, 245 preventing write skew, 248, 261 read committed isolation, 236 recursive query support, 54 representing graphs, 51 Index | 579 serializable snapshot isolation (SSI), 261 snapshot isolation support, 239, 242 WAL-based replication, 160 XML and JSON support, 30, 42 pre-splitting, 212 Precision Time Protocol (PTP), 290 predicate locks, 259 predictive analytics, 533-536 amplifying bias, 534 ethics of (see ethics) feedback loops, 536 preemption of datacenter resources, 418 of threads, 298 Pregel processing model, 425 primary keys, 85, 556 compound primary key (Cassandra), 204 primary-secondary replication (see leaderbased replication) privacy, 536-543 consent and freedom of choice, 538 data as assets and power, 540 deleting data, 463 ethical considerations (see ethics) legislation and self-regulation, 542 meaning of, 539 surveillance, 537 tracking behavioral data, 536 probabilistic algorithms, 16, 466 process pauses, 295-299 processing time (of events), 469 producers (message streams), 440 programming languages dataflow languages, 504 for stored procedures, 255 functional reactive programming (FRP), 504 logic programming, 504 Prolog (language), 61 (see also Datalog) promises (asynchronous operations), 135 property graphs, 50 Cypher query language, 52 Protocol Buffers (data format), 117-121 field tags and schema evolution, 120 provenance of data, 531 publish/subscribe model, 441 publishers (message streams), 440 punch card tabulating machines, 390 580 | Index pure functions, 48 putting computation near data, 400 Q Qpid (messaging), 444 quality of service (QoS), 285 Quantcast File System (distributed filesystem), 398 query languages, 42-48 aggregation pipeline, 48 CSS and XSL, 44 Cypher, 52 Datalog, 60 Juttle, 504 MapReduce querying, 46-48 recursive SQL queries, 53 relational algebra and SQL, 42 SPARQL, 59 query optimizers, 37, 427 queueing delays (networks), 282 head-of-line blocking, 15 latency and response time, 14 queues (messaging), 137 quorums, 179-182, 556 for leaderless replication, 179 in consensus algorithms, 368 limitations of consistency, 181-183, 334 making decisions in distributed systems, 301 monitoring staleness, 182 multi-datacenter replication, 184 relying on durability, 309 sloppy quorums and hinted handoff, 183 R R-trees (indexes), 87 RabbitMQ (messaging), 137, 444 leader-based replication, 153 race conditions, 225 (see also concurrency) avoiding with linearizability, 331 caused by dual writes, 452 dirty writes, 235 in counter increments, 235 lost updates, 242-246 preventing with event logs, 462, 507 preventing with serializable isolation, 252 write skew, 246-251 Raft (consensus algorithm), 366 sensitivity to network problems, 369 term number, 368 use in etcd, 353 RAID (Redundant Array of Independent Disks), 7, 398 railways, schema migration on, 496 RAMCloud (in-memory storage), 89 ranking algorithms, 424 RDF (Resource Description Framework), 57 querying with SPARQL, 59 RDMA (Remote Direct Memory Access), 276 read committed isolation level, 234-237 implementing, 236 multi-version concurrency control (MVCC), 239 no dirty reads, 234 no dirty writes, 235 read path (derived data), 509 read repair (leaderless replication), 178 for linearizability, 335 read replicas (see leader-based replication) read skew (transaction isolation), 238, 266 as violation of causality, 340 read-after-write consistency, 163, 524 cross-device, 164 read-modify-write cycle, 243 read-scaling architecture, 161 reads as events, 513 real-time collaborative editing, 170 near-real-time processing, 390 (see also stream processing) publish/subscribe dataflow, 513 response time guarantees, 298 time-of-day clocks, 288 rebalancing partitions, 209-214, 556 (see also partitioning) automatic or manual rebalancing, 213 dynamic partitioning, 212 fixed number of partitions, 210 fixed number of partitions per node, 212 problems with hash mod N, 210 recency guarantee, 324 recommendation engines batch process outputs, 412 batch workflows, 403, 420 iterative processing, 424 statistical and numerical algorithms, 428 records, 399 events in stream processing, 440 recursive common table expressions (SQL), 54 redelivery (messaging), 445 Redis (database) atomic operations, 243 durability, 89 Lua scripting, 255 single-threaded execution, 253 usage example, 4 redundancy hardware components, 7 of derived data, 386 (see also derived data) Reed–Solomon codes (error correction), 398 refactoring, 22 (see also evolvability) regions (partitioning), 199 register (data structure), 325 relational data model, 28-42 comparison to document model, 38-42 graph queries in SQL, 53 in-memory databases with, 89 many-to-one and many-to-many relation‐ ships, 33 multi-object transactions, need for, 231 NoSQL as alternative to, 29 object-relational mismatch, 29 relational algebra and SQL, 42 versus document model convergence of models, 41 data locality, 41 relational databases eventual consistency, 162 history, 28 leader-based replication, 153 logical logs, 160 philosophy compared to Unix, 499, 501 schema changes, 40, 111, 130 statement-based replication, 158 use of B-tree indexes, 80 relationships (see edges) reliability, 6-10, 489 building a reliable system from unreliable components, 276 defined, 6, 22 hardware faults, 7 human errors, 9 importance of, 10 of messaging systems, 442 Index | 581 software errors, 8 Remote Method Invocation (Java RMI), 134 remote procedure calls (RPCs), 134-136 (see also services) based on futures, 135 data encoding and evolution, 136 issues with, 134 using Avro, 126, 135 using Thrift, 135 versus message brokers, 137 repeatable reads (transaction isolation), 242 replicas, 152 replication, 151-193, 556 and durability, 227 chain replication, 155 conflict resolution and, 246 consistency properties, 161-167 consistent prefix reads, 165 monotonic reads, 164 reading your own writes, 162 in distributed filesystems, 398 leaderless, 177-191 detecting concurrent writes, 184-191 limitations of quorum consistency, 181-183, 334 sloppy quorums and hinted handoff, 183 monitoring staleness, 182 multi-leader, 168-177 across multiple datacenters, 168, 335 handling write conflicts, 171-175 replication topologies, 175-177 partitioning and, 147, 200 reasons for using, 145, 151 single-leader, 152-161 failover, 157 implementation of replication logs, 158-161 relation to consensus, 367 setting up new followers, 155 synchronous versus asynchronous, 153-155 state machine replication, 349, 452 using erasure coding, 398 with heterogeneous data systems, 453 replication logs (see logs) reprocessing data, 496, 498 (see also evolvability) from log-based messaging, 451 request routing, 214-216 582 | Index approaches to, 214 parallel query execution, 216 resilient systems, 6 (see also fault tolerance) response time as performance metric for services, 13, 389 guarantees on, 298 latency versus, 14 mean and percentiles, 14 user experience, 15 responsibility and accountability, 535 REST (Representational State Transfer), 133 (see also services) RethinkDB (database) document data model, 31 dynamic partitioning, 212 join support, 34, 42 key-range partitioning, 202 leader-based replication, 153 subscribing to changes, 456 Riak (database) Bitcask storage engine, 72 CRDTs, 174, 191 dotted version vectors, 191 gossip protocol, 216 hash partitioning, 203-204, 211 last-write-wins conflict resolution, 186 leaderless replication, 177 LevelDB storage engine, 78 linearizability, lack of, 335 multi-datacenter support, 184 preventing lost updates across replicas, 246 rebalancing, 213 search feature, 209 secondary indexes, 207 siblings (concurrently written values), 190 sloppy quorums, 184 ring buffers, 450 Ripple (cryptocurrency), 532 rockets, 10, 36, 305 RocksDB (storage engine), 78 leveled compaction, 79 rollbacks (transactions), 222 rolling upgrades, 8, 112 routing (see request routing) row-oriented storage, 96 row-based replication, 160 rowhammer (memory corruption), 529 RPCs (see remote procedure calls) Rubygems (package manager), 428 rules (Datalog), 61 S safety and liveness properties, 308 in consensus algorithms, 366 in transactions, 222 sagas (see compensating transactions) Samza (stream processor), 466, 467 fault tolerance, 479 streaming SQL support, 466 sandboxes, 9 SAP HANA (database), 93 scalability, 10-18, 489 approaches for coping with load, 17 defined, 22 describing load, 11 describing performance, 13 partitioning and, 199 replication and, 161 scaling up versus scaling out, 146 scaling out, 17, 146 (see also shared-nothing architecture) scaling up, 17, 146 scatter/gather approach, querying partitioned databases, 207 SCD (slowly changing dimension), 476 schema-on-read, 39 comparison to evolvable schema, 128 in distributed filesystems, 415 schema-on-write, 39 schemaless databases (see schema-on-read) schemas, 557 Avro, 122-127 reader determining writer’s schema, 125 schema evolution, 123 dynamically generated, 126 evolution of, 496 affecting application code, 111 compatibility checking, 126 in databases, 129-131 in message-passing, 138 in service calls, 136 flexibility in document model, 39 for analytics, 93-95 for JSON and XML, 115 merits of, 127 schema migration on railways, 496 Thrift and Protocol Buffers, 117-121 schema evolution, 120 traditional approach to design, fallacy in, 462 searches building search indexes in batch processes, 411 k-nearest neighbors, 429 on streams, 467 partitioned secondary indexes, 206 secondaries (see leader-based replication) secondary indexes, 85, 557 partitioning, 206-209, 217 document-partitioned, 206 index maintenance, 495 term-partitioned, 208 problems with dual writes, 452, 491 updating, transaction isolation and, 231 secondary sorts, 405 sed (Unix tool), 392 self-describing files, 127 self-joins, 480 self-validating systems, 530 semantic web, 57 semi-synchronous replication, 154 sequence number ordering, 343-348 generators, 294, 344 insufficiency for enforcing constraints, 347 Lamport timestamps, 345 use of timestamps, 291, 295, 345 sequential consistency, 351 serializability, 225, 233, 251-266, 557 linearizability versus, 329 pessimistic versus optimistic concurrency control, 261 serial execution, 252-256 partitioning, 255 using stored procedures, 253, 349 serializable snapshot isolation (SSI), 261-266 detecting stale MVCC reads, 263 detecting writes that affect prior reads, 264 distributed execution, 265, 364 performance of SSI, 265 preventing write skew, 262-265 two-phase locking (2PL), 257-261 index-range locks, 260 performance, 258 Serializable (Java), 113 Index | 583 serialization, 113 (see also encoding) service discovery, 135, 214, 372 using DNS, 216, 372 service level agreements (SLAs), 15 service-oriented architecture (SOA), 132 (see also services) services, 131-136 microservices, 132 causal dependencies across services, 493 loose coupling, 502 relation to batch/stream processors, 389, 508 remote procedure calls (RPCs), 134-136 issues with, 134 similarity to databases, 132 web services, 132, 135 session windows (stream processing), 472 (see also windows) sessionization, 407 sharding (see partitioning) shared mode (locks), 258 shared-disk architecture, 146, 398 shared-memory architecture, 146 shared-nothing architecture, 17, 146-147, 557 (see also replication) distributed filesystems, 398 (see also distributed filesystems) partitioning, 199 use of network, 277 sharks biting undersea cables, 279 counting (example), 46-48 finding (example), 42 website about (example), 44 shredding (in relational model), 38 siblings (concurrent values), 190, 246 (see also conflicts) similarity search edit distance, 88 genome data, 63 k-nearest neighbors, 429 single-leader replication (see leader-based rep‐ lication) single-threaded execution, 243, 252 in batch processing, 406, 421, 426 in stream processing, 448, 463, 522 size-tiered compaction, 79 skew, 557 584 | Index clock skew, 291-294, 334 in transaction isolation read skew, 238, 266 write skew, 246-251, 262-265 (see also write skew) meanings of, 238 unbalanced workload, 201 compensating for, 205 due to celebrities, 205 for time-series data, 203 in batch processing, 407 slaves (see leader-based replication) sliding windows (stream processing), 472 (see also windows) sloppy quorums, 183 (see also quorums) lack of linearizability, 334 slowly changing dimension (data warehouses), 476 smearing (leap seconds adjustments), 290 snapshots (databases) causal consistency, 340 computing derived data, 500 in change data capture, 455 serializable snapshot isolation (SSI), 261-266, 329 setting up a new replica, 156 snapshot isolation and repeatable read, 237-242 implementing with MVCC, 239 indexes and MVCC, 241 visibility rules, 240 synchronized clocks for global snapshots, 294 snowflake schemas, 95 SOAP, 133 (see also services) evolvability, 136 software bugs, 8 maintaining integrity, 529 solid state drives (SSDs) access patterns, 84 detecting corruption, 519, 530 faults in, 227 sequential write throughput, 75 Solr (search server) building indexes in batch processes, 411 document-partitioned indexes, 207 request routing, 216 usage example, 4 use of Lucene, 79 sort (Unix tool), 392, 394, 395 sort-merge joins (MapReduce), 405 Sorted String Tables (see SSTables) sorting sort order in column storage, 99 source of truth (see systems of record) Spanner (database) data locality, 41 snapshot isolation using clocks, 295 TrueTime API, 294 Spark (processing framework), 421-423 bytecode generation, 428 dataflow APIs, 427 fault tolerance, 422 for data warehouses, 93 GraphX API (graph processing), 425 machine learning, 428 query optimizer, 427 Spark Streaming, 466 microbatching, 477 stream processing on top of batch process‐ ing, 495 SPARQL (query language), 59 spatial algorithms, 429 split brain, 158, 557 in consensus algorithms, 352, 367 preventing, 322, 333 using fencing tokens to avoid, 302-304 spreadsheets, dataflow programming capabili‐ ties, 504 SQL (Structured Query Language), 21, 28, 43 advantages and limitations of, 416 distributed query execution, 48 graph queries in, 53 isolation levels standard, issues with, 242 query execution on Hadoop, 416 résumé (example), 30 SQL injection vulnerability, 305 SQL on Hadoop, 93 statement-based replication, 158 stored procedures, 255 SQL Server (database) data warehousing support, 93 distributed transaction support, 361 leader-based replication, 153 preventing lost updates, 245 preventing write skew, 248, 257 read committed isolation, 236 recursive query support, 54 serializable isolation, 257 snapshot isolation support, 239 T-SQL language, 255 XML support, 30 SQLstream (stream analytics), 466 SSDs (see solid state drives) SSTables (storage format), 76-79 advantages over hash indexes, 76 concatenated index, 204 constructing and maintaining, 78 making LSM-Tree from, 78 staleness (old data), 162 cross-channel timing dependencies, 331 in leaderless databases, 178 in multi-version concurrency control, 263 monitoring for, 182 of client state, 512 versus linearizability, 324 versus timeliness, 524 standbys (see leader-based replication) star replication topologies, 175 star schemas, 93-95 similarity to event sourcing, 458 Star Wars analogy (event time versus process‐ ing time), 469 state derived from log of immutable events, 459 deriving current state from the event log, 458 interplay between state changes and appli‐ cation code, 507 maintaining derived state, 495 maintenance by stream processor in streamstream joins, 473 observing derived state, 509-515 rebuilding after stream processor failure, 478 separation of application code and, 505 state machine replication, 349, 452 statement-based replication, 158 statically typed languages analogy to schema-on-write, 40 code generation and, 127 statistical and numerical algorithms, 428 StatsD (metrics aggregator), 442 stdin, stdout, 395, 396 Stellar (cryptocurrency), 532 Index | 585 stock market feeds, 442 STONITH (Shoot The Other Node In The Head), 158 stop-the-world (see garbage collection) storage composing data storage technologies, 499-504 diversity of, in MapReduce, 415 Storage Area Network (SAN), 146, 398 storage engines, 69-104 column-oriented, 95-101 column compression, 97-99 defined, 96 distinction between column families and, 99 Parquet, 96, 131 sort order in, 99-100 writing to, 101 comparing requirements for transaction processing and analytics, 90-96 in-memory storage, 88 durability, 227 row-oriented, 70-90 B-trees, 79-83 comparing B-trees and LSM-trees, 83-85 defined, 96 log-structured, 72-79 stored procedures, 161, 253-255, 557 and total order broadcast, 349 pros and cons of, 255 similarity to stream processors, 505 Storm (stream processor), 466 distributed RPC, 468, 514 Trident state handling, 478 straggler events, 470, 498 stream processing, 464-481, 557 accessing external services within job, 474, 477, 478, 517 combining with batch processing lambda architecture, 497 unifying technologies, 498 comparison to batch processing, 464 complex event processing (CEP), 465 fault tolerance, 476-479 atomic commit, 477 idempotence, 478 microbatching and checkpointing, 477 rebuilding state after a failure, 478 for data integration, 494-498 586 | Index maintaining derived state, 495 maintenance of materialized views, 467 messaging systems (see messaging systems) reasoning about time, 468-472 event time versus processing time, 469, 477, 498 knowing when window is ready, 470 types of windows, 472 relation to databases (see streams) relation to services, 508 search on streams, 467 single-threaded execution, 448, 463 stream analytics, 466 stream joins, 472-476 stream-stream join, 473 stream-table join, 473 table-table join, 474 time-dependence of, 475 streams, 440-451 end-to-end, pushing events to clients, 512 messaging systems (see messaging systems) processing (see stream processing) relation to databases, 451-464 (see also changelogs) API support for change streams, 456 change data capture, 454-457 derivative of state by time, 460 event sourcing, 457-459 keeping systems in sync, 452-453 philosophy of immutable events, 459-464 topics, 440 strict serializability, 329 strong consistency (see linearizability) strong one-copy serializability, 329 subjects, predicates, and objects (in triplestores), 55 subscribers (message streams), 440 (see also consumers) supercomputers, 275 surveillance, 537 (see also privacy) Swagger (service definition format), 133 swapping to disk (see virtual memory) synchronous networks, 285, 557 comparison to asynchronous networks, 284 formal model, 307 synchronous replication, 154, 557 chain replication, 155 conflict detection, 172 system models, 300, 306-310 assumptions in, 528 correctness of algorithms, 308 mapping to the real world, 309 safety and liveness, 308 systems of record, 386, 557 change data capture, 454, 491 treating event log as, 460 systems thinking, 536 T t-digest (algorithm), 16 table-table joins, 474 Tableau (data visualization software), 416 tail (Unix tool), 447 tail vertex (property graphs), 51 Tajo (query engine), 93 Tandem NonStop SQL (database), 200 TCP (Transmission Control Protocol), 277 comparison to circuit switching, 285 comparison to UDP, 283 connection failures, 280 flow control, 282, 441 packet checksums, 306, 519, 529 reliability and duplicate suppression, 517 retransmission timeouts, 284 use for transaction sessions, 229 telemetry (see monitoring) Teradata (database), 93, 200 term-partitioned indexes, 208, 217 termination (consensus), 365 Terrapin (database), 413 Tez (dataflow engine), 421-423 fault tolerance, 422 support by higher-level tools, 427 thrashing (out of memory), 297 threads (concurrency) actor model, 138, 468 (see also message-passing) atomic operations, 223 background threads, 73, 85 execution pauses, 286, 296-298 memory barriers, 338 preemption, 298 single (see single-threaded execution) three-phase commit, 359 Thrift (data format), 117-121 BinaryProtocol, 118 CompactProtocol, 119 field tags and schema evolution, 120 throughput, 13, 390 TIBCO, 137 Enterprise Message Service, 444 StreamBase (stream analytics), 466 time concurrency and, 187 cross-channel timing dependencies, 331 in distributed systems, 287-299 (see also clocks) clock synchronization and accuracy, 289 relying on synchronized clocks, 291-295 process pauses, 295-299 reasoning about, in stream processors, 468-472 event time versus processing time, 469, 477, 498 knowing when window is ready, 470 timestamp of events, 471 types of windows, 472 system models for distributed systems, 307 time-dependence in stream joins, 475 time-of-day clocks, 288 timeliness, 524 coordination-avoiding data systems, 528 correctness of dataflow systems, 525 timeouts, 279, 557 dynamic configuration of, 284 for failover, 158 length of, 281 timestamps, 343 assigning to events in stream processing, 471 for read-after-write consistency, 163 for transaction ordering, 295 insufficiency for enforcing constraints, 347 key range partitioning by, 203 Lamport, 345 logical, 494 ordering events, 291, 345 Titan (database), 50 tombstones, 74, 191, 456 topics (messaging), 137, 440 total order, 341, 557 limits of, 493 sequence numbers or timestamps, 344 total order broadcast, 348-352, 493, 522 consensus algorithms and, 366-368 Index | 587 implementation in ZooKeeper and etcd, 370 implementing with linearizable storage, 351 using, 349 using to implement linearizable storage, 350 tracking behavioral data, 536 (see also privacy) transaction coordinator (see coordinator) transaction manager (see coordinator) transaction processing, 28, 90-95 comparison to analytics, 91 comparison to data warehousing, 93 transactions, 221-267, 558 ACID properties of, 223 atomicity, 223 consistency, 224 durability, 226 isolation, 225 compensating (see compensating transac‐ tions) concept of, 222 distributed transactions, 352-364 avoiding, 492, 502, 521-528 failure amplification, 364, 495 in doubt/uncertain status, 358, 362 two-phase commit, 354-359 use of, 360-361 XA transactions, 361-364 OLTP versus analytics queries, 411 purpose of, 222 serializability, 251-266 actual serial execution, 252-256 pessimistic versus optimistic concur‐ rency control, 261 serializable snapshot isolation (SSI), 261-266 two-phase locking (2PL), 257-261 single-object and multi-object, 228-232 handling errors and aborts, 231 need for multi-object transactions, 231 single-object writes, 230 snapshot isolation (see snapshots) weak isolation levels, 233-251 preventing lost updates, 242-246 read committed, 234-238 transitive closure (graph algorithm), 424 trie (data structure), 88 triggers (databases), 161, 441 implementing change data capture, 455 implementing replication, 161 588 | Index triple-stores, 55-59 SPARQL query language, 59 tumbling windows (stream processing), 472 (see also windows) in microbatching, 477 tuple spaces (programming model), 507 Turtle (RDF data format), 56 Twitter constructing home timelines (example), 11, 462, 474, 511 DistributedLog (event log), 448 Finagle (RPC framework), 135 Snowflake (sequence number generator), 294 Summingbird (processing library), 497 two-phase commit (2PC), 353, 355-359, 558 confusion with two-phase locking, 356 coordinator failure, 358 coordinator recovery, 363 how it works, 357 issues in practice, 363 performance cost, 360 transactions holding locks, 362 two-phase locking (2PL), 257-261, 329, 558 confusion with two-phase commit, 356 index-range locks, 260 performance of, 258 type checking, dynamic versus static, 40 U UDP (User Datagram Protocol) comparison to TCP, 283 multicast, 442 unbounded datasets, 439, 558 (see also streams) unbounded delays, 558 in networks, 282 process pauses, 296 unbundling databases, 499-515 composing data storage technologies, 499-504 federation versus unbundling, 501 need for high-level language, 503 designing applications around dataflow, 504-509 observing derived state, 509-515 materialized views and caching, 510 multi-partition data processing, 514 pushing state changes to clients, 512 uncertain (transaction status) (see in doubt) uniform consensus, 365 (see also consensus) uniform interfaces, 395 union type (in Avro), 125 uniq (Unix tool), 392 uniqueness constraints asynchronously checked, 526 requiring consensus, 521 requiring linearizability, 330 uniqueness in log-based messaging, 522 Unix philosophy, 394-397 command-line batch processing, 391-394 Unix pipes versus dataflow engines, 423 comparison to Hadoop, 413-414 comparison to relational databases, 499, 501 comparison to stream processing, 464 composability and uniform interfaces, 395 loose coupling, 396 pipes, 394 relation to Hadoop, 499 UPDATE statement (SQL), 40 updates preventing lost updates, 242-246 atomic write operations, 243 automatically detecting lost updates, 245 compare-and-set operations, 245 conflict resolution and replication, 246 using explicit locking, 244 preventing write skew, 246-251 V validity (consensus), 365 vBuckets (partitioning), 199 vector clocks, 191 (see also version vectors) vectorized processing, 99, 428 verification, 528-533 avoiding blind trust, 530 culture of, 530 designing for auditability, 531 end-to-end integrity checks, 531 tools for auditable data systems, 532 version control systems, reliance on immutable data, 463 version vectors, 177, 191 capturing causal dependencies, 343 versus vector clocks, 191 Vertica (database), 93 handling writes, 101 replicas using different sort orders, 100 vertical scaling (see scaling up) vertices (in graphs), 49 property graph model, 50 Viewstamped Replication (consensus algo‐ rithm), 366 view number, 368 virtual machines, 146 (see also cloud computing) context switches, 297 network performance, 282 noisy neighbors, 284 reliability in cloud services, 8 virtualized clocks in, 290 virtual memory process pauses due to page faults, 14, 297 versus memory management by databases, 89 VisiCalc (spreadsheets), 504 vnodes (partitioning), 199 Voice over IP (VoIP), 283 Voldemort (database) building read-only stores in batch processes, 413 hash partitioning, 203-204, 211 leaderless replication, 177 multi-datacenter support, 184 rebalancing, 213 reliance on read repair, 179 sloppy quorums, 184 VoltDB (database) cross-partition serializability, 256 deterministic stored procedures, 255 in-memory storage, 89 output streams, 456 secondary indexes, 207 serial execution of transactions, 253 statement-based replication, 159, 479 transactions in stream processing, 477 W WAL (write-ahead log), 82 web services (see services) Web Services Description Language (WSDL), 133 webhooks, 443 webMethods (messaging), 137 WebSocket (protocol), 512 Index | 589 windows (stream processing), 466, 468-472 infinite windows for changelogs, 467, 474 knowing when all events have arrived, 470 stream joins within a window, 473 types of windows, 472 winners (conflict resolution), 173 WITH RECURSIVE syntax (SQL), 54 workflows (MapReduce), 402 outputs, 411-414 key-value stores, 412 search indexes, 411 with map-side joins, 410 working set, 393 write amplification, 84 write path (derived data), 509 write skew (transaction isolation), 246-251 characterizing, 246-251, 262 examples of, 247, 249 materializing conflicts, 251 occurrence in practice, 529 phantoms, 250 preventing in snapshot isolation, 262-265 in two-phase locking, 259-261 options for, 248 write-ahead log (WAL), 82, 159 writes (database) atomic write operations, 243 detecting writes affecting prior reads, 264 preventing dirty writes with read commit‐ ted, 235 WS-* framework, 133 (see also services) WS-AtomicTransaction (2PC), 355 590 | Index X XA transactions, 355, 361-364 heuristic decisions, 363 limitations of, 363 xargs (Unix tool), 392, 396 XML binary variants, 115 encoding RDF data, 57 for application data, issues with, 114 in relational databases, 30, 41 XSL/XPath, 45 Y Yahoo!

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 16 Mar 2017

A transaction log can be made tamper-proof by periodically signing it with a hardware security module, but that does not guarantee that the right transactions went into the log in the first place. It would be interesting to use cryptographic tools to prove the integrity of a system in a way that is robust to a wide range of hardware and software issues, and even potentially malicious actions. Cryptocurrencies, blockchains, and distributed ledger technologies such as Bitcoin, Ethereum, Ripple, Stellar, and various others [71, 72, 73] have sprung up to explore this area. I am not qualified to comment on the merits of these technologies as currencies or mechanisms for agreeing contracts. However, from a data systems point of view they contain some interesting ideas. Essentially, they are distributed databases, with a data model and transaction mechanism, in which different replicas can be hosted by mutually untrusting organizations.

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multi-object transactions, need for, The need for multi-object transactions versus relational modelconvergence of models, Convergence of document and relational databases data locality, Data locality for queries document-partitioned indexes, Partitioning Secondary Indexes by Document, Summary, Building search indexes domain-driven design (DDD), Event Sourcing DRBD (Distributed Replicated Block Device), Leaders and Followers drift (clocks), Clock Synchronization and Accuracy Drill (query engine), The divergence between OLTP databases and data warehouses Druid (database), Deriving several views from the same event log Dryad (dataflow engine), Dataflow engines dual writes, problems with, Keeping Systems in Sync, Dataflow: Interplay between state changes and application code duplicates, suppression of, Duplicate suppression(see also idempotence) using a unique ID, Operation identifiers, Multi-partition request processing durability (transactions), Durability, Glossary duration (time), Unreliable Clocksmeasurement with monotonic clocks, Monotonic clocks dynamic partitioning, Dynamic partitioning dynamically typed languagesanalogy to schema-on-read, Schema flexibility in the document model code generation and, Code generation and dynamically typed languages Dynamo-style databases (see leaderless replication) E edges (in graphs), Graph-Like Data Models, Reduce-Side Joins and Groupingproperty graph model, Property Graphs edit distance (full-text search), Full-text search and fuzzy indexes effectively-once semantics, Fault Tolerance, Exactly-once execution of an operation(see also exactly-once semantics) preservation of integrity, Correctness of dataflow systems elastic systems, Approaches for Coping with Load Elasticsearch (search server)document-partitioned indexes, Partitioning Secondary Indexes by Document partition rebalancing, Fixed number of partitions percolator (stream search), Search on streams usage example, Thinking About Data Systems use of Lucene, Making an LSM-tree out of SSTables ElephantDB (database), Key-value stores as batch process output Elm (programming language), Designing Applications Around Dataflow, End-to-end event streams encodings (data formats), Encoding and Evolution-The Merits of SchemasAvro, Avro-Code generation and dynamically typed languages binary variants of JSON and XML, Binary encoding compatibility, Encoding and Evolutioncalling services, Data encoding and evolution for RPC using databases, Dataflow Through Databases-Archival storage using message-passing, Distributed actor frameworks defined, Formats for Encoding Data JSON, XML, and CSV, JSON, XML, and Binary Variants language-specific formats, Language-Specific Formats merits of schemas, The Merits of Schemas representations of data, Formats for Encoding Data Thrift and Protocol Buffers, Thrift and Protocol Buffers-Datatypes and schema evolution end-to-end argument, Cloud Computing and Supercomputing, The end-to-end argument-Applying end-to-end thinking in data systemschecking integrity, The end-to-end argument again publish/subscribe streams, End-to-end event streams enrichment (stream), Stream-table join (stream enrichment) Enterprise JavaBeans (EJB), The problems with remote procedure calls (RPCs) entities (see vertices) epoch (consensus algorithms), Epoch numbering and quorums epoch (Unix timestamps), Time-of-day clocks equi-joins, Reduce-Side Joins and Grouping erasure coding (error correction), MapReduce and Distributed Filesystems Erlang OTP (actor framework), Distributed actor frameworks error handlingfor network faults, Network Faults in Practice in transactions, Handling errors and aborts error-correcting codes, Cloud Computing and Supercomputing, MapReduce and Distributed Filesystems Esper (CEP engine), Complex event processing etcd (coordination service), Membership and Coordination Services-Membership serviceslinearizable operations, Implementing Linearizable Systems locks and leader election, Locking and leader election quorum reads, Implementing linearizable storage using total order broadcast service discovery, Service discovery use of Raft algorithm, Using total order broadcast, Distributed Transactions and Consensus Ethereum (blockchain), Tools for auditable data systems Ethernet (networks), Cloud Computing and Supercomputing, Unreliable Networks, Can we not simply make network delays predictable?packet checksums, Weak forms of lying, The end-to-end argument Etherpad (collaborative editor), Collaborative editing ethics, Doing the Right Thing-Legislation and self-regulationcode of ethics and professional practice, Doing the Right Thing legislation and self-regulation, Legislation and self-regulation predictive analytics, Predictive Analytics-Feedback loopsamplifying bias, Bias and discrimination feedback loops, Feedback loops privacy and tracking, Privacy and Tracking-Legislation and self-regulationconsent and freedom of choice, Consent and freedom of choice data as assets and power, Data as assets and power meaning of privacy, Privacy and use of data surveillance, Surveillance respect, dignity, and agency, Legislation and self-regulation, Summary unintended consequences, Doing the Right Thing, Feedback loops ETL (extract-transform-load), Data Warehousing, Example: analysis of user activity events, Keeping Systems in Sync, Glossaryuse of Hadoop for, Diversity of storage event sourcing, Event Sourcing-Commands and eventscommands and events, Commands and events comparison to change data capture, Event Sourcing comparison to lambda architecture, The lambda architecture deriving current state from event log, Deriving current state from the event log immutability and auditability, State, Streams, and Immutability, Designing for auditability large, reliable data systems, Operation identifiers, Correctness of dataflow systems Event Store (database), Event Sourcing event streams (see streams) events, Transmitting Event Streamsdeciding on total order of, The limits of total ordering deriving views from event log, Deriving several views from the same event log difference to commands, Commands and events event time versus processing time, Event time versus processing time, Microbatching and checkpointing, Unifying batch and stream processing immutable, advantages of, Advantages of immutable events, Designing for auditability ordering to capture causality, Ordering events to capture causality reads as, Reads are events too stragglers, Knowing when you’re ready, The lambda architecture timestamp of, in stream processing, Whose clock are you using, anyway?

The Stack: On Software and Sovereignty
by Benjamin H. Bratton
Published 19 Feb 2016

Speaking of reserve currencies, Bitcoin introduces addressable scarcity not in direct relation to the sum of mined minerals or national currencies, but by the mathematics of solving increasingly difficult problems toward an eventual arbitrary limit of 21 million “coins.” There is much to explore with Bitcoin, blockchains and related initiatives, such as Ethereum, but it is also the monetary platform of choice of secessionist projects for which the metaphysical expulsion of externalities is the paramount program, as important if not more than the disintermediation of central banks. The version of Bitcoin that we have (other currencies may fork or follow) is exemplary of the future-archaic quality of many Stack innovations.

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See also platform economics Anthropocenic, 58, 103 of borders, 173 capitalist, 56 City layer, 159–160 Cloud model, 137 electronics, mining and trading in, 82–83 zero-sum, 336 economy of additive manufacturing, 202 of cognitive capital, 110, 116 computational, 328 computer-controlled, 58–60 of contemporary warfare, 248 digital, 196 of energy, 92, 106–107 Facebook, 127 Google, 136–138, 159, 444n26 of identity, 270 of information, 199 of mobility, 280 of prostheticization, 273 of reversible partitions, 21 of scarcity, 208 and sovereignty and territory, 114, 316 ecopolitics, 100 ecosystems, 129, 178, 185, 336, 456n6 Ecumenopolis (Dioxiadis), 178 Eisenman, Peter, 410n50 Elden, Stewart, 335, 379n12 electricity, 93, 95, 141 electronics, mining and trading in, 82–83 electronic waste, 83 Elysium (Blomkamp), 311, 323, 444n27 emergency accommodating, 103–104 designing for/designing with, 101–104, 321, 325 ecojurisdictions in response to, 99–100 ecological, 105–106, 295, 305 ecopolitics emerging by, 100 exceptional, 103–104, 173, 321–322 permanent, 104 progress in response to, 321 sovereign decision and, 20, 32, 102–103 state of, 32–33, 99 emergent, the, 9 empty space, 30, 380n20 enclosure and escape, 22, 32–33, 149–150, 172–176, 303 “end of history,” 321 “End of Sykes-Picot, The” 430n65 energy alternative sources, 259 ecologies of, 98–104 economy, 92 efficiencies, 140–141 energy-information network, 93, 95 footprint, of planetary-scale computing, 82–83, 92–96, 106–107, 113, 140–141, 258–260, 303–304 grid, 92–96, 140, 152, 201, 294–295 needs, predicted, 113 political loyalty and availability, 141 polities, subdivided, 99–100 post-Anthropocene, 217 Eneropa, 99 Engelbart, Douglas, 343 Enlightenment, 251, 426n46 entertaining securitization, 156 entertainment identifier registry (EIDR), 207 entrance/exit, 149–150, 313, 315, 317, 371 envelope-interface borders, 172–173 envelopes airports, 156–157 architectural, 23, 165–167, 195, 303, 311–313, 323 cars as, 238 City layer, 12, 70, 148–149, 152, 154–155, 163–171, 173 as interface, 167 mixed, designing for, 168–172 paper, 46, 195 physical, 167 software, 167 urban, 168–172 urban platform, 180 User position in relation to, 252 environmental migrants/refugees, 100–101 Epicureanism, inverse, 358 equilibrium, cybernetic, 59, 158 error detection, 50 espionage, 398n21 Estates of The Oaks, 311 Estonia, 399n36, 446n42 Ethereum, 336 ethics, 258, 285, 362 European nomos of World Wars I and II, 25–26 European Space Agency, 181 European Union, 309 exception in Apple model, 130 automation of, 33 becomes the rule, 102–103, 111 emergency, 103–104, 173, 321–322 emergent order of, 110–111 inside and outside the law, 102 interface, 357 legitimate state of, 104 normalizing, 23, 32–33, 39, 103–104 reversibility of, 21, 32–33, 39, 145 sovereignty over, 20–21, 32, 105, 341 state of emergency as, 99, 105 territories of, 114 exclusion agency and, 173–175 augmented reality and, 236, 241–242 elective, 316–317 societal, 308–309, 311–312, 317 exclusive totalities, 245 exit.

Boom and Bust: A Global History of Financial Bubbles
by William Quinn and John D. Turner
Published 5 Aug 2020

‘Public finance and economic growth: the case of Holland in the seventeenth century’, Journal of Economic History, 71, 1–39, 2011. Gentzkow, M. and Shapiro, J. M. ‘Media bias and reputation’, Journal of Political Economy, 114, 280–316, 2006. Gerard, D. Attack of the 50 Foot Blockchain: Bitcoin, Blockchain, Ethereum and Smart Contracts, David Gerard (self-published), 2017. Gilmore, N. R. ‘Henry George Ward, British publicist for Mexican mines’, Pacific Historical Review, 31, 35–47, 1963. Gissing, G. The Whirlpool. London: Penguin Classics, 2015. Gjerstad, S. and Smith, V. L. ‘Monetary policy, credit extension, and housing bubbles: 2008 and 1929’, Critical Review, 21, 269–300, 2009.

The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power
by Max Chafkin
Published 14 Sep 2021

At the end of 2019, the organization recruited a new CEO, James O’Neill, a longtime employee at Thiel’s companies (and one of Thiel’s suggestions to run the FDA). At the time of my visit, SENS had been enjoying some modest success, having spun out several research projects into early-stage companies and attracted a number of new donors, including Vitalik Buterin, the creator of the technology behind the Ethereum cryptocurrency and, years earlier, a Thiel Fellow. But Thiel himself had not been among those recent contributors. His last donation to SENS had been in 2016. De Grey also mentioned that, despite talking up SENS’s work often in the press, Thiel had never actually visited the nonprofit’s laboratory, which is located just eight miles from Palantir’s Palo Alto office.

Narrative Economics: How Stories Go Viral and Drive Major Economic Events
by Robert J. Shiller
Published 14 Oct 2019

Narrative economics, the study of the viral spread of popular narratives that affect economic behavior, can improve our ability to anticipate and prepare for economic events. It can also help us structure economic institutions and policy. To get a feel for where we are going, let’s begin by considering one such popular narrative, recently in full swing. Bitcoin, the first of thousands of privately issued cryptocurrencies—including Litecoin, Ripple, Ethereum, and Libra—has generated enormous levels of talk, enthusiasm, and entrepreneurial activity. These narratives surrounding Bitcoin, the most remarkable cryptocurrency in history as judged by the speculative enthusiasm for it and its market price rather than its actual use in commerce, provide an intuitive basis for discussing the basic epidemiology of narrative economics (which we explore in detail in chapter 3).

The Age of Em: Work, Love and Life When Robots Rule the Earth
by Robin Hanson
Published 31 Mar 2016

“Time Spent in Primary Activities and Percent of the Civilian Population Engaging in Each Activity, Averages per Day by Sex, 2012 Annual Averages.” Bureau of Labor Statistics Economic News Release. June 20. http://www.bls.gov/news.release/atus.t01.htm. Buterin, Vitalik. 2014. “White Paper: A Next-Generation Smart Contract and Decentralized Application Platform.” April. https://www.ethereum.org/pdfs/EthereumWhitePaper.pdf. Caplan, Bryan. 2008. “The Totalitarian Threat.” In Global Catastrophic Risks, edited by Nick Bostrom and Milan Ćirković, 504–519. Oxford University Press, July 17. Caplan, Bryan, and Stephen Miller. 2010. “Intelligence Makes People Think Like Economists: Evidence from the General Social Survey.”

Connectography: Mapping the Future of Global Civilization
by Parag Khanna
Published 18 Apr 2016

With the Bali Trade Facilitation Agreement of 2013, the harmonization of customs administration (cutting red tape) could add $1 trillion to world GDP and create twenty million jobs. A study undertaken by the World Economic Forum and Bain estimates that further aligning supply chain standards would boost world GDP by an enormous 5 percent, while implementation of all current WTO accords would deliver only 1 percent growth. The Ethereum blockchain platform will allow for standardized and transparent contracts between trading parties beyond any single jurisdiction and, when combined with real-time data sharing on supply chain transactions, can substantially reduce the cost of insuring trade. Open trade and open borders further reorganize the world into functional circuits.