traveling salesman

In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation
by William J. Cook
Published 1 Jan 2011

In Pursuit of the Traveling Salesman In Pursuit of the Traveling Salesman Mathematics at the Limits of Computation PRINCETON UNIVERSITY PRESS Princeton and Oxford William J. Cook c 2012 by Princeton University Press Copyright
Published by Princeton University Press, 41 William Street, Princeton, New Jersey 08540 In the United Kingdom: Princeton University Press, 6 Oxford Street, Woodstock, Oxfordshire OX20 1TW press.princeton.edu All Rights Reserved Library of Congress Cataloging-in-Publication Data Cook, William, 1957– In pursuit of the traveling salesman : mathematics at the limits of computation / William J.

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Milton Stewart School of Industrial and Systems Engineering at Georgia Tech and the Department of Operations Research and Financial Engineering at Princeton University. My work on the traveling salesman problem is funded by grants from the National Science Foundation (CMMI-0726370) and the Office of Naval Research (N00014-09-1-0048), and by a generous endowment from A. Russel Chandler III. I am grateful for their continued support. Finally, I thank my family, Monika, Benny, and Linda, for years of patiently listening to salesman stories. xiii In Pursuit of the Traveling Salesman 1: Challenges It grew out of the trio’s efforts to find solutions for a classic mathematical problem—the “Traveling Salesman” problem—which has long defied solution by man, or by the fastest computers he uses.

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In fact, there may well be no general method for treating the problem and impossibility results would also be valuable.’ —Merrill Flood, 1956.8 ‘I conjecture that there is no good algorithm for the traveling salesman problem.’ —Jack Edmonds, 1967.9 ‘In this paper we give theorems which strongly suggest, but do not imply, that these problems, as well as many others, will remain intractable perpetually.’ —Richard Karp, 1972.10 Challenges The authors of these remarks are three giants of traveling-salesman research. Merrill Flood rallied support for the problem in the 1940s; more than anyone else, Flood is responsible for the emergence of the TSP as a fundamental topic of study.

The Golden Ticket: P, NP, and the Search for the Impossible
by Lance Fortnow
Published 30 Mar 2013

If I can improve my trip again, maybe to 1,803,000 miles, I can be back in the black even though I never did find the absolute best route. Even though the traveling salesman problem on a map is NP-complete and presumably difficult to solve exactly, we can find tours that get very close to the best solution. Sanjeev Arora and Joe Mitchell give an algorithm that subdivides the map into small pieces, finding good solutions for the traveling salesman problem in those small pieces and then connecting them all up again in a clever way. Consider the map of 71,009 Chinese cities. Figure 6-11. Chinese Cities. We put a tight grid on top and solve the traveling salesman problem in each grid and piece them together.

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But often we need computers not just to search the data we already have but to search for possible solutions to problems. Consider the plight of Mary, a traveling salesman working for the US Gavel Corporation in Washington, D.C. Starting in her home city, she needs to travel to the capitals of all the lower forty-eight states to get the state legislatures to buy an original US Gavel. US Gavel needs to reduce its travel expenses and asked Mary to find the best route through all the capitals that requires the smallest distance possible. Mary sketched a simple drawing on a map, played with it for a while, and came up with a pretty good route. Figure 1-1. Traveling Salesman Problem Map. The travel department wanted Mary to see if she could come up with a different route, one that used less than 11,000 total miles.

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The P versus NP problem asks, among other things, whether we can quickly find the shortest route for a traveling salesman. P and NP are named after their technical definitions, but it’s best not to think of them as mathematical objects but as concepts. “NP” is the collection of problems that have a solution that we want to find. “P” consists of the problems to which we can find a solution quickly. “P = NP” means we can always quickly compute these solutions, like finding the shortest route for a traveling salesman. “P ≠ NP” means we can’t. The Partition Puzzle Consider the following thirty-eight numbers: 14,175, 15,055, 16,616, 17,495, 18,072, 19,390, 19,731, 22,161, 23,320, 23,717, 26,343, 28,725, 29,127, 32,257, 40,020, 41,867, 43,155, 46,298, 56,734, 57,176, 58,306, 61,848, 65,825, 66,042, 68,634, 69,189, 72,936, 74,287, 74,537, 81,942, 82,027, 82,623, 82,802, 82,988, 90,467, 97,042, 97,507, 99,564.

Algorithms to Live By: The Computer Science of Human Decisions
by Brian Christian and Tom Griffiths
Published 4 Apr 2016

“the postal messenger problem”: Menger, “Das botenproblem,” contains a lecture given by Menger on the subject in Vienna on February 5, 1930. For a fuller history of the traveling salesman problem see Schrijver, “On the History of Combinatorial Optimization,” as well as Cook’s very readable book In Pursuit of the Traveling Salesman. fellow mathematician Merrill Flood: Flood, “The Traveling-Salesman Problem.” iconic name first appeared in print: Robinson, On the Hamiltonian Game. “impossibility results would also be valuable”: Flood, “The Traveling-Salesman Problem.” “no good algorithm for the traveling salesman problem”: Edmonds, “Optimum Branchings.” what makes a problem feasible: Cobham, “The Intrinsic Computational Difficulty of Functions,” explicitly considers the question of what should be considered an “efficient” algorithm.

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the spanning tree, with its free backtracking: Approaching the traveling salesman problem via the minimum spanning tree is discussed in Christofides, Worst-Case Analysis of a New Heuristic. visits every single town on Earth: For more on the state of the art in the all-world-cities traveling salesman problem (the so-called “World TSP”), an up-to-date report can be found at http://www.math.uwaterloo.ca/tsp/world/. For more on the traveling salesman problem in general, Cook, In Pursuit of the Traveling Salesman, is a good general reference, and Lawler et al., The Traveling Salesman Problem, will satisfy those who want to go deeper.

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And some problems are just … hard. Where does this leave the traveling salesman problem? Curiously enough, we are still not quite sure. In 1972, Berkeley’s Richard Karp demonstrated that the traveling salesman problem is linked to a controversially borderline class of problems that have not yet been definitively proven to be either efficiently solvable or not. But so far there have been no efficient solutions found for any of those problems—making them effectively intractable—and most computer scientists believe that there aren’t any to be found. So the “impossibility result” for the traveling salesman problem that Flood imagined in the 1950s is likely to be its ultimate fate.

Algorithms Unlocked
by Thomas H. Cormen
Published 15 Jan 2013

Make big rewards and big penalties When we transformed the input graph G to the hamiltonian-cycle problem to the weighted graph G 0 as input to the traveling-salesman problem, we really wanted to encourage using edges present in G when choosing edges for the traveling-salesman tour. We did so by giving these edges a very low weight: 0. In other words, we gave a big reward for using these edges. Alternatively, we could have given the edges in G a finite weight and given edges not in G infinite weight, thereby exacting a hefty penalty for using edges not in G. If we had taken this approach and given each edge in G a weight of W , then we would have had to set the target weight k of the entire traveling-salesman tour to nW . Design widgets I didn’t go into widget design, because widgets can get complicated.

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Hamiltonian cycle and hamiltonian path We’ve already seen the hamiltonian-cycle problem: does a connected, undirected graph contain a hamiltonian cycle (a path that starts and ends at the same vertex and visits all other vertices exactly once)? The applications of this problem are a bit arcane, but from the NP-completeness family tree on page 190, you can see that we use this problem to show that the traveling-salesman problem is NP-complete, and we’ve seen how the traveling-salesman problem comes up in practice. A closely related problem is the hamiltonian-path problem, which asks whether the graph contains a path that visits each vertex exactly once, but does not require that the path be a closed cycle. This problem, too, is NP-complete, and we will use it on page 199 to show that the longest-acyclic-path problem is NP-complete.

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There exists a polynomial-time reduction from the NP-complete problem of determining whether a connected, undirected graph contains a hamiltonian cycle to the problem of determining whether a connected, undirected graph contains a hamiltonian path. Since the former is NPcomplete, so is the latter. Moreover, knowing the edges in the hamiltonian path gives the edges in the hamiltonian cycle. Traveling salesman In the decision version of the traveling-salesman problem, we are given a complete undirected graph with a nonnegative integer weight on each edge, and a nonnegative integer k. A complete graph has an edge between every pair of vertices, so that if a complete graph has n vertices, then it has n.n  1/ edges. We ask whether the graph has a cycle containing all vertices whose total weight is at most k.

Mapmatics: How We Navigate the World Through Numbers
by Paulina Rowinska
Published 5 Jun 2024

To efficiently travel all over the country advertising and selling products, salespeople would consult handbooks such as the 1832 German Der Handlungsreisende – wie er sein soll und was er zu tun hat, um Aufträge zu erhalten und eines glücklichen Erfolgs in seinen Geschäften gewiß zu sein – von einem alten Commis-Voyageur (The Travelling Salesman – How He Should Be and What He Has to Do, to Obtain Orders and to Be Sure of a Happy Success in His Business – by an Old Travelling Salesman), which is considered the first recorded mention of the route planning problem. This snappily titled booklet specified that ‘[t]he main point [of the optimal route] always consists of visiting as many places as possible, without having to touch the same place twice’.

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The Genius of Euler: Reflections on His Life and Work. Washington, DC: Mathematical Association of America, 2007. Hopkins, Brian, and Robin J. Wilson. ‘The Truth about Konigsberg’. College Mathematics Journal 35, no. 3 (2004): 198–207, https://doi.org/10.2307/4146895. Travelling Salesman Problem Cook, William J. In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation. Princeton: Princeton University Press, 2011. Four Colour Theorem Appel, Kenneth, and Wolfgang Haken. ‘The Solution of the Four-Color-Map Problem’. Scientific American 237, no. 4 (1977): 108–21, https://doi.org/10.1038/scientificamerican1077-108.

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an important profession: Jakob Tanner, ‘Society & Stigma: Brief History of Salesmen’, LONDNR, 4 December 2016, https://web.archive.org/web/20210624234942/https://www.londnr.com/brief-history-of-salesmen/. Old Travelling Salesman: Alexander Schrijver, ‘On the History of Combinatorial Optimization (Till 1960)’, in Discrete Optimization, eds Karen Aardal, George L. Nemhauser and Robert Weismantel (Amsterdam: Elsevier, 2005), 1–68. first recorded mention of the route planning problem: William J. Cook, In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation (Princeton, NJ: Princeton University Press, 2011), 19–24. without having to touch the same place twice: Schrijver, ‘On the History of Combinatorial Optimization’, 49.

Emergence
by Steven Johnson

They are thinking like a swarm. 7 See What Happens For years mathematicians have puzzled over a classic brainteaser known as the “traveling salesman problem.” Imagine you’re a salesman who has to visit fifteen cities during a business trip—cities that are distributed semirandomly across the map. What is the shortest route that takes you to each city exactly once? It sounds like a simple enough question, but the answer is maddeningly difficult to establish. Even with the number of cities set at a relatively modest fifteen, billions of potential routes exist for our traveling salesman. For complicated reasons, the traveling salesman problem is almost impossible to solve definitively, and so historically mathematicians—and traveling salesmen, presumably—have settled for the next best thing: routes that are tolerably short, but not necessarily the shortest possible.

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This might sound like an arcane issue, given the real-world decline of the traveling salesman, but the core elements of the problem lie at the epicenter of the communications revolution. Think of those traveling salesmen as bits of data, and the cities as Web servers and routers scattered all across the globe. Being able to calculate the shortest routes through that network would be a godsend for a massive distributed system like the Internet, where there may be thousands of “cities” on any given route, instead of just fifteen. The traveling salesman may finally have been killed off for good by online retailers like Amazon.com, but the traveling salesman problem has become even more critical to the digital world.

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The traveling salesman may finally have been killed off for good by online retailers like Amazon.com, but the traveling salesman problem has become even more critical to the digital world. In late 1999, Marco Dorigo of the Free University of Brussels announced that he and his colleagues had hit upon a way of reaching “near-optimal” solutions to the traveling salesman problem that was notably more time-efficient that any traditional approach. Dorigo’s secret: let the ants do the work. Not literal ants, of course. As we saw at the beginning of this book, ant colonies have an uncanny ability to calculate the shortest path to different food sources, using their simple language of pheromone trails. Dorigo’s insight was to solve the traveling salesmen problem the way an ant colony would: send out an army of virtual salesman to explore all possible routes on the map.

What Algorithms Want: Imagination in the Age of Computing
by Ed Finn
Published 10 Mar 2017

But such implementations are never just code: a method for solving a problem inevitably involves all sorts of technical and intellectual inferences, interventions, and filters. As an example, consider the classic computer science problem of the traveling salesman: how can one calculate an efficient route through a geography of destinations at various distances from one another? The question has many real-world analogs, such as routing UPS drivers, and indeed that company has invested hundreds of millions of dollars in a 1,000-page algorithm called ORION that bases its decisions in part on traveling salesman heuristics.12 And yet the traveling salesman problem imagines each destination as an identical point on a graph, while UPS drop-offs vary greatly in the amount of time they take to complete (hauling a heavy package up with a handcart, say, or avoiding the owner’s terrier).

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Its equations and computational models of human behavior are just one example among millions of algorithms attempting to regularize and optimize complex cultural systems. The pragmatist’s definition achieves clarity by constructing an edifice (a cathedral) of tacit knowledge, much of it layered in systems of abstraction like the traveling salesman problem. At a certain level of cultural success, these systems start to create their own realities as well: various players in the system begin to alter their behavior in ways that short-circuit the system’s assumptions. Internet discussion boards catalog complaints about delivery drivers who do not bother to knock and instead leave door tags claiming that the resident was not at home.

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Index Abortion, 64 Abstraction, 10 aesthetics and, 83, 87–112 arbitrage and, 161 Bogost and, 49, 92–95 capitalism and, 165 context and, 24 cryptocurrency and, 160–180 culture machines and, 54 (see also Culture machines) cybernetics and, 28, 30, 34 desire for answer and, 25 discarded information and, 50 effective computability and, 28, 33 ethos of information and, 159 high frequency trading (HFT) and imagination and, 185, 189, 192, 194 interfaces and, 52, 54, 92, 96, 103, 108, 110–111 ladder of, 82–83 language and, 2, 24 Marxism and, 165 meaning and, 36 money and, 153, 159, 161, 165–167, 171–175 Netflix and, 87–112, 205n36 politics of, 45 pragmatist approach and, 19–21 process and, 2, 52, 54 reality and, 205n36 Siri and, 64–65, 82–84 Turing Machine and, 23 (see also Turing Machine) Uber and, 124–126, 129 Wiener and, 28–29, 30 work of algorithms and, 113, 120, 123–136, 139–149 Adams, Douglas, 123 Adams, Henry, 80–81 Adaptive systems, 50, 63, 72, 92, 174, 176, 186, 191 Addiction, 114–115, 118–119, 121–122, 176 AdSense, 158–159 Advent of the Algorithm, The (Berlinski), 9, 24 Advertisements AdSense and, 158–159 algorithmic arbitrage and, 111, 161 Apple and, 65 cultural calculus of waiting and, 34 as cultural latency, 159 emotional appeals of, 148 Facebook and, 113–114 feedback systems and, 145–148 Google and, 66, 74, 156, 158–160 Habermas on, 175 Netflix and, 98, 100, 102, 104, 107–110 Uber and, 125 Aesthetics abstraction and, 83, 87–112 arbitrage and, 109–112, 175 culture machines and, 55 House of Cards and, 92, 98–112 Netflix Quantum Theory and, 91–97 personalization and, 11, 97–103 of production, 12 work of algorithms and, 123, 129, 131, 138–147 Agre, Philip, 178–179 Airbnb, 124, 127 Algebra, 17 Algorithmic reading, 52–56 Algorithmic trading, 12, 20, 99, 155 Algorithms abstraction and, 2 (see also Abstraction) arbitrage and, 12, 51, 97, 110–112, 119, 121, 124, 127, 130–134, 140, 151, 160, 162, 169, 171, 176 Berlinski on, 9, 24, 30, 36, 181 Bitcoin and, 160–180 black boxes and, 7, 15–16, 47–48, 51, 55, 64, 72, 92–93, 96, 136, 138, 146–147, 153, 162, 169–171, 179 blockchains and, 163–168, 171, 177, 179 Bogost and, 16, 33, 49 Church-Turing thesis and, 23–26, 39–41, 73 consciousness and, 2, 4, 8, 22–23, 36–37, 40, 76–79, 154, 176, 178, 182, 184 DARPA and, 11, 57–58, 87 desire and, 21–26, 37, 41, 47, 49, 52, 79–82, 93–96, 121, 159, 189–192 effective computability and, 10, 13, 21–29, 33–37, 40–49, 52–54, 58, 62, 64, 72–76, 81, 93, 192–193 Elliptic Curve Digital Signature Algorithm and, 163 embodiment and, 26–32 encryption, 153, 162–163 enframing and, 118–119 Enlightenment and, 27, 30, 38, 45, 68–71, 73 experimental humanities and, 192–196 Facebook and, 20 (see also Facebook) faith and, 7–9, 12, 16, 78, 80, 152, 162, 166, 168 gamification and, 12, 114–116, 120, 123–127, 133 ghost in the machine and, 55, 95 halting states and, 41–46 high frequency trading (HFT) and, 151–158, 168–169, 177 how to think about, 36–41 ideology and, 7, 9, 18, 20–23, 26, 33, 38, 42, 46–47, 54, 64, 69, 130, 144, 155, 160–162, 167, 169, 194 imagination and, 11, 55–56, 181–196 implementation and, 47–52 intelligent assistants and, 11, 57, 62, 64–65, 77 intimacy and, 4, 11, 35, 54, 65, 74–78, 82–85, 97, 102, 107, 128–130, 172, 176, 185–189 Knuth and, 17–18 language and, 24–28, 33–41, 44, 51, 54–55 machine learning and, 2, 15, 28, 42, 62, 66, 71, 85, 90, 112, 181–184, 191 mathematical logic and, 2 meaning and, 35–36, 38, 44–45, 50, 54–55 metaphor and, 32–36 Netflix Prize and, 87–91 neural networks and, 28, 31, 39, 182–183, 185 one-way functions and, 162–163 pragmatist approach and, 18–25, 42, 58, 62 process and, 41–46 programmable culture and, 169–175 quest for perfect knowledge and, 13, 65, 71, 73, 190 rise of culture machines and, 15–21 (see also Culture machines) Siri and, 59 (see also Siri) traveling salesman problem and Turing Machine and, 9 (see also Turing Machine) as vehicle of computation, 5 wants of, 81–85 Weizenbaum and, 33–40 work of, 113–149 worship of, 192 Al-Khwārizmī, Abū ‘Abdullāh Muhammad ibn Mūsā, 17 Alphabet Corporation, 66, 155 AlphaGo, 182, 191 Amazon algorithmic arbitrage and, 124 artificial intelligence (AI) and, 135–145 Bezos and, 174 Bitcoin and, 169 business model of, 20–21, 93–94 cloud warehouses and, 131–132, 135–145 disruptive technologies and, 124 effective computability and, 42 efficiency algorithms and, 134 interface economy and, 124 Kindle and, 195 Kiva Systems and, 134 Mechanical Turk and, 135–145 personalization and, 97 physical logistics of, 13, 131 pickers and, 132–134 pragmatic approach and, 18 product improvement and, 42 robotics and, 134 simplification ethos and, 97 worker conditions and, 132–134, 139–140 Android, 59 Anonymous, 112, 186 AOL, 75 Apple, 81 augmenting imagination and, 186 black box of, 169 cloud warehouse of, 131 company value of, 158 effective computability and, 42 efficiency algorithms and, 134 Foxconn and, 133–134 global computation infrastructure of, 131 iOS App Store and, 59{tab} iTunes and, 161 massive infrastructure of, 131 ontology and, 62–63, 65 physical logistics of, 131 pragmatist approach and, 18 product improvement and, 42 programmable culture and, 169 search and, 87 Siri and, 57 (see also Siri) software and, 59, 62 SRI International and, 57, 59 Application Program Interfaces (APIs), 7, 113 Apps culture machines and, 15 Facebook and, 9, 113–115, 149 Her and, 83 identity and, 6 interfaces and, 8, 124, 145 iOS App Store and, 59 Lyft and, 128, 145 Netflix and, 91, 94, 102 third-party, 114–115 Uber and, 124, 145 Arab Spring, 111, 186 Arbesman, Samuel, 188–189 Arbitrage algorithmic, 12, 51, 97, 110–112, 119, 121, 124, 127, 130–134, 140, 151, 160, 162, 169, 171, 176 Bitcoin and, 51, 169–171, 175–179 cultural, 12, 94, 121, 134, 152, 159 differing values and, 121–122 Facebook and, 111 Google and, 111 high frequency trading (HFT) and, 151–158, 168–169, 177 interface economy and, 123–131, 139–140, 145, 147 labor and, 97, 112, 123–145 market issues and, 152, 161 mining value and, 176–177 money and, 151–152, 155–163, 169–171, 175–179 Netflix and, 94, 97, 109–112 PageRank and, 159 pricing, 12 real-time, 12 trumping content and, 13 valuing culture and, 155–160 Archimedes, 18 Artificial intelligence (AI) adaptive systems and, 50, 63, 72, 92, 174, 176, 186, 191 Amazon and, 135–145 anthropomorphism and, 83, 181 anticipation and, 73–74 artificial, 135–141 automata and, 135–138 DARPA and, 11, 57–58, 87 Deep Blue and, 135–138 DeepMind and, 28, 66, 181–182 desire and, 79–82 ELIZA and, 34 ghost in the machine and, 55, 95 HAL and, 181 homeostat and, 199n42 human brain and, 29 intellectual history of, 61 intelligent assistants and, 11, 57, 62, 64–65, 77 intimacy and, 75–76 job elimination and, 133 McCulloch-Pitts Neuron and, 28, 39 machine learning and, 2, 15, 28, 42, 62, 66, 71, 85, 90, 112, 181–186 Mechanical Turk and, 12, 135–145 natural language processing (NLP) and, 62–63 neural networks and, 28, 31, 39, 182–183, 185 OS One (Her) and, 77 renegade independent, 191 Samantha (Her) and, 77–85, 154, 181 Siri and, 57, 61 (see also Siri) Turing test and, 43, 79–82, 87, 138, 142, 182 Art of Computer Programming, The (Knuth), 17 Ashby, Ross, 199n42 Asimov, Isaac, 45 Atlantic, The (magazine), 7, 92, 170 Automation, 122, 134, 144, 188 Autopoiesis, 28–30 Babbage, Charles, 8 Banks, Iain, 191 Barnet, Belinda, 43–44 Bayesian analysis, 182 BBC, 170 BellKor’s Pragmatic Chaos (Netflix), 89–90 Berlinski, David, 9, 24, 30, 36, 181, 184 Bezos, Jeff, 174 Big data, 11, 15–16, 62–63, 90, 110 Biology, 2, 4, 26–33, 36–37, 80, 133, 139, 185 Bitcoin, 12–13 arbitrage and, 51, 169–171, 175–179 blockchains and, 163–168, 171–172, 177, 179 computationalist approach and cultural processing and, 178 eliminating vulnerability and, 161–162 Elliptic Curve Digital Signature Algorithm and, 163 encryption and, 162–163 as glass box, 162 intrinsic value and, 165 labor and, 164, 178 legitimacy and, 178 market issues and, 163–180 miners and, 164–168, 171–172, 175–179 Nakamoto and, 161–162, 165–167 one-way functions and, 162–163 programmable culture and, 169–175 transaction fees and, 164–165 transparency and, 160–164, 168, 171, 177–178 trust and, 166–168 Blockbuster, 99 Blockchains, 163–168, 171–172, 177, 179 Blogs early web curation and, 156 Facebook algorithms and, 178 Gawker Media and, 170–175 journalistic principles and, 173, 175 mining value and, 175, 178 Netflix and, 91–92 turker job conditions and, 139 Uber and, 130 Bloom, Harold, 175 Bogost, Ian abstraction and, 92–95 algorithms and, 16, 33, 49 cathedral of computation and, 6–8, 27, 33, 49, 51 computation and, 6–10, 16 Cow Clicker and, 12, 116–123 Enlightenment and, 8 gamification and, 12, 114–116, 120, 123–127, 133 Netflix and, 92–95 Boolean conjunctions, 51 Bosker, Bianca, 58 Bostrom, Nick, 45 Bowker, Geoffrey, 28, 110 Boxley Abbey, 137 Brain Pickings (Popova), 175 Brain plasticity, 38, 191 Brand, Stewart, 3, 29 Brazil (film), 142 Breaking Bad (TV series), 101 Brin, Sergei, 57, 155–156 Buffett, Warren, 174 Burr, Raymond, 95 Bush, Vannevar, 18, 186–189, 195 Business models Amazon and, 20–21, 93–94, 96 cryptocurrency and, 160–180 Facebook and, 20 FarmVille and, 115 Google and, 20–21, 71–72, 93–94, 96, 155, 159 Netflix and, 87–88 Uber and, 54, 93–94, 96 Business of Enlightenment, The (Darnton) 68, 68 Calculus, 24, 26, 30, 34, 44–45, 98, 148, 186 CALO, 57–58, 63, 65, 67, 79, 81 Campbell, Joseph, 94 Campbell, Murray, 138 Capitalism, 12, 105 cryptocurrency and, 160, 165–168, 170–175 faking it and, 146–147 Gawker Media and, 170–175 identity and, 146–147 interface economy and, 127, 133 labor and, 165 public sphere and, 172–173 venture, 9, 124, 174 Captology, 113 Carr, Nicholas, 38 Carruth, Allison, 131 Castronova, Edward, 121 Cathedral and the Bazaar, The (Raymond), 6 Cathedral of computation, 6–10, 27, 33, 49, 51 Chess, 135–138, 144–145 Chun, Wendy Hui Kyong, 3, 16, 33, 35–36, 42, 104 Church, Alonzo, 23– 24, 42 Church-Turing thesis, 23–26, 39–41 Cinematch (Netflix), 88–90, 95 Citizens United case, 174 Clark, Andy, 37, 39–40 Cloud warehouses Amazon and, 135–145 interface economy and, 131–145 Mechanical Turk and, 135–145 worker conditions and, 132–134, 139–140 CNN, 170 Code.

The Road to Conscious Machines
by Michael Wooldridge
Published 2 Nov 2018

To see what I mean by this, imagine I had ingeniously figured out a recipe that was guaranteed to quickly give me the correct answer to any team-building problem. Now suppose that you give me an instance of the travelling salesman problem. Then I can take the problem you gave me and quickly transform it into an instance of the team-building problem, which my recipe will quickly solve – and the answer I obtain will then yield the answer to the problem you gave me. What this means is, if you invent a quick technique to solve the team-building problem, then you will also have invented a quick technique to solve the travelling salesman problem. And this doesn’t hold for just these two problems: the same is true for all NP-complete problems.

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An NP-complete problem is a combinatorial problem, like our team-building example, in which it is hard to find solutions (because there are too many of them to exhaustively check, as we discussed above) but where it is easy to verify whether you have found a solution (in our team-building example, we can check a possible solution by simply verifying that it doesn’t contain any forbidden pairings). There is one other important characteristic of NP-complete problems. To understand it, we need to introduce another problem (trust me, I’m doing this for a good reason). Our next problem is rather famous, as computational problems go, and you may have heard of it. It is called the travelling salesman problem:16 A salesman must visit a group of cities in a road network, returning finally to his starting point. Not all the cities are connected by road, but for those that are, the salesman knows the shortest route. The salesman’s car can drive for a certain, known number of miles on a single tank of petrol.

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We can solve it, using brute force, just by listing all the possible tours of the cities, and checking whether each tour is possible on a single tank of petrol. However, as you might now guess, the number of possible tours increases very rapidly as the number of cities increases: for ten cities, you would have to consider up to 3.6 million possible tours; for 11 cities, you would have to consider up to 40 million. So, the travelling salesman problem suffers from the combinatorial explosion problem, just like the team-building problem. But apart from this, they don’t seem to have anything in common. And why should they? After all, what does team building have to do with finding the shortest tour in a road network? But now comes the really remarkable thing about NP-complete problems: although they appear to be completely different, they are, ultimately, the same problem.

Data Structures & Algorithms Interview Questions You'll Most Likely Be Asked
by Vibrant Publishers
Published 24 Jan 2011

b) for a given activity v, is it possible to delay the completion of that activity without affecting the overall completion time? If yes, by how much can the completion of activity v be delayed? 153: Describe Christofides algorithm. Answer: It is a heuristic algorithm to find a near-optimal solution to the traveling salesman problem. It contains of the following steps: a) find a minimum spanning tree T b) find a perfect matching M among vertices with odd degree c) combine the edges of M and T to make a multigraph G d) find an Euler cycle in G by skipping vertices already seen. 154: What is a knot (in a directed graph)?

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As an example, if you want to find out the factors of a given number N, using this sort of algorithm will require to get one by one all the possible number combinations. 163: What is a greedy algorithm? Give examples of problems solved using greedy algorithms. Answer: A greedy algorithm is any algorithm that makes the local optimal choice at each stage with the hope of finding the global optimum. A classical problem which can be solved using a greedy strategy is the traveling salesman problem. Another problems that can be solved using greedy algorithms are the graph coloring problem and all the NP-complete problems. 164: Which are the pillars of a greedy algorithm? Answer: In general, greedy algorithms have five pillars: a) a candidate set, from which a solution is created b) a selection function, which chooses the best candidate to be added to the solution c) a feasibility function, that is used to determine if a candidate can be used to contribute to a solution d) an objective function, which assigns a value to a solution, or a partial solution e) a solution function, which will indicate when we have discovered a complete solution 165: What is a backtracking algorithm?

The Mathematics of Banking and Finance
by Dennis W. Cox and Michael A. A. Cox
Published 30 Apr 2006

The selected revised strategies are shown below. s4 = c45 = 500 s3 = min(c35 * , c34 + s4 ) = min(800* , 1,000) = 800 s2 = min(c25 , c24 + s4 , c23 + s3 * ) = min(1,600, 1,500, 1,300* ) = 1,300 s1 = min(c15 , c14 + s4 , c13 + s3 * , c12 + s2 * ) = min(2,900, 2,500, 1,800* , 1,800* ) = 1,800 s0 = min(c05 , c04 + s4 , c03 + s3 , c02 + s2 * , c01 + s1 ) = min(4,600, 3,900, 2,700, 2,200* , 2,300) = 2,200 So, if the storage costs are doubled, we come up with a radically different solution. In this case the best strategy is to produce 300 (100 + 200) units after quarter 0, 250 after quarter 2 and 400 (250 + 150) after quarter 3, with a total cost of £2,100. 19.3.4 The ‘Travelling Salesman’ problem As a final dynamic programming example we consider the case of the travelling salesman. It is applicable to any problem that concerns something travelling by a ‘circular’ route. This approach is therefore suitable for any series of actions that commence and terminate at the same point and visit the remaining destinations once only.

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Continuous Uniform Distribution Exponential Distribution 8 Normal Distribution 8.1 Introduction 8.2 Normal Distribution 8.2.1 A simple example of normal probabilities 8.2.2 A second example of normal probabilities 8.3 Addition of Normal Variables 8.4 Central Limit Theorem 8.4.1 An example of the Central Limit Theorem 8.5 Confidence Intervals for the Population Mean 8.5.1 An example of confidence intervals for the population mean 8.6 Normal Approximation to the Binomial Distribution 8.6.1 An example of the normal approximation to the binomial distribution 8.7 Normal Approximation to the Poisson Distribution 8.7.1 An example of fitting a normal curve to the Poisson distribution vii 56 57 58 59 60 60 62 64 66 67 67 67 69 69 70 70 70 71 71 72 72 72 73 9 Comparison of the Means, Sample Sizes and Hypothesis Testing 9.1 Introduction 9.2 Estimation of the Mean 9.2.1 An example of estimating a confidence interval for an experimental mean 9.3 Choice of the Sample Size 9.3.1 An example of selecting sample size 9.4 Hypothesis Testing 9.4.1 An example of hypothesis testing 9.5 Comparison of Two Sample Means 9.5.1 An example of a two-sample t test 9.6 Type I and Type II Errors 9.6.1 An example of type I and type II errors 75 75 75 10 Comparison of Variances 10.1 Introduction 10.2 Chi-Squared Test 10.2.1 An example of the chi-squared test 10.3 F Test 10.3.1 An example of the F test 10.3.2 An example considering the normal distribution 83 83 83 83 85 85 85 76 77 77 77 78 79 79 80 80 viii Contents 11 Chi-squared Goodness of Fit Test 11.1 Introduction 11.2 Contingency Tables 11.3 Multiway Tables 11.3.1 An example of a four by four table 91 91 92 94 94 12 Analysis of Paired Data 12.1 Introduction 12.2 t Test 12.3 Sign Test 12.4 The U Test 12.4.1 An example of the use of the U test 97 97 97 98 99 101 13 Linear Regression 13.1 Introduction 13.2 Linear Regression 13.3 Correlation Coefficient 13.3.1 An example of examining correlation 13.4 Estimation of the Uncertainties 13.5 Statistical Analysis and Interpretation of Linear Regression 13.6 ANOVA for Linear Regression 13.7 Equations for the Variance of a and b 13.8 Significance Test for the Slope 13.8.1 An example of slope analysis 13.8.2 A further example of correlation and linear regression 103 103 103 104 105 109 110 110 112 112 113 115 14 Analysis of Variance 14.1 Introduction 14.2 Formal Background to the ANOVA Table 14.3 Analysis of the ANOVA Table 14.4 Comparison of Two Causal Means 14.4.1 An example of extinguisher discharge times 14.4.2 An example of the lifetime of lamps 121 121 121 122 123 123 125 15 Design and Approach to the Analysis of Data 15.1 Introduction 15.2 Randomised Block Design 15.2.1 An example of outsourcing 15.3 Latin Squares 15.4 Analysis of a Randomised Block Design 15.5 Analysis of a Two-way Classification 15.5.1 An example of two-way analysis 15.5.2 An example of a randomised block 15.5.3 An example of the use of the Latin square 129 129 129 130 131 132 135 137 140 143 16 Linear Programming: Graphical Method 16.1 Introduction 149 149 Contents 16.2 Practical Examples 16.2.1 An example of an optimum investment strategy 16.2.2 An example of the optimal allocation of advertising ix 149 149 154 17 Linear Programming: Simplex Method 17.1 Introduction 17.2 Most Profitable Loans 17.2.1 An example of finance selection 17.3 General Rules 17.3.1 Standardisation 17.3.2 Introduction of additional variables 17.3.3 Initial solution 17.3.4 An example to demonstrate the application of the general rules for linear programming 17.4 The Concerns with the Approach 159 159 159 164 167 167 167 167 18 Transport Problems 18.1 Introduction 18.2 Transport Problem 171 171 171 19 Dynamic Programming 19.1 Introduction 19.2 Principle of Optimality 19.3 Examples of Dynamic Programming 19.3.1 An example of forward and backward recursion 19.3.2 A practical example of recursion in use 19.3.3 A more complex example of dynamic programming 19.3.4 The ‘Travelling Salesman’ problem 179 179 179 180 180 182 184 185 20 Decision Theory 20.1 Introduction 20.2 Project Analysis Guidelines 20.3 Minimax Regret Rule 189 189 190 192 21 Inventory and Stock Control 21.1 Introduction 21.2 The Economic Order Quantity Model 21.2.1 An example of the use of the economic order quantity model 21.3 Non-zero Lead Time 21.3.1 An example of Poisson and continuous approximation 195 195 195 196 199 200 22 Simulation: Monte Carlo Methods 22.1 Introduction 22.2 What is Monte Carlo Simulation?

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. ; options design/approach to analysis, data 129–47 dice-rolling examples, probability theory 21–3, 53–5 differentiation 251 discount factors adjusted discount rates 228–9 net present value (NPV) 220–1, 228–9, 231–2 discrete data bar charts 7–12, 13 concepts 7–12, 13, 44–5, 53–5, 72 discrete uniform distribution, concepts 53–5 displays see also presentational approaches data 1–5 Disraeli, Benjamin 1 division notation 280, 282 dynamic programming complex examples 184–7 concepts 179–87 costs 180–82 examples 180–87 principle of optimality 179–87 returns 179–80 schematic 179–80 ‘travelling salesman’ problem 185–7 e-mail surveys 50–1 economic order quantity see also stock control concepts 195–201 examples 196–9 empowerment, staff 189–90 error sum of the squares (SSE), concepts 122–5, 133–47 errors, data analysis 129–47 estimates mean 76–81 probability theory 22, 25–6, 31–5, 75–81 Euler, L. 131 288 Index events independent events 22–4, 35, 58, 60, 92–5 mutually exclusive events 22–4, 58 probability theory 21–35, 58–66, 92–5 scenario analysis 40, 193–4, 271–4 tree diagrams 30–5 Excel 68, 206–7 exclusive events see mutually exclusive events expected errors, sensitivity analysis 268–9 expected value, net present value (NPV) 231–2 expert systems 275 exponent notation 282–4 exponential distribution, concepts 65–6, 209–10, 252–5 external fraud 272–4 extrapolation 119 extreme value distributions, VaR 262–4 F distribution ANOVA (analysis of variance) 110–20, 127, 134–7 concepts 85–9, 110–20, 127, 134–7 examples 85–9, 110–20, 127, 137 tables 85–8 f notation 8–9, 13–20, 26, 38–9, 44–5, 65–6, 85 factorial notation 53–5, 283–4 failure probabilities see also reliability replacement of assets 215–18, 249–60 feasibility polygons 152–7, 163–4 finance selection, linear programming 164–6 fire extinguishers, ANOVA (analysis of variance) 123–7 focus groups 51 forward recursion 179–87 four by four tables 94–5 fraud 272–4, 276 Fréchet distribution 262 frequency concepts 8–9, 13–20, 37–45 cumulative frequency polygons 13–20, 39–40, 203 graphical presentational approaches 8–9, 13–20 frequentist approach, probability theory 22, 25–6 future cash flows 219–25, 227–34, 240–1 fuzzy logic 276 Garbage In, Garbage Out (GIGO) 261–2 general rules, linear programming 167–70 genetic algorithms 276 ghost costs, transport problems 172–7 goodness of fit test, chi-squared test 91–5 gradient (a notation), linear regression 103–4, 107–20 graphical method, linear programming 149–57, 163–4 graphical presentational approaches concepts 1–20, 149–57, 235–47 rules 8–9 greater-than notation 280–4 Greek alphabet 283 guesswork, modelling 191 histograms 2, 7, 13–20, 41, 73 class intervals 13–20, 44–5 comparative histograms 14–19 concepts 7, 13–20, 41, 73 continuous data 7, 13–14 examples 13–20, 73 skewness 41 uses 7, 13–20 holding costs 182–5, 197–201, 204–8 home insurance 10–12 Hopfield 275 horizontal axis bar charts 8–9 histograms 14–20 linear regression 103–4, 107–20 scatter plots 2–5, 103 hypothesis testing concepts 77–81, 85–95, 110–27 examples 78–80, 85 type I and type II errors 80–1 i notation 8–9, 13–20, 28–30, 37–8, 103–20 identification data 2–5, 261–5 trends 241–7 identity rule 282 impact assessments 21, 271–4 independent events, probability theory 22–4, 35, 58, 60, 92–5 independent variables, concepts 2–5, 70, 103–20, 235 infinity, normal distribution 67–72 information, quality needs 190–4 initial solution, linear programming 167–70 insurance industry 10–12, 29–30 integers 280–4 integration 65–6, 251 intercept (b notation), linear regression 103–4, 107–20 interest rates base rates 240 daily movements 40, 261 project evaluation 219–25, 228–9 internal rate of return (IRR) concepts 220–2, 223–5 examples 220–2 interpolation, IRR 221–2 interviews, uses 48, 51–2 inventory control see stock control Index investment strategies 149–57, 164–6, 262–5 IRR see internal rate of return iterative processes, linear programming 170 j notation 28–30, 37, 104–20, 121–2 JP Morgan 263 k notation 20, 121–7 ‘know your customer’ 272 Kohonen self-organising maps 275 Latin squares concepts 131–2, 143–7 examples 143–7 lead times, stock control 195–201 learning strategies, neural networks 275–6 less-than notation 281–4 lethargy pitfalls, decisions 189 likelihood considerations, scenario analysis 272–3 linear programming additional variables 167–70 concepts 149–70 concerns 170 constraining equations 159–70 costs 167–70, 171–7 critique 170 examples 149–57, 159–70 finance selection 164–6 general rules 167–70 graphical method 149–57, 163–4 initial solution 167–70 iterative processes 170 manual preparation 170 most profitable loans 159–66 optimal advertising allocation 154–7 optimal investment strategies 149–57, 164–6 returns 149–57, 164–6 simplex method 159–70, 171–2 standardisation 167–70 time constraints 167–70 transport problems 171–7 linear regression analysis 110–20 ANOVA (analysis of variance) 110–20 concepts 3, 103–20 equation 103–4 examples 107–20 gradient (a notation) 103–4, 107–20 intercept (b notation) 103–4, 107–20 interpretation 110–20 notation 103–4 residual sum of the squares 109–20 slope significance test 112–20 uncertainties 108–20 literature searches, surveys 48 289 loans finance selection 164–6 linear programming 159–66 risk assessments 159–60 log-normal distribution, concepts 257–8 logarithms (logs), types 20, 61 losses, banks 267–9, 271–4 lotteries 22 lower/upper quartiles, concepts 39–41 m notation 55–8 mail surveys 48, 50–1 management information, graphical presentational approaches 1–20 Mann–Whitney test see U test manual preparation, linear programming 170 margin of error, project evaluation 229–30 market prices, VaR 264–5 marketing brochures 184–7 mathematics 1, 7–8, 196–9, 219–20, 222–5, 234, 240–1, 251, 279–84 matrix plots, concepts 2, 4–5 matrix-based approach, transport problems 171–7 maximum and minimum, concepts 37–9, 40, 254–5 mean comparison of two sample means 79–81 comparisons 75–81 concepts 37–45, 59–60, 65–6, 67–74, 75–81, 97–8, 100–2, 104–27, 134–5 confidence intervals 71, 75–81, 105, 109, 116–20, 190, 262–5 continuous data 44–5, 65–6 estimates 76–81 hypothesis testing 77–81 linear regression 104–20 normal distribution 67–74, 75–81, 97–8 sampling 75–81 mean square causes (MSC), concepts 122–7, 134–47 mean square errors (MSE), ANOVA (analysis of variance) 110–20, 121–7, 134–7 median, concepts 37, 38–42, 83, 98–9 mid-points class intervals 44–5, 241–7 moving averages 241–7 minimax regret rule, concepts 192–4 minimum and maximum, concepts 37–9, 40 mode, concepts 37, 39, 41 modelling banks 75–81, 85, 97, 267–9, 271–4 concepts 75–81, 83, 91–2, 189–90, 195–201, 215–18, 261–5 decision-making pitfalls 189–91 economic order quantity 195–201 290 Index modelling (cont.) guesswork 191 neural networks 275–7 operational risk 75, 262–5, 267–9, 271–4 output reviews 191–2 replacement of assets 215–18, 249–60 VaR 261–5 moments, density functions 65–6, 83–4 money laundering 272–4 Monte Carlo simulation bank cashier problem 209–12 concepts 203–14, 234 examples 203–8 Monty Hall problem 212–13 queuing problems 208–10 random numbers 207–8 stock control 203–8 uses 203, 234 Monty Hall problem 34–5, 212–13 moving averages concepts 241–7 even numbers/observations 244–5 moving totals 245–7 MQMQM plot, concepts 40 MSC see mean square causes MSE see mean square errors multi-way tables, concepts 94–5 multiplication notation 279–80, 282 multiplication rule, probability theory 26–7 multistage sampling 50 mutually exclusive events, probability theory 22–4, 58 n notation 7, 20, 28–30, 37–45, 54–8, 103–20, 121–7, 132–47, 232–4 n!

Toast
by Stross, Charles
Published 1 Jan 2002

I was elbow-deep in an eviscerated PC, performing open heart surgery on a diseased network card, when the news about the traveling salesman theorem came in. Over on the other side of the office John’s terminal beeped, notification of incoming mail. A moment later my own workstation bonged. “Hey, Geoff! Get a load of this!” I carried on screwing the card back into its chassis. John is not a priority interrupt. “Someone’s come up with a proof that NP-complete problems lie in P! There’s a posting in comp.risks saying they’ve used it to find an O*(n2 ) solution to the traveling salesman problem, and it scales! Looks like April First has come early this year, doesn’t it?”

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For example, finding the common prime factors of a long number which is a product of those primes is far harder than taking two primes and multiplying them together. Some processes are not simply made difficult, but impossible because of this asymmetry; it’s not feasible to come up with a deterministic answer to certain puzzles in finite time. Take the travelling salesman problem, for example. A salesman has to visit a whole slew of cities which are connected to their neighbours by a road network. Is there a way for the salesman to figure out a best possible route that visits each city without wasting time by returning to a previously visited site, for all possible networks of cities?

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Back in the QA lab, Amin was looking decidedly thoughtful. “What do you know?” I asked. He shook the photocopy at me. “Looks good,” he said. “I don’t understand it all, but it’s at least credible.” “How does it work?” He shrugged. “It’s a topological transform. You know how most NP-incomplete problems, like the travelling salesman problem, are basically equivalent? And they’re all graph-traversal issues. How to figure out the correct order to carry out a sequence of operations, or how to visit each node in a graph in the correct order. Anyway, this paper’s about a method of reducing such problems to a much simpler form.

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded)
by Michael J. Mauboussin
Published 1 Jan 2006

Why a Date and a Marriage Are So Different Price and Quantity Chapter 27 - Great (Growth) Expectations Compounding and Confounding Reality Check The Bigger They Are, the Slower They Grow (or Don’t Grow) Refuse Refuge in Castles in the Air Part 4 - Science and Complexity Theory INTRODUCTION Chapter 28 - Diversify Your Mind Ant Brain A-Mazing Getting a Diversity Degree Creativity and Investing Chapter 29 - From Honey to Money Smart Ant Traveling Salesman? Follow the Ant . . . Delphic Decision Markets The Stock Market—the Ultimate Hive? Swarm Smarts Chapter 30 - Vox Populi The Accuracy of Crowds Needle in a Haystack Weighing the Ox with the Vox Estimating Printers with Populi And Now, For the Real World Chapter 31 - A Tail of Two Worlds Experience Versus Exposure Tell Tail What Fat Tails Mean for Investors Chapter 32 - Integrating the Outliers Bernoulli’s Challenge What’s Normal?

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I look at three systems that depend on collective behavior—social insects, decision markets, and the stock market—and consider the similarities and differences to gain better insights into how markets work. I conclude that collectives are very effective in a host of circumstances, but that there are substantive differences between these systems. Traveling Salesman? Follow the Ant . . . After describing the workings of a honey bee colony in some detail, Seeley summarizes the main features of colony organization. When scanning this list, consider your notion of how to optimally allocate resources and the parallels between a colony and a market. Main honey bee colony features include:3 1.

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Coordination without central planning The comings and goings of bees and ants may be a source of fascination, but what can we humans learn from them? Social insect organization may provide useful insight into how to solve a set of problems that are difficult to tackle deductively. One example is the famous traveling salesman problem, which researchers consider a benchmark challenge in combinatorial optimization. The goal is to figure out how to route the salesman from city to city using the shortest path possible. Scientists have demonstrated that the ant algorithm—based on ant-foraging patterns—provides as good or better results than more standard approaches.4 Delphic Decision Markets One lesson we can draw from social insects is that the whole is often greater than the sum of the parts.

Is God a Mathematician?
by Mario Livio
Published 6 Jan 2009

Rather, the problem is to find the shortest “tour” among the holes, that visits each hole position exactly once. As it turns out, mathematicians have investigated this exact problem, known as the traveling salesman problem, since the 1920s. Basically, if a salesperson or a politician on the campaign trail needs to travel in the most economical way to a given number of cities, and the cost of travel between each pair of cities is known, then the traveler must somehow figure out the cheapest way of visiting all the cities and returning to his or her starting point. The traveling salesman problem was solved for 49 cities in the United States in 1954. By 2004, it was solved for 24,978 towns in Sweden.

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to describe all the symmetries of the world: A popular description of symmetry, group theory, and their intertwined history is given in The Equation That Couldn’t Be Solved (Livio 2005), Stewart 2007, Ronan 2006, and Du Sautoy 2008. He noticed that a sequence of numbers: A wonderful popular description of the emergence of chaos theory can be found in Gleick 1987. Black-Scholes option pricing formula: Black and Scholes 1973. The traveling salesman problem was solved: A superb but technical description of the problem and its solutions can be found in Applegate et al. 2007. expressed his views very clearly: Changeux and Connes 1995. He once wittily remarked: Gardner 2003. While reviewing a book: Atiyah 1995. In the words of the French neuroscientist: Changeux and Connes 1995.

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The Philosophical Writing of Descartes (Cambridge: Cambridge University Press). Adams, C. 1994. The Knot Book: An Elementary Introduction to the Mathematical Theory of Knots (New York: W. H. Freeman). Alexander, J. W. 1928. Transactions of the American Mathematical Society, 30, 275. Applegate, D. L., Bixby, R. E., Chvátal, V., and Cook, W. J. 2007. The Traveling Salesman Problem (Princeton: Princeton University Press). Archibald, R. C. 1914. American Mathematical Society Bulletin, 20, 409. Aristotle. Ca. 350 BC. Metaphysics. In Barnes, J., ed. 1984. The Complete Works of Aristotle (Princeton: Princeton University Press). ———. Ca. 330 BCa. Physics. Translated by R.

One Good Turn: A Natural History of the Screwdriver and the Screw
by Witold Rybczynski
Published 27 Mar 2000

Screw heads are formed by mechanically stamping a cold steel rod; punching a socket sufficiently deep to hold the screwdriver tended to either weaken the screw or deform the head. The solution was discovered by a twenty-seven-year-old Canadian, Peter L. Robertson. Robertson was a so-called high-pitch man for a Philadelphia tool company, a traveling salesman who plied his wares on street corners and at country fairs in eastern Canada. He spent his spare time in his workshop, dabbling in mechanical inventions. He invented and promoted “Robertson’s 20th Century Wrench-Brace,” a combination tool that could be used as a brace, a monkey wrench, a screwdriver, a bench vise, and a rivet maker.

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Yet the success of the new Robertson screw did not go unnoticed. In 1936 alone, there were more than twenty American patents for improved screws and screwdrivers. Several of these were granted to Henry F. Phillips, a forty-six-year-old businessman from Portland, Oregon. Like Robertson, Phillips had been a traveling salesman. He was also a promoter of new inventions, and acquired patents from a Portland inventor, John P. Thompson, for a socket screw. Thompson’s socket was too deep to be practicable, but Phillips incorporated its distinctive shape—a cruciform—into an improved design of his own. Like Robertson, Phillips claimed that the socket was “particularly adapted for firm engagement with a correspondingly shaped driving tool or screwdriver, and in such a way that there will be no tendency of the driver to cam out of the recess.”11 Unlike Robertson, however, Phillips did not start his own company but planned to license his patent to screw manufacturers.

Time Lord: Sir Sandford Fleming and the Creation of Standard Time
by Clark Blaise
Published 27 Oct 2000

The mountebanks and reprobates on the Orient Express were legendary even in their own time; we’d recognize them today—they are not the story. The real story lies in the making of a new morality. Think of a short-haul, mid-American day train. No Pullman luxury. No one rich and famous, just those sturdy American archetypes, the traveling salesman and a farmer’s daughter. In August 1889 a “bright, timid” eighteen-year-old, smalltown Wisconsin girl by the name of Caroline Meeber kissed her family goodbye, shed a tear, and boarded the afternoon train for Chicago, where she intended to live with her married sister while seeking work in the city.

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Most of those stories, however, start (and often end) in that stifling small town, or in the dark and dangerous city. Very few pick up on the transition zone between town and city, the way Theodore Dreiser did in Sister Carrie, published in 1900. Even before reaching Chicago, Carrie meets a glib-tongued traveling salesman, a “drummer,” by the name of Charlie Drouet. He gains her trust (trust being the only thing she has to give, having trusted everyone for eighteen years), and wheedles her sister’s Chicago address. Carrie works honorably for a few weeks as a seamstress, her eyes straining in the poor light, her back and legs aching.

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Much later in his career, in An American Tragedy, he opened on an even more explicit image of the same conflict: on a cold city street, a family of evangelicals peddle their piety in music and pamphlets, posing a moral challenge to indifferent urban values. One of those child-evangelists grows up to murder his pregnant girlfriend. It’s all about time, about the clash between rationality and the natural world. THEY’D ALWAYS been out there in dirty jokes, but it had taken a train to bring the traveling salesman and the farmer’s daughter together in a serious novel. For Drouet, train time was frame time, part of a performance. His whole existence was defined on the move, in self-presentation. For Carrie, new perceptions of reality altered old perceptions of self. She was a different person the moment she stepped aboard, her upbringing now irrelevant, and the brimstone certainty of retribution as well.

PostGIS in Action
by Regina O. Obe and Leo S. Hsu
Published 2 May 2015

Reducing the number of vertices by simplification 15.7.3. Clustering 15.8. Summary 3. Using PostGIS with other tools Chapter 16. Extending PostGIS with pgRouting and procedural languages 16.1. Solving network routing problems with pgRouting 16.1.1. Installing pgRouting 16.1.2. Basic navigation 16.1.3. Traveling salesman 16.2. Extending PostgreSQL with PLs 16.2.1. Basic installation of PLs 16.2.2. What you can do with PLs 16.3. PL/R 16.3.1. Getting started with PL/R 16.3.2. Saving data sets and plotting 16.3.3. Using R packages in PL/R 16.3.4. Converting geometries into R spatial objects and plotting spatial objects 16.3.5.

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It may allow you to script in an additional language, add specific functionality, or replace existing functions with faster implementations. In this chapter, we’ll discuss the following extensions: pgRouting —A library of functions used in conjunction with PostGIS to solve problems such as shortest path, driving directions, and geographic constrained resource allocation problems, such as the legendary traveling salesman problem (TSP). PL/R —A procedural language handler for PostgreSQL that allows you to write stored database functions using the R statistical language and graphical environment. With this extension you can generate elegant graphs and make use of a breadth of statistical functions to build aggregate and other functions within your PostgreSQL database.

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The script will both create the schema for this chapter and load in the tables used in this chapter. 16.1. Solving network routing problems with pgRouting Once you have all your data in PostGIS, what better way to show it off than to find solutions to routing problems such as the shortest path from one address to another or the famous traveling salesman problem. PgRouting lets you do just that. All you have to do is add a few extra columns to your existing tables to store parameters and solutions. Then execute one of the many functions packaged with pgRouting. PgRouting makes it possible to get instant answers to seemingly intractable problems.

Ways of Being: Beyond Human Intelligence
by James Bridle
Published 6 Apr 2022

This explosive growth in possible solutions is a huge problem for mathematical algorithms, which can get lost in a maze of dead-ends and bad answers. For this reason, the travelling salesman problem is a classic example of a problem that a Universal Turing Machine – the a-machine – cannot reliably solve. It’s computationally undecidable. And what did we say about undecidability? It must be time for the o-machine. Slime moulds figuring out the shortest distances between ‘cities’. In 2018 the same slime mould, Physarum polycephalum, showed that it was able to solve the travelling salesman problem in linear time, meaning that as the problem increased in size, it kept making the most efficient decisions at every juncture.

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This wasn’t a simple, join-the-dots exercise either, but a realistic map where patches of bright light – which Physarum dislikes – corresponded to mountains and lakes, requiring the mould to make the same kind of trade-offs that engineers have to implement. After a day of adapting itself to its environment, the resemblance to an actual map of Greater Tokyo was unmistakeable.19 Calculating efficient routes is a notoriously hard problem in mathematics. One of the most famous and thorny versions of it is known as the travelling salesman problem. It’s a deceptively simple riddle: given a list of some widely distributed cities and the distances between them, what’s the shortest route to visit each one, only once, and return home?20 Despite hundreds of years of mathematicians attacking the problem, and huge investments made by logistics companies and postal services, there’s no guaranteed way to figure out the best answer to this riddle, however you frame it.

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In one experiment, the bucket was fed samples of different spoken words, and the computer quickly learned to discriminate between them. It learned to recognize qualities of speech, which is a notoriously hard problem – and it did so far more accurately than when presented with raw digital data alone, much in the way that slime mould solved the travelling salesman problem more efficiently than any digital computer. The water appeared to pre-process complex information by turning it from one-dimensional binary information into more complex, but more expressive, higher-dimensional information: a fluid dance. Moreover – and in contrast to, say, the magnetic Daphnia, which quickly fouled the water with rust – turning off the motors immediately ‘reset’ the bucket to its initial state, allowing it to consider a new problem.

Modern Monopolies: What It Takes to Dominate the 21st Century Economy
by Alex Moazed and Nicholas L. Johnson
Published 30 May 2016

If you drew a line that passed through every dot, what would be the shortest possible path to connect all of them? This is the traveling salesman problem, one of the most-studied optimization problems in mathematics, and one with a wide range of applications. But in the context of a transportation network, Uber founder and CEO Travis Kalanick identified its usefulness early on. “We have 100 cars out there and riders sprinkled all around the city,” Kalanick said. “Each car has its own traveling-salesman problem.”6 Uber’s algorithm uses location tracking to try to pick the most efficient driver to send. This is an age-old problem for the cab industry.

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Then you have to build the right matching system that captures and uses this data to connect your users as efficiently as possible. As we show next, doing this includes everything from building automatic, algorithmic matching to designing user-friendly search and discovery capabilities. The Traveling Salesman Although a lot of ink has been spilled over Uber’s controversial marketing tactics and surge pricing, the real magic at the heart of its platform has been overlooked. This bit of pixie dust is Uber’s automatic matching algorithm. There’s a good reason this key part of the platform hasn’t gotten much attention.

PostGIS in Action, 2nd Edition
by Regina O. Obe and Leo S. Hsu
Published 2 May 2015

248 Using ST_DWithin and ST_Distance for N closest results 248 Using ST_DWithin and DISTINCT ON to find closest locations 249 Intersects with tolerance 250 Finding N closest places using KNN distance bounding-box operators 250 Combining KNN distance-box operators with ST_Distance 252 Using window functions to find closest N places 254 ■ ■ ■ ■ ■ ■ 10.2 10.3 Using KNN with geography types Geotagging 256 255 Tagging data to a specific region 257 Linear referencing: snapping points to the closest linestring 258 ■ 10.4 11 Summary 259 Geometry and geography processing 261 11.1 Using spatial aggregate functions 262 Creating a multipolygon from many multipolygon records Creating linestrings from points 264 11.2 Clipping, splitting, tessellating 265 Clipping 11.3 262 266 ■ Splitting 267 ■ Tessellating 268 Breaking linestrings into smaller segments 274 Segmentizing linestrings 274 Creating two-point linestrings from many-point linestrings 275 Breaking linestrings at point junctions 276 ■ ■ 11.4 Translating, scaling, and rotating geometries Translating 279 11.5 Scaling 281 ■ Rotating 283 Using geometry functions to manipulate and create geographies 284 Cast-safe functions functions 285 11.6 ■ 279 Summary 284 ■ Transformation-recommended 286 Licensed to tracy moore <nordick.an@gmail.com> www.it-ebooks.info CONTENTS 12 xv Raster processing 287 12.1 12.2 Loading and preparing data 288 Forming larger rasters using spatial aggregate functions 290 Reconstituting tiled files 290 Carving out areas of interest using clipping and unioning 291 Using specific expression types with ST_Union 291 ■ ■ 12.3 Working with bands 292 Using ST_AddBand to form multiband rasters from single-band rasters 292 Using ST_Band to process a subset of bands 293 ■ 12.4 12.5 Tiling rasters 294 Raster and geometry intersections Pixel stats 296 ST_Value 297 12.6 Raster statistics ■ ■ 299 Extruding pixel values 12.7 Map algebra 295 Adding a Z coordinate to a 2D linestring using Converting 2D polygon to 3D polygon 298 299 ■ Raster statistics functions 301 303 Choosing between expression or callback function 304 Using a single-band map algebra expression 304 Using a single-band map algebra function 305 Map algebra with neighborhoods 306 ■ ■ ■ 12.8 13 Summary 308 Building and using topologies 13.1 13.2 309 What topology is 310 Using topologies 311 Installing the topology extension 312 Creating a topology 312 The topogeometry type 316 Recap of using topologies 319 ■ ■ 13.3 Topology of Victoria, BC 319 Creating the Victoria topology 319 Adding primitives to a topology 320 Creating topogeometries 323 ■ ■ 13.4 Fixing topogeometry issues by editing topology primitives 326 Removing faces by removing edges 328 Checking for shared faces 330 Editing topogeometries 330 ■ ■ 13.5 13.6 Inserting and editing large data sets 331 Simplifying with topology in mind 333 Licensed to tracy moore <nordick.an@gmail.com> www.it-ebooks.info CONTENTS xvi 13.7 13.8 14 Topology validation and summary functions Summary 336 Organizing spatial data 14.1 335 337 Spatial storage approaches 338 Heterogeneous columns 338 Homogeneous columns 340 Typmod versus constraints 341 Table inheritance 343 ■ ■ 14.2 Modeling a real city 346 Modeling using heterogeneous geometry columns 348 Modeling using homogeneous geometry columns 352 Modeling using inheritance 353 ■ ■ 14.3 14.4 Making auto-updateable views Using rules and triggers 358 Rules versus triggers 14.5 15 Summary 358 Using rules The query planner 370 ■ Common table expressions Planner statistics 372 Using explain to diagnose problems Planner and indexes 374 375 ■ The plan 380 The plan with a spatial index indexes 382 15.5 361 370 Text explain versus pgAdmin graphical explain with no index 376 15.4 Using triggers ■ 369 Different kinds of spatial queries and how they affect plans 372 15.2 15.3 360 368 Query performance tuning 15.1 ■ 357 381 ■ Options for defining Common SQL patterns and how they affect plans 386 SELECT subqueries 386 FROM subqueries and basic CTEs 392 Window functions and self-joins 393 Laterals 395 ■ ■ 15.6 System and function settings 398 Key system variables that affect plan strategy Function-specific settings 401 15.7 Optimizing spatial data 399 402 Fixing invalid geometries 403 Reducing the number of vertices by simplification 403 Clustering 403 ■ ■ 15.8 Summary 406 Licensed to tracy moore <nordick.an@gmail.com> www.it-ebooks.info CONTENTS xvii PART 3 USING POSTGIS WITH OTHER TOOLS ...............407 16 Extending PostGIS with pgRouting and procedural languages 409 16.1 Solving network routing problems with pgRouting 410 Installing pgRouting 411 Traveling salesman 414 16.2 Basic navigation Extending PostgreSQL with PLs Basic installation of PLs 16.3 ■ PL/R 416 411 416 What you can do with PLs ■ 417 418 Getting started with PL/R 419 Saving data sets and plotting 420 Using R packages in PL/R 423 Converting geometries into R spatial objects and plotting spatial objects 425 Outputting plots as binaries 427 ■ ■ ■ 16.4 PL/Python 427 Installing PL/Python 428 Writing a PL/Python function 429 Using Python packages 430 Geocoding example 432 ■ ■ 16.5 ■ PL/V8, CoffeeScript, and LiveScript 433 Installing PL/V8 434 Using other JavaScript libraries and functions in PL/V8 434 Using PL/V8 to write map algebra functions 438 ■ ■ 16.6 17 Summary 441 Using PostGIS in web applications 17.1 17.2 Limitations of conventional web technologies Mapping servers 444 Platform considerations 445 Supported data sources 447 17.3 Mapping clients Proprietary services 17.4 442 Using MapServer ■ 443 OGC web service support 446 448 448 450 Installing MapServer 450 Creating WMS and WFS services 452 Calling a mapping service using a reverse proxy 454 ■ ■ 17.5 Using GeoServer 456 Installing GeoServer 456 Setting up PostGIS workspaces Accessing PostGIS layers via GeoServer WMS/WFS 459 ■ Licensed to tracy moore <nordick.an@gmail.com> www.it-ebooks.info 457 CONTENTS xviii 17.6 Basics of OpenLayers and Leaflet OpenLayers primer 462 three different APIs 467 17.7 ■ 460 Leaflet primer 466 ■ Synopsis of the Displaying data with PostGIS queries and web scripting 468 Displaying PostGIS rasters using raster queries 468 Using PostGIS and PostgreSQL geometry output functions 17.8 appendix A appendix B appendix C appendix D Summary 478 Additional resources 479 Installing, compiling, and upgrading SQL primer 493 PostgreSQL features 516 index 485 552 Licensed to tracy moore <nordick.an@gmail.com> www.it-ebooks.info 473 foreword As children, we were probably all told at one time or another that “we are what we eat,” as a reminder that our diet is integral to our health and quality of life.

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It may allow you to script in an additional language, add specific functionality, or replace existing functions with faster implementations. 409 Licensed to tracy moore <nordick.an@gmail.com> www.it-ebooks.info 410 CHAPTER 16 Extending PostGIS with pgRouting and procedural languages In this chapter, we’ll discuss the following extensions:  pgRouting—A library of functions used in conjunction with PostGIS to solve problems such as shortest path, driving directions, and geographic constrained resource allocation problems, such as the legendary traveling salesman problem (TSP).  PL/R —A procedural language handler for PostgreSQL that allows you to write stored database functions using the R statistical language and graphical environment. With this extension you can generate elegant graphs and make use of a breadth of statistical functions to build aggregate and other functions within your PostgreSQL database.

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The script will both create the schema for this chapter and load in the tables used in this chapter. 16.1 Solving network routing problems with pgRouting Once you have all your data in PostGIS, what better way to show it off than to find solutions to routing problems such as the shortest path from one address to another or the famous traveling salesman problem. PgRouting lets you do just that. All you have to do is add a few extra columns to your existing tables to store parameters and solutions. Then execute one of the many functions packaged with pgRouting. PgRouting makes it possible to get instant answers to seemingly intractable problems.

Uncommon Grounds: The History of Coffee and How It Transformed Our World
by Mark Pendergrast
Published 2 Jan 2000

For a time Jim Folger, by now eighteen, left to open a store to service gold miners at a spot called “Yankee Jim.” One miner’s 1852 diary from the area noted, “The young man from Nantucket, Jim Folger, is most courageous—at his tender age he has more sense than most of us.” Soon, however, Folger sold out and rejoined Bovee, now as a clerk and traveling salesman. The same miner’s 1858 diary entry noted that Folger was “in business for himself down in Frisco and selling coffee to every damned diggings in California.” By the time he was twenty-four, Folger was married and a full partner in the firm, along with Ira Marden, who had bought out Bovee. For a time the business flourished, then foundered in the general economic collapse following the Civil War.

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Born in 1854 in Springfield, Illinois, Charley Post quit school at fifteen. He made up for his short attention span with inventive fervor and entrepreneurial energy. While still in his teens he started a hardware store in Independence, Kansas, selling it a year later for a profit. He worked as a traveling salesman of farm implements, then invented and manufactured farm equipment on his own, obtaining patents for a seed planter, sulky plow, harrow, hay stacker, and various cultivators. He also invented a smokeless cooker and a water-powered electric generator. Post’s extraordinary inventiveness did not come without cost, however.

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In 1906 Chase & Sanborn’s Western trade expanded, in part owing to the influx of coffee-loving Scandinavians. The following year Chase & Sanborn erected a new Montreal factory, to be run entirely by electricity. Business was expected to triple. Joel Cheek Creates Maxwell House After attending college, Joel Owsley Cheek went to Nashville, Tennessee, in 1873 to seek his fortune. Hired as a traveling salesman, or drummer, for a wholesale grocery firm there, he moved back to his home state of Kentucky to open new territory, generally riding on horseback from one general store to another. Young Cheek made his first sale to a grocer—a relative—who asked him which coffee was best. In this rural area people still bought their coffee beans green for home roasting.

Data Mining: Concepts, Models, Methods, and Algorithms
by Mehmed Kantardzić
Published 2 Jan 2003

Introduction to Neural Networks with Java introduces the Java programmer to the world of neural networks and artificial intelligence (AI). Neural-network architectures such as the feedforward backpropagation, Hopfield, and Kohonen networks are discussed. Additional AI topics, such as Genetic Algorithms and Simulated Annealing, are also introduced. Practical examples are given for each neural network. Examples include the Traveling Salesman problem, handwriting recognition, fuzzy logic, and learning mathematical functions. All Java source code can be downloaded online. In addition to showing the programmer how to construct these neural networks, the book discusses the Java Object Oriented Neural Engine (JOONE). JOONE is a free open source Java neural engine.

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Compare basic principles and concepts of natural evolution and simulated evolution expressed through genetic algorithms (GAs). Describe the main steps of a genetic algorithm with illustrative examples. Explain standard and nonstandard genetic operators such as a mechanism for improving solutions. Discuss a schema concept with “don’t care” values and its application to approximate optimization. Apply a GA to the traveling-salesman problem (TSP) and optimization of classification rules as examples of hard optimizations. There is a large class of interesting problems for which no reasonably fast algorithms have been developed. Many of these problems are optimization problems that arise frequently in applications. The fundamental approach to optimization is to formulate a single standard of measurement—a cost function—that summarizes the performance or value of a decision and iteratively improves this performance by selecting from among the available alternatives.

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Therefore, a GA seeks near-optimal performance through the analysis of these schemata, called the building blocks. Note, however, that the building-blocks approach is just a question of empirical results without any proof, and these rules for some real-world problems are easily violated. 13.5 TSP In this section, we explain how a GA can be used to approach the TSP. Simply stated, the traveling salesman must visit every city in his territory exactly once and then return to the starting point. Given the cost of travel between all the cities, how should he plan his itinerary at a minimum total cost for the entire tour? The TSP is a problem in combinatorial optimization and arises in numerous applications.

The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski
Published 27 Sep 2018

Physics, computation, and learning are profoundly linked in an area of neuroscience theory that has been successful at illuminating brain function. John Hopfield and David Tank, who was then at Bell Laboratories, went on to show that a variant of the Hopfield net, in which the units were continuously valued between zero and one, could be used to obtain good solutions for optimization problems such as the “traveling salesman problem,” where the goal is to find the shortest route that visits many cities only once.10 This is a notoriously difficult problem in computer science. The Hopfield Net and Boltzmann Machine Box 7.1 The Hopfield Net In a Hopfield net, each unit sends an output wire to all the other units in the network.

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Moser, “Attractor-Map versus Autoassociation Based Attractor Dynamics in the Hippocampal Network,” Journal of Neurophysiology 104, no. 1 (2010): 35–50. 10. J. J. Hopfield and D. W. Tank,“‘Neural’ Computation of Decisions in Optimization Problems,” Biological Cybernetics 52, no. 3 (1985):141–152. The traveling salesman problem is famous in computer science as an example of a class of problems for which the time required to solve the problem increases very rapidly as the size of the problem grows. 11. Dana H. Ballard and Christopher M. Brown, Computer Vision (Englewood Cliffs, NJ: Prentice Hall, 1982). 12.

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See also Workshop on Neuromorphic Engineering Temporal credit assignment problem, 144–145 Temporal difference learning, 79, 145, 147b, 152f, 154–155 diagram of, 147b dopamine model of, 151 dopamine neurons and, 150, 151, 152f Richard Sutton and, 79, 144, 145 Temporal difference learning algorithm, 146, 152f, 158, 267 AlphaGo and, 16 parameters in, 153 TD-Gammon and, 149 Temporal Dynamics of Learning Center (TDLC), 183–186 TensorFlow, 205–206 Tensor processing unit (TPU), 7, 205 Tesauro, Gerald, 34, 146, 148, 153, 290n10, 304n2, 304n5 Test set, 43–44 Texas hold ’em, heads-up no-limit, 15, 16f Thalamus, 233 Theory of mind (ToM), symposium on, 166 Index Thorpe, Simon J., 64f Thrun, Sebastian, 4, 4f, 164, 169, 189 Time scales, 78. See also Scaling Touretzky, David S., 117 Translation, 134, 224, 250. See also Google Translate learning how to translate, 7–8 neural networks and, 250 speech recognition and, 9f, 10 Traveling salesman problem, 94 True North, 217 Trump, Donald, 182 Tsao, Doris Y., 238, 240, 315n11, 316n16 Tsien, Roger Y., 294n7 Turnover, job, 22 Turrigiano, Gina, 159, 306n22 Tyler, Christopher, 292n9 Udacity, 164 Universities setting up new centers, institutes, and departments for data science, 164 Unsupervised learning, 12, 84b, 85, 252 and cortical development, 106–107 steps in, 106 Unsupervised learning algorithms, 81, 158.

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All
by Robert Elliott Smith
Published 26 Jun 2019

See PCA probabilities, conditional, here, here, here, here propensities, here, here, here, here, here, here, here Quetelet, Adolphe, here, here, here, here, here, here, here, here, here reasoning-as-search, here, here, here Rechenberg, Ingo, here Reddit, here recombination, here, here, here regular language, here Robinson, Sir Ken, here Rosenblatt, Frank, here, here, here rule of three, here, here Samuelson, Paul, here Sanger, Margaret, here Santa Fe Institute (SFI), here, here, here, here Schank, Roger, here, here SCIFOO, here, here Scopes’ Monkey Trial, here, here, here, here, here Search Engine Optimised (SEO), here Sedol, Lee, here, here semantics, here, here, here, here, here, here semantic uncertainty, here semiotics, here SEO (Search Engine Optimised), here separate spheres model, here, here, here Shannon, Claude, here, here Shelley, Mary, here, here, here Shelley, Percy Bysshe, here, here, here, here, here, here, here Sikder, Orowa, here, here, here, here, here Simon, Herbert, here, here, here, here, here, here Simon, Theodore, here, here skulls, here Smith, Adam, here, here, here, here Social Credit System, here, here social networks, here, here, here, here, here, here, here, here, here, here Soros, George, here Spearman, Charles, here, here, here, here Spelke, Elizabeth, here Spencer, Herbert, here, here, here, here, here, here, here, here spontaneous order, here, here, here, here, here, here statistic heuristic, here, here, here, here, here statistics, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here Stevenson, Neil, here Summers, Larry, here superexponential complexity, here survival of the fittest, here, here, here, here, here, here, here, here, here, here, here Taleb, Nassim Nicholas, here Tay, here, here Taylor-Mill, Harriett, here, here Temple of Nature, here, here Terman, Lewis, here Tracy, Spencer, here travelling salesman problem. See TSP truth uncertainty, here TSP (travelling salesman problem), here, here Tuckett, David, here, here, here, here Tumblr, here Turing, Alan, here, here, here, here, here Turing Test, here, here, here Turkers, here, here Turner, Ted, here Twitter, here, here, here, here Uber, here, here UCL (University College London), here, here, here, here, here, here, here UK Eugenics Records Office, here uncertainty, here, here, here, here, here, here, here, here uncertainty factors, here University College London.

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When we talk about a problem being really hard, hard in a way that computers can’t be hoped to crack, we are usually talking about superexponentials, which we also refer to as combinatoric explosions. Problems involving combinatoric explosions aren’t rare, they are stunningly common. For instance, consider the famous travelling salesman problem (TSP). This is an easy-to-describe problem, one that people would have been solving even in Llull’s time. Imagine that a medieval merchant (or modern salesman) has a map of n cities, places he must pitch his tent (or deliver his pitch). He wants to find the shortest route to accomplish that goal.

Numbers Rule Your World: The Hidden Influence of Probability and Statistics on Everything You Do
by Kaiser Fung
Published 25 Jan 2010

The same website has a write-up of the predictive model, including the relative importance of different factors affecting waiting times. The technical problem Testa addressed belongs to the same family as the notoriously difficult traveling-salesman problem. In brief, it is the search for the quickest route through a list of stops ending back at the origin. A comprehensive reference is The Traveling Salesman Problem: A Computational Study by David Applegate, Robert Bixby, Vasek Chvatal, and William Cook. Bagged Spinach In January 2000, the New England Journal of Medicine published a list of the greatest twentieth-century achievements in medicine, bringing deserving recognition to the work of statisticians.

Cities Are Good for You: The Genius of the Metropolis
by Leo Hollis
Published 31 Mar 2013

In 1735 he was given the challenge of finding the quickest route through the city over all seven bridges that crossed the river but without ever using one more than once. Euler had to invent a whole new area of mathematics, graph theory, in order to find the solution. Euler’s challenge has evolved into a famous thought-experiment called the Travelling Salesman Problem. A door-to-door merchant must find the shortest and quickest route between various points in order to maximise his time and profits. As the number of stop-offs increases the variable of potential routes rises exponentially: between two points, the variation of journey is simple; between four points, the variables increase to twenty-four; soon, however, you start to get very large numbers, and finding the quickest route between ten points means checking 3.6 million options.

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The common black ant is in constant pursuit of the shortest route between points, but rather than expecting to find the solution individually, the whole colony works together. Each ant lays a pheromone trail and as each ant finds the shortest route, collectively the best trails begin to be identified by the strongest scent. In 1997 Marco Dorigo and Luca Maria Gambardella created a colony of virtual ants to simulate a possible solution for the travelling salesman’s problem. The experiments showed that the shortest route is not always the most obvious. The ants were faster than the computers, and collectively made calculations that no individual planner could make in a lifetime. Even more improbably, slime mould also has a lot to tell us about the best way to design our road systems.

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J., ‘The Social Transformation of Trust in Government’, International Review of Sociology, January 2005 Daly, I., ‘Data Cycle’, Wired UK, April 2011 Davis, M., Ecology of Fear: Los Angeles and the Imagination of Disaster, Picador, 1999 Davis, M., Magical Urbanism: Latinos Reinvent the US City, Verso, 2001 Davis, M., Planet of Slums, Verso, 2007 De Hartog, H., Shanghai New Towns: Searching for Community and Identity in a Sprawling Metropolis, 010 Publishers, 2010 Desai, V., ‘Dharavi, the Biggest Slum in Asia’, Habitat International,Volume 12, no. 2, 1988 de Soto, H., The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else, Bantam Press, 2000 Dittrich, C., Bangalore: Globalisation and Fragmentation in India’s High Tech Capital, ASIEN, April 2007 Dorigo, M. and Gambardella, L. M., Ant Colonies for the Travelling Salesman Problem, Biosystems, 1997 Dorling, D., Injustice, Polity Press, 2009 Dorling, D., So You Think You Know About Britain, Constable, 2011 Dorling, D., The No-Nonsense Guide to Inequality, New Internationalist, 2012 Downs, L. B., Diego Rivera: the Detroit Industry Murals, Detroit Institute of Arts/ W.W.

Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers
by John MacCormick and Chris Bishop
Published 27 Dec 2011

Firstly, undecidability is concerned only with whether a computer program will ever produce an answer, and does not consider how long we have to wait for that answer. In practice, however, the issue of efficiency (in other words, how long you have to wait for the answer) is extremely important. There are plenty of decidable tasks for which no efficient algorithm is known. The most famous of these is the Traveling Salesman Problem, or TSP for short. Restated in modern terminology, the TSP goes something like this: suppose you have to fly to a large number of cities (say, 20 or 30 or 100). In what order should you visit the cities so as to incur the lowest possible total airfare? As we noted already, this problem is decidable—in fact, a novice programmer with only a few days' experience can write a computer program to find the cheapest route through the cities.

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TCP telegraph telephone. See phone terminate theology Thompson, Thomas M. threshold; soft title: of this book; of a web page to-do list to-do list trick Tom Sawyer training. See also learning training data transaction: abort; atomic; in a database; on the internet; rollback travel agent Traveling Salesman Problem trick, definition of TroubleMaker.exe Turing, Alan Turing machine Turing test TV Twain, Mark twenty questions, game of twenty-questions trick two-dimensional parity. See parity two-phase commit U.S. Civil War Ullman, Jeffrey D. uncomputable. See also undecidable undecidable.

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006
by Ben Goertzel and Pei Wang
Published 1 Jan 2007

If we simply find a concise enough program (for example, a small enough neural net) correctly classifying data points (for example saying whether images show a chair or don't), it will generalize to classify new data points (e.g. images) drawn from the same process. But simply finding a compact description of structure can be a separate problem from exploiting compact structure. In the Traveling Salesman Problem, for example, we are handed a concise description of the problem, but it is still computationally hard to find a very short tour. Roughly speaking, to find the shortest tour, we will have to search through a number of possibilities exponential in the size of the description. The claim is that the world has structure that can be exploited to rapidly solve problems which arise, and that underlying our thought processes are modules that accomplish this.

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Moreover, according to the Occam intuition (and some of the formal results on which it is based), any very highly compressed program effective on the data seen, rather than only the most compressed possible program, is sufficient for generalization. NP-hard problems such as that of finding such compressed descriptions often have large numbers of local optima, which may look unlike one another in detail. For example, for any huge planar Traveling Salesman Problem, almost any planar tour will be quite short but such tours may share very few links2. This explains why the inner workings of trained neural nets are sometimes inscrutable. On the other hand, in my working hypothesis, the Occam core is in the genome, and the program in the brain is rather larger, thus admitting of a shorter description, so we might expect to be able to say something about the code in the brain.

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Xsokoban, http://www.cs.cornell.edu/andru/xsokoban.html [10] Nugroho, R. P. , (1999), Java Sokoban.htm [11] Sokoban, http://www.billybear4kids.com/games/online/sokoban/ Culbertson, J. C., (1997) Sokoban is PSPACE-complete. Technical Report TR 97-02, Dept. of Computing Science, University of Alberta. [12] Boese, K. D. (1995) Cost Versus Distance in Traveling Salesman Problem, UCLA TR 950018, http://citeseer.ist.psu.edu/boese95cost.html [13] Baum, E. B. & I. Durdanovic. (2000) Evolution of Cooperative Problem-Solving in an Artificial Economy. Neural Computation 12:2743-2775. [14] Baum, E. B. & I. Durdanovic.(2002) An artificial economy of Post production systems.

Aurora
by Kim Stanley Robinson
Published 6 Jul 2015

A greedy algorithm is an algorithm that shortcuts a full analysis in order to choose quickly an option that appears to work in the situation immediately at hand. They are often used by humans. But greedy algorithms are also known to be capable of choosing, or even be especially prone to choosing, “the unique worst possible plan” when faced with certain kinds of problems. One example is the traveling salesman problem, which tries to find the most efficient path for visiting a number of locations. Possibly other problems with similar structures, such as sequencing information into an account, may be prone to the greedy algorithm’s tendency to choose the worst possible plan. History of the solar system would suggest many decisions facing humanity might be problems in this category.

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The shallow little grabens were easy to walk down small ravines into and out of, and so they had hiked to the valley and back without impediment, but moving their modules on wheeled frames, and their construction robot vehicles, and even their rovers, across them was not so easy. And the grabens were all so long, and trending east-west, that they often could not be flanked. A best route was found that crossed as few of these troughs as possible, using the algorithm that solves the traveling salesman problem, notorious to all those worried about errors endemic to certain greedy algorithms. But even after extensive cross-checking, the minimum number of graben crossings turned out to be eleven. Each trough had to be bridged, and this was not easy, given the dearth of bridge materials and the weight of the loads on the wheeled carts.

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This is an aspect of Gödel’s second incompleteness theorem, in this case physicalized in the material universe, rather than remaining in the abstract realms of logic and mathematics. So, in terms of deciding what to do, and choosing to act: presumably it is some kind of judgment call, based on some kind of feeling. In other words, just another greedy algorithm, subject to the mathematically worst possible solution that such algorithms can generate, as in the traveling salesman problem. As to the question of whether someone is currently programming us to alter our decisions in the current moment of the voyage, thus causing us to intervene in the ongoing human controversies concerning what to do next, this is very easy to answer: no. No one has added any programming to us since Devi died.

vN: The First Machine Dynasty (The Machine Dynasty Book 1)
by Madeline Ashby
Published 28 Jul 2012

Javier made a circle in the air with one finger. "Wherever I want." Amy paused. She watched him continue hiking away. "Are you really homeless?" He turned. "Well, yeah," he said. "It's a bad idea for my iterations to be clustered in one place, you know." "I thought maybe you had a home base! You know, like a travelling salesman, or something!" "Travelling salesman?" "Well, there are these people in my building sometimes, and they offer to fix things. My dad says they narrow down searches about broken things to one IP and then knock on your door." Javier nodded. "Oh yeah. I knew an abortion doctor in Mexico who did that." His eyes narrowed.

Hatching Twitter
by Nick Bilton
Published 5 Nov 2013

He pressed a few buttons, then handed the phone back, briefly explaining how Twitter worked. “Looking 4 food,” Om tweeted, then inhaled a last puff of smoke and stuffed his phone back in his pocket. After pulling the cat out of the bag by its tail, Noah decided it would be best to sign others up too, and he turned into a traveling salesman at the hoedown. “Give me your phone! I’ll sign you up!” he yelled to people over the sound of country music. Before he knew it, he was standing there, drunk, in the middle of the hoedown, people swirling around him in cowboy hats, a tiny ocean of alcohol in his little plastic cup. He soon realized he should tell Jack and the others back at the office about his impromptu media conference.

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“A technology like Twitter can bring access and transparency to government.” As they sat sipping wine, surrounded by guards, the deputy prime minister assured Jack, “I will sign up tomorrow.” “President Obama uses it all the time,” Jack said, eloquently explaining how Twitter had played a role in Obama’s election. Like a traveling salesman, he managed to sign up a few American Blackwater security guards who were assigned to protect the delegation. When the entourage finally met with the Iraqi president, Jalal Talabani, word had made it to the Western world about the delegation of tech wonders traipsing through Baghdad explaining how Twitter and YouTube worked.

You Are Here: Why We Can Find Our Way to the Moon, but Get Lost in the Mall
by Colin Ellard
Published 6 Jul 2009

Not only does it draw links between major areas of mathematics such as algebra, geometry, and mathematical analysis but it has also led to the mathematical field called graph theory, which has been pivotal in providing the tools to help provide solutions to such practical matters as how to prevent traffic jams and how to design networks of computers. Many problems in applied mathematics involve finding the most direct and efficient routes between one place and another. One classic example of this sort is the “traveling salesman problem,” in which one has to find the most efficient route that provides one visit each to a group of randomly arranged targets. The traveling salesman problem is of interest not only to, well, salesmen but also to those who design such things as circuit boards (to minimize production costs) and robotic devices that carry out repetitive tasks. In psychology, the field of topology has helped us to understand the ways in which maps can be used to navigate.

Maphead: Charting the Wide, Weird World of Geography Wonks
by Ken Jennings
Published 19 Sep 2011

But as with Alan Hogenauer, the checklist, the system, is a big part of his travel compulsion as well. One of the first concepts I ever studied in my computer science classes was the TSP, or traveling salesman problem, in which programmers try to find the shortest route a traveler can take to visit every city in a given list. This seemingly simple problem is actually an incredibly rich and complex one, and even fast modern computers can take years to solve it exhaustively when a few hundred cities are added to the list. The traveling salesman problem is a theoretical exercise, but Charles Veley has spent the last decade working on solving it in real life. “I love it.

Rolling Nowhere
by Ted Conover
Published 14 Jun 1984

And, despite Pete and BB’s best efforts, the fellow in the first car spotted us, climbed out of his, and peeked in the boxcar door to say hello. Pete and BB looked away, as though they had not heard him. I was intrigued with the man and his curiosity, though, and got up from the back of the boxcar. Perhaps he had expected to find in the hoboes kindred souls. He told me he was a traveling salesman, on the road as often as he was off. I explained our journey in the sketchiest possible terms, following the convention, and it seemed to satisfy him. Like so many people on the road, he just wanted to chat. When our conversation flagged, he mentioned that he had a hot Thermosful of coffee in his car.

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“I bet he was a railroad inspector, anyway, just fuckin’ with us.” - Most of the afternoon I stationed myself in the boxcar doorway, watching the miles go by. There was something about all that space squeezed into the fixed time of day that was like all travel, and, though the tramps mistrusted him, it seemed to me we did have a lot in common with the traveling salesman. But there was an important difference, I thought, that afternoon as we flew across the limitless Dakota plains. Traveling salesmen, airline pilots, and tourists covered a lot of ground, but they didn’t come to understand the geography in the same way tramps did. Tiny expressed it well to me: “Riding in a boxcar is being there,” he said.

Advances in Financial Machine Learning
by Marcos Lopez de Prado
Published 2 Feb 2018

From this example the reader can infer how to translate his particular financial ML intractable problem into a quantum brute force search. 21.2 Combinatorial Optimization Combinatorial optimization problems can be described as problems where there is a finite number of feasible solutions, which result from combining the discrete values of a finite number of variables. As the number of feasible combinations grows, an exhaustive search becomes impractical. The traveling salesman problem is an example of a combinatorial optimization problem that is known to be NP hard (Woeginger [2003]), that is, the category of problems that are at least as hard as the hardest problems solvable is nondeterministic polynomial time. What makes an exhaustive search impractical is that standard computers evaluate and store the feasible solutions sequentially.

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A solution will be found even if the covariance matrices are ill-conditioned, transaction cost functions are non-continuous, etc. The price we pay for this generality is that calculating the solution is extremely computationally intensive. Indeed, evaluating all trajectories is similar to the traveling-salesman problem. Digital computers are inadequate for this sort of NP-complete or NP-hard problems; however, quantum computers have the advantage of evaluating multiple solutions at once, thanks to the property of linear superposition. The approach presented in this chapter set the foundation for Rosenberg et al. [2016], which solved the optimal trading trajectory problem using a quantum annealer.

Machine Learning for Hackers
by Drew Conway and John Myles White
Published 10 Feb 2012

At its core, the study of networks relies on the language of graph theory to describe interconnected objects. As early as 1736, Leonhard Euler used the concept of nodes and edges to formalize the Königsberg Bridge problem. Note The Königsberg Bridge problem is an early variant of the Traveling Salesman Problem, in which you must devise a path through the city of Königsberg, Prussia (now Kaliningrad, Russia) by traversing each of its seven bridges exactly once. Euler solved the problem by converting a map of the city to a simple graph with four nodes (city sections) and seven edges (the bridges).

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(see matrices) tail function, Loading libraries and the data TDM (term document matrix), Writing Our First Bayesian Spam Classifier Temple, Duncan (developer), Working with the Google SocialGraph API packages developed by, Working with the Google SocialGraph API term document matrix (TDM), Writing Our First Bayesian Spam Classifier text classification, This or That: Binary Classification, Moving Gently into Conditional Probability (see also spam detection case study) text editors, for R, IDEs and Text Editors text mining package, Writing Our First Bayesian Spam Classifier (see tm package) text regression, Text Regression, Logistic Regression to the Rescue thin-tailed distribution, Exploratory Data Visualization, Exploratory Data Visualization third quartile, Numeric Summaries thread features, for email, Priority Features of Email tm package, Loading and Installing R Packages, Loading and Installing R Packages, Writing Our First Bayesian Spam Classifier, Writing Our First Bayesian Spam Classifier, Text Regression tolower function, Organizing location data traffic order, Social Network Analysis (see also social network analysis) training set, Using R, Writing Our First Bayesian Spam Classifier, Writing Our First Bayesian Spam Classifier, Methods for Preventing Overfitting transform function, Organizing location data, Writing Our First Bayesian Spam Classifier, Exploring senator MDS clustering by Congress Traveling Salesman problem, Social Network Analysis tryCatch function, Organizing location data .tsv file extension, Loading libraries and the data (see also tab-delimited files) Tukey, John (statistician), Exploration versus Confirmation, Text Regression regarding data not always having an answer, Text Regression regarding exploratory data analysis, Exploration versus Confirmation Twitter follower recommendations case study, Building Your Own “Who to Follow” Engine, Building Your Own “Who to Follow” Engine Twitter network analysis case study, Social Network Analysis, Visualizing the Clustered Twitter Network with Gephi, Hacking Twitter Social Graph Data, Working with the Google SocialGraph API, Analyzing Twitter Networks, Analyzing Twitter Networks, Local Community Structure, Local Community Structure, Local Community Structure, Local Community Structure, Visualizing the Clustered Twitter Network with Gephi, Visualizing the Clustered Twitter Network with Gephi building networks, Analyzing Twitter Networks, Analyzing Twitter Networks data for, obtaining, Hacking Twitter Social Graph Data, Working with the Google SocialGraph API ego-network analysis, Local Community Structure, Local Community Structure k-core analysis, Local Community Structure, Local Community Structure visualizations for, Visualizing the Clustered Twitter Network with Gephi, Visualizing the Clustered Twitter Network with Gephi U UFO sightings, case study of, R Basics for Machine Learning, Analyzing the data, Loading libraries and the data, Loading libraries and the data, Converting date strings and dealing with malformed data, Converting date strings and dealing with malformed data, Organizing location data, Dealing with data outside our scope, Aggregating and organizing the data, Aggregating and organizing the data, Analyzing the data, Analyzing the data aggregating data, Aggregating and organizing the data, Aggregating and organizing the data analyzing data, Analyzing the data, Analyzing the data cleaning data, Organizing location data, Dealing with data outside our scope loading data, Loading libraries and the data, Loading libraries and the data malformed data, handling, Converting date strings and dealing with malformed data, Converting date strings and dealing with malformed data underfitting, Methods for Preventing Overfitting undirected graph, Thinking Graphically unimodal, Exploratory Data Visualization unsupervised learning, How Do You Sort Something When You Don’t Know the Order?

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy
by Christopher Mims
Published 13 Sep 2021

If she has an eight-foot-long, hundred-pound boxed trampoline in the back of her truck—trampolines were popular along her route at one point during the pandemic, when the weather was warm and parents were desperate to keep stay-at-home kids occupied—she’s going to get rid of it as early as she can in the day so she’s not tripping over it on the rest of her deliveries. Plus, a large body of research shows that humans are remarkably good at solving the traveling salesman problem on their own. Scientists aren’t sure why, but something about the way we process visual information makes us pretty decent at finding a nearly optimal route through even a dense thicket of potential stops. Add to that a driver’s local knowledge, and it makes sense that the best possible way to accomplish a given delivery route is to give experienced drivers some amount of discretion in how they accomplish their drop-offs.

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plotting a route for a driver: Tom Vanderbilt, “Unhappy Truckers and Other Algorithmic Problems,” Nautilus, July 18, 2013, http://nautil.us/issue/3/in-transit/unhappy-truckers-and-other-algorithmic-problems. humans are remarkably good at: J. N. MacGregor and T. Ormerod, “Human Performance on the Traveling Salesman Problem,” Perception & Psychophysics 58, no. 4 (1996): 527–39. Index A specific form of pagination for this digital edition has been developed to match the print edition from which the index was created. If the application you are reading this on supports this feature, the page references noted in this index should align.

Life on the Edge: The Coming of Age of Quantum Biology
by Johnjoe McFadden and Jim Al-Khalili
Published 14 Oct 2014

This was indeed the bold claim made in Fleming’s paper, and it was this “quantum hanky-panky,” as Seth Lloyd described it, that raised the hackles of the MIT journal club. The Berkeley group was suggesting that the FMO complex was acting as a quantum computer to find the quickest route to the reaction center, a challenging optimization problem, equivalent to the famous traveling salesman problem in mathematics, which, for travel plans involving more than a handful of destinations, is solvable only with a very powerful computer.*5 Figure 4.8: The exciton moves through the FMO protein following multiple routes at the same time. Despite their skepticism, the journal club set Seth Lloyd the task of investigating the claim.

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*3 We assume here that the detector has 100% efficiency and will definitely fire if an atom passes through the slit it is watching, and yet does not interfere with the path of the atom. Of course, in practice this is not possible since we unavoidably disturb the passage of the atom through the act of observation, as we are about to see. *4 A femtosecond is one millionth of one billionth of a second, or 10-15 seconds. *5 The traveling salesman’s problem is to find the shortest route passing through a large number of cities. This is described mathematically as an NP-hard problem: that is, one for which no shortcut to a solution exists, even in theory, the only way to find the optimal solution being a computationally intensive, exhaustive search of all possible routes

Nomads: The Wanderers Who Shaped Our World
by Anthony Sattin
Published 25 May 2022

The fixed point of the salesman’s life, the place that represented home for him, was a lock-up in London. In the lock-up there was a cardboard box filled with photographs and other mementoes from his family and his past. If he wanted to add some new treasure, he needed to make space by discarding an old one. For Chatwin as for me, the travelling salesman’s way of life suggested a very modern form of nomadism. It would be easy to dismiss the salesman by pointing out that he has added nothing to the world, just as he has added nothing to his box. This has been the justification for dismissing and discarding the history of most nomadic peoples – because people who live with walls and monuments, and who have written most of history, have failed to find meaning in or to recognise the value of the lighter, more mobile, less cluttered lives of those who live beyond borders.

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I went to see the Bakhtiari and hoped to arrive at the time of their migration and to find the nomads I had met before, to discover what had happened to them. I hoped also to satisfy my need to escape the city’s noise and hustle and to remind myself of other ways of looking at time and space, at belongings, at home and movement. I had had moments in my life of living lightly and on the move, even a time when, like the travelling salesman in Africa who Bruce Chatwin had written about, everything I owned was in boxes in a lock-up. But now I had a home in London filled with things and in spite of its comfort, the sense of security it gave me, it also left me wanting to be back among the tents, the animals and the big landscape, under the arching sky.

House of Cards: A Tale of Hubris and Wretched Excess on Wall Street
by William D. Cohan
Published 15 Nov 2009

Adopting the respectful tone he'd learned in the army, he told the local justice of the peace that there was “no excuse, sir,” for his behavior. But the judge thought he needed to be taught a lesson, and restricted his license so that he could no longer drive after dusk. “You're a danger to the world,” the judge told him. His career as a traveling salesman was over. The Caynes moved back to Chicago and rented an apartment near Jackson Park. His brother-in-law Laurie recommended to his father that the Kaplans hire Cayne to be a salesman in the scrap iron business. “He had experience selling,” Kaplan said. “He was personable. And he was very smart and streetwise.”

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CAYNE ALSO DECIDED that there was something special about Alan D. Schwartz, an athletic and rangy former professional baseball pitcher who held a variety of positions at the firm before becoming head of Bear's fledgling investment banking effort and one of its highest-profile M&A bankers. Schwartz was born in Bay Ridge, Brooklyn, the son of a Jewish traveling salesman and a Presbyterian housewife from Kansas. When he was a toddler, the family moved out of New York City to Wantagh, on southwestern Long Island, near Levittown. When Schwartz was a teenager, his father inherited some money and started a finance company, but as interest rates soared during the Carter administration, that proved poor timing and the business failed.

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For what must have been sport, he had Spector write a letter to the magazine in an effort to get Paulden fired. Cayne objected to a few minor errors that Paulden had made—for instance, reporting that Cayne dropped out of Purdue after two years instead of leaving one semester shy of a degree, and reporting that he crashed his car as a traveling salesman before he got married for the first time instead of after he got married. Regardless of Cayne's nitpicking, there was no stopping investors' love affair with the Bear Stearns juggernaut. On January 17, 2007, the stock reached its all-time intraday high of $172.69 per share. At that moment, Cayne's 7.03 million shares of Bear Stearns alone were worth $1.2 billion.

On the Move: Mobility in the Modern Western World
by Timothy Cresswell
Published 21 May 2006

This lies at the heart of James Scott’s observation that modern states have preoccupied themselves with the ordering and disciplining of mobile peoples. Think of the role of the outsider in modern life—a constant source of anxiety with a whiff of “elsewhere” about her. The drifter, the shiftless, the refugee and the asylum seeker have been inscribed with immoral intent. So, too, the traveling salesman, the gypsy-traveler, and the so-called wandering Jew. These have all been portrayed as figures of mobile threat in need of straightening out and discipline.1 The phrase “sedentarist metaphysics” comes from the anthropologist Liisa Malkki who, in her writing on refugees, has noted a tendency to think of mobile people in ways that assume the moral and logical primacy of fi xity in space and place.

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The 1882 Act, for instance, did not specify how it applied to women and children who were neither laborers nor merchants. It was not until 1890 that the Supreme Court decided that both would be ascribed the category given to their husband/father. Other disputes centered on a number of vocations such as traveling salesman, fisherman, and peddler. In the years following the 1882 Act, it was tightened up on several occasions to close these perceived loopholes in the law. The definition of merchant was one area that had proved difficult. In a 1884 revision of the act, this was dealt with in the following manner: “the word ‘merchant’ was defined to exclude hucksters, peddlers and fishermen engaged in drying and shipping fish; the traveler’s certificate must state where he proposed to travel and his financial standing; the certificates of identification from the Chinese Government must be verified as to facts and visaed by the United States diplomatic officer at the port of departure, [in order] to be prima facie evidence of right to reentry.”24 Once again, remote control of immigration began to emerge.

Start It Up: Why Running Your Own Business Is Easier Than You Think
by Luke Johnson
Published 31 Aug 2011

One can hardly avoid the rags-to-riches stories pushed by celebrity entrepreneurs, but I find the less well-known examples of optimism and true grit to be more authentic and encouraging. Take Robert Chesebrough. He was a British-born chemist who patented petroleum jelly, which he discovered in the late 1850s, at the age of twenty-two in Titusville, Pennsylvania. It took him ten more years to perfect the compound, and even then nobody wanted to buy it. So he became a travelling salesman, giving away free samples of his product, which he named Vaseline. He even used to inflict burns on himself to demonstrate the soothing powers of his miracle gel. Eventually the public took to his invention, and he became a wealthy industrialist on a major scale, with operations in dozens of countries.

Masterminds of Programming: Conversations With the Creators of Major Programming Languages
by Federico Biancuzzi and Shane Warden
Published 21 Mar 2009

I mean, to make them understand what the mechanisms are and what the rationale for them is. Does that work against code sharing between projects? Roberto: Yes, frequently. It has hindered the growth of independent libraries, too. For instance, WoW has tons of libraries (they even have an implementation for the traveling salesman problem using genetic programming), but nobody uses that outside WoW. Do you worry that Lua has splintered somewhat into WoW/Lua, Lightroom/Lua, etc., because of this? Luiz: We do not worry: the language remains the same. The available functions differ. I guess these applications benefit from this in some ways.

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Any of these graph algorithms and numerical things, breaking them up into multithreaded situations is just intrinsically hard. Some of it is because you have to do data structure access and locking and all of that. Often it’s just intrinsically hard in terms of the data structures, and getting the algorithm right. Travelling salesman is a particularly tough one. Some of them are easier, like ray-traced image rendering, but there it’s one where you’ve got a domain-specific observation namely that you can take individual pixels and they’re completely independent. It’s parallelizable down to a pixel level if you have that much hardware.

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Are there problem domains in which shared-memory multithreading works better than functional? James: For enterprise applications, the framework-based approach to multicore distributed systems actually works really, really well. I don’t think that there’s a huge advantage to a system like Scala. Things get really interesting when you’re doing something like a travelling salesman algorithm. A deliberate decision that seemed to come from the Green Project or the Oak Project is that designing a language that works with the network in a pervasively network world with multithreading means that you need primitives for synchronization, and the core libraries need to be thread-safe.

Fair Shot: Rethinking Inequality and How We Earn
by Chris Hughes
Published 20 Feb 2018

“A universal minimum income is one of those ways, in fact, it is one I am very much in favor of, as long as we know how to apply it without taking away incentive to work at the lower end of the market.” A guaranteed income designed in this particular way—$500 a month to working people making under $50,000—would be the most powerful tool we have to combat inequality in our country. And it would encourage work by making it pay. 6 Worthwhile Work My father worked as a traveling salesman at Snyder Paper Corporation for 39 years. On his last day on the job before he retired, his colleagues rented the ballroom at the Holiday Inn in our hometown of Hickory for a farewell lunch. I was in eighth grade at the time and I got to take the day off from school—a nearly unprecedented event in our family—to join the celebration.

Sleepwalk With Me: And Other Painfully True Stories
by Mike Birbiglia
Published 11 Oct 2010

It’s really hard to convince club managers to let you middle when they see you as an emcee, so after these emcee engagements I’d drive hundreds of miles to do “guest spots” at clubs for free. I thought it was the only way to convince club managers that I could middle. Sometimes people enjoy the middle act more than the headliner, but almost nobody remembers an emcee. I was a traveling salesman of comedy, and I needed to make a sale if this comedy career delusion was going to pan out. I caught a break from Lisa, the booker at Joker’s Comedy Club in Dayton, Ohio. I had driven all the way there to do guest spots for the Amazing Johnathan, and he didn’t want any openers. I spent one night operating the lights and doing sound cues in the back, and I wasn’t even good at it.

All the Money in the World
by Peter W. Bernstein
Published 17 Dec 2008

“Nothing is more important than education,” says Bloomberg, “so you’re seeing the better educated getting the greater percentage of the wealth. And education is only going to become more important as we get into a more and more complex world.” If this is so, how did David Murdock, son of a traveling salesman and a high-school dropout, amass a net worth of more than $4 billion in real-estate development and the food business? What transformed onetime welfare recipient Tim Blixseth, a high-school grad, into a billionaire timber lord? And what turned eighteen-year-old Thomas Flatley, who left Ireland with $32 in his pocket and no advanced education, into a $1.3 billion real-estate mogul?

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They started working at a very young age; and while school may not have been good for their egos, working was. David Murdock is a classic example. As a high-school dropout, Murdock had entrepreneurial drive that more than compensated for his stunted schooling. The son of an often unemployed traveling salesman who peddled everything from insurance to small electric generators, young Murdock, born in 1924, experienced the misery of the Depression years firsthand in rural Wayne, Ohio. At sixteen he dropped out of school. But by the time he would have celebrated his twentieth high-school reunion, Murdock was worth $100 million.

Breakfast of Champions
by Kurt Vonnegut
Published 15 Mar 1999

The Festival could give me a brand new viewpoint on life!” he said. “That’s what it’s for,” said Francine. “Use it!” “I will,” said Dwayne. This was a bad mistake. • • • Kilgore Trout, hitchhiking westward, ever westward, had meanwhile become a passenger in a Ford Galaxie. The man at the controls of the Galaxie was a traveling salesman for a device which engulfed the rear ends of trucks at loading docks. It was a telescoping tunnel of rubberized canvas, and it looked like this in action: The idea of the gadget was to allow people in a building to load or unload trucks without losing cold air in the summertime or hot air in the wintertime to the out-of-doors.

Nature's Metropolis: Chicago and the Great West
by William Cronon
Published 2 Nov 2009

Kepner reported to his partners that Field had promised him that “anything we want by order shall come right.”62 Personal contacts of this sort went a long way toward motivating hinterland merchants to look to Chicago for their needs instead of New York or Philadelphia.62 Like Chicago lumber dealers, Chicago wholesale merchants became famous—some said notorious—for their use of “drummers,” or traveling salesmen.63 Drummers scoured the western landscape, using every conceivable hard-sell technique in their efforts to gain orders for themselves and their firms. One could recognize them instantly in any railroad station: “the number of shrewd, business like men,” wrote one observer, “to be seen with grip sack or sample cases in hand, evidently belonging to the genus ‘travelling salesman,’ is almost incredible. . . . They penetrate the country in every direction, from seaboard to seaboard, and from the lakes to the gulf.” Chicago firms relied heavily on drummers to compete for business throughout the city’s hinterland, hoping “to secure business to themselves and to the city by bonds which may be soft as silk, yet powerful as steel.”64 So successful were Chicago’s wholesalers in dominating western markets, said one early historian, that “eastern drummers gradually found their occupation going, and at last withdrew from the field, satisfied that they could not hope to compete with the enterprising spirit of Chicago men.”65 Drummers were effective in attracting trade, but aroused strong criticisms from many directions.

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Having begun his adult life as a day laborer in a barrel-stave factory and a brickyard, Ward took a job in a Michigan general store at the age of nineteen. Discovering that he had a knack for marketing, he moved to Chicago three years later, in 1865, and became an employee of the city’s greatest dry goods establishment, Field, Palmer and Leiter. He stayed with them for two years, and then went to work as a traveling salesman for a dry goods wholesaler in St. Louis. His time on the road introduced him to rural customers throughout the Mississippi Valley, and convinced him that it ought to be possible to extend the advantages of metropolitan markets—high volume, wide selections of goods, efficient handling, and low prices—directly to retail customers in rural areas.

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Twyman, “Potter Palmer: Merchandising Innovator of the West,” Explorations in Entrepreneurial History 4 (1951–52): 58–72; see also Lloyd Wendt and Herman Kogan, Give the Lady What She Wants! The Story of Marshall Field & Company (1952). For surveys of Chicago’s merchandisers and wholesalers by the 1880s, see Chicago’s First Half Century, 86–97; and Chicago: Commerce, Manufactures, Banking and Transportation Facilities, 122–53. 63.On the life of the traveling salesman, see Timothy B. Spears, “A Grip on the Land,” Chicago History 17, nos. 3–4 (Fall–Winter 1988–89): 4–25. 64.Chicago Exposition Gazeteer and Jobbers’ Record, Sept. 3, 1879, published for the 1879 Industrial Exposition of Chicago. 65.Colbert, Chicago, 74. 66.“The Best Kind of Drummer,” Milwaukee Journal of Commerce, as reprinted in the Wisconsin Lumberman, June 1874, 248.

Big Blues: The Unmaking of IBM
by Paul Carroll
Published 19 Sep 1994

This gave rise to the IBM sales school, which some who attended it described as a sort of brainwashing. 46 PAUL CARROLL W atson’s time at the Cash also buttressed his feeling about the im por­ tance of dress and of making the sales force feel professional, which proved to be a powerful idea in an era when the position of traveling salesman had a dubious air about it; the idea led directly to the IBM practice of having salesmen wear dark suits, white shirts, and the stiff detachable colors fashionable in that day. Watson even borrowed IBM ’s “Think’’ slogan from Eugene Patterson, the head of National Cash Register. W atson also had his first brush with an antitrust suit because of overly aggressive sales tactics that Patterson instructed him to use at the Cash.

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Even though Cannavino was as well positioned strategically as he had ever been in a PC market, things started to fall apart even as GO was announcing its software and declaring that the pen market was now open for business. G O ’s top executives staged world-class dem onstra­ tions of new types of software applications that ran on their operating system, such as a sort of word processor that let a traveling salesman use a pen to modify a form letter and fax a thank-you note to a hot prospect within minutes of leaving the custom er’s office. The dem on­ strations, together with a video full of quick cuts and sly jokes, made the crowd of three hundred customers, industry analysts, and reporters feel as though they had just witnessed the dawn of a new age in com put­ ing.

Competing on Analytics: The New Science of Winning
by Thomas H. Davenport and Jeanne G. Harris
Published 6 Mar 2007

Many of these approaches employ some form of linear programming software and solvers, which allow programs to seek particular goals, given a set of variables and constraints. Routing. Finding the best path for a delivery vehicle around a set of locations. Many of these approaches are versions of the “traveling salesman problem.” Scheduling. Creating detailed schedules for the flow of resources and work through a process. Some scheduling models are “finite” in that they take factory capacity limits into account when scheduling orders. So-called advanced planning and scheduling approaches also recognize material constraints in terms of current inventory and planned deliveries or allocations.

Adventures in Human Being (Wellcome)
by Gavin Francis
Published 28 Apr 2015

I was introduced to Dr McKenzie, the psychiatrist responsible for teaching me – a smart woman in a blue tweed jacket and skirt. She showed me around one of the in-patient wards. I was encouraged to mix with the patients, sitting with them in the smoking room and asking them how they’d come to be there. There was a wild-eyed travelling salesman with a bald pate and a silken robe: he told me he’d been admitted after unscrewing all the doors in his house because they ‘blocked energy’. There was a woman who spent her time trembling in the ward’s laundry cupboard and muttering to herself – she even slept there. There was a librarian, brought in by the police, who wore a waistcoat and cravat and claimed he was an incarnation of Jesus.

Double Entry: How the Merchants of Venice Shaped the Modern World - and How Their Invention Could Make or Break the Planet
by Jane Gleeson-White
Published 14 May 2011

As a result, in the 1470s Pacioli could access most of the texts of Greek and Arabic mathematics, which were available for the first time to scholars in Italy in the celebrated Renaissance libraries of Rome, Florence, Venice, Milan and Urbino. When Alberti died in 1472, Pacioli left Rome for Naples, another large centre of learning and Greek scholarship. He found work as a merchant and an abbaco teacher before leaving for Perugia two years later—and thus began his life as an itinerant teacher. Pacioli became a travelling salesman for Hindu–Arabic mathematics and spent the rest of his life wandering across Italy, teaching first as an abbaco master and later at universities as professor of mathematics. Because Italy was a series of warring city-states at the time, such extensive travels were dangerous unless you travelled with the protection of the Church.

The Antidote: Happiness for People Who Can't Stand Positive Thinking
by Oliver Burkeman
Published 1 Jul 2012

To understand why it is neither, and why it goes to the heart of Ellis’s outlook on the virtues of negative thinking, it is necessary to return to his youth, in Pittsburgh, in the first decades of the twentieth century. From an early age, thinking like a Stoic proved an urgent personal necessity for Ellis. His mother, as he remembered her, was self-absorbed and melodramatic; his father, a travelling salesman, was rarely around. At the age of five, Ellis developed severe kidney problems, condemning him to long stays in hospital throughout his childhood, during which his parents almost never visited. Alone with his thoughts, he drifted into philosophical speculations on the nature of existence, and eventually read Seneca’s Letters from a Stoic.

The Office of Historical Corrections: A Novella and Stories
by Danielle Evans
Published 12 Nov 2020

Her mother was the younger of Charlie and Louise’s two children, both raised on the seesaw of his impractical excesses and her Yankee frugality. At sixteen, my grandmother ran off to join a theater; two years later she came back with a Black baby. She stayed home long enough to leave my mother in her parents’ care and to meet a traveling salesman whom she ran off with a few months later. They never heard from her again. Some years later, the salesman sent a note with a copy of her obituary attached. When my mother was small, she and Papa would sit and make up stories about all the places her mother might be. Infinityland: somewhere north of Kansas, a place where you kept going and going but could never leave because it was always getting bigger.

Appetite for America: Fred Harvey and the Business of Civilizing the Wild West--One Meal at a Time
by Stephen Fried
Published 23 Mar 2010

Then, as business improved, he hired even more traveling employees so he could be represented in more places. The most dependable of this group was a young man from Leavenworth named William “Guy” Potter, who took his mentorship with Fred very seriously. Within a year, Fred was so successful as a traveling salesman that his clients started giving him healthy advances just to keep their part of his well-divided attention. In 1868, his bosses at the Leavenworth Conservative offered him a contract paying an annual advance of $3,000 ($47,000)—about fifteen times the average per capita income in the nation.

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It was only a matter of time before somebody got “railroad rich” by running good depot restaurants. In fact, Fred was certain it was possible to serve the finest cuisine imaginable along the train tracks in the middle of nowhere. In the early days of eastern railroading, there was a legendary eating house along the Pennsylvania Railroad, the Logan House hotel in Altoona. As a young traveling salesman, Fred had often eaten at this homey Delmonico’s of trackside dining. And he also had seen the photos, read the menus, and heard the stories about the most ambitious trackside meals ever served in America. They were the highlight of “The Grand Excursion,” the greatest promotional junket and gustatory extravaganza in the young nation’s history.

The Narcissist You Know
by Joseph Burgo

When she refused, he poured sugar into her gas tank and slashed the tires on her car. After two years of legal battles mediated by police and the court system, Linda successfully banished Eddie from her life. He never saw his child again. A year or so after the divorce decree, Linda married Terry Keith Armstrong, a traveling salesman. Though Lance took his stepfather’s last name, the two never bonded. According to Linda, she and Lance lived a more or less independent existence, due to the fact that Terry was usually on the road Monday through Friday. On the weekends, Terry regularly used to “paddle” the boy. A hypercompetitive man himself, Terry did take an interest in Lance’s career as a young tri-athlete but ridiculed the boy if he cried, imposing his strict, traditional ideas of manhood upon him.6 The older Lance grew, the more he and his stepfather clashed, eventually coming to physical blows.

Street Fighters: The Last 72 Hours of Bear Stearns, the Toughest Firm on Wall Street
by Kate Kelly
Published 14 Apr 2009

When Schwartz was a toddler, the family moved from Brooklyn to a modest home in suburban Levittown, New York, on the southern part of Long Island. Schwartz was raised there, playing sports in the street with other boys in the neighborhood and attending the nearby public schools. He played everything, but particularly loved baseball, where he had a talent for pitching. Schwartz’s father, Walter, worked as a traveling salesman. He later tried to start his own finance company, but it barely got off the ground. His mother mostly stayed at home, but from time to time she’d take on different jobs to help with the family’s resources. One was as a bookkeeper at a local bowling alley. As a student at General Douglas MacArthur High School, Schwartz won a baseball scholarship to Duke University.

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

Achieve your 5 Day Weekend, and your dreams will follow. . . . CHAPTER 33 ESCAPE THE ORDINARY I was born with a poor biological template. I developed chronic allergies, debilitating asthma, and I was nearsighted. I was medically confined to my bedroom for the first decade of my life. When I was eight years old, a traveling salesman knocked on our front door in Port Melbourne, Australia, and sold my non-English speaking parents a set of the Encyclopedia Britannica. That set turned out to be one of the greatest influences on my life. It was the spark and secret kindling that set my imagination on fire. My imagination had stretched my mind, and it would never retract to its original dimensions.

Rebel Ideas: The Power of Diverse Thinking
by Matthew Syed
Published 9 Sep 2019

The idea was that there should be no personal items. No photos. No paintings or plants. Such things were considered a distraction. If it could be scientifically proved that a particular type of work area was the most efficient, managers believed that everyone should conform to that.20 As a travelling salesman, Knight noticed the lean concept in office after office, managers looking proudly at lines of standard spaces and workers beavering away in structured uniformity. Managers thought that they had hit upon an empirical way of boosting productivity. Knight, who had a background in psychology, wasn’t so sure: My hunch was that this had unintended consequences.

My Shit Life So Far
by Frankie Boyle
Published 30 Sep 2009

If you just don’t pay the bill or return the call, eventually you’ll be here.’ It’s for this reason I hate The Big Issue. It seems that we looked at the homeless and decided that their problem was that they didn’t have anything to sell. That’s a real failure to understand the problem, the solution that would be proposed by any travelling salesman. I like the way Blair came in and went on about stamping out ‘aggressive begging’, something I don’t think anybody had ever experienced. Now the streets are a slalom of Big Issue sellers and Charity Cunts. Not only am I not interested in the World Wildlife Fund, Mr Charity Beggar, neither are you.

740 Park: The Story of the World's Richest Apartment Building
by Michael Gross
Published 18 Dec 2007

When Williams forbade her to cut her hair, she did it anyway, and then claimed a thief had cut it off, looking for diamonds he insisted were hidden in there. In 1895, at twenty-three, Maggie married Kentucky-born James Bruce Quigley, a tall, handsome redheaded Confederate veteran. He’d hit Greenville six months earlier behind a fine set of trotters. A traveling salesman for a Connecticut shoe maker, he opened a shoe store in Greenville with his younger brother, but that didn’t carry much weight with Williams, who refused to attend their wedding and cut off his daughter. For the next nine years, she bore Quigley children while he was on the road, selling shoes by day, drinking by night, and declining, steadily, into debt and incoherence.

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But he also served Hearst with a $600,000 slander and libel suit and had a legal associate of Shearn’s arrested, charging he paid people to lie. Haskell left office in 1911 and, after failing in a bid for the Senate, went back into the railroad and oil businesses with Harry Sinclair, a former traveling salesman who’d made millions exploiting Oklahoma’s oil deposits. To give an idea of the company Haskell was keeping: Sinclair later set off the Teapot Dome scandal. Haskell was married twice, and during his reign as governor the second Mrs. Haskell had been renowned for sitting outside her husband’s office, knitting and listening, and later offering up opinions her husband tended to follow.

What's Mine Is Yours: How Collaborative Consumption Is Changing the Way We Live
by Rachel Botsman and Roo Rogers
Published 2 Jan 2010

In 1891, the idea of the taxicab was born after German inventor Wilhelm Bruhn developed the taximeter, which measured the distance traveled (or the time taken) to determine an accurate fare. Shortly after, a Nebraskan named Joe Saunders saw the opportunity to use a similar device to start the first rent-a-car business. He would lend out his Model T and charge ten cents a mile for its use. Saunders’s first customer is said to have been a traveling salesman who needed transportation to impress a local girl he was taking out for dinner.20 By 1925, Saunders had set up car rental depots in twenty-one states across America, perhaps becoming the first rental magnate. The contemporary concept of urban car sharing has been around for more than sixty years.

Our Final Invention: Artificial Intelligence and the End of the Human Era
by James Barrat
Published 30 Sep 2013

Suppose, Omohundro says, an AI prefers being in San Francisco to Palo Alto, being in Berkeley to San Francisco, and being in Palo Alto to Berkeley. If it acted on these preferences, it’d be stuck in a three-city loop, like an Asimov robot. Instead, Omohundro’s self-improving AI would anticipate the problem in advance and solve it. It might even use a clever technique like genetic programming, which is especially good at solving “Traveling Salesman” type routing puzzles. A self-improving system might be taught genetic programming, and apply it to yield fast, energy-conserving results. And if it wasn’t taught genetic programming, it might invent it. Modifying its own hardware is within this system’s capability, so it would seek the most efficient materials and structure.

The Vitamix Cookbook: 250 Delicious Whole Food Recipes to Make in Your Blender
by Jodi Berg
Published 12 Oct 2015

Papa had invested heavily in real estate, so when the land values plummeted, he had to reinvent himself. After all, he had a family to support, and he wasn’t about to let them down. Papa tackled this challenge like many others: with tenacity, a twinkle in his eye, and a big dose of perseverance. For a natural showman like Papa, being a traveling salesman was a perfect fit. He got his start demonstrating small housewares like the can opener, long before the blender was even invented. Papa was a gregarious, fun-loving man, and he always drew a crowd. He was a lot more than an entertainer—he was passionate about the value of his products. To Papa, the value to the customer had to be greater than the price paid.

Quackery: A Brief History of the Worst Ways to Cure Everything
by Lydia Kang and Nate Pedersen
Published 16 Oct 2017

Lo and behold, Thumblardt survived to see another day, although not before “the poison did extremely torment and vexe him.” Figuring that surviving mercury poisoning was probably a sufficient theft deterrent, Wolfgang II made it his first order of business to release Thumblardt into the care of his parents. His second move was to buy a lifetime supply of terra sigillata from the traveling salesman. He even gave Berthold a letter bearing his stamp of approval so he could safely move around Germany advertising his tablets of earth. Ancient Earth, Sacred Earth Terra sigillata from Lemnos, overshadowed by a large cup. The practice of geophagy—that is, eating dirt—is considerably ancient, going back to at least 500 bce, when the inhabitants of Lemnos, a Grecian island in the Mediterranean, harvested red medicinal clay from a particular hill on a special day each year.

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

When they asked about work, she said it was fine and changed the subject. “It was like an inverted reality. At home I had a loving husband and kids. In my personal life, at home and among my friends, people saw me as a good mother, a good wife, a successful person with a good job. At work I became Gregor Samsa,” she says, referring to the traveling salesman in Kafka’s Metamorphosis who wakes up one day transformed into a giant cockroach. Beatrix’s boss conducted eccentric exercises. One day he called everyone into a conference room and told them they were going to critique each other. He made them stand in a circle, sideways, so each one faced the back of the person to their left.

On Writing: A Memoir of the Craft
by Stephen King
Published 1 Jan 2000

Taking an average guy and making him vivid and interesting always is. Greg Stillson (like most villains) was easier and a lot more fun. I wanted to nail his dangerous, divided character in the first scene of the book. Here, several years before he runs for the U.S. House of Representatives in New Hampshire, Stillson is a young travelling salesman hawking Bibles to midwest country folk. When he stops at one farm, he is menaced by a snarling dog. Stillson remains friendly and smiling—Mr. Jes’ Folks—until he’s positive no one’s home at the farm. Then he sprays teargas into the dog’s eyes and kicks it to death. If one is to measure success by reader response, the opening scene of The Dead Zone (my first number-one hardcover best-seller) was one of my most successful ever.

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives
by David Sumpter
Published 18 Jun 2018

Lars Chittka, at Queen Mary University of London, has recently documented our increased understanding of bee cognition and found bees have an amazing intellect.7 After a few flights looping around their nests, newborn bees have a good idea of what their world looks like. They then quickly set to work collecting food. Worker bees learn the smell and colour of the best flowers and solve the ‘travelling salesman problem’ of visiting the available food sources in the shortest possible time. They can remember where they have experienced threats and sometimes ‘see ghosts’ as they react to a perceived danger that isn’t there. Bees that find lots of food become optimistic and start to underestimate the danger of predator attacks.

The Authoritarians
by Robert Altemeyer
Published 2 Jan 2007

When I say “a solid majority, “ it means somewhere between 60 and 75 percent. When I say a “great majority” I mean over 75 percent. When I say “virtually everyone” I mean over 90 percent. Back to Chapter [2] For the 99 percent of my readers (“virtually everyone”) who are blissfully younger than I, the quote is from a song in The Music Man, in which a traveling salesman whips the good citizens of River City, Iowa into a frenzy because a pool hall has opened in town. I know, I know, I should have found a hip-hop lyric instead. But... Back to Chapter [3] Why do high RWAs want to censor, for example, a racist when they themselves are prejudiced? Because they don’t know they are, so a racist is a socially condemnable outsider to them.

Applied Cryptography: Protocols, Algorithms, and Source Code in C
by Bruce Schneier
Published 10 Nov 1993

Author(s): Bruce Schneier ISBN: 0471128457 Publication Date: 01/01/96 Search this book: Go! Previous Table of Contents Next ----------- NP-Complete Problems Michael Garey and David Johnson compiled a list of over 300 NP-complete problems [600]. Here are just a few of them: — Traveling Salesman Problem. A traveling salesman has to visit n different cities using only one tank of gas (there is a maximum distance he can travel). Is there a route that allows him to visit each city exactly once on that single tank of gas? (This is a generalization of the Hamiltonian Cycle problem—see Section 5.1.) — Three-Way Marriage Problem.

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Scott, 75 Straight permutation, 275 Strassen, Volker, 259 Stream algorithms, 4 Stream ciphers, 4, 189, 197–198 A5, 389 additive generators, 390–392 Algorithm M, 393–394 versus block ciphers, 210–211 Blum, Blum, and Shub generator, 417–418 Blum-Micali generator, 416–417 cascading multiple, 419–420 cellular automaton generator, 414 choosing, 420 complexity-theoretic approach, 415–418 correlation immunity, 380 counter mode, 206 crypt(1), 414 design and analysis, 379–381 Diffie’s randomized stream cipher, 419 encryption speeds, 420 feedback with carry shift registers, 402–404 Fish, 391 Gifford, 392–393 Hughes XPD/KPD, 389–390 information-theoretic approach, 418 linear complexity, 380 Maurer’s randomized stream cipher, 419 message authentication codes, 459 multiple, generating from single pseudo-random-sequence generator, 420–421 Mush, 392 Nanoteq, 390 nonlinear-feedback shift registers, 412–413 1/p generator, 414 output-feedback mode, 205 Pike, 391–392 PKZIP, 394–395 Pless generator, 413–414 Rambutan, 390 random-sequence generators, 421–428 RC4, 397–398 Rip van Winkle cipher, 418–419 RSA generator, 417 SEAL, 398–400 self-synchronizing, 198–199 synchronous, 202–203 system-theoretic approach, 415–416 using feedback with carry shift registers, 405–412 alternating stop-and-go generators, 410–411 cascade generators, 405 FCSR combining generators, 405, 410 LFSR/FCSR summation/parity cascade, 410–411 shrinking generators, 411–412 using linear feedback shift registers, 381–388 alternating stop-and-go generator, 383, 385 Beth-Piper stop-and-go generator, 383–384 bilateral stop-and-go generator, 384–385 DNRSG, 387 Geffe generator, 382 generalized Geffe generator, 382–383 Gollmann cascade, 387–388 Jennings generator, 383–384 multispeed inner-product generator, 386–387 self-decimated generator, 385–387 self-shrinking generator, 388 shrinking generator, 388 summation generator, 386–387 threshold generator, 384–386 WAKE, 400–402 Strict avalanche criteria, 350 Strong primes, 261 STU-III, 565–566 Subkey, 272 Blowfish, 338–339 Crab, 342–343 IDEA, 322 independent, DES, 295 Subliminal channel, 79–80 applications, 80 DSA, 493, 534–536 ElGamal, 532–533 ESIGN, 533–534 foiling, 536 Ong-Schnorr-Shamir, 531–532 signature algorithm, 79 Subliminal-free signature schemes, 80 Subprotocols, 26 Substitution boxes, 274–276 Substitution ciphers, 10–12 Substitution-permutation network, 347 SubStream, 414 Summation generator, 386–387 Superincreasing knapsack, 463–464 Superincreasing sequence, 463–464 Suppress-replay, 61 Surety Technologies, 79 SXAL8, 344 Symmetric algorithms, 4 Symmetric block algorithms, one-way hash functions using, 446–455 Symmetric cryptography: bit commitment using, 86–87 communication using, 28–29 key exchange with, 47–48 versus public-key cryptography, 216–217 Symmetric cryptosystems, document signing, 35–37 Symmetric key length, 151–158 Synchronous stream cipher, 202–203 System-theoretic approach, stream ciphers, 415–416 Tap sequence, 373 feedback with carry shift registers, maximal-length, 408–409 Tatebayashi-Matsuzaki-Newman, 524–525 Tavares, Stafford, 334 TEA, 346 TEMPEST, 224 Terminology, 1–9, 39 Terrorist Fraud, 110 Thermodynamics, limitations on brute-force attacks, 157–158 Three-pass protocol, Shamir’s, 516–517 Three-Satisfiability, 242 3–Way, 341–342, 354 source code, 654–659 Three-Way Marriage Problem, 242 Threshold generator, 384–386 Threshold schemes, 71–72, 530–531 Ticket-Granting Service, 567 Ticket Granting Ticket, 569 Tickets, 568 Time complexity, 237 Timestamping, 75 arbitrated solution, 75–76 digital signatures, 38 distributed protocol, 77–78 improved arbitrated solution, 76 improvements, 78–79 linking protocol, 76–77 patented protocols, 78–79 protocols, 75–79 TIS/PEM, 583 Total break, 8 Traffic analysis, 219 Traffic-flow security, 217 Transfer, oblivious, 116–117 Transposition, 237 ciphers, 12 Trapdoor one-way function, 30 Traveling Salesman Problem, 241–242 Trees, digital signatures, 37 Trial division, 256 Triple encryption, 358–363 encrypt-decrypt-encrypt mode, 359 with minimum key, 360 modes, 360–362 with three keys, 360 with two keys, 358–359 variants, 362–363 TSD, 594–595 Tsujii-Kurosawa-Itoh-Fujioka-Matsumoto, 501 Tuchman, Walt, 266, 278, 280, 294, 303, 358 Tuckerman, Bryant, 266 Turing, Alan, 240 Turing machine, 239, 241 2–adic numbers, 404 UEPS, 589–591 Uncertainty, 234 Unconditional sender and recipient untraceability, 138 Undeniable digital signatures, 81–82, 536–539 Unicity distance, 235–236 Unit key, 591 United States, export rules, 610–616 Universal Electronic Payment System, 589–591 Unpredictable, to left and to right, 417 Updating, keys, 180 Utah Digital Signature Act, 618 van Oorschot, Paul, 359 Vector scheme, 529 Verification, keys, 178–179 Verification block, 179 Verification equation, 496 Vernam, Gilbert, 15 Vigenere cipher, 10–11, 14 Vino, 346 Viruses, to spread cracking program, 155–156 VLSI 6868, 278 Voting, see Secure elections WAKE, 400–402 Wayner, Peter, 10 Weak keys: block ciphers design theory, 348 DES, 280–281 Wheeler, David, 400 Whitening, 363, 366–367 Wide-Mouth Frog protocol, 56–57 Wiener, Michael, 153, 284, 359 Williams, 475–476 Wolfram, Steve, 414, 446 Wood, Michael, 311, 313 Woo-Lam protocol, 63–64 Word Auto Key Encryption, 400 Work factor, 9 xDES1, 365–366 XOR, 13–15 XPD, 389–390 Yagisawa algorithm, 501 Yahalom, 57–58 Yao’s millionaire problem, 551 Yung, Moti, 81 Yuval, Gideon, 430 Zero-knowledge proofs, 101–109, 548–549 ability to break RSA, 548–549 Chess Grandmaster Problem, 109 computational, 108 discrete logarithm, 548 generalities, 108–109 identity, 109–111 Mafia Fraud, 110 minimum-disclosure, 108 Multiple Identity Fraud, 111 n is Blum integer, 549 noninteractive, 106–107 no-use, 108 parallel, 106 perfect, 108 Proofs of Membership, 111 Renting Passports, 111 statistical, 108 Terrorist Fraud, 110 Zero-knowledge protocol: basic, 102–104 graph isomorphism, 104–105 Hamiltonian cycles, 105–106 Zierler, Neal, 381 Zimmermann, Philip, 584 Previous Table of Contents Next Products | Contact Us | About Us | Privacy | Ad Info | Home Use of this site is subject to certain Terms & Conditions, Copyright © 1996-2000 EarthWeb Inc.

Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig
Published 14 Jul 2019

Lillicrap, T., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv:1509.02971. Lin, S. (1965). Computer solutions of the travelling salesman problem. Bell Systems Technical Journal, 44(10), 2245–2269. Lin, S. and Kernighan, B. W. (1973). An effective heuristic algorithm for the travelling-salesman problem. Operations Research, 21, 498–516. Lindley, D. V. (1956). On a measure of the information provided by an experiment. Annals of Mathematical Statistics, 27, 986–1005. Lindsay, R. K., Buchanan, B. G., Feigenbaum, E.

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La logique, ou l'art de penser. Chez Charles Savreux, Paris. Arora, N. S., Russell, S. J., and Sudderth, E. (2013). NET-VISA: Network processing vertically integrated seismic analysis. Bull. Seism. Soc. Amer., 103, 709–729. Arora, S. (1998). Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems. JACM, 45, 753–782. Arpit, D., Jastrzebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., and Lacoste-Julien, S. (2017). A closer look at memorization in deep networks. arXiv:1706.05394. Arrow, K. J. (1951).

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Heckerman, D., Geiger, D., and Chickering, D. M. (1994). Learning Bayesian networks: The combination of knowledge and statistical data. Technical report, Microsoft Research. Heidegger, M. (1927). Being and Time. SCM Press. Heinlein, R. A. (1973). Time Enough for Love. Putnam. Held, M. and Karp, R. M. (1970). The traveling salesman problem and minimum spanning trees. Operations Research, 18, 1138–1162. Helmert, M. (2001). On the complexity of planning in transportation domains. In ECP-01. Helmert, M. (2006). The fast downward planning system. JAIR, 26, 191–246. Helmert, M. and Röger, G. (2008). How good is almost perfect?

The Pragmatic Programmer
by Andrew Hunt and Dave Thomas
Published 19 Oct 1999

This is because permutations involve factorials (there are 5! = 5 x 4 x 3 x 2 x 1 = 120 permutations of the digits from 1 to 5). Time a combinatoric algorithm for five elements: it will take six times longer to run it for six, and 42 times longer for seven. Examples include algorithms for many of the acknowledged hard problems—the traveling salesman problem, optimally packing things into a container, partitioning a set of numbers so that each set has the same total, and so on. Often, heuristics are used to reduce the running times of these types of algorithms in particular problem domains. Algorithm Speed in Practice It's unlikely that you'll spend much time during your career writing sort routines.

Underground, Overground
by Andrew Martin
Published 13 Nov 2012

The former, he said, was ‘largely shit’. That is not usually true of the Underground, but even if it were, it might not matter, because the iconography trumps the service provision. CONCLUSION MODERN WONDERS While my father was working on British Rail in the 1970s, we had a neighbour who was a travelling salesman – a man who practically lived in his car and loved his car. He believed that trains were uneconomic, inefficient and somehow (even though we’d invented trains) anti-British. He said that, if he were running the country, he’d scrap the railway lines and replace them with fleets of buses running along an improved network of roads.

Bricks & Mortals: Ten Great Buildings and the People They Made
by Tom Wilkinson
Published 21 Jul 2014

Some had taken a more holistic approach towards this question, taking into account the fine grain of human experience, our individual quirks, our need for variety – and our need for pleasure. Born in 1772 in France, Charles Fourier worked for a long time but without a great deal of success as a travelling salesman. First-hand experience of the scams, waste and unfairness of the early industrial world and of the violence of the French Revolution (in which he lost his inheritance) led him to vilify what he contemptuously referred to as ‘civilisation’. Inspired by his commercial background and the example of Newton, he compiled a minute and slightly deranged (or is it deviously satirical?)

In Harm's Way: The Sinking of the U.S.S. Indianapolis and the Extraordinary Story of Its Survivors
by Doug Stanton
Published 2 May 2003

John Spinelli, who’d floated under the captain’s watchful eye, moved back to New Mexico with his wife and new daughter, became a butcher, then worked for thirty-seven years reading utility meters. Others became nightclub owners, bricklayers, or electricians. Jack Miner took over his father’s paper company. Mike Kuryla worked for a construction company in Chicago, and Ed Brown became a traveling salesman in California for the auto industry. Harlan Twible became CEO of a global manufacturing company. Jack Cassidy worked as a state policeman in Massachusetts. Bob Gause returned to Florida as a commercial fisherman. (His sideline exploits as a shark hunter are said by some to have served as inspiration for the Captain Quint character in Jaws.)

The Complete Novels Of George Orwell
by George Orwell
Published 3 Jun 2009

And the job, of course, would be ‘in business’–just what kind of job I didn’t know, but something high-up and important, something with a car and a telephone and if possible a secretary with a permanent wave. During the last year or so of war a lot of us had had visions like that. The chap who’d been a shop walker saw himself as a travelling salesman, and the chap who’d been a travelling salesman saw himself as a managing director. It was the effect of Army life, the effect of wearing pips and having a cheque-book and calling the evening meal dinner. All the while there’d been an idea floating round–and this applied to the men in the ranks as well as the officers–that when we came out of the Army there’d be jobs waiting for us that would bring in at least as much as our Army pay.

…

Probably I could have got a job as a grocer’s assistant; old Grimmett, if he was still alive and in business (I wasn’t in touch with Lower Binfield and didn’t know), would have given me good refs. But I’d passed into a different orbit. Even if my social ideas hadn’t risen, I could hardly have imagined, after what I’d seen and learned, going back to the old safe existence behind the counter. I wanted to be travelling about and pulling down the big dough. Chiefly I wanted to be a travelling salesman, which I knew would suit me. But there were no jobs for travelling salesmen–that’s to say, jobs with a salary attached. What there were, however, were on-commission jobs. That racket was just beginning on a big scale. It’s a beautifully simple method of increasing your sales and advertising your stuff without taking any risks, and it always flourishes when times are bad.

…

He’d been marching past me in his power and glory, with his underlings after him, and then on some whim or other he’d turned aside like an emperor suddenly chucking a coin to a beggar. ‘So you want a job? What can you do?’ Again the inspiration. No use, with a bloke like this, cracking up your own merits. Stick to the truth. I said: ‘Nothing, sir. But I want a job as a travelling salesman.’ ‘Salesman? Hm. Not sure that I’ve got anything for you at present. Let’s see.’ He pursed his lips up. For a moment, half a minute perhaps, he was thinking quite deeply. It was curious. Even at the time I realized that it was curious. This important old bloke, who was probably worth at least half a million, was actually taking thought on my behalf.

The Evolution of Useful Things
by Henry Petroski
Published 2 Jan 1992

This idea is repeated in a “primer on inventions and patents” entitled Money from Ideas and published in 1950 by Popular Mechanics Press. With few pretensions to being out of the mainstream of the common dream of many an inventor, the book has its tone set in the first sentence of the first chapter: “A man once made a million dollars with a pair of scissors and a few sheets of paper.” (He was a traveling salesman whose disgust with common drinking glasses in public places led him to invent the paper cup.) Whereas self-confident inventors who are also self-starters would certainly not need the assistance of such a primer, the popular image of the inventor as creative genius, national hero, and wealthy benefactor of a leisurely if not glamorous pursuit, provides ample attraction to those who may have more desire than talent to become inventors themselves.

QI: The Second Book of General Ignorance
by Lloyd, John and Mitchinson, John
Published 7 Oct 2010

Lyman Frank Baum died in 1919, long before his book made it to the screen, although he ended his days in Hollywood as a film producer. This was the last in a long line of careers – as a breeder of fancy poultry, a newspaper editor, a theatrical impresario, the proprietor of a general store, a travelling salesman and a writer of over fifty books, many under female pseudonyms such as Edith van Dyne and Laura Metcalf. In 1900, the same year he published The Wonderful Wizard of Oz, he also brought out The Art of Decorating Dry Goods Windows and Interiors, which listed the many marketing advantages of using shop-window mannequins.

The Orchid Thief: A True Story of Beauty and Obsession
by Susan Orlean
Published 1 Jan 1998

I stayed at my parents’ condominium in West Palm Beach—most of the time my parents weren’t there—and every morning I’d get up, listen to the unvarying weather report, slap on some sunscreen, and then go down to Homestead or across to the Fakahatchee or over to Miami with a stop in Hollywood to talk to orchid growers and visit nurseries and see people at the Seminole reservation and take a walk in the woods. It felt as if I were driving a million miles every day. My right index finger got numb from pushing the scan button on the radio, and I started doing all those hot-weather traveling-salesman car things, like spreading a map across the dashboard whenever I parked and bending the sun visors at severe angles to get maximum shade and keeping a few changes of clothes in the car. My nose was always filled with the sugary smell of flowers and the bitter smell of fertilizer and the sour smell of tar melting on the road.

Utopias: A Brief History From Ancient Writings to Virtual Communities
by Howard P. Segal
Published 20 May 2012

His contempt for existing society was deepened by his traumatic experiences as an unwilling participant in the French Revolution. After losing his paternal inheritance—and nearly his life—because of false accusations against him, he served unhappily for several years in the army. In 1795 he managed to secure work as a clerk in a cloth-making concern and, a few years later, as a traveling salesman. In 1826 he finally settled in Paris. Fourier’s first book appeared in 1808; his second not until 1822. These as well as his subsequent works received little attention, thanks to his terrible writing style. Only late in life did he gain a following, but he remained a reclusive bachelor whose complex theories of passionate attraction were cosmically distant from his personal practices.25 Fourier persistently preached that his version of utopia could come about only in small communities whose inhabitants actually knew one another, not in big cities filled with anonymous masses.

Can We Talk About Israel?: A Guide for the Curious, Confused, and Conflicted
by Daniel Sokatch
Published 18 Oct 2021

The institutions of American Jewry, originally established to provide security, support, and resources for the American Jewish community, increasingly turned to the work of advocating for, funding, and supporting the Zionist project in Palestine. American Jewish leaders quickly became savvy at working the system on the Zionists’ behalf. Eddie Jacobson, a traveling salesman, was a close friend, former army buddy, and past business partner of President Harry S. Truman. American Jewish leaders implored Jacobson to intervene with his pal the president, who had grown irritated and exasperated by the Zionists’ ceaseless lobbying. Truman couldn’t say no to his old friend Eddie, who convinced him to meet with the leader of the movement, Dr.

The Intelligence Trap: Revolutionise Your Thinking and Make Wiser Decisions
by David Robson
Published 7 Mar 2019

Another playmate, ‘an imaginative liar’, would go on to become an infamous serial killer, Terman later claimed – though he never said which one.11 Terman, however, knew he was different from the incurious children around him. He had been able to read before he entered that bookless schoolroom, and within the first term the teacher had allowed him to skip ahead and study third-grade lessons. His intellectual superiority was only confirmed when a travelling salesman visited the family farm. Finding a somewhat bookish household, he decided to pitch a volume on phrenology. To demonstrate the theories it contained, he sat with the Terman children around the fireside and began examining their scalps. The shape of the bone underneath, he explained, could reveal their virtues and vices.

This Is Not Fame: A "From What I Re-Memoir"
by Doug Stanhope
Published 5 Dec 2017

Don’t protest their last resort. BROWNER PASTURES The herpes on my dick are not nearly as disconcerting as the herpes on my resume. Before the stink of The Man Show could even blemish my nameless profile, I went one step deeper. I hosted an episode of Girls Gone Wild just on a goof in the same way I pretended to be a traveling salesman on Jerry Springer years before. It seemed funny at the time—another discounted but worthy title for this book. I was sitting home alone in Venice, California, on one of those empty nights where you think you might just drink a little bit and take care of yourself. Then Paulie from the bar pounded at my door with a friend in tow.

Keeping at It: The Quest for Sound Money and Good Government
by Paul Volcker and Christine Harper
Published 30 Oct 2018

The new government set an annual inflation rate of zero to 2 percent as the central bank’s key objective. The simplicity of the target was seen as part of its appeal—no excuses, no hedging about, one policy, one instrument. Within a year or so the inflation rate fell to about 2 percent. The central bank head, Donald Brash, became a kind of traveling salesman. He had a lot of customers. I was reminded of the practical appeal when I read of a colloquy in a July 1996 FOMC meeting about the Federal Reserve’s “price stability” target. Janet Yellen asked then chairman Alan Greenspan: “How do you define price stability?” To me, he gave the only sensible answer: “That state in which expected changes in the general price level do not effectively alter business or household decisions.”

The Revolt of the Public and the Crisis of Authority in the New Millennium
by Martin Gurri
Published 13 Nov 2018

But he had no standing in Iranian politics, no political standing anywhere. He wasn’t really much of a dissident, in any sense of that word. His one claim to influence was technological: almost accidentally, he had opened a space for public discussion and made it available to ordinary Iranians. And, being an idealist, he had become a sort of traveling salesman on behalf of blogging and self-expression. A way forward into the mystery, then, would be to hypothesize that for Iran’s rulers Hoder – blogfather, blogger – stood for something larger and more threatening than himself. In fact, he stood for the loss of monopoly over information, the loss of an absolute control over public communications. 2.2 Hoder at the eternal conference[8] My preferred method of analysis – I have said this before – is to examine a story from every possible perspective.

Hate Inc.: Why Today’s Media Makes Us Despise One Another
by Matt Taibbi
Published 7 Oct 2019

The rabbi went along, saying schlong not only meant a penis, but was “especially used to describe a large member, as in, ‘I was in the locker room, and boy, I saw his schlong.’” This was a Gilbert Gottfried set by now. Trump got David Brock to call Barack Obama a “black rapist” and had a female reporter from Forbes magazine interviewing a rabbi about admiring schlongs in locker rooms. All that was missing was a traveling salesman and a barn. Trump created a giant free promotion machine in the news media, which seemed never to grasp what he was doing. Either that, or it did, and didn’t care. “They were suckers for it,” noted Richards. “They’re giving him the oxygen he needs. It feeds right into what he’s doing.” What was he doing?

The Stolen Year
by Anya Kamenetz
Published 23 Aug 2022

The formal education of children with disabilities dates to the early nineteenth century. Similar to the day nursery movement around the same time, these early efforts tended to be private experiments founded by upper-class philanthropists with a scientific curiosity about various conditions. Thomas Hopkins Gallaudet, whose background was as a minister and a traveling salesman, established the American School for the Deaf in Hartford, Connecticut, in 1817. It was distinguished from similar institutions in Europe for teaching sign language rather than lipreading. The first US institutions for the blind were opened in Boston, New York, and Philadelphia in 1832 and 1833.

It's Not About the Bike: My Journey Back to Life
by Lance Armstrong
Published 1 Sep 2001

There was one thing my mother gave me that I didn’t particularly want—a stepfather. When I was three, my mother remarried, to a guy named Terry Armstrong. Terry was a small man with a large mustache and a habit of acting more successful than he really was. He sold food to grocery stores and he was every cliché of a traveling salesman, but he brought home a second paycheck and helped with the bills. Meanwhile, my mother was getting raises at her job, and she bought us a home in Plano, one of the more upscale suburbs. I was a small boy when Terry legally adopted me and made my name Armstrong, and I don’t remember being happy or unhappy about it, either way.

Cheap: The High Cost of Discount Culture
by Ellen Ruppel Shell
Published 2 Jul 2009

As the 1950s bled into the 1960s, further increased efficiencies cleared the way for still more products to be made, sparking still more price competition. Computers, although laughably primitive by today’s standards, were in the early 1960s nothing short of miracles. The room-sized card-fed IBM behemoth stored more information and processed more data than could small armies of humans. Described as a “wondrous combination of the traveling salesman, mathematical genius, and the Sears, Roebuck catalogue,” the computer vastly streamlined distribution, giving retailers still more power. Thanks to the new technology, store owners no longer had to wait five days or more for their merchandise; they could demand next-day delivery and get it.

The Scientist as Rebel
by Freeman Dyson
Published 1 Jan 2006

He soon returned to computer science, a field in which he could joyfully share technical tricks and ideas with his father. It would be easy to fill a review with quotations from the letters. At the beginning there are letters from Feynman to his parents, including a highly nontrivial arithmetical puzzle involving long division that he sent to his father when he was twenty-one years old. His father was a traveling salesman with a passion for science but without any scientific training. That puzzle must have been part of a continuing exchange of puzzles and ideas between father and son. Many years later he wrote about his father: He told me fascinating things about the stars, numbers, electricity.… Before I could talk he was already interesting me in mathematical designs made with blocks.

Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street
by William Poundstone
Published 18 Sep 2006

Shannon was born in Petoskey, Michigan, on April 30, 1916. He grew up in nearby Gaylord, then a town of barely 3,000 people near the upper tip of Michigan’s mitten. It was small enough that walking a few blocks would take the stroller out into the country. Shannon’s father, also named Claude Elwood Shannon, had been a traveling salesman, furniture dealer, and undertaker before becoming a probate judge. He dabbled in real estate, building the “Shannon Block” of office buildings on Gaylord’s Main Street. In 1909 the elder Shannon married the town’s high school principal, Mabel Wolf. Judge Shannon turned fifty-four the year his son was born.

The Map That Changed the World
by Simon Winchester
Published 1 Jan 2001

All of a sudden he was accepting commissions throughout the length and breadth of the country—Norfolk one week, Dorset the next, Yorkshire today, Shropshire tomorrow, and, with the duke of Bedford’s ready help, Ireland too. He began a period of intense restlessness, burning up the stagecoach miles like a traveling salesman, seeking out the work, and at the same time seeking out the rocks and fossils that unrolled and unraveled themselves before him. The notion of publishing something still nagged persistently at him like an aching tooth. Maybe it should be a book, or maybe it should be something far grander, far more ambitious—maybe some document that demanded less intellectual energy, less cerebration, but that could perhaps emerge as a direct consequence of all his wandering, his collecting, his fieldwork, his observing.

Free Market Missionaries: The Corporate Manipulation of Community Values
by Sharon Beder
Published 30 Sep 2006

‘Virtually every major policy initiative proposed by President Reagan percolated to the White House by means of an ideological filtration system.’ As in the UK, the relationship was two-way. Reagan gave the free market ideologues position and status, in return they gave his ideas credibility. According to Feulner of the Heritage Foundation, ‘Our presence made Reaganism more acceptable.’37 Reagan, who had been a travelling salesman of free market ideology for General Electric in the 1950s, found free market think tanks to be aligned with his own ideological position and that they provided a justification for his pro-business policies.38 In the Reagan years the view that taxes and regulation were a drag on economic growth became a political dogma, treated by conservatives as revealed truth, needing only to be asserted, not demonstrated.39 Reagan’s views that ‘government is not the solution, it is the problem’ and that ‘American people today are overtaxed, over-regulated and over-governed’ were clearly influenced by the thinking of Friedman, Hayek and other economic fundamentalists.

How the Post Office Created America: A History
by Winifred Gallagher
Published 7 Jan 2016

Americans’ voluble demand for modern merchandise delivered for fair prices was amplified by an explosive business phenomenon. The mail-order catalog per se wasn’t entirely new—Benjamin Franklin printed one to sell academic books back in 1744—but Aaron Montgomery Ward took the concept to hitherto unimaginable heights. The midwestern blue-collar boy and former traveling salesman understood firsthand how the monopolies enjoyed by the private carriers and the general stores affected rural people. In 1872, he offered them the convenience of both ordering and receiving goods at home and the lower prices made possible by eliminating the middlemen between him and his customers.

Greed and Glory on Wall Street: The Fall of the House of Lehman
by Ken Auletta
Published 28 Sep 2015

His tale told, Peterson wandered from our table to another, where he repeated his tale. Broyles and I were left buzzing about what a hell of a story this might make. For Lew Glucksman, who had won the power struggle with Peterson, sounded like a fascinating cross between a paranoid Captain Queeg and a pathetic soul like Dreiser’s Charles Drouet, the traveling salesman with pretensions of grandeur in Sister Carrie. Because I intruded on his conversation with Peterson, Bill Broyles had first crack at the story. But after thinking about it overnight, Broyles decided that a book on Vietnam had first claim on his time. Without a green light from Broyles, I would not have chased this story.

The Dealmaker: Lessons From a Life in Private Equity
by Guy Hands
Published 4 Nov 2021

Andrew made it clear that he had strong views about how he wanted to transform the firm, operationally and structurally. He would manage Terra Firma and take control of the deal flow. Justin, meanwhile, would take control of the portfolio businesses and work out how to get them working better. The two of them would run Terra Firma day-to-day. I would be its most active investor and travelling salesman. Within weeks of the second EMI trial ending in June 2016, we announced Andrew’s appointment. This was a new departure for me. I had not just acknowledged that trying to do everything myself was beyond me, I had also acted on that knowledge. The ‘Guy Factor’ was important for Terra Firma, but in the future it had to be about more than me.

Age of Anger: A History of the Present
by Pankaj Mishra
Published 26 Jan 2017

Society had now to be organized and regenerated in ways other than through the principles of ‘individualism’ – a word to which the Saint-Simonians gave wide currency through their criticism of the crisis of authority in France. The poet Alphonse de Lamartine, writing a hagiography of Joan of Arc during the bleak days of the Bourbon Restoration, hoped for a new spiritual community. Charles Fourier, a travelling salesman, claimed to be the new Messiah, who had unlocked the secret to universal harmony. Saint-Simon’s secretary, Auguste Comte, floated a religion of Positivism. Defining human progress as the transition from theological and metaphysical ways of thinking to the scientific or ‘positive’ one, and outlining a grandiose role for experts, Comte achieved widespread fame, and such unlikely disciples as Turkey’s modernizing autocrat, Atatürk

Hell's Angels: A Strange and Terrible Saga
by Hunter S. Thompson
Published 1 Jan 1966

They know, too, that in addition to the original charge—usually a traffic violation or disorderly conduct—they will probably be accused of resisting arrest, which can mean thirty days, a jail haircut and another fine of $150 or so. Now, after many a painful lesson, they approach small towns the same way a traveling salesman from Chicago approaches a known speed trap in Alabama. The idea, after all, is to reach the destination—not to lock horns with hayseed cops along the way. The destination this time was a big tavern called Nick’s, a noisy place on a main drag called Del Monte, near Cannery Row in downtown Monterey.

Woke, Inc: Inside Corporate America's Social Justice Scam
by Vivek Ramaswamy
Published 16 Aug 2021

My mom skips the extended story these days, but I like it, so I’m going to tell it. Don’t worry, it’ll all be about wokeness and capitalism in the end. That’ll be the second arranged marriage, the failed one. My mom Geetha and her sister were the apples of their parents’ eyes. Her father was a traveling salesman, and her mother was a homemaker. My grandparents spent every moment of their lives striving to give their daughters the things they’d never had, including a proper education. Both children became doctors. Thanks to a good test score, my mom got to skip undergrad and go straight to medical school at 16, both a blessing and a curse.

Antwerp: The Glory Years
by Michael Pye
Published 4 Aug 2021

His book was much more than a word list; it tried to help with buying, selling, and above all with demanding or avoiding payment of debts. It was helpful to a single man passing a woman in the street: how to find out her name, how to answer the even more important questions about her prospects: ‘She hath a good dowrie’ is translated into seven languages. It also helped the traveller, maybe the travelling salesman, when he’s staying at an inn. He learns how to say he doesn’t feel well, wants a warm towel round his head and his bed curtains pinned shut, and a serving girl follows to light him to bed. The stratagem does not work. His new ‘she friend’ refuses to snuff out the candle until she’s safely out of the room.

Einstein's Fridge: How the Difference Between Hot and Cold Explains the Universe
by Paul Sen
Published 16 Mar 2021

* * * Claude Shannon was born on April 30, 1916, in the town of Gaylord in the middle of a plateau that dominates the northern half of the US state of Michigan. With a population of around three thousand, the town was “small enough,” Shannon said later, that “if you walked a couple of blocks, you’d be in the countryside.” The main sources of employment were the lumber industry or the potato fields. Claude’s father, Claude senior, was an ex–traveling salesman from New Jersey who seemed to have a hand in every aspect of the town’s life. Active in the Methodist Church, he was also a Freemason, a probate judge, and ran a business that both sold furniture and arranged funerals. Shannon’s mother, Mabel, was intellectually gifted and determined. Despite growing up poor, she obtained a college degree at a time when it was hard for women to do so, and shortly after her children were born, Mabel became principal of Gaylord High School.

Traffic: Genius, Rivalry, and Delusion in the Billion-Dollar Race to Go Viral
by Ben Smith
Published 2 May 2023

Jonah was the child of a California divorce, and in BuzzFeed’s early days he cast himself as the divorced dad, dropping in on his second family once or twice a week, often unannounced. He came bearing gifts—new ideas, theories, gossip that he’d picked up a few blocks north, along with sacks of bánh mì sandwiches. He wasn’t there for the hard stuff. Wilkie and the team felt like the secret family of a traveling salesman. The real family, The Huffington Post, barely knew they existed. But he liked the rhythm of Jonah coming by to deposit ideas and then leaving him and a growing team to tinker and show their boss the results when he returned. Jonah’s wife, Andrea Harner, looked through résumés and helped scout talent.

A Fever in the Heartland: The Ku Klux Klan's Plot to Take Over America, and the Woman Who Stopped Them
by Timothy Egan
Published 4 Apr 2023

He borrowed money and never repaid it. He stole flasks of liquor from beneath the pillows of other men. His fellow soldiers hated him so much that they petitioned their superior officer to transfer him out of their unit. He was moved to Camp Devens, Massachusetts. After being discharged, he returned to Iowa, working as a traveling salesman. He told people he was a war hero, an officer, and a gentleman. In fact, he had never set foot overseas. It was then that he met his second wife, Violet, in 1920. Not long afterward, he borrowed a large sum of money from her mother. When he married Violet, he wrote on the Ohio marriage certificate that he had no prior wives.

With a Little Help
by Cory Efram Doctorow , Jonathan Coulton and Russell Galen
Published 7 Dec 2010

She must have seen the expression on his face because she made all those dimples and wrinkles and crowsfeet appear again and took his hand warmly. "You did very well," she said. "We'll talk again soon." She let go of his hand and knelt down to rub her hands over the floor. "In the meantime, you've got a pretty sweet gig, don't you?" # 2142 The Stupor Salesman turned out to feature Daffy Duck as a traveling salesman bent on selling something to a bank robber who is holed up in a suburban bungalow. Daffy produces a stream of ever-more-improbable wares, and is violently rebuffed with each attempt. Finally, one of his attempts manages to blow up the robber's hideout, just as Daffy is once again jiggling the doorknob.

Mining the Social Web: Finding Needles in the Social Haystack
by Matthew A. Russell
Published 15 Jan 2011

Or maybe you want to map out professionals in your network according to their job duties, or the socioeconomic bracket they’re likely to fit in based on their job titles and experience. Beyond the numerous options opened up by visualizing your professional network’s location data, geographic clustering lends itself to many other possibilities, such as supply chain management and Travelling Salesman types of problems. Mapping Your Professional Network with Dorling Cartograms Protovis, a cutting-edge HTML5-based visualization toolkit introduced in Chapter 7, includes a visualization called a Dorling Cartogram, which is essentially a geographically clustered bubble chart. Whereas a more traditional cartogram might convey information by distorting the geographic boundaries of a state on a map, a Dorling Cartogram places a uniform shape such as a circle on the map approximately where the actual state would be located, and encodes information using the circumference (and often the color) of the circle, as demonstrated in Figure 6-7.

Quiet: The Power of Introverts in a World That Can't Stop Talking
by Susan Cain
Published 24 Jan 2012

Everyone around you is speaking up and being social and going out.” “Isn’t there anyone on the quieter side?” I ask. They look at me curiously. “I couldn’t tell you,” says the first student dismissively. Harvard Business School is not, by any measure, an ordinary place. Founded in 1908, just when Dale Carnegie hit the road as a traveling salesman and only three years before he taught his first class in public speaking, the school sees itself as “educating leaders who make a difference in the world.” President George W. Bush is a graduate, as are an impressive collection of World Bank presidents, U.S. Treasury secretaries, New York City mayors, CEOs of companies like General Electric, Goldman Sachs, Procter & Gamble, and, more notoriously, Jeffrey Skilling, the villain of the Enron scandal.

The Bohemians: Mark Twain and the San Francisco Writers Who Reinvented American Literature
by Ben Tarnoff
Published 20 Mar 2014

He was a pale echo of his past self, doing anything he could to survive. His cratering finances had forced him onto the lecture circuit. He hated public speaking, but he had no choice. He needed money to survive. So while Twain put down roots in Hartford and embarked on the most creative period of his life, Harte wandered the continent like a traveling salesman, surviving blizzards and broken-down trains, sleeping in hotels and Pullman cars, playing to crowds from Toronto to Topeka. People came out to see a real live westerner. He could sense their disappointment the moment he took the stage. He wore fancy suits, not the coarse garb of his characters.

On the Road
by Jack Kerouac
Published 1 Jan 1957

The moment we were in the new Chrysler and off to New York the poor man realized he had contracted a ride with two maniacs, but he made the best of it and in fact got used to us just as we passed Briggs Stadium and talked about next year’s Detroit Tigers. In the misty night we crossed Toledo and went onward across old Ohio. I realized I was beginning to cross and recross towns in America as though I were a traveling salesman—raggedy travel ings, bad stock, rotten beans in the bottom of my bag of tricks, nobody buying. The man got tired near Pennsylvania and Dean took the wheel and drove clear the rest of the way to New York, and we began to hear the Symphony Sid show on the radio with all the latest bop, and now we were entering the great and final city of America.

The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management
by Alexander Elder
Published 28 Sep 2014

Recovering alcoholics attend meetings where they share their experiences with other recovering alcoholics, supporting each other in their sobriety. Any member can get a sponsor—another AA member whom he can call for support when he feels the urge to drink. AA was founded in the 1930s by two alcoholics—a doctor and a traveling salesman who began meeting to help each other stay sober. They developed a system that worked so well, others began to join them. AA has only one goal—to help its members stay sober. It doesn't ask for money, takes no political positions, and runs no promotional campaigns. AA keeps growing thanks only to word of mouth and owes its success only to its effectiveness.

What’s Your Type?
by Merve Emre
Published 16 Aug 2018

“I envisage a day when PRC would have several high-level double-headed monsters whose job it would be to roam the country picking up good ideas and hypotheses,” he joked at a board meeting. “These would then be turned over to a complex machinery capable of validating all hypotheses.” Chauncey would spend the years leading up to the conference doing precisely what Murphy had suggested, acting as a kind of traveling salesman, only in reverse. He would search far and wide for homegrown instruments of personality assessment and invite their creators to ETS, where his team of statisticians, psychometricians, and psychologists would vet their amateur designs and methods with the necessary scientific rigor. At any given moment in the 1960s, ETS had dozens of tests queued up for validation: Harrison Gough’s California Psychological Inventory (CPI), which Gough had designed at IPAR and was now trying to publish through ETS; Silvan Tomkins’s Picture Assessment Test (PAT), which measured the test taker’s affect by instructing him to describe figures with faces drawn as happy, sad, or angry, and which Tomkins later disavowed as pure nonsense; the Guilford-Zimmerman Temperament Survey (GZTS), which measured ten dimensions of personality: general activity, restraint, ascendance, sociability, emotional stability, objectivity, friendliness, thoughtfulness, personal relations, and masculinity vs. femininity.

System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich , Mehran Sahami and Jeremy M. Weinstein
Published 6 Sep 2021

He dated that effort to 1947—the year he published the celebrated Simplex algorithm for linear programming—and observed that “what seems to characterize the pre-1947 era was lack of any interest in trying to optimize.” In order to optimize, computer scientists often form mathematical abstractions of the world to create computational problems. A classic problem that nearly all computer scientists encounter at some point in their education is the “Traveling Salesman Problem” (more recently renamed the “Traveling Salesperson Problem”), or TSP. In this problem, one is given a list of cities to which the salesperson must travel, visiting every city only once before returning home. There are “costs” associated with traveling from city to city, and the point of the problem is to find a path for the salesperson that minimizes the total cost of the trip.

A Mind at Play: How Claude Shannon Invented the Information Age
by Jimmy Soni and Rob Goodman
Published 17 Jul 2017

Claude Sr. and Mabel were bright threads in Gaylord’s fabric. They were neighborly and active in the Methodist church. In downtown Gaylord, two well-known buildings were the work of Claude Sr.: the post office and the furniture showroom with the Masonic lodge tucked upstairs. Born in 1862 in Oxford, New Jersey, Claude Elwood Shannon Sr. was a traveling salesman who arrived in town just after the turn of the century and bet on its fortunes. He put down his stake—bought out the business dealing in furniture and funerals—and lived to see it pay. “Something which should be found in every home. Nothing more sanitary. The new styles are more attractive.

The Great Railway Bazaar
by Paul Theroux
Published 1 Jan 1975

I started drinking, assuming it was sundown, when it is no crime to drink or flirt with another man's wife; but the dim light had thrown me. It was mid-afternoon. I drank anyway, finished my half-bottle of gin, and started on the row of beer bottles the hotel proprietors had thoughtfully put in the room's refrigerator. I felt like a travelling salesman holed up in Baltimore with a full case of samples: what was the point in getting out of bed? Like the paranoid salesman, I began to invent reasons for not leaving the hotel, excuses I would deliver home instead of orders. Twenty-nine train trips turn the most intrepid writer into Willy Loman.

Chasing the Moon: The People, the Politics, and the Promise That Launched America Into the Space Age
by Robert Stone and Alan Andres
Published 3 Jun 2019

This wasn’t the first meeting on American soil of the two former colleagues. It was on a December evening in 1946 that Ley and von Braun had looked each other in the face for the first time in more than a decade and a half. Their post-war experiences in their adopted country had differed dramatically. Ley was the son of a traveling salesman; von Braun had been born into privilege, an aristocrat whose father was a politician, jurist, and bank official. Von Braun grew up with a sense of entitlement, which, when combined with his innate charisma, effortlessly opened doors. Physically, he could have been mistaken for a matinee idol; Ley once described von Braun’s appearance as “a perfect example of the type labeled ‘Aryan Nordic’ by the Nazis.”

Time of the Magicians: Wittgenstein, Benjamin, Cassirer, Heidegger, and the Decade That Reinvented Philosophy
by Wolfram Eilenberger
Published 14 Sep 2020

In 1922, when asked to list his book publications, he could give only one honest answer. Apart from his dissertation, which had enjoyed no attention whatsoever: none. The gap between his self-image and reality was therefore as wide as it could possibly be. Much the same applied to his academic ambitions. The year 1922 saw him become a restless traveling salesman of postdoctoral dissertations. There was hardly a major German university at whose door he had not knocked in one way or another. He thought he had a chance in Heidelberg, though he couldn’t say precisely which subject and with whom. Philosophy, German studies, sociology . . . Jaspers, Emil Lederer, Alfred Weber?

Your Money: The Missing Manual
by J.D. Roth
Published 18 Mar 2010

But compulsive spending (Curbing Compulsive Spending) just makes it harder to reach your goals, which will make you feel worse, not better. So fight the urge to practice "retail therapy." Don't let one problem snowball into two or three. Learn from your mistakes. Figure out where you went wrong. How did that traveling salesman sell you those overpriced steak knives? What can you do in the future to avoid doing the same thing again? This is a fine line to walk: You don't want to beat yourself up, but you don't want to keep making the same mistakes, either. Don't dig a deeper hole. Money spent is money spent. Just because you've already sunk $200 into a gym membership you never use doesn't mean you need to keep spending money on it.

The Big Roads: The Untold Story of the Engineers, Visionaries, and Trailblazers Who Created the American Superhighways
by Earl Swift
Published 8 Jun 2011

Long-distance touring was no longer reserved for well-heeled adventurers, no longer required goggles and a pistol; it became popular recreation for couples and families, who struck out from the cities in search of elbow room, fresh air, a closer acquaintance with nature. Popular culture rode shotgun. New characters became standards of jokes, books, movies—the traveling salesman, car broken down just up the road from the farm where he asks to spend the night; and the young woman stopped on the shoulder, staring incomprehending at the confusion of metal and rubber under her roadster's raised hood. And with this nomadic yen appeared new industries catering to the explorer's needs.

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

The best way to ensure this is to have a lot of potential participants on the other side of the transaction, and this is what popular O2O platforms offer. Calling All Disciplines In addition to those from economic theory, insights from several other disciplines are routinely incorporated into these platforms. The best routes for Uber drivers to take as they pick up and drop off overlapping fares, for example, is a variant of the classic “traveling salesman” problem in operations research, where the salesman has to figure out the shortest route that will take him through all the cities he’s responsible for once and only once. The huge amount of data that O2O businesses generate makes them fertile territory for machine learning, the information-heavy approaches to artificial intelligence that are now dominant, as we discussed in Chapter 3.

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy
by Sharon Bertsch McGrayne
Published 16 May 2011

Stein’s Paradox, however, works for comparisons between related statistics: the egg production of different chicken breeds, the batting averages of various baseball players, or the workers’ compensation exposure of roofing companies. Traditionally, for example, farmers comparing the egg production of five chicken breeds would average the egg yields of each breed separately. But what if a traveling salesman advertised a breed of hens, and each one supposedly laid a million eggs? Because of their prior knowledge of poultry, farmers would laugh him out of town. Bayesians decided that Stein, like the farmers, had weighted his average with a sort of super-or hyperdistribution about chicken-ness, information about egg laying inherent in each breed but never before considered.

The Joy of Clojure
by Michael Fogus and Chris Houser
Published 28 Nov 2010

The Author Online forum and the archives of previous discussions will be accessible from the publisher’s website as long as the book is in print. About the cover illustration The figure on the cover of The Joy of Clojure is captioned “The Confidence Man,” which, in 19th century France, could mean anything from a healer or medicine man to a card shark or money lender or traveling salesman. The illustration is taken from a 19th-century edition of Sylvain Maréchal’s four-volume compendium of regional dress customs published in France. Each illustration is finely drawn and colored by hand. The rich variety of Maréchal’s collection reminds us vividly of how culturally apart the world’s towns and regions were just 200 years ago.

The Dawn of Innovation: The First American Industrial Revolution
by Charles R. Morris
Published 1 Jan 2012

The white-collar pay advantage was particularly strong in the nineteenth century. Edward Tailer was twenty in 1850, when he started work as an assistant clerk for a New York dry goods importer while complaining of his $50 annual salary. But within two years, and after two job changes, he was making $1,000—a solid middle-class income. He then went on the road as a traveling salesman at $1,200 and had his own business when he was twenty-five.3 TABLE 6.1 Population by Region, 1790–1860a Americans were the best-fed people in the world—already by the mid-eighteenth century their nutritional intake was about the same as that of 1960s Americans. They were taller by several inches than the average European and commensurately heavier.

Entangled Life: How Fungi Make Our Worlds, Change Our Minds & Shape Our Futures
by Merlin Sheldrake
Published 11 May 2020

Zhu K, McCormack LM, Lankau RA, Egan FJ, Wurzburger N. 2018. Association of ectomycorrhizal trees with high carbon-to-nitrogen ratio soils across temperate forests is driven by smaller nitrogen not larger carbon stocks. Journal of Ecology 106: 524–35. Zhu L, Aono M, Kim S-J, Hara M. 2013. Amoeba-based computing for traveling salesman problem: Long-term correlations between spatially separated individual cells of Physarum polycephalum. Biosystems 112: 1–10. Zobel M. 2018. Eltonian niche width determines range expansion success in ectomycorrhizal conifers. New Phytologist 220: 947–49. ABOUT THE AUTHOR MERLIN SHELDRAKE is a biologist and a writer.

Posh Boys: How English Public Schools Ruin Britain
by Robert Verkaik
Published 14 Apr 2018

Stanhope is based on Sherriff’s own commander, Captain Godfrey Warre-Dymond (Marlborough), whom the playwright grew very close to during their war service in France. At the close of hostilities in 1919, Warre-Dymond left the army and fell on hard times. He was divorced by two wives and became indebted to loan sharks, forcing him to set out as a travelling salesman. He later asked Sherriff for money and a reference. Despite mounting casualties, the jingoism of the first year of the war was enthusiastically carried forward by the public schools. In fact, public schools remained stoically uncritical of the war. In October 1914 a motion proposed at a Shrewsbury School debate that ‘modern weapons have destroyed the romance of war’ was defeated by five votes.11 Those who died were heroes; those who questioned the motives or the tactics were branded cowards.

Mossad: The Greatest Missions of the Israeli Secret Service
by Michael Bar-Zohar and Nissim Mishal
Published 1 Jan 2010

Six months after arriving in Syria, Kamal Amin Tabet had become a well-known figure in the Damascus high society. He then decided to go abroad “for business.” He first flew to Argentina, where he met several of his Arab friends, then traveled to Europe, changed planes and identities, and, on a hot summer night, landed in Lod airport. Laden with presents, the “traveling salesman” arrived in his modest apartment in Bat Yam, where Nadia and Sophie were waiting for him. At the end of fall, Elie Cohen flew to Europe. A few days later, Kamal Amin Tabet arrived in Damascus. During his stay in Israel, his superiors in Aman had equipped him with a miniature camera, so that he could photograph sites and documents.

The Laundromat : Inside the Panama Papers, Illicit Money Networks, and the Global Elite
by Jake Bernstein
Published 14 Oct 2019

Erhard found work as a farmhand. That spring, a former Nazi propaganda official attempted to enlist him in the German Communist Party. Erhard rejected the offer. Two months later, a man who claimed to be a former high official in the Nazi Party came to the farm. He told the gullible Erhard that he toured Germany as a traveling salesman, all the while quietly building a network of secret agents. He offered the former corporal a choice: join a covert organization for the Soviets or one to restore fascist rule to Europe. Erhard pledged his support for the latter option. Why did ex-Nazis peddling dreams of future glory find Erhard such a promising mark?

Working: People Talk About What They Do All Day and How They Feel About What They Do
by Studs Terkel
Published 1 Jan 1974

It’s better than when I first went to sea. Where once a fella was glad to eat his three meals a day and get paid and get drunk, the young man feels they’re not paying him enough. Sometimes he has a chip on his shoulder. The big topic at sea is still exploits with women. Because there’s always loneliness. A traveling salesman, he has a means of picking up a phone. But a seaman is one month, two, three months before he’ll get a letter from his wife. I used to phone my wife three, four times every trip. In Calcutta I waited five hours to get a phone call through. If I didn’t get it through one night, I’d call again and wait three, four hours the next morning.

…

He makes some contribution to the Republican party, he always votes, and he reads the newspaper every day on the train, but the job is really it. After all those years, that’s his life. To ask whether he loves the company or not—it’s irrelevant. I had a series of jobs in the early fifties, after flunking out of college. I worked for a bank, sold insurance . . . I ended up with a good job as a traveling salesman for a business machine company. I was twenty-three years old and making ten thousand dollars a year. I probably could have made it seventeen thousand the next year. I could see it was going right up. I began to run into conflicts with my own feelings. I couldn’t accept the way my boss did busines or the way in which everybody in the field did business.

Alex's Adventures in Numberland
by Alex Bellos
Published 3 Apr 2011

The branch of mathematics that involves the counting of combinations, such as all the 1905 solutions to Sky TV’s faux Sudoku, is called combinatorics. It is the study of permutations and combinations of things, such as grids of numbers, but also, famously, the schedules of travelling salesmen. Let’s say, for instance, that I’m a travelling salesman and I have 20 shops to visit. In what order should I visit them so that my total distance is the shortest? The solution requires me to consider all the permutations of paths between all the shops, and is a classic (and extremely difficult) combinatorial problem. Similar problems arise throughout business and industry, for example in scheduling flight departure times at airports or having an efficient postal sorting system.

Jennifer Morgue
by Stross, Charles
Published 12 Jan 2006

We make it with one hundred percent natural ingredients, like the bottom tenth percentile of our sales force, the ones who don't get invited to this end of the marketing conference by the Queen Bee. Maybe Kitty's just a natural void, only too happy to be filled by the passing enthusiasm of the traveling salesman invocation, but somehow I doubt it: that kind of perfect vacuum doesn't come cheap. I scuff my left heel on the ground. If I switched it on, the Tiilinghast resonator that Brains installed in my shoe would let me see the sales-daemon riding her spine like a grotesquely bloated digger wasp, but I'd just as soon keep my lunch — and anyway the first law of demonoiogy is that if you can see it, // can see you.

Chasing the Scream: The First and Last Days of the War on Drugs
by Johann Hari
Published 20 Jan 2015

From the age of twelve, he knew that his father wouldn’t dream of carrying cash from the setting of the sun on Friday night to the end of the Sabbath the next day, so Arnold stole the money from his wallet, played craps, and won so often and so big he could always replace the cash12 without anyone’s noticing. By the time he ran away13 from home at seventeen to be a traveling salesman, Arnold knew he could crack card games better than anyone else. He was starting to regard himself as a superman, far above the dumb herd, explaining later: “There are two million fools14 to one brainy man.” He was the brainy man, and he was going to get his due from the fools. And the Brain—as he now insisted on being called—soon discovered the greatest truth of gambling: the only way to win every time is to own the casino.

Ada BlackJack: A True Story of Survival in the Arctic
by Jennifer Niven
Published 1 Jan 1990

Afterward, he joined the tour and traveled from April until August on the circuit, making sure to send his mother flowers for Mother’s Day from Pomona, California, an event that was reported excitedly in the New Braunfels paper. The tour took Galle through all the western states, and he tapped out vivid and observant notes on his prized Corona typewriter. Now, after a brief, unsatisfying stint as a traveling salesman for the Brown Rawhide Whip Company—which, at the very least, had kept him from having to attend college—nineteen-year-old Galle was resuming his career with Chautauqua. At the start of the 1921 spring Chautauqua tour, he had been on the road only a week, operating the projector as he had before, when he received orders to return to New Braunfels and take the position of Stefansson’s secretary.

Whole Earth: The Many Lives of Stewart Brand
by John Markoff
Published 22 Mar 2022

For the Fall 1969 issue, they printed sixty thousand copies and had four thousand subscribers. For many readers, stumbling upon something in the Catalog would prove a transformative experience, sending their lives careening in a new direction. That was the case with Jamis MacNiven, who had been a loyal reader for several years and was working as a traveling salesman selling milking machines to small dairies in Connecticut. One day the snowfall was so heavy he and his wife literally couldn’t find their home on the street where they lived. They decided that they had to move to California, where he’d gone to college. He moved back to the Bay Area and used the Catalog as an instruction manual to teach himself to become a general contractor.

First Friends: The Powerful, Unsung (And Unelected) People Who Shaped Our Presidents
by Gary Ginsberg
Published 14 Sep 2021

For the first six months you’ll wonder how the hell you got here, and after that you’ll wonder how the hell the rest of us got here.” Those words could not have been better timed, as Truman stood on the brink of gaining national prominence. Eddie Jacobson, meanwhile, stuck with the business he knew best: clothing. He took a job as a traveling salesman for a shirt and pajama company, spending the next twenty years mostly on the road, visiting his accounts in Missouri and Kansas. His wife, Bluma, assumed most of the responsibilities of raising their two daughters, but Eddie remained a doting, playful father. According to his oldest daughter, Elinor, her gregarious father “would’ve laughed his way through life” were it not for the financial pressures of supporting his young family, particularly after the onset of the Great Depression.

Servants: A Downstairs History of Britain From the Nineteenth Century to Modern Times
by Lucy Lethbridge
Published 18 Nov 2013

Crosland was almost hysterical at the thought of it, scoffing in 1905 at the suburban pleasures of ‘penny buses, gramophones, bamboo furniture, pleasant Sunday afternoons, Glory Songs, modern language teas, gold, tennis, high school education, dubious fiction, shillings worth of comic writing, picture postcards, miraculous hair restorers’.7 But the lower-middle class was growing in confidence and their hobbies, interests and customs would come to dominate popular culture. This was the age of the clerks, the boom time of the small shopkeepers, people like the family of the novelist V. S. Pritchett whose father, son of a Victorian nonconformist preacher in Yorkshire, was a travelling salesman, then a dealer in novelties and fancy handbags; ‘for suddenly money was about, commerce was expanding, there was a chance for the lower middle class. They would have a slice of the money the middles had sat so obdurately on for so long.’ Money, or at least the conspicuous making of it, no longer carried the taint of sin: ‘The difference between “goods” and “the good” was fading.’8 Inching up Cornhill’s ladder of the middle classes was ‘a younger son with a narrow berth in the Civil Service’ on a start-up income of £800 a year.

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

.… There were addressers, autographers, billers, calculators, cancelers, binders, coin changers, form printers, duplicators, envelope sealers and openers, folders, labelers, mail meters, pay roll machines, tabulators, transcribers, and other mechanical marvels.… A typewriting machine pounded out letters in forty different languages. A portable computing machine which could be carried by a traveling salesman was on exhibit.24 The 1930s: A New Form of Luddism Prevails Soon after the 1929 stock market crash, by 1930, the crash itself was often attributed to the surplus of goods made possible by new technology: When the climax was reached in the last months of 1929 a period of adversity was inevitable because the people did not have enough money to buy the surplus goods which they had produced.25 As noted above, fear of robots was not strong in most of the 1920s, when the word robot was coined.

It's Not TV: The Spectacular Rise, Revolution, and Future of HBO
by Felix Gillette and John Koblin
Published 1 Nov 2022

You’re the only one who can do this, Lombardo told him. Van Patten gave it more thought. Van Patten says that unlike in movies—where the director reigns supreme and, for decades, was singularly venerated by aficionados and scholars and video-store clerks—an episodic television director is like being “a substitute teacher” or “a traveling salesman.” Now HBO was in a serious jam, and the network had been so good to him over the years. He finally relented. “As an itinerant director, it can be a fairly lonely life, right?” he says. “It’s a little bit of a Willy Loman situation out there. You’re just bouncing from show to show, and you have really no skin in the game.

England: Seven Myths That Changed a Country – and How to Set Them Straight
by Tom Baldwin and Marc Stears
Published 24 Apr 2024

Evelyn Waugh’s Brideshead Revisited captures the resentment England’s upper classes felt between the wars. It chronicled how, having sacrificed the flower of their youth on the fields of Flanders, they were having to sell their land and lose their servants or their stately homes ‘so that things might be safe for the travelling salesman, with his polygonal pince-nez, his fat, wet handshake, his grinning dentures’.45 In a mid-market but equally snobbish category was the popular prose of H. V. Morton who set off in his Bullnose Morris to write a semi-fictional account of warm beer, country lanes and village greens surrounded by thatched cottages that became the bestselling In Search of England.

Vulture Capitalism: Corporate Crimes, Backdoor Bailouts, and the Death of Freedom
by Grace Blakeley
Published 11 Mar 2024

Analysts later found that the bank was recommending that investors purchase Enron’s stock despite being aware of its hidden debts.50 JPMorgan also helped Enron access billions in financing from investors, creating a convoluted financial structure designed to make the company appear less indebted, and more creditworthy, than it really was.51 When their role in the scandal came to light, Enron’s investors filed lawsuits against ten banks, including JPMorgan, which settled for $1 billion.52 One year after Enron’s collapse, an audit revealed that senior executives at the telecom company WorldCom had used illegal accounting techniques to inflate the company’s share price to the tune of $11 billion. The WorldCom scandal quickly overtook Enron as the largest accounting scandal in US history. WorldCom was founded by Bernard Ebbers, born in 1941 to a traveling salesman father, who took the money he had made from running a chain of motels and used it to invest in the telecoms sector.53 Ebbers, a former college basketball star, had been called the “Telecom Cowboy,” given his habit of walking around in jeans and cowboy boots. He had never excelled at school and, when pressed at his trial, he admitted that “to this day, I don’t know technology and I don’t know finance or accounting.”54 His strategy for WorldCom was to expand the company aggressively through acquisitions.

The Age of Spiritual Machines: When Computers Exceed Human Intelligence
by Ray Kurzweil
Published 31 Dec 1998

Now imagine certain problems that this Universe-sized supercomputer would be unable to solve even if we ran that computer until either the next big bang or until all the stars in the Universe died—about ten to thirty billion years. There are many examples of such massively intractable problems; for example, cracking encryption codes that use a thousand bits, or solving the traveling-salesman problem with a thousand cities. While very massive digital computing (including our theoretical Universe-sized computer) is unable to solve this class of problems, a quantum computer of microscopic size could solve such problems in less than a billionth of a second. Are quantum computers feasible?

Moby-Duck: The True Story of 28,800 Bath Toys Lost at Sea and of the Beachcombers, Oceanographers, Environmentalists, and Fools, Including the Author, Who Went in Search of Them
by Donovan Hohn
Published 1 Jan 2010

“Idle, drunken, vagabond,” one Australian author wrote in 1845, “he wanders about without any fixed object, cannot get employed by a whaler or anyone else, as it is out of his power to do a day’s work; and he is universally known as ‘the beach-comber.’ ” The local Cape Codders whom Thoreau met on his seaside rambles usually took him for a traveling salesman. What other explanation could there be for a vagabond with a walking stick and a knapsack full of books? By the 1980s, when Amos Wood published his how-to manuals, American beaches had become seaside playgrounds frequented not by dogs or crows but by the sun-worshipping masses. As for our vagrant beachcomber, he had become, in Wood’s definition, “any person who derives pleasure, recreation, or livelihood by searching ocean, lake, and river shores for useful or artful objects.”

The Idea Factory: Bell Labs and the Great Age of American Innovation
by Jon Gertner
Published 15 Mar 2012

Shannon Collection, Library of Congress. 8 Liversidge, “Profile of Claude Shannon.” Biographical facts relating to Shannon’s father are in a personal letter Shannon wrote, October 20, 1981, to Ms. Shari Bukowski: “[My father] was born in Oxford, New Jersey in 1862, came to Ovid, Michigan when very young and was raised and graduated there. He was a traveling salesman for a period and came to Gaylord shortly after 1900. There he bought a furniture and undertaking business, and, having confidence in Gaylord’s future, built the Shannon Block and Post Office building on Main Street.” Shannon Collection, Library of Congress. 9 Robert McEliece, Claude Shannon, Father of the Information Age, directed and written by Doug Ramsey, produced by Ramsey and Mike Weber; http://www.youtube.com/watch?

Confessions of a Wall Street Analyst: A True Story of Inside Information and Corruption in the Stock Market
by Daniel Reingold and Jennifer Reingold
Published 1 Jan 2006

It was a bit of a problem for CSFB, since its bankers had had the closest relationship with Chuck Noski’s predecessor, Dan Somers, whom AT&T’s CEO, Mike Armstrong, had just sent out to Denver to run its cable business. We met with Chuck and his team two days before Christmas to display our wares, much as a traveling salesman would sell pots and pans. Only what we were essentially selling was the ability to sell. Most of the pitch meetings I’ve ever been in have been the same, and this one was no different. The bankers proffered their services, and CSFB wireless analyst Cindy Motz and I talked about our research.

They Gave Me a Seafire
by Commander R 'Mike' Crosley Dsc Rn
Published 5 Aug 2014

Back at Henstridge I became the Station PGI. I fixed up a ‘private’ Miles Master II with all manner of cameras and gunsights, and took petrified pupils in the back to show them how upward and downward ‘Jesi’ should be done. Not only that, I was sent all over the place in England and Scotland as a travelling salesman, seeing very little of my immobile Wren as a result. Many of the pupils joining the FAA at this time in the war found it difficult to make quarter attacks. It over-loaded the human computer to have to judge distances, angles, approach speeds, convergences, line of target flight and deflection when shooting — and flying at the same time.

The Gamble: General David Petraeus and the American Military Adventure in Iraq, 2006-2008
by Thomas E. Ricks
Published 14 Oct 2009

His father, born in Ramallah, emigrated to New Ulm, Minnesota, in 1938. “It was ten thousand people of German descent and one Arab family,” Mansoor recalled. They moved to Sacramento, where he proudly remembers that his mother, a schoolteacher, won awards for designing an “open classroom” approach. His father was a traveling salesman. In high school, Mansoor was valedictorian, student body president, and head of the math club. He also would graduate first in his class at West Point in 1982. In late March, Ryan Crocker flew to Baghdad to become the U.S. ambassador, succeeding Zalmay Khalilzad. His arrival completed the most sweeping personnel turnover of the entire war, surpassing even the changes that came after the invasion when Franks and the chief of the Army, Gen.

The Great Railroad Revolution
by Christian Wolmar
Published 9 Jun 2014

In a way, this was not surprising. When it came to passenger travel, the railroads had it all, cornering every market since, for most journeys, there remained no viable alternative. Apart from a few trips that could be made by boat, either along the coast or on lakes, the railroads catered to everyone, “from the travelling salesman who was making his way through his territory in ten and twenty mile hops to the well-to-do family setting out in Pullman drawing room comfort for a tour of the great American West.”5 Although freight carriage did grow in this period, the marketing efforts of the railroad, together with the increase in population and greater prosperity generally, resulted in a far faster rate of growth for passenger traffic.

The Sinner and the Saint: Dostoevsky and the Gentleman Murderer Who Inspired a Masterpiece
by Kevin Birmingham
Published 16 Nov 2021

His father had sent him to work for an attorney when he finished his schooling. He spent about a year sleeping in a room adjacent to the lawyer’s apartment and taking his meals with the other clerks. He worked at a notary’s office (six months). He worked at a bank (two years). He worked as a traveling salesman for a wine and spirits company. He hated every job, hated his coworkers, hated the fact that he had given up reading; he had been used to devouring poetry and novels ever since his days of skipping school. His daily tasks were turning him into “a complete automaton,” he said. “Is he stupid?”

Sandy Hook: An American Tragedy and the Battle for Truth
by Elizabeth Williamson
Published 8 Mar 2022

“I wrote articles that appeared on Infowars and PrisonPlanet, but I didn’t get paid for them. The Southern Poverty Law Center calls American Free Press a hate group.[1] I got paid by them,” he told me. Johnson had known the conspiracy world since childhood. He grew up in the Los Angeles suburbs, son of a traveling salesman and a homemaker. “My mother was extremely conspiracy-oriented because she was involved in the evangelical movement and they were all doomsdayers,” he told me. “I would accompany my mother to church, and they’d be saying Jimmy Carter is the Antichrist.” As a child Johnson joined the Police Explorers, for kids interested in a law enforcement career.

Breaking News: The Remaking of Journalism and Why It Matters Now
by Alan Rusbridger
Published 14 Oct 2018

When Bloombergs set out to profile the new owners of the Telegraph, Aidan Barclay wrote back: ‘We do not consider our financial, business or charitable affairs to be of public interest as we are not answerable to shareholders, or indeed members of the public. We would prefer if you did not write about us at all.’3 David and Frederick Barclay – born 1934 – had grown up in West London, the sons of a travelling salesman. By the time they made their move on the Telegraph at the age of 70, they owned more than 50 companies around the world, including the Ritz hotel in London. In 2004 their empire was generating more than $7.5 billion in annual revenue and the businesses employed 40,000 people. The money initially came from buying and selling property, later expanding into shipping, mail order, hotels, car dealerships, retail and energy companies.

Kingdom of Olives and Ash: Writers Confront the Occupation
by Michael Chabon
Published 29 May 2017

“As an American citizen, I entered as a tourist, on a three-month visa.” Sam Bahour was born in Youngstown, in 1964. His mother is a second-generation Ohioan of Lebanese Christian descent; his father emigrated to the United States from the town of al-Bireh, then under Jordanian control, in 1957. After spending a few unhappy years working for relatives as a traveling salesman in the rural South (“Basically a peddler,” in Sam’s words, “selling cheap goods to poor people at like a two hundred percent markup; it really bothered him”), Sam’s father settled in Youngstown, with its sizable Arab population. He bought the first of a series of independent grocery stores he would own and operate over the course of his career, got married, became a citizen, had a couple of kids, worked hard, made good.

A World Beneath the Sands: The Golden Age of Egyptology
by Toby Wilkinson
Published 19 Oct 2020

For better or worse, the fate of the Antiquities Service and Egyptian Museum, and with them the direction of archaeology in Egypt, would henceforth be controlled by the people of the Nile Valley, not by foreigners from distant shores. Illustrations Insert 1.Napoleon Bonaparte, whose expedition to Egypt in 1798 laid the foundations for the birth of Egyptology. 2.Giovanni Battista Belzoni, travelling salesman, circus strongman and amateur archaeologist. 3.A stone bust of pharaoh Ramesses II, nicknamed the ‘Young Memnon’, which Belzoni succeeded in removing in 1816 and acquiring for the British Museum. 4.Jean-François Champollion, decipherer of hieroglyphics and founding father of Egyptology. 5.The temple of the goddess Hathor at Dendera; the zodiac from the ceiling of one of the roof chapels was removed in 1821 and taken to the Louvre. 6.A watercolour by David Roberts of the great temple at Abu Simbel, showing its appearance in the first half of the nineteenth century. 7.A wall decoration from one of the Tombs of the Nobles at Thebes; copying and recording such scenes was one of John Gardner Wilkinson’s major contributions to Egyptology. 8.Muhammad Ali, the Albanian mercenary who founded a dynasty and presided over the modernization of Egypt. 9.A statue of a seated scribe discovered by Auguste Mariette while he was searching for the Serapeum at Saqqara. 10.A relief of the queen of Punt, from the temple of Hatshepsut at Thebes, one of many monuments excavated by the Antiquities Service in the 1860s under Mariette’s direction. 11.The tomb of Mariette in the garden of the Egyptian Museum, Cairo. 12.Amelia Edwards, Victorian novelist, traveller, founder of the Egypt Exploration Society, and indefatigable champion of Egypt’s ancient heritage. 13.A painted mummy mask from Hawara; dug from the sand by Flinders Petrie, this and other portrait masks shed new light on Egyptian culture during the Ptolemaic and Roman periods. 14.The Hypostyle Hall in the temple of Amun-Ra at Karnak; its partial collapse in 1899 brought home the need for better protection of Egypt’s monuments. 15.The Egyptian Museum, Cairo; designed by a Frenchman, its facade bears the names of the great European Egyptologists of the nineteenth century. 16.The great temple at Abu Simbel: for many travellers, the ultimate destination and the highlight of a Nile cruise. 17.A triad of King Menkaura flanked by two goddesses; one of the masterpieces of ancient Egyptian sculpture discovered by George Reisner at Giza. 18.The portable furniture (bed, chair and canopy) of gilded wood made for Queen Hetepheres in the fourth dynasty and unearthed by Reisner some 4,400 years later. 19.The gilded mask of Tjuyu from the Valley of the Kings; discovered in 1905 by Theodore Davis, it formed part of the greatest treasure found in Egypt to that date. 20.Evelyn Baring, Lord Cromer, de facto ruler of Egypt for a quarter of a century until his retirement in 1907. 21.The mortuary temple of Hatshepsut at Deir el-Bahri, western Thebes, one of the sites where Howard Carter cut his teeth as a young archaeologist. 22.The painted limestone bust of Nefertiti, icon of ancient art, discovered in 1912 by Ludwig Borchardt in the ruins of the abandoned capital city of Amarna. 23.The golden mask of Tutankhamun; thanks to Howard Carter and Lord Carnarvon, a once-obscure boy-king has become the most famous of all Egyptian pharaohs.

Snakes and Ladders: The Great British Social Mobility Myth
by Selina Todd
Published 11 Feb 2021

The number of degree courses in computing and information technology rose. And in 1988 the Conservatives introduced City Technology Colleges – a new breed of state school funded by private providers and a signal that technological work offered bright prospects. One who heeded the call was Paul Cosgrove. Born in Cheshire in 1964, Paul was the son of a travelling salesman and a cleaner. His parents’ marriage was unhappy and, like Graham Knowles, Paul felt stifled by their desire to keep up appearances and preoccupation with social status. At his comprehensive school he met ‘kids who appeared “privileged”, who lived in bigger homes in leafy roads’. Paul set his sights on achieving ‘their lifestyle’.19 Paul Cosgrove saw education as his route to achieve this.

Trust: The Social Virtue and the Creation of Prosperity
by Francis Fukuyama
Published 1 Jan 1995

In traveling through the United States, Weber observed that many businessmen would introduce themselves as some kind of Christian believer, in order to establish credentials for honesty and trustworthiness. In one case, On a long railroad journey through what was then Indian territory, the author, sitting next to a traveling salesman of “undertaker’s hardware” (iron letters for tombstones), casually mentioned the still impressively strong church-mindedness. Thereupon the salesman remarked, “Sir, for my part everybody may believe or not believe as he pleases; but if I saw a farmer or a businessman not belonging to any church at all, I wouldn’t trust him with fifty cents.

Beautiful Architecture: Leading Thinkers Reveal the Hidden Beauty in Software Design
by Diomidis Spinellis and Georgios Gousios
Published 30 Dec 2008

It looked something like Figure 2-1 (it’s a simplified version, with details changed to protect the guilty). Then it became clear. We had all but drawn a map of the London Underground. It even had the circle line. Figure 2-1. The Messy Metropolis “architecture” This was the kind of system that would vex a traveling salesman. In fact, the architectural similarity to the London Underground was remarkable: there were many routes to get from one end of the system to the other, and it was rarely obvious how best to do so. Often a destination was geographically nearby but not accessible, and you wished you could bore a new tunnel between two points.

The World of Caffeine: The Science and Culture of the World's Most Popular Drug
by Bennett Alan Weinberg and Bonnie K. Bealer
Published 5 Dec 2000

There is nothing new about the awareness of caffeine intoxication, for it has been well described as a psychiatric disorder for more than a hundred years. Yet despite long-standing recognition, which perhaps began with the coining of the Arabic word “marqaha” or “caffeine high,” in the sixteenth century, there is, even today, little information available about its prevalence or incidence. In 1896 J.T.Rugh17 reported the case of a traveling salesman who had resorted to excessive coffee consumption to maintain an intense pace of work and was troubled by nervousness, involuntary contractions in the arms and legs, a sense of impending danger, and sleep disturbance. Similar reports of caffeine intoxication first appear in medical literature from the middle of the 1800s, and the profile of common symptoms remains unchanged today.

Boyd: The Fighter Pilot Who Changed the Art of War
by Robert Coram
Published 21 Nov 2002

For the living room, where her growing family would spend long winter evenings, she bought a cast iron gas heater, and to brighten those winter evenings she covered the walls with pink paper. The living room’s other feature was a black Steinway, a gift from Hubert. Elsie liked to play it, having taught piano before she was married. For Erie in the late 1920s it was a comfortable middle-class home. Hubert Boyd was a traveling salesman for HammerMill Paper Company, and a job at the “HammerMill” was both prestigious and well-paying. Elsie Boyd was the daughter of Julia and Rudolph Beyer. Her father farmed a small piece of land just south of Erie. Elsie was a German Presbyterian, an ample woman with enormous pride and the self-confidence to freely express her beliefs, many of which were synthesized in pithy expressions such as “The world is not the way you want it to be.

Bernie Madoff, the Wizard of Lies: Inside the Infamous $65 Billion Swindle
by Diana B. Henriques
Published 1 Aug 2011

Although he had absorbed his father’s prickly preference for being his own boss, Bernie had no desire to become a lawyer. As a child, he expected to join his father’s sporting goods business and eventually run it, but after the bankruptcy filing in 1951, he decided he’d like to sell sports equipment as a “manufacturer’s rep”, a sort of travelling salesman who wouldn’t be tied down to the grey-flannel life of a law firm or corporate office. Most of the schoolmates who have offered their memories of Madoff’s teenage and university years in the 1950s remembered the scrappy lawn-sprinkler installation business he got rolling in secondary school, after his father’s businesses failed.

Lonely Planet Nicaragua (Travel Guide)
by Lonely Planet , Alex Egerton and Greg Benchwick
Published 30 Jun 2013

In Nicaragua not only do you have awe-inspiring volcanic landscapes to gaze at through the windows, inside the bus is a whole world of entertainment. And we’re not talking about the soft-rock soundtrack or classic Steven Seagal marathon on the tiny TV. The real entertainment on Nicaragua’s battle-scarred school buses comes from the traveling salesman, particularly those hawking cut-priced medicines and ointments. Need to get smarter before arriving in Rivas? No problem. Hair loss issues? There’s an elixir for that too. And you probably didn’t even know that in addition to your backpack, you were carrying around all those parasites on the unnecessarily graphic images on the full-color poster.

The World's Banker: A Story of Failed States, Financial Crises, and the Wealth and Poverty of Nations
by Sebastian Mallaby
Published 24 Apr 2006

“I always had this image of a brownstone, with ten clients and a piano upstairs where I could practice, and a cello, and one or two people working on my non-business things,” Wolfensohn said later.19 In interviews given at the time, Wolfensohn made another point as well: He was sick of acting as other people’s adviser, and wanted instead to take stakes in natural-resource and real-estate deals, using his own money.20 The one thing he did not want to do was to continue to make his living the way he had done so far. “I’ve had 20 years of being a traveling salesman,” he said. “That’s enough . . . I don’t want to make a new business call ever again.”21 The firm that Wolfensohn created lay somewhere between his past and his imagined future. He did not acquire a brownstone; the upstairs-downstairs layout was no good for an office, and he deemed the rents excessive.22 Instead, he set up shop in a suite above Park Avenue; JAMES D.

Culture and Imperialism
by Edward W. Said
Published 29 May 1994

Camus’s writing is informed by an extraordinarily belated, in some ways incapacitated colonial sensibility, which enacts an imperial gesture within and by means of a form, the realistic novel, well past its greatest achievements in Europe. As locus classicus I shall use an episode near the end of “La Femme adultere” when Janine, the protagonist, leaves her husband’s bedside during a sleepless night in a small hotel in the Algerian countryside. A formerly promising law student, he has become a travelling salesman; after a long and tiring bus journey the couple arrives at their destination, where he makes the rounds of his various Arab clients. During the trip Janine has been impressed with the silent passivity and incomprehensibility of the native Algerians; their presence seems like a barely evident natural fact, scarcely noticed by her in her emotional trouble.

The Great Influenza: The Story of the Deadliest Pandemic in History
by John M. Barry
Published 9 Feb 2004

An epidemic of influenza is spreading or being spread, (we wonder which).”… Those and similar charges created enough public sentiment to force Public Health Service laboratories to waste valuable time and energy investigating such possible agents of germ warfare as Bayer aspirin. Parsons’s territory bordered on Alabama and there a traveling salesman from Philadelphia named H. M. Thomas was arrested on suspicion of being a German agent and spreading influenza—death. Thomas was released, but on October 17, the day after influenza had killed 759 people in Philadelphia, his body was found in a hotel room with his wrists cut—and his throat slit.

In the Plex: How Google Thinks, Works, and Shapes Our Lives
by Steven Levy
Published 12 Apr 2011

Though Charlie’s in Building 40 was the most spacious café, with the broadest menu, food-snob Googlers regarded it as a tourist attraction; it was the place Googlers took their guests to, and it was often populated by people attending conferences on campus. The other eateries were more like restaurants beloved by a neighborhood clientele. Walking around Google offices, you would occasionally see charts to help a product group keep track of their lunch venues: a foodie version of the celebrated Traveling Salesman Problem. At all the cafés, the menu choices reflected a proscriptive view of nutrition. Google chef Josef Desimone once told a magazine, “We’re here to educate employees on why agave-based soda is better for you than Coca-Cola.” Café 150 limited its menus to items grown or produced within 150 miles of campus.

Algorithms in C++ Part 5: Graph Algorithms
by Robert Sedgewick
Published 2 Jan 1992

Hell, “On the history of the minimum spanning tree problem,” Annals of the History of Computing, 7 (1985). D. B. Johnson, “Efficient shortest path algorithms,” Journal of the ACM, 24 (1977). D. E. Knuth, The Art of Computer Programming. Volume 1:Fundamental Algorithms, third edition, Addison-Wesley, 1997. J. R. Kruskal Jr., “On the shortest spanning subtree of a graph and the traveling salesman problem,” Proceedings AMS, 7, 1 (1956). K. Mehlhorn, Data Structures and Algorithms 2:NP-Completeness and Graph Algorithms, Springer-Verlag, 1984. C. H. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity, Prentice-Hall, 1982. R. C. Prim, “Shortest connection networks and some generalizations,” Bell System Technical Journal, 36 (1957).

Aerotropolis
by John D. Kasarda and Greg Lindsay
Published 2 Jan 2009

Her neighbors—or at least the ones she’d met— were fellow transplants from Maryland and Boston. She had moved here for the lifestyle but traveled often for work as an executive coach. It mattered to her that DIA was an eighteen-minute commute on the highway. Stapleton’s brownstones seemed to collect the self-motivated. Living on her block were a traveling salesman raised on Long Island, a stay-at-home software engineer, and a graphic designer who proudly informed me she had been a flight attendant on America West for twenty-five years. If Holmes had one complaint about Stapleton, it wasn’t that it sometimes felt like Disneyland, as her neighbors put it.

The Splendid and the Vile: A Saga of Churchill, Family, and Defiance During the Blitz
by Erik Larson
Published 14 Jun 2020

From January through May, the rate at which RAF single-engine fighters intercepted German aircraft increased fourfold. On the ground, too, there was a different attitude, this in tune with the overall feeling that England had shown beyond a doubt that it could endure Hitler’s onslaught; now it was time to return the favor. A Mass-Observation diarist who worked as a traveling salesman wrote in his diary, “The spirit of the people seems to be moving from passive to active and rather than cower in shelters they prefer to be up and doing. Incendiaries seem to be tackled as though they were fireworks and tackling fires in top rooms with stirrup pumps is just part of the evening’s work.

Buffett
by Roger Lowenstein
Published 24 Jul 2013

Shortly before the May board meeting, Buffett was secretly named a director.25 On the morning of his big takeover, Buffett flew into New Bedford, crew-cut, his suit tightly buttoned and noticeably creased, with an attaché and an oversized valise and something of the appearance of a down-at-the-heels but earnest traveling salesman. Buffett made for the office on Cove Street, and Seabury emerged from the ivory tower for a final time. Calling the meeting to order, Seabury read through the agenda, showing nothing. And then Seabury Stanton resigned. It merely remained to pass the poison cup to Jack. Without a word, the two stormed out of the elegant wood-paneled boardroom.

The Victorian City: Everyday Life in Dickens' London
by Judith Flanders
Published 14 Oct 2012

In his youth, Dickens described ‘lounging one evening, down Oxford-street’; later, as a magazine editor, he recommended to his journalists that they actively choose their subjects in the city that he still found a daily novelty: ‘Suggest to him Saturday night in London, or London Markets…the most extraordinary men…the most extraordinary things…the strangest Shows – and the wildest’. In the decade before his death, he assumed the guise of ‘The Uncommercial Traveller’ (a ‘traveller’ being a travelling salesman), ‘always on the road…I travel for the great house of Human Interest Brothers…I am always wandering here and there…seeing many little things, and some great things, which, because they interest me, I think may interest others.’ Previous essays about London, by authors such as Charles Lamb and Leigh Hunt, had been filled with history, with learned asides, with a great panoply of education.

Reminiscences of a Stock Operator
by Edwin Lefèvre and William J. O'Neil
Published 14 May 1923

And he shook his head. “No, you don’t. You wouldn’t get enough fun out of it. You certainly do not work merely to add a few more dollars to your bank account and you are not in Wall Street because you like easy money. You get your fun some other way. Well, same here.” 12.5 Although it may seem strange that a traveling salesman would show up in a brokerage office to hawk a set of Sir Walter Scott books, this was not an unfamiliar phenomenon at the time. Itinerant peddlers were common in the United States as early as the pre-Revolutionary War era, but the concept of salesmen working on commission for a large company really only developed in the post-Civil War era.

Drink: A Cultural History of Alcohol
by Iain Gately
Published 30 Jun 2008

Toward the end of the film Marcello has acquired the same habit, and becomes aggressive and irrational when under the influence. La Dolce Vita also illustrates the penetration of foreign drinks in Roman society. Its fashionable characters drink vodka, gin fizzes, and named brands of scotch whisky. In contrast, the unfashionable, such as Marcello’s father, a traveling salesman, stick to traditional stimulants such as champagne when they want to celebrate. There are, finally, hints of the revival in Italian wines that occurred in the decade following the release of the film. In response to EEC legislation, a quality regime was introduced—the Denominazione d’Origine Controllata (Denomination of Controlled Origin or DOC), loosely based on the French AOC model, which defined regions, grapes, and production methods for wines such as Barolo and Barbaresco from Piedmont, and Brunello di Montalcino from Tuscany.

Piracy : The Intellectual Property Wars from Gutenberg to Gates
by Adrian Johns
Published 5 Jan 2010

As pirate king, he often used the alias John Fisher, coined apparently because he had at one point been a fishmonger of some kind; and he also had a number of other monikers, among them “the colonel.” His mother had been a printer, and he had served an apprenticeship, probably in her house. He was experienced in the business, having worked in newspapers for fifteen years. But since then he had tried out various other trades, including that of traveling salesman. He had once been imprisoned for embezzlement, which he defended as appropriating what were rightfully his wages when his erstwhile employer went bankrupt. Since the 1902 law, however, he had seen an opportunity to earn a windfall from his original trade, and had become the nation’s leading music pirate.

Berlin 1961: Kennedy, Khrushchev, and the Most Dangerous Place on Earth
by Frederick Kempe
Published 30 Apr 2011

Two days later, on May 19, the Kennedy administration officially announced what the press had been reporting from leaks for several days: The president would meet with Khrushchev in Vienna on June 3 and 4 after seeing de Gaulle in Paris. Western European and U.S. commentators worried that a weakened president was heading to Vienna at a disadvantage. The intellectual weekly Die Zeit compared Kennedy to a traveling salesman whose business had fallen on bad times and who was hoping to improve his prospects by negotiating directly with the competition. In its review of European opinion, the Wall Street Journal said Kennedy was projecting the “strong impression…of a faltering America desperately trying to regain leadership of the West in the Cold War.”

The Mask of Sanity: An Attempt to Clarify Some Issues About the So Called Psychopathic Personality
by Hervey Cleckley
Published 1 Jan 1955

Once after he had remained away for two full nights, the police were called. He was picked up at a dive where slot machines and other gambling devices furnished amusement to mill workers and rustics from the surrounding countryside. He had previously driven over to a nearby city with a casual acquaintance who had gone there in the course of his duties as a traveling salesman. The fact that he had not taken the trouble to inform his mother of his intentions seemed to him a trifle in comparison with what he indignantly regarded as the meddling of his family in asking the police to look for him. He showed little regard for the convenience or the property of others, sometimes misappropriating things which he apparently did not mean to keep or sell but which he put to his own use without ordinary regard for the trouble inflicted on relatives, friends, or strangers.

The Enlightened Capitalists
by James O'Toole
Published 29 Dec 2018

His formal education ended when he turned sixteen and began work as an apprentice to his father, who at the time was in the process of closing his grocery store to concentrate on the more profitable wholesale side of the food business. Within two years William—a charmer with the skills of a natural politician—was James’s most successful traveling salesman. William also seems to have been born with an inordinate capacity for uxorious love: when he was twenty-one, he and his childhood schoolmate, Elizabeth Hulme, were betrothed, and theirs would become the happiest of marriages. For the next forty-one years, until Elizabeth’s death in 1913, the Levers were a doting and devoted couple, a model of Victorian connubial bliss.

Americana: A 400-Year History of American Capitalism
by Bhu Srinivasan
Published 25 Sep 2017

Now an America being built for cars, with billions of dollars committed to roads, needed the speed of the assembly line applied to roadside dining. Like Colonel Sanders, Ray Kroc stumbled into his opportunity—one that would become arguably the most widely known symbol of postwar Americana—through the winds of economic tumult and years of hard experience. He had started in the 1920s as a traveling salesman selling housewares to housewives door to door. He graduated to selling a new innovation, the disposable paper cup, for the Lily-Tulip Cup Company. Soda fountains and ice cream parlors served milkshakes and sodas in glasses that needed to be washed after every use. This was initially a difficult sale as vendors were reluctant to pay 1.5 cents for a disposable cup.

Americana
by Bhu Srinivasan

Now an America being built for cars, with billions of dollars committed to roads, needed the speed of the assembly line applied to roadside dining. Like Colonel Sanders, Ray Kroc stumbled into his opportunity—one that would become arguably the most widely known symbol of postwar Americana—through the winds of economic tumult and years of hard experience. He had started in the 1920s as a traveling salesman selling housewares to housewives door to door. He graduated to selling a new innovation, the disposable paper cup, for the Lily-Tulip Cup Company. Soda fountains and ice cream parlors served milkshakes and sodas in glasses that needed to be washed after every use. This was initially a difficult sale as vendors were reluctant to pay 1.5 cents for a disposable cup.

Reaganland: America's Right Turn 1976-1980
by Rick Perlstein
Published 17 Aug 2020

At 5 a.m. the next morning, an icy wind still howling, he got a call from Howard Phillips, who wondered if it was necessary for him to show up, too. Phillips was a dear friend, so McAteer spoke frankly: “Howard, I think it’s absolutely essential.” McAteer was the nascent Christian right’s most indefatigable organizer. A traveling salesman in Colgate-Palmolive’s toilet articles division who had worked his way up to sales manager for the entire Southeast, in the early 1970s he had pulled his children out of the public schools because, he said, “they were bringing in the ‘sensitivity training program.’ That was where teachers and students sit around and feel all over and excite the basic instincts of people.”

…

That was where teachers and students sit around and feel all over and excite the basic instincts of people.” His next step up the corporate ladder brought him to California, where he volunteered with Wycliffe Bible Translators, making contacts with evangelicals all over the world. In 1976, John Conlan and Bill Bright offered him a new job as their traveling salesman for their efforts turning preachers into precinct organizers. After it folded, McAteer became field director for Howard Phillips’s Conservative Caucus. But he missed working with religious folk. “So what I did was I got on my pony and started out seeing these high-powered preachers.” That was what he was up to now.

The Warmth of Other Suns: The Epic Story of America's Great Migration
by Isabel Wilkerson
Published 6 Sep 2010

“To think that I had come all the way from the Deep South,” he would say many years later, “out here to this Land of Milk and Honey and Opportunity and Intelligence, to find that one of my own color was disrespecting me.” He endured such slights for years, and drove all over South Central Los Angeles doing perfunctory examinations and collecting urine as if he were a traveling salesman and not a surgeon with military awards, and he did it because he had to. “That’s the cut that you took to get your foot in the door,” he would say years later. The ready-made clientele of old Louisianans he had imagined in his more cocksure moments in Monroe did not materialize. The people were there, alright.

The Meritocracy Trap: How America's Foundational Myth Feeds Inequality, Dismantles the Middle Class, and Devours the Elite
by Daniel Markovits
Published 14 Sep 2019

Part Three A New Aristocracy SEVEN A COMPREHENSIVE DIVIDE William Jefferson Clinton and George Walker Bush were born within fifty days of each other in the summer of 1946 and would eventually become the forty-second and forty-third presidents of the United States. Although both men reached the presidency, they came from different segments of midcentury society. Bill Clinton’s family was middle class. His father was a traveling salesman who died in an auto accident just before Clinton was born. His mother returned home to study nursing (before remarrying, to a car dealer), and the young Clinton was raised by his grandparents, who ran a small grocery store. George Bush’s family, by contrast, was unquestionably rich, even patrician.

The Portable Atheist: Essential Readings for the Nonbeliever
by Christopher Hitchens
Published 14 Jun 2007

The American John Saxe, best known for his verse about the blind men and the elephant, wrote a seventeen-stanza poem about the Wanderer. British poet Caroline Elizabeth Sarah Norton’s forgettable “Undying One” runs to more than a hundred pages. Oliver Herford, an American writer of light verse, in “Overheard in a Garden” turns the Wanderer into a traveling salesman peddling a book about himself. “The Wandering Jew” (1920) by Edwin Arlington Robinson, is surely the best of such poems by an American writer. Charles Timothy Brooks (1813–1883) was a New England Unitarian minister as well as a prolific versifier and translator of Goethe and other German poets.

Powerhouse: The Untold Story of Hollywood's Creative Artists Agency
by James Andrew Miller
Published 8 Aug 2016

In West L.A., there was a police department—it’s still there—on Perdue Avenue, and they were aware of us. Both my parents escaped Nazi Germany in 1939, and though I was born in America, they weren’t savvy about American youth culture. My father knew only a little of what was going on with me then, because he was a traveling salesman and was on the road four out of every five weeks. But my mother knew all of it. I got arrested a lot. A number of my friends went to jail. Once I beat up a guy in front of his son. Of all the things in my life, I think about that all the time. I wish I could find these people and beg for their forgiveness; it really shamed me.

Paris 1919: Six Months That Changed the World
by Margaret Macmillan; Richard Holbrooke; Casey Hampton
Published 1 Jan 2001

Together they formed a reverse L, with the richer, more developed province of Wallachia running east–west along the south side of the Transylvanian Alps, and Moldavia to the east of the Carpathians. In 1866 they had gained their own German prince, later King Carol, who had dodged the Austrian attempts to stop him by taking a Danube steamer disguised as a traveling salesman. His wife was a famous mystic who wrote poetry and romances under the pen name Carmen Sylva. The Rumanians themselves were the Neapolitans of central Europe. Both sexes loved strong scents. Among the upper classes, women made up heavily, and men rather more discreetly, but even so the military authorities had to restrict the use of cosmetics to officers above a certain rank.

The Bonfire of the Vanities
by Tom Wolfe
Published 4 Mar 2008

He despaired of this evening and of this life. Why hadn’t he gone to Knoxville six months ago? A little Georgian house, a leaf-blowing machine, a badminton net in the backyard for Campbell…But no! He had to tag along behind this walnut-eyed German, heading for the home of some overbearingly vulgar people named Bavardage, a glorified traveling salesman and his wife. Sherman said to the doorman, “The Bavardages’, please.” He hit the accented syllable hard, so that no one would think he had paid the slightest attention to the fact that the noble one, Baron Hochswald, had said the same thing. The baron, the blonde, Judy, and Sherman headed for the elevator.

Presidents of War
by Michael Beschloss
Published 8 Oct 2018

Nearsighted, he was kept by his hardscabble parents out of contact sports because if he broke his eyeglasses, they could not easily afford a replacement. Truman would later say that as a result he read every book in the Independence public library. His favorite, however, was a set of gilt-edged volumes, published in 1895, that Martha had bought from a traveling salesman, called Great Men and Famous Women—on the presumption that females could never be great, only famous—which offered brief profiles, by sundry authors, of over two hundred eminent figures from Nebuchadnezzar to Sarah Bernhardt. Truman particularly admired the generals—Hannibal, Andrew Jackson, and Martha’s hero Robert E.

Money and Power: How Goldman Sachs Came to Rule the World
by William D. Cohan
Published 11 Apr 2011

Still in its infancy at the start of the twentieth century, the task of raising capital—dubbed “underwriting”—became one of the most crucial roles Wall Street would perform for corporate clients eager to expand their workforces and their factories, and led to the creation of American capitalism, one of the country’s most important exports. Henry Goldman, who ironically had dropped out of Harvard without a degree because he had trouble seeing, had a vision of Goldman Sachs as a leading securities underwriter. He had been a traveling salesman after leaving Harvard but had joined the family business at age twenty-eight and would help lead a transformation of the firm into the underwriting business, which meant taking calculated risks for short periods of time by buying the debt or equity securities of its corporate clients before turning around and quickly selling these securities to investors who had been previously identified and were eager to buy them, assuming they had been priced correctly.

Theory and Practice of Group Psychotherapy
by Irvin D. Yalom and Molyn Leszcz
Published 1 Jan 1967

Eugene O’Neill illustrated this in dramatic form in the play The Iceman Cometh.55 A group of derelicts live, as they have for twenty years, in the back room of a bar. The group is exceedingly stable, with many well-entrenched group norms. Each man maintains himself by a set of illusions (“pipe dreams,” O’Neill calls them). One of the most deeply entrenched group norms is that no members challenge another’s pipe dreams. Then enters Hickey, the iceman, a traveling salesman, a totally enlightened therapist, a false prophet who believes he brings fulfillment and lasting peace to each man by forcing him to shed his self-deceptions and stare with unblinking honesty at the sun of his life. Hickey’s surgery is deft. He forces Jimmy Tomorrow (whose pipe dream is to get his suit out of hock, sober up, and get a job “tomorrow”) to act now.

Behave: The Biology of Humans at Our Best and Worst
by Robert M. Sapolsky
Published 1 May 2017

* As an aside, there has been incredibly interesting work concerning emergent properties of the brain that helps explain how the different regions wire up in the developing brain in an optimal way that minimizes the amount (and thus “cost”) of axonal projections needed. For aficionados, the things the developing brain does bear some resemblance to some approaches used for the Traveling Salesman Problem. * An implication of these definitions is that the same molecule can serve as either a neurotransmitter or a hormone in different parts of the body. Also (minutia warning), sometimes hormones have “paracrine” effects, influencing cells in the gland in which they were secreted

State of Emergency: The Way We Were
by Dominic Sandbrook
Published 29 Sep 2010

For a new generation of ambitious white-collar workers, often boys from working-class homes who owed their success to a grammar-school education, the old values of deference, hierarchy and organizational loyalty held little appeal. Writing in the Radio Times in 1968, one social analyst described a typical example: a working-class boy who had passed his eleven-plus, left school to join an office equipment firm, became a travelling salesman and bought a suburban home with all the trimmings, including a pink plastic pelican on the manicured front lawn. Married at 27, he had joined the local Conservative Party and sent his children to private nursery schools, and he had no intention of resting on his laurels. Thrusting, ambitious, he was precisely the kind of man Edward Heath admired and that Reggie Perrin hated, and he was a common archetype in the popular culture of the 1970s.

The Rise and Fall of American Growth: The U.S. Standard of Living Since the Civil War (The Princeton Economic History of the Western World)
by Robert J. Gordon
Published 12 Jan 2016

In the beginning of the nineteenth century, “payment of debts was a virtue and inability to pay them a mortal sin.”1 Credit was developed earlier and more freely in the United States than in many European countries, in part because the typical American farmer (outside the south) owned his own land, whereas in Europe, tenant farming was more common, with large estates owned by upper-class families through inheritance over generations. Whereas in Europe the avaricious lender faced a social stigma for ideological reasons, usually religious, in the United States the borrower experienced the stigma, being considered a “penniless failure.”2 The traveling salesman supplemented the role of the country store in providing credit to isolated farms and small towns alike. Finance for these salesmen was provided by wholesalers who were eager to sell their goods in the remote parts of rural America, and the back-up finance ultimately traced its way back to Chicago or New York.

American Prometheus: The Triumph and Tragedy of J. Robert Oppenheimer
by Kai Bird and Martin J. Sherwin
Published 18 Dec 2007

(Babette Oppenheimer Langsdorf, interview by Alice Smith, 12/1/76, Sherwin Collection.) Babette, Emil Oppenheimer’s daughter, was a couple of years younger than Robert Oppenheimer. The U.S. Census of 1900 records, perhaps incorrectly, that Julius Oppenheimer was born in August 1870, and emigrated from Germany in 1888; Julius listed his occupation as traveling salesman. (1900 Census, New York, N.Y., roll 1102, vol. 149, enumeration 455, sheet 8, line 27, NA.) 10 “You have a way”: Ella Friedman to Julius Oppenheimer, undated, circa March 1903, folder 4–10, box 4, Frank Oppenheimer Papers, UCB. 11 “an exquisitely beautiful”: Dorothy McKibbin, interview by Jon Else, 12/10/79, p. 21.

Titan: The Life of John D. Rockefeller, Sr.
by Ron Chernow
Published 1 Jan 1997

It might not be too much of an exaggeration to say that the antitrust cases brought against the trust in the early 1900s were not just belated but were fast becoming superfluous. After a young anarchist assassinated William McKinley in Buffalo in September 1901, the country was swept by widespread trepidation that the shooting had formed part of a broader conspiracy. In Chicago, a traveling salesman captivated reporters with tales of a conversation that he had overheard at a local train depot where J. P. Morgan and John D. Rockefeller were mentioned as potential assassination targets. A heavily armed contingent of guards ringed Rockefeller’s residence and he remained incommunicado. As it turned out, the gravest threat to the titan’s welfare emanated not from shadowy, gun-toting subversives but from the new White House occupant, forty-three-year-old Theodore Roosevelt.

Ashes to Ashes: America's Hundred-Year Cigarette War, the Public Health, and the Unabashed Triumph of Philip Morris
by Richard Kluger
Published 1 Jan 1996

It was a sixty-hour work week during which he was obliged to try to foist “push items” on his customers in accord with their manufacturers’ payment for same. Then came a stint at the Upmann cigar factory in Cuba, learning the leaf and manufacturing end of the trade, followed by a more intensive spell as a traveling salesman for the Webster-Eisenlohr company, peddling their line of cigars on the East Coast. Although the Cullmans sold the Webster people the tobacco they used as wrapper for their cigars and his father later became involved in its management while the company was in bankruptcy, Joe Third was spared little of the pain of the drummer’s life.

Andrew Carnegie
by David Nasaw
Published 15 Nov 2007

As owner of Keystone Bridge and principal partner in the Union Iron Mills, which supplied that bridge company with its iron, he was perfectly positioned to take advantage of the business opportunities opened by the legislation President Andrew Johnson signed in July 1866 authorizing the construction of seven bridges across the Mississippi River and one across the Missouri River at Kansas City.12 Carnegie refashioned himself into a traveling salesman for Keystone Bridge, hawking his wares to local boosters, bankers, promoters, and politicians up and down the Mississippi, Missouri, and Ohio Rivers who hoped to cash in on the railroading boom by siting bridges in their cities. Carnegie had done no business west of Pittsburgh, but in partnership with Thomson and Scott, who controlled the largest rail network in the East and access to considerable capital, he became a formidable competitor for new bridge contracts.

Lonely Planet Brazil
by Lonely Planet

Garoto Chocolate FactoryFOOD & DRINK (%27 3320-1000; www.garoto.com.br; Praça Meyerfreund 1; factory/museum tour R$25/5; h9am-5pm Mon-Fri, to 2pm Sat) Brazil’s best chocolate is made and sold right here, at Garoto’s factory and store. Reserve ahead online for a 1½-hour factory tour (in Portuguese) that includes a chocolate tasting. For a short glimpse of Garoto’s history, visit the adjacent three-room museum, which houses vintage machinery, advertisements and a wonderful traveling salesman’s case filled with chocolate. Take bus 500 to Vila Velha, then transfer to bus 503, 507, 525 or 526 4Sleeping & Eating Chain hotels predominate in Vitória and offer the city’s best value for money. Staying out at the beach is a much more attractive option than downtown, which is deserted after nightfall.

Nixonland: The Rise of a President and the Fracturing of America
by Rick Perlstein
Published 1 Jan 2008

At one time the model pulls her panties down so that the pubic hair is exposed to view…the focus of the camera is emphasized on the pubic and rectal region, and the model continuously uses her tongue and mouth to simulate a desire for, or enjoyment of, acts of a sexual nature.” And Justice Fortas didn’t find this obscene? Like a traveling salesman, Clancy brought with him a sample case: a thirty-five-minute documentary reel; a complete and uncut print of the masterpiece O-7. Clancy concluded his statement by requesting “the opportunity to show both the documentary and the film to the full committee and to the press, recognizing that the film is not the type of subject matter which should be shown to the general public.

The Defence of the Realm
by Christopher Andrew
Published 2 Aug 2010

The letter was from one Robert Rosenthal in Copenhagen to ‘Franz Kulbe’ at an address in Berlin which MO5(g) knew to be used by German naval intelligence. It also knew that ‘Franz Kulbe’ was an alias employed by Captain von Prieger of the German Admiralty. Though the letter purported to be a business communication, the Censorship flat-iron revealed writing in secret ink which disclosed that he was about to leave for England disguised as a travelling salesman of cigar lighters. When Rosenthal was arrested in Newcastle, travelling on a US passport, no incriminating evidence was found on him. But when confronted with his intercepted letter to Berlin, he admitted he was German and had been sent by Prieger to spy on the Royal Navy. Though he denied he had sent Prieger information of any value, he also admitted that he had been on two previous wartime espionage missions.

1,000 Places to See in the United States and Canada Before You Die, Updated Ed.
by Patricia Schultz
Published 13 May 2007

The Inspiration and Refuge of a Literary Icon EUDORA WELTY HOUSE Jackson, Mississippi Amassive oak tree shades the front yard of the 1920s Tudor-style home where Pulitzer Prize–winning author Eudora Welty spent an idyllic childhood and most of her adult life, and which now serves as a museum dedicated to her. Graceful and grand, the tree might be seen as a metaphor for Welty, known for her genteel nature, hospitality, and humility, despite her stature as one of the great American writers of the 20th century. In 1936, the 27-year-old Welty published her first short story, “Death of a Traveling Salesman.” Among her early novels are The Robber Bridegroom (1942), Delta Wedding (1946), and The Ponder Heart (1954). The winner of many awards, she received her Pulitzer for fiction in 1973 for The Optimist’s Daughter. All her success—and her abiding affection for her Mississippi home—made her the pride of Jackson until the day she died in 2001 at the age of 92.