fudge factor

The (Honest) Truth About Dishonesty: How We Lie to Everyone, Especially Ourselves
by Dan Ariely
Published 27 Jun 2012

First, we need to recognize that dishonesty is largely driven by a person’s fudge factor and not by the SMORC. The fudge factor suggests that if we want to take a bite out of crime, we need to find a way to change the way in which we are able to rationalize our actions. When our ability to rationalize our selfish desires increases, so does our fudge factor, making us more comfortable with our own misbehavior and cheating. The other side is true as well; when our ability to rationalize our actions is reduced, our fudge factor shrinks, making us less comfortable with misbehaving and cheating. When you consider the range of undesirable behaviors in the world from this stand-point—from banking practices to backdating stock options, from defaulting on loans and mortgages to cheating on taxes—there’s a lot more to honesty and dishonesty than rational calculations.

…

This is where our amazing cognitive flexibility comes into play. Thanks to this human skill, as long as we cheat by only a little bit, we can benefit from cheating and still view ourselves as marvelous human beings. This balancing act is the process of rationalization, and it is the basis of what we’ll call the “fudge factor theory.” To give you a better understanding of the fudge factor theory, think of the last time you calculated your tax return. How did you make peace with the ambiguous and unclear decisions you had to make? Would it be legitimate to write off a portion of your car repair as a business expense? If so, what amount would you feel comfortable with?

…

Although most people haven’t consciously figured out (much less announced) their acceptable rate of lying like this young man, this overall approach seems to be quite accurate; each of us has a limit to how much we can cheat before it becomes absolutely “sinful.” Trying to figure out the inner workings of the fudge factor—the delicate balance between the contradictory desires to maintain a positive self-image and to benefit from cheating—is what we are going to turn our attention to next. CHAPTER 2 Fun with the Fudge Factor Here’s a little joke for you: Eight-year-old Jimmy comes home from school with a note from his teacher that says, “Jimmy stole a pencil from the student sitting next to him.” Jimmy’s father is furious.

The Irrational Bundle
by Dan Ariely
Published 3 Apr 2013

First, we need to recognize that dishonesty is largely driven by a person’s fudge factor and not by the SMORC. The fudge factor suggests that if we want to take a bite out of crime, we need to find a way to change the way in which we are able to rationalize our actions. When our ability to rationalize our selfish desires increases, so does our fudge factor, making us more comfortable with our own misbehavior and cheating. The other side is true as well; when our ability to rationalize our actions is reduced, our fudge factor shrinks, making us less comfortable with misbehaving and cheating. When you consider the range of undesirable behaviors in the world from this standpoint—from banking practices to backdating stock options, from defaulting on loans and mortgages to cheating on taxes—there’s a lot more to honesty and dishonesty than rational calculations.

…

This is where our amazing cognitive flexibility comes into play. Thanks to this human skill, as long as we cheat by only a little bit, we can benefit from cheating and still view ourselves as marvelous human beings. This balancing act is the process of rationalization, and it is the basis of what we’ll call the “fudge factor theory.” To give you a better understanding of the fudge factor theory, think of the last time you calculated your tax return. How did you make peace with the ambiguous and unclear decisions you had to make? Would it be legitimate to write off a portion of your car repair as a business expense? If so, what amount would you feel comfortable with?

…

Although most people haven’t consciously figured out (much less announced) their acceptable rate of lying like this young man, this overall approach seems to be quite accurate; each of us has a limit to how much we can cheat before it becomes absolutely “sinful.” Trying to figure out the inner workings of the fudge factor—the delicate balance between the contradictory desires to maintain a positive self-image and to benefit from cheating—is what we are going to turn our attention to next. CHAPTER 2 Fun with the Fudge Factor Here’s a little joke for you: Eight-year-old Jimmy comes home from school with a note from his teacher that says, “Jimmy stole a pencil from the student sitting next to him.” Jimmy’s father is furious.

Elliptic Tales: Curves, Counting, and Number Theory
by Avner Ash and Robert Gross
Published 12 Mar 2012

This agrees with what we found by direct calculation. 204 CHAPTER 13 For each p, we call the function ζC,p (T) the local zeta-function of C. Now we can define the Hasse–Weil zeta-function of C, denoted Z(C, s), except for a certain fudge factor we will explain but not define. You simply substitute T = p−s in ζC,p (T) and then multiply all these local factors together:  Z(C, s) = ζC,p (p−s ) × fudge. p good Here is a rough idea of where the fudge factor comes from. We call a prime p “good” for C if C modulo p is nonsingular, which means C(Fac p ) has no ac singular point on it, where Fp is as usual an algebraic closure of Fp . We call p “bad” if it is not good.

…

For a given curve C, there are only a finite number of bad primes. The factor called “fudge” is a product of certain functions of p−s for the bad primes p. These functions are defined in a complicated way we can’t go into here. (However, when C is an elliptic curve, we will be able to tell you how to define the fudge factors.) The fudge factors are hard to define, but they are essential to flesh out our understanding of C modulo p for all primes p. In particular, they are necessary to make a certain functional equation discussed below work out correctly. Let’s continue our example with C = P1 , a projective line. To get the Hasse–Weil zeta-function of P1 , we have to substitute T = p−s and then multiply all these local factors together:  Z(P1 , s) = (1 − p−s )−1 (1 − pp−s )−1 (13.1) p There are no bad primes in this example, because P1 modulo p is nonsingular for all p.

…

The numerator of the Hasse–Weil zetafunction of C is the product over good primes of polynomials of degree 2g evaluated at p−s , times a fudge factor from the bad primes. The reciprocal of this numerator is called the L-function of C, and is written L(C, s). In the case of a curve, we have the formula  L(C, s) = fudge × p good 1 , fp (p−s ) where fp (T) is some polynomial of degree 2g. The theory tells us that the constant term of fp is always equal to 1, and various theorems give us bounds on the absolute values of the coefficients of fp . So we can write this as L(C, s) = fudge ×  1 p good 1 + c1,p p−s + c2,p p−2s + · · · + c2g,p p−2gs . (Remember that although we don’t say here what the fudge factor is, it is known and can be determined.)

Principles of Corporate Finance
by Richard A. Brealey , Stewart C. Myers and Franklin Allen
Published 15 Feb 2014

In this step only market risks are relevant. Avoid Fudge Factors in Discount Rates Think back to our example of project Z, where we reduced forecasted cash flows from $1 million to $900,000 to account for a possible failure of technology. The project’s PV was reduced from $909,100 to $818,000. You could have gotten the right answer by adding a fudge factor to the discount rate and discounting the original forecast of $1 million. But you have to think through the possible cash flows to get the fudge factor, and once you forecast the cash flows correctly, you don’t need the fudge factor. Fudge factors in discount rates are dangerous because they displace clear thinking about future cash flows.

…

If he was right in the first place, and the true bias is 10%, then adding a 10% fudge factor to the discount rate understates PV. The fudge factor also makes long-lived projects look much worse than quick-payback projects.18 Discount Rates for International Projects In this chapter we have concentrated on domestic investments. In Chapter 27 we say more about investments made internationally. Here we simply warn against adding fudge factors to discount rates for projects in developing economies. Such fudge factors are too often seen in practice. It’s true that markets are more volatile in developing economies, but much of that risk is diversifiable for investors in the U.S., Europe, and other developed countries.

…

There is a Third Law, but that is for another chapter. 2Adding a fudge factor to the cost of capital also favors quick-payback projects and penalizes longer-lived projects, which tend to have lower rates of return but higher NPVs. Adding a 5% fudge factor to the discount rate is roughly equivalent to reducing the forecast and present value of the first year’s cash flow by 5%. The impact on the present value of a cash flow 10 years in the future is much greater, because the fudge factor is compounded in the discount rate. The fudge factor is not too much of a burden for a 2- or 3-year project, but an enormous burden for a 10- or 20-year project. 3If you doubt this, try some simple experiments.

Ruby by example: concepts and code
by Kevin C. Baird
Published 1 Jun 2007

I knew the approximate ratio of the word processor’s 62 C ha pt er 4 word count versus the output of wc (I call this the fudge factor), and I could certainly do the math, but I wanted something that would do all of this for me automatically. Let’s take a look. The Code #!/usr/bin/env ruby # word_count.rb class String def num_matches(thing_to_match) return self.split(thing_to_match).size - 1 end # num_matches end # String BAR_LENGTH = 20 # to match these calculations with the output of some word processors  FUDGE_FACTOR = 0.82  def word_count(files)  Multiplying Strings  output = '' total_word_count = 0 files.each do |filename| file_word_count = word_count_for_file(filename) output += "#{filename} has #{file_word_count} words.

…

/usr/bin/env ruby # word_count.rb class String def num_matches(thing_to_match) return self.split(thing_to_match).size - 1 end # num_matches end # String BAR_LENGTH = 20 # to match these calculations with the output of some word processors  FUDGE_FACTOR = 0.82  def word_count(files)  Multiplying Strings  output = '' total_word_count = 0 files.each do |filename| file_word_count = word_count_for_file(filename) output += "#{filename} has #{file_word_count} words.\n" total_word_count += file_word_count end # each file return output + '-' * BAR_LENGTH + "\n" + "Total word count = #{total_word_count}" + " (#{(total_word_count * FUDGE_FACTOR)})" end # word_count  def word_count_for_file(filename) f = File.new(filename, 'r') contents = f.read() f.close() spaces = contents.num_matches(' ') breaks = contents.num_matches("\n") false_doubles = contents.num_matches(" \n") double_spaces = contents.num_matches(' ') hyphens = contents.num_matches('-') false_doubles += double_spaces + hyphens words = spaces + breaks - false_doubles + 1 return words end # word_count_for_file puts word_count(ARGV) Te xt M a ni pul at io n 63 How It Works We start out by adding a new method called num_matches to the String class ( ).

…

\n" total_word_count += file_word_count end # each file return output + '-' * BAR_LENGTH + "\n" + "Total word count = #{total_word_count}" + " (#{(total_word_count * FUDGE_FACTOR)})" end # word_count  def word_count_for_file(filename) f = File.new(filename, 'r') contents = f.read() f.close() spaces = contents.num_matches(' ') breaks = contents.num_matches("\n") false_doubles = contents.num_matches(" \n") double_spaces = contents.num_matches(' ') hyphens = contents.num_matches('-') false_doubles += double_spaces + hyphens words = spaces + breaks - false_doubles + 1 return words end # word_count_for_file puts word_count(ARGV) Te xt M a ni pul at io n 63 How It Works We start out by adding a new method called num_matches to the String class ( ). It simply returns the number of times the argument appears within the calling String. I also define top-level constants called BAR_LENGTH ( ), which is just for visual formatting, and FUDGE_FACTOR ( ), which I already noted is the ratio between the two different word-counting programs I was working with. We then define the word_count method ( ), which takes the files argument. You’ll notice on the last line of the script that this program takes an arbitrary number of filenames as its argument, which is different from our earlier scripts that would only deal with a single file at a time.

Relevant Search: With Examples Using Elasticsearch and Solr
by Doug Turnbull and John Berryman
Published 30 Apr 2016

You can see this in sum of from the previous explain: 3.19292, sum of: 3.19292, weight(title:alien in 223) [PerFieldSimilarity] 3.6.3. Practical caveats to the vector space model Although the vector space model provides a general framework for discussing Lucene’s scoring, it’s far from a complete picture. Numerous fudge factors have been shown to improve scoring in practice. Perhaps most fundamentally, the ways matches are combined by compound queries into a larger score isn’t always a summation. You’ve seen through the | symbol that the “max” of two fields is often taken. There’s also often a coord factor that directly punishes compound matches missing some of their components (coord multiplies the resulting dot product by <the number of matches> / <the total query terms>).

…

Another important note about this dot product is that it’s often normalized by dividing the magnitude of each vector: For dot products, normalization converts the score to a 0–1. This rebalances the equation to account for features that tend to have high weights, and those that tend to have smaller weights.[3] For search, given all the fudge factors in Lucene scoring and the peculiarities of field statistics, you should never attempt to compare scores between queries without a great deal of deep customization to make them comparable. 3 Astute readers will recognize this as the cosine similarity. As stated previously, the sparse vector representation of text is known as the bag of words model.

…

Taken together, Lucene’s classic similarity measures a term’s weight in a piece of text as follows: TF weighted × IDF weighted × fieldNorm Revisiting the fieldWeight calculation, you see this formula in play: 0.4414702, fieldWeight in 31, product of: 1.4142135, tf(freq=2.0), with freq of: 2.0, termFreq=2.0 3.9957323, idf(docFreq=1, maxDocs=40) 0.078125, fieldNorm(doc=31) Lucene’s next default similarity: BM25 Over the years, an alternate approach to computing a TF × IDF score has become prevalent in the information retrieval community: Okapi BM25. Because of its proven high performance on article-length text, Lucene’s BM25 similarity will be rolling out as the default similarity for Solr/Elasticsearch, even as you read this book. What is BM25? Instead of “fudge factors” as discussed previously, BM25 bases its TF × IDF “fudges” on more-robust information retrieval findings. This includes forcing the impact of TF to reach a saturation point. Instead of the impact of length (fieldNorms) always increasing, its impact is computed relative to the average document length (above-average docs weighted down, below-average boosted).

Isaac Newton
by James Gleick
Published 1 Jan 2003

Newton, by contrast, set himself, and science, the obligation to exclude nothing and calculate everything. As Westfall says, “So completely has modern physical science modeled itself on the Principia that we can scarcely realize how unprecedented such calculations were.” It was impossible, given the available data, and sometimes he cheated. Westfall, “Newton and the Fudge Factor,” Science 179 (February 23, 1973): 751. Also Nicholas Kollerstrom, “Newton’s Lunar Mass Error,” Journal of the British Astronomical Association 95 (1995): 151. For another example of what Whiteside calls “the delicate art of numerical cookery,” see Math VI: 508–36. 23. Principia 807. 24. Principia 806. 25.

…

A History of the Royal Society, with Memoirs of the Presidents. London: 1848. Westfall, Richard S. Force in Newton’s Physics: The Science of Dynamics in the Seventeenth Century. London: Macdonald, 1971. ———. Never at Rest: A Biography of Isaac Newton. Cambridge: Cambridge University Press, 1980. ———. “Newton and the Fudge Factor,” Science 179: 751. ———. Science and Religion in Seventeenth-Century England. New Haven: Yale University Press, 1958. ———. “Short-Writing and the State of Newton’s Conscience, 1662.” Notes and Records of the Royal Society 18 (1963): 10. Whiston, William. Memoirs of the Life and Writings of Mr.

In the Age of the Smart Machine
by Shoshana Zuboff
Published 14 Apr 1988

An operator who worked regularly with the calculator models de- scribed his frustration at not having access to their underlying assump- tions and algorithms, which he called "fudge factors": 278 AUTHORITY: THE SPIRITUAL DIMENSION OF POWER The models use equations, fudge factors, put in there to make sure you make quality pulp. If you don't know these fudge factors, they can work against you. I had to talk the engineers into explaining the fudge factors to me. The assumptions of the designers are never explained to us. You cannot really be controlling the process if you don't understand these things. As long as it's a black box to me, all I can do is babysit the computer.

The Knowledge Machine: How Irrationality Created Modern Science
by Michael Strevens
Published 12 Oct 2020

The role of Kuhn’s ideas in contemporary history of science is laid out succinctly in Mario Biagioli’s short paper “Productive Illusions.” 47 science’s biggest names can be seen discarding: See, respectively, Fisher, “Has Mendel’s Work Been Rediscovered?”; Richardson et al., “There Is No Highly Conserved Embryonic Stage in the Vertebrates”; Holton, “Subelectrons, Presuppositions, and the Millikan-Ehrenhaft Dispute”; Westfall, “Newton and the Fudge Factor” (the quote is from p. 753). In each case, accusations of impropriety have spurred illuminating debates about the culpability of the scientists and the damage done to science—with some writers arguing for little culpability and less damage—but there is scant doubt that a certain amount of deliberate misrepresentation took place. 48 Eddington’s original presentation: Dyson, Eddington, and Davidson, “Determination of the Deflection of Light.” 52 Pasteur and Pouchet had sparred: This story is told in Collins and Pinch, The Golem, which also contains a brief, accessible, and rather unsympathetic account of Eddington’s maneuvers. 52 a combative and unfair disputant: A balanced biography that takes the notebooks into account is Patrice Debré’s Louis Pasteur. 53 the industry-supported group is considerably more likely: On soda: Bes-Rastrollo et al., “Financial Conflicts of Interest and Reporting Bias Regarding the Association between Sugar-Sweetened Beverages and Weight Gain.”

…

The Thirty Years War. London: Jonathan Cape, 1938. Weinberg, S. Dreams of a Final Theory. New York: Pantheon, 1992. Weiner, J. The Beak of the Finch. New York: Knopf, 1994. Westfall, R. S. Never at Rest: A Biography of Isaac Newton. Cambridge: Cambridge University Press, 1983. Westfall, R. S. “Newton and the Fudge Factor.” Science 179 (1973): 751–8. Whewell, W. Astronomy and General Physics Considered with Reference to Natural Theology. Vol. 3 of The Bridgewater Treatises on the Power, Wisdom and Goodness of God as Manifested in the Creation. London: William Pickering, 1833. Whewell, W. History of the Inductive Sciences, from the Earliest to the Present Times.

Arguing With Zombies: Economics, Politics, and the Fight for a Better Future
by Paul Krugman
Published 28 Jan 2020

But the New Keynesian models that have come to dominate teaching and research assume that people are perfectly rational and financial markets are perfectly efficient. To get anything like the current slump into their models, New Keynesians are forced to introduce some kind of fudge factor that for reasons unspecified temporarily depresses private spending. (I’ve done exactly that in some of my own work.) And if the analysis of where we are now rests on this fudge factor, how much confidence can we have in the models’ predictions about where we are going? The state of macro, in short, is not good. So where does the profession go from here? VII. FLAWS AND FRICTIONS Economics, as a field, got in trouble because economists were seduced by the vision of a perfect, frictionless market system.

UNIX® Network Programming, Volume 1: The Sockets Networking API, 3rd Edition
by W. Richard Stevens, Bill Fenner, Andrew M. Rudoff
Published 8 Jun 2013

The historical definition in this bullet is the Berkeley implementation, dating back to 4.2BSD, and copied by many others. • Berkeley-derived implementations add a fudge factor to the backlog: It is multiplied by 1.5 (p. 257 of TCPv1 and p. 462 of TCPv2). For example, the commonly specified backlog of 5 really allows up to 8 queued entries on these systems, as we show in Figure 4.10. The reason for adding this fudge factor appears lost to history [Joy 1994]. But if we consider the backlog as specifying the maximum number of completed connections that the kernel will queue for a socket ([Borman 1997b], as discussed shortly), then the reason for the fudge factor is to take into account incomplete connections on the queue

…

This time, the variable pointed to by lenp will return with the amount of information stored in the buffer, and this variable is allocated by the caller. A pointer to the buffer is also returned to the caller. Since the size of the routing table or the number of interfaces can change between the two calls to sysctl, the value returned by the first call contains a 10% fudge factor (pp. 639 – 640 of TCPv2). Section 18.5 get_ifi_info Function (Revisited) 501 Figure 18.16 shows the first half of the get_ifi_info function. 3 struct ifi_info * 4 get_ifi_info(int family, int doaliases) 5 { 6 int flags; 7 char *buf, *next, *lim; 8 size_t len; 9 struct if_msghdr *ifm; 10 struct ifa_msghdr *ifam; 11 struct sockaddr *sa, *rti_info[RTAX_MAX]; 12 struct sockaddr_dl *sdl; 13 struct ifi_info *ifi, *ifisave, *ifihead, **ifipnext; route/get_ifi_info.c 14 buf = Net_rt_iflist(family, 0, &len); 15 16 ifihead = NULL; ifipnext = &ifihead; 17 18 19 20 21 22 lim = buf + len; for (next = buf; next < lim; next += ifm->ifm_msglen) { ifm = (struct if_msghdr *) next; if (ifm->ifm_type == RTM_IFINFO) { if (((flags = ifm->ifm_flags) & IFF_UP) == 0) continue; /* ignore if interface not up */ 23 24 25 26 27 28 sa = (struct sockaddr *) (ifm + 1); get_rtaddrs(ifm->ifm_addrs, sa, rti_info); if ( (sa = rti_info[RTAX_IFP]) !

…

M., xxii fflags member, 405 fflush function, 400 – 402 fgets function, 15, 121, 125 – 126, 128, 141 – 142, 153, 167 – 169, 171, 245, 287, 292, 400 – 401, 536, 851, 915 – 916, 924 Index Fiber Distributed Data Interface, see FDDI FIFO (first in, first out), 243 FILE structure, 402, 679 file structure, 455, 459, 694 – 695, 706, 829 file table, 421 File Transfer Protocol, see FTP fileno function, 168, 400 filter member, 405 filtering ICMPv6 type, 740 – 741 imperfect multicast, 555 perfect, 555 FIN (finish flag, TCP header), 39 – 40, 179, 789 FIN_WAIT_1 state, 40 – 41 FIN_WAIT_2 state, 41, 128, 944 finish flag, TCP header, see FIN Fink, R., 879, 888, 949 FIOASYNC constant, 234, 467 – 468, 664 FIOGETOWN constant, 467 – 468 FIONBIO constant, 234, 467 – 468 FIONREAD constant, 234, 399, 409, 467 – 468 FIOSETOWN constant, 467 – 468 firewall, 893, 948 first in, first out, see FIFO flags member, 405 flock structure, 834 flooding broadcast, 558 SYN, 108, 948 flow control, 35 UDP lack of, 257 – 261 flow label field, IPv6, 871 Floyd, S., 35, 215, 870 – 871, 947 – 948, 952 FNDELAY constant, 234 fopen function, 851 fork function, 15 – 16, 26, 53, 95, 111 – 115, 118, 120, 122, 126, 132, 139, 175, 243, 263, 368 – 369, 371, 373 – 377, 379 – 380, 405, 420 – 423, 430, 432, 446 – 448, 464, 577, 609, 612 – 614, 675 – 677, 679, 681, 698, 707, 717, 817 – 818, 820, 822 – 823, 825 – 827, 829 – 830, 837, 842, 850, 934, 944, 946 definition of, 111 format prefix, 878 formats binary structures, data, 148 – 151 data, 147 – 151 text strings, data, 147 – 148 four-way handshake, 45 SCTP, 45 – 46 fpathconf function, 209 fprintf function, 344, 365, 369 – 370, 439, 443 fputs function, 9, 11, 121, 125, 168 – 169, 245, 288, 400 – 402, 680, 919 FQDN (fully qualified domain name), 303, 309, 317, 340 UNIX Network Programming fragmentation, 56 – 57, 59, 719, 737, 739, 771 – 772, 870, 873, 883 – 884, 914, 926, 945 and broadcast, IP, 537 – 538 and multicast, IP, 571 offset field, IPv4, 871 frame type, 532, 534 – 535, 555, 791 – 792 Franz, M., xxiii free function, 508, 684 free_ifi_info function, 471, 478 source code, 480 freeaddrinfo function, 321, 327, 345 definition of, 321 FreeBSD, 20 – 24, 78, 108, 197, 260 – 262, 299, 405, 469, 473, 497, 538, 658, 666, 710, 775, 882 – 883, 891, 897, 904, 926, 934, 939 – 940 freehostent function, 347 definition of, 347 frequently asked question, see FAQ fseek function, 400 fsetpos function, 400 fstat function, 406 fstat program, 897 FTP (File Transfer Protocol), 20, 62, 201, 311 – 312, 360, 362, 366, 375, 662, 914, 947 fudge factor, 106, 500 full-duplex, 36, 415 Fuller, V., 874, 949 fully buffered standard I/O stream, 401 fully qualified domain name, see FQDN function destructor, 690 system call versus, 891 wrapper, 11 – 13 gai_strerror function, 320 – 321 definition of, 321 Ganguly, S., 285, 953 Garcia, M., 267, 952 Garfinkel, S.

The Grand Design
by Stephen Hawking and Leonard Mlodinow
Published 14 Jun 2010

Elegance, for example, is not something easily measured, but it is highly prized among scientists because laws of nature are meant to economically compress a number of particular cases into one simple formula. Elegance refers to the form of a theory, but it is closely related to a lack of adjustable elements, since a theory jammed with fudge factors is not very elegant. To paraphrase Einstein, a theory should be as simple as possible, but not simpler. Ptolemy added epicycles to the circular orbits of the heavenly bodies in order that his model might accurately describe their motion. The model could have been made more accurate by adding epicycles to the epicycles, or even epicycles to those.

The Most Powerful Idea in the World: A Story of Steam, Industry, and Invention
by William Rosen
Published 31 May 2010

IF THE CAST IRON used for pots and pans was the most mundane version of the element, the most sublime was steel. As with all iron alloys, carbon is steel’s critical component. In its simplest terms, wrought iron has essentially no minimum amount of carbon, just as there is no maximum carbon content for cast iron. As a result, the recipe for either has a substantial fudge factor. Not so with steel. Achieving steel’s unique combination of strengths demands a very narrow range of carbon: between 0.25 percent and a bit less than 2 percent. For centuries* this has meant figuring out how to initiate the process whereby carbon insinuates itself into iron’s crystalline structure, and how to stop it once it achieves the proper percentage.

…

Except during times of dramatic depopulation, such as the Black Death of the fourteenth century, or extremely large additions to the stock of arable land, as with Europe’s discovery of the New World, growth in land per worker has been negligible for centuries, so small that its effect on growth can be eliminated in the simplest calculations. The second component, growth in capital3 per worker—that is, all the buildings, machinery, tools, and so on—explains only about 24 percent of total growth. However, since the growth in the amount of land and capital per worker together doesn’t equal the overall growth rate, a fudge factor must be used, called the residual: what’s left over. This also means that the residual, despite the ass-backward way it is calculated, amounts to at least three-quarters of the total increase in economic growth since 1800. That’s a big chunk of activity defined by subtracting everything else, a little like a ten-drawer file cabinet with seven drawers marked “Miscellaneous.”

The God Equation: The Quest for a Theory of Everything
by Michio Kaku
Published 5 Apr 2021

(He did not realize this at the time, but this was the solution to the question asked by Richard Bentley. The universe did not collapse under gravity because the universe was expanding, overcoming the tendency to collapse.) In order to find a static universe, Einstein was forced to add a fudge factor (called the cosmological constant) into his equations. By adjusting its value by hand, he could cancel out the expansion or contraction of the universe. Later, in 1929, astronomer Edwin Hubble, by using the giant Mount Wilson Observatory telescope in California, was able to make a startling discovery.

Economics Rules: The Rights and Wrongs of the Dismal Science
by Dani Rodrik
Published 12 Oct 2015

The standards of the profession require that the modeler make only some general claims about how what he or she is doing is relevant to the real world. It is left to the reader or the user of the model to infer the specific circumstances in which the model can help us better understand reality.§ This fudge factor increases the chances of malpractice. Models lifted out of their original context can be used in settings for which they are inappropriate. At the empirical end of economics, such as labor and development economics, where almost all economists work directly with data and real-world evidence, paradoxically the problems may be even more severe.

They Have a Word for It A Lighthearted Lexicon of Untranslatable Words & Phrases-Sarabande Books (2000)
by Howard Rheingold
Published 10 Mar 2020

/aux.frais(French) Items you are likely to forget to include when making a budget. [noun] Here's a problem that vexes small households and sovereign nations: figuring out in advance how you are going to spend your money. No matter how thoroughly you do your research, how conscientiously you build in fudge factors, Serious Business 127 how hard you work at sticking to a prearranged budget, doesn't it seem as if you always end up outspending your target amount, especially if it is an annual budget? How does this happen? Did you remember to include the cost of replacing a cracked windshield or removing an impacted wisdom tooth?

Our Robots, Ourselves: Robotics and the Myths of Autonomy
by David A. Mindell
Published 12 Oct 2015

I asked my colleague Jon How, one of the principals on the project, how many such thresholds there are in a system like that. His reply: “Many, many, many.” In fact the “configuration file” for the MIT vehicle contained nearly a thousand lines of text, setting hundreds of variables: sensor positions and calibrations, fudge factors to align the sensors with one another, how to deal with sun dazzle, etc. Machine learning techniques can help reduce this reliance on parameters, but they still rely on human programmers for their basic structure. How points out that core algorithms generally rely heavily on accurate models of uncertainty in the world.

Shipping Greatness
by Chris Vander Mey
Published 23 Aug 2012

The testing constant is a function of the size of your test team. In the spreadsheet shown in Figure 4-1, I’ve added some calculations to ensure that tasks don’t end on the weekends. Because this model uses “ideal” developer days for estimates, it is critical to build a buffer into your dates, but not the engineering task estimates. A buffer is a “fudge factor” that accommodates unforeseen problems and general productivity losses. Some teams estimate that approximately three out of five days are productive. Anything could be happening in those two days, but it’s likely some combination of meetings, broken builds, marriage problems, and false starts.

Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It
by Erica Thompson
Published 6 Dec 2022

Even I probably wouldn’t change π (although my colleague at the London School of Economics Leonard Smith points out that I do change the value of π every time I use a digital computer). And yet what you are doing, in making a model, is exactly that: simplifying reality away from the messy truth towards something that happens to be more tractable. Why is it that it feels totally acceptable to make simplifications or add in empirically derived fudge factors along some dimensions, but to do so along others would be complete sacrilege even if it resulted in a model that could make demonstrably better predictions? Model laws are not real laws If model quantities are not real quantities, what about model laws? Perhaps we were getting close to that when we discussed π and the conservation of mass.

The Healing of America: A Global Quest for Better, Cheaper, and Fairer Health Care
by T. R. Reid
Published 15 Aug 2009

Colombia, with health care funded by a steeply progressive tax code, topped the chart on this scale, followed closely by western European countries and Japan. Finally, the WHO experts took all these factors, tabulated each country’s score on each measure, and arrived at its rating of “overall performance.” But this score was adjusted by one more fudge factor: a comparison of each country’s actual performance on national health care to the overall performance it should have been able to achieve, considering its level of education and the amount of money it spends on health care.With this ultimate wrinkle factored in, the report finally came up with its ranking of “overall performance” in all 191 member nations.

Frequently Asked Questions in Quantitative Finance
by Paul Wilmott
Published 3 Jan 2007

First, if we do start to move outside the Black-Scholes world then chances are it will be the diffusion coefficient that we must change from its usual to accommodate new models. Second, if we want to fudge our option prices, to massage them into line with traded prices for example, we can only do so by fiddling with this diffusion coefficient, i.e. what we now know to be the volatility. This derivation tells us that our only valid fudge factor is the volatility. Black-Scholes for Accountants The final derivation of the Black-Scholes equation requires very little complicated mathematics, and doesn’t even need assumptions about Gaussian returns, all we need is for the variance of returns to be finite. The Black-Scholes analysis requires continuous hedging, which is possible in theory but impossible, and even undesirable, in practice.

Quarantine
by Greg Egan
Published 13 Dec 1994

‘Oh, nobody’s done anything. Nothing’s changed. It just… all seems even more stupid and oppressive than usual, today. I read an article in Physical Review this morning: a whole new treatment of the measurement problem. They add a few more dimensions to space-time; throw in a few nonlinearities, asymmetries and assorted fudge factors; and—miracle of miracles!—the collapse of the wave falls out the other end.’ I know I should have dutifully silenced her half-way through the word ‘measurement’—if only for the sake of appearances—but the hypocrisy would have been too much. She says, ‘People are wasting valuable time, heading down paths that I know are blind alleys.

Duped: Double Lives, False Identities, and the Con Man I Almost Married
by Abby Ellin
Published 15 Jan 2019

What if they claimed to be six feet tall but were a mere five foot three in real life? People would notice that kind of discrepancy. But if they said they were five four and were really five three? That was a little more plausible. What’s an inch among lovers?3 Behavioral economist Dan Ariely, author of The (Honest) Truth About Dishonesty, said we all cheat by a “fudge factor” of roughly 15 percent.4 But some of us advance to such big lies that we’re practically drowning in the gooey concoction. The Commander certainly used fragments of the truth, combined them with untruths, and shot it all out of a cannon. He really was a doctor in private practice in Beverly Hills, and had a PhD and an MD.

Carjacked: The Culture of the Automobile and Its Effect on Our Lives
by Catherine Lutz and Anne Lutz Fernandez
Published 5 Jan 2010

With greater model diversity and the niche marketing of those models to a complex set of demographics that began most vigorously in the 1970s, the automobile became more than a marker of class.21 And in an environment where credit is sold so aggressively, the car today is less a reliable sign of hard work done and money earned than of hard work yet to be done and money yet to be earned. (Now the more appropriate comment to a new car buyer might be “Congratulations on your debt!”) Despite these fudging factors, class remains legible in one’s car, a fact that provides some of the sweetest pleasures to those who drive more expensive and late-model cars. Even the way some talk about how they drive sounds a lot like how they think about getting ahead. Said one man: “I have a life philosophy. If you do what the herd does, you get what the herd gets.”

Predictably Irrational, Revised and Expanded Edition: The Hidden Forces That Shape Our Decisions
by Dan Ariely
Published 19 Feb 2007

On the other hand, we’re selfish, and we want to benefit from cheating. On the surface, these two motivations seem contradictory, but our flexible psychology allows us to act on both of them when we cheat “just by a bit”—benefiting financially from cheating while at the same time managing not to feel bad about ourselves. I think of this as an individual “fudge factor,” or a fuzzy conscience. One way to look at the experiments described in Chapters 11 and 12 is to think about them as an examination of what happens when people wrestle with conflicting interests. When we placed participants in situations in which they were torn between wanting to behave honorably and wanting to benefit financially, they usually succumbed to temptation but only by a little bit.

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

Institutional Investor observed that the median annualized return between 2010 and 2019 of ESG equity funds with a track record of at least 10 years and $100 million in assets was only 11.98 percent, while the S&P 500 had returned an annualized 13.56 percent. Volatility was identical across both indices, suggesting that ESG assets underperformed not only on an absolute basis but on a risk-adjusted basis as well.10 Many of the analyses in favor of ESG outperformance are cherry-picked. Fudge factors include which companies to include versus exclude, the relevant time horizon to examine, what benchmark indices to use, and so on. Those are fundamentally subjective decisions, often made by the very people who know what conclusion they wish to reach. In sum, the existence of dueling data sets shouldn’t surprise anyone—in this case, the “data” itself is a charade.

Your Money or Your Life: 9 Steps to Transforming Your Relationship With Money and Achieving Financial Independence: Revised and Updated for the 21st Century
by Vicki Robin , Joe Dominguez and Monique Tilford
Published 31 Aug 1992

“But must I keep track of every cent?” you may ask. Yes, every cent! Why every cent, rather than just rounding off to the nearest dollar, or using approximate figures? Because this helps to establish important lifelong habits. After all, how big is a “Finagler’s Constant”? What’s the definition of a “Fudge Factor”? How close is “close enough”? Granted, in practice many FIers settle into rounding to the dollar, but that’s as far as they slip. Human nature being what it is, if you start cheating, even “just a little bit,” that little bit tends to get bigger and soon you’ll find yourself thinking, “Well, I don’t have to write everything down, just the major expenses”; and then, “Well, I’ve done this for a month now, so I think I’ll start rounding it off to the nearest thousand.”

The Ultimate Engineer: The Remarkable Life of NASA's Visionary Leader George M. Low
by Richard Jurek
Published 2 Dec 2019

I mentioned that one of the things that made Apollo feasible was Jim Webb’s original estimate of between $20 to $40 billion,” he explained. Apollo came in at around $24 billion; in his original Low committee report, he had estimated the program at just $7 billion. Webb, an old budget hand, knew the game of government budgets. He multiplied Low’s estimate by a healthy fudge factor. It worked. “With Webb’s high estimate, we never had a problem in defending Apollo costs again during the early days of the program,” he explained to Paine.16 “The agency’s initial budget request in 1970 started out at $4.5 billion for 1971. It was based on moving out with speed on the Space Task Group’s planning,” Low remembered.

Infinite Powers: How Calculus Reveals the Secrets of the Universe
by Steven Strogatz
Published 31 Mar 2019

No one could imagine a universe so immense with stars so remote, much farther away than the planets. Today we know that is exactly the case, but back then it was inconceivable. So the Earth-centered cosmology, for all its faults, seemed like the more plausible picture. Suitably modified by the ancient Greek astronomer Ptolemy with epicycles, equants, and other fudge factors, the theory could be made to account reasonably well for planetary motion and it kept the calendar in line with seasonal cycles. The Ptolemaic system was clunky and complicated, but it worked well enough to last into the late Middle Ages. Two books published in 1543 marked a turning point, the beginning of the scientific revolution.

Day We Found the Universe
by Marcia Bartusiak
Published 6 Apr 2009

Stellar objects would be gravitationally drawn to one another, closer and closer over time. Ultimately, the universe would collapse under the inescapable pull of gravity. So, to avoid this cosmic calamity and match his theory with then-accepted astronomical observations, Einstein altered his famous equation, adding the term λ (the Greek letter lambda), a fudge factor that came to be called the “cosmological constant.” This new ingredient was an added energy that permeated empty space and exerted an outward “pressure” on it. This repulsive field—a kind of antigravity, actually—exactly balanced the inward gravitational attraction of all the matter in his closed universe, keeping it from moving.

The Invention of Science: A New History of the Scientific Revolution
by David Wootton
Published 7 Dec 2015

‘The Accuracy of Tycho Brahe’s Instruments’. Journal for the History of Astronomy 9 (1978): 42–53. Westfall, Richard S. ‘The Development of Newton’s Theory of Color’. Isis (1962): 339–58. ———. Never at Rest: A Biography of Isaac Newton. Cambridge: Cambridge University Press, 1980. ———. ‘Newton and the Fudge Factor’. Science 179 (1973): 751–8. ———. ‘Science and Technology during the Scientific Revolution: An Empirical Approach’. In Renaissance and Revolution. Humanists, Scholars, Craftsmen and Natural Philosophers in Early Modern Europe. Ed. JV Field and FA James. Cambridge: Cambridge University Press, 1997: 63–72. ———.

…

Galileo’s second letter on sunspots (1612), in Galilei & Scheiner, On Sunspots (2008), 107–70. 48. ‘Préface sur le traité du vuide’, in Pascal, Oeuvres complètes (1964), Vol. 2, 772–85. 49. Dear, Discipline and Experience (1995), 15, 180; for an example published by Riccioli in 1651, 78. 50. Palmerino, ‘Experiments, Mathematics, Physical Causes’ (2010); and Westfall, ‘Newton and the Fudge Factor’ (1973). 51. The legal issues and their history were recently summarized in the House of Lords judgement on Regina v. Pendleton, 13 Dec. 2001. 52. Locke, An Essay (1690), 333. 53. Hobbes, Humane Nature (1650), 38–9; quoted in Hacking, The Emergence of Probability (2006), 48 54. Wotton, Reflections upon Ancient and Modern Learning (1694), 301. 55.

Tesla: Man Out of Time
by Margaret Cheney
Published 1 Jan 1981

The new physics boiled with debates over waves versus particles and about Einstein’s special theory of relativity, which Tesla—with strong cosmic theories of his own—rejected outright. When Einstein’s general theory of relativity was published in 1916, even its creator had been unable to accept fully the dynamic universe that it implied. So disturbed was Einstein by this that he built into his calculations a “fudge factor” that preserved the possibility that the universe might after all prove to be stable and unchanging. To Tesla this was just added proof that the relativists didn’t know what they were talking about. He himself was working on a theory of the universe to be disclosed in good time, and he had long ago propounded (but not published) his own dynamic theory of gravity.

Fool Me Twice: Fighting the Assault on Science in America
by Shawn Lawrence Otto
Published 10 Oct 2011

When he turned to astronomers for verification of his theory, he found that almost all of them held the notion that the universe existed in a steady state and there was no motion on a grand scale. So in deference to their observational experience, Einstein adjusted his general theory calculations with a mathematical “fudge factor”—the cosmological constant—that made the universe seem to be steady. Lemaître had independently been working off the same mathematical principles that Einstein had originally laid out, and in 1927 he wrote a dissenting paper in which he argued that the universe must be expanding, and that if it was, the redshifted light from stars was the result of this expansion.

To Explain the World: The Discovery of Modern Science
by Steven Weinberg
Published 17 Feb 2015

Buchwald and M. Feingold, Newton and the Origin of Civilization (Princeton University Press, Princeton, N.J., 2014). 14. See S. Chandrasekhar, Newton’s Principia for the Common Reader (Clarendon, Oxford, 1995), pp. 472–76; Westfall, Never at Rest, pp. 736–39. 15. R. S. Westfall, “Newton and the Fudge Factor,” Science 179, 751 (1973). 16. See G. E. Smith, “How Newton’s Principia Changed Physics,” in Interpreting Newton: Critical Essays, ed. A. Janiak and E. Schliesser (Cambridge University Press, Cambridge, 2012), pp. 360–95. 17. Voltaire, Philosophical Letters, trans. E. Dilworth (Bobbs-Merrill Educational Publishing, Indianapolis, Ind., 1961), p. 61. 18.

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

One theory is that the Universe will continue its expansion forever. Alternatively, if there’s enough stuff, then the force of the Universe’s own gravity will stop the expansion, resulting in a final “big crunch.” Unless, of course, there’s an antigravity force. Or if the “cosmological constant,” Einstein’s “fudge factor,” is big enough. I’ve had to rewrite this paragraph three times over the past several months because the physicists can’t make up their minds. The latest speculation apparently favors indefinite expansion. Personally, I prefer the idea of the Universe closing in again on itself as more aesthetically pleasing.

The Rights of the People
by David K. Shipler
Published 18 Apr 2011

Souter’s key point was not that the Atwater arrest was justifiable—indeed, he and the majority found it full of “gratuitous humiliations imposed by a police officer who was (at best) exercising extremely poor judgment.” But the justices did not think that one policeman’s overreaction should induce the Court to ban all such misdemeanor arrests and thus “mint a new rule of constitutional law.” The fudge factor in the text of the Fourth Amendment is the word “unreasonable,” which presents judges with latitude for indulging their predilections for or against police power. Courts have held that to be reasonable, an arrest must balance two competing factors: its intrusion on personal privacy versus its weight in promoting government interests, as O’Connor noted in her Atwater dissent.

Big Bang
by Simon Singh
Published 1 Jan 2004

Later he would call the cosmological constant the greatest blunder of his entire life. As he wrote in a letter to Lemaître: ‘Since I have introduced this term I had always a bad conscience…I am unable to believe that such an ugly thing should be realised in nature.’ Although Einstein was keen to abandon his cosmic fudge factor, cosmologists who still believed in an eternal, static universe were convinced that the cosmological constant was an essential and valid part of general relativity. Even some Big Bang cosmologists had become quite fond of it and were reluctant to lose it. By retaining the cosmological constant and varying its value, they could tweak their theoretical models of the Big Bang and modify the universe’s expansion.

The Billion-Dollar Molecule
by Barry Werth

Observed Murcko, “In all the things we do with proteins, what we’re really doing is calculating all the forces between atoms: the stretches and the bends, the nonbonded interactions and the tugs between charged particles. But not all the equations we use to describe those interactions are accurate. Some of them are fudge factors. Some of them are thought to be correct even though the experimental data they’re based on are wrong, only nobody knows that because nobody’s gone back and double-checked the experiments. Some are pure guesses. There are assumptions, biases. There’s user error. There’s imprecision in the hardware and software.

The Better Angels of Our Nature: Why Violence Has Declined
by Steven Pinker
Published 24 Sep 2012

A team of statisticians led by Michael Spagat and Neil Johnson found these estimates incredible and discovered that a disproportionate number of the surveyed families lived on major streets and intersections—just the places where bombings and shootings are most likely.69 An improved study conducted by the World Health Organization came up with a figure that was a quarter of the Lancet number, and even that required inflating an original estimate by a fudge factor of 35 percent to compensate for lying, moves, and memory lapses. Their unadjusted figure, around 110,000, is far closer to the battle-death body counts.70 Another team of epidemiologists extrapolated from retrospective surveys of war deaths in thirteen countries to challenge the entire conclusion that battle deaths have declined since the middle of the 20th century.71 Spagat, Mack, and their collaborators have examined them and shown that the estimates are all over the map and are useless for tracking war deaths over time.72 What about the report of 5.4 million deaths (90 percent of them from disease and hunger) in the civil war in the Democratic Republic of the Congo?

…

Myth of reversal in civilian war deaths: Human Security Centre, 2005, p. 75; Goldstein, 2011; Roberts, 2010; White, in press. 67. Civilian deaths in the Civil War: Faust, 2008. 68. Lancet study: Burnham et al., 2006. 69. Bias in epidemiological studies: Human Security Report Project, 2009; Johnson et al., 2008; Spagat, Mack, Cooper, & Kreutz, 2009. 70. Fudge factor: Bohannon, 2008. 71. Retrospective surveys of war deaths: Obermeyer, Murray, & Gakidou, 2008. 72. The trouble with surveys: Spagat et al., 2009. 73. Claim of 5.4 million deaths in DRC: Coghlan et al., 2008. 74. Problems with DRC estimate: Human Security Report Project, 2009. 75. Famine and disease decline during war: Human Security Report Project, 2009. 76.

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

Heathrow sees more traffic than Britain has citizens. The world’s busiest hub, Atlanta’s Hartsfield-Jackson, has a daytime population larger than Orlando’s and an annual one that would rank it as the twelfth most populous nation on earth. (It’s also the state of Georgia’s largest employer.) All of these figures have a sky-high fudge factor, failing to account for fliers counted twice or more. The media research firm Arbitron made a better measurement a few years ago. It estimated ninety-two million Americans—nearly one in three—had flown at least once in the past twelve months. A clear and bright line separates those of us who fly and those of us who don’t.

Derivatives Markets
by David Goldenberg
Published 2 Mar 2016

In order to hedge the option in the Binomial model, we have to have a hedge ratio different from 1, except for Case 1. Further, it generally has to be less than 1.0 in order to hedge the option. When we replicated the option for European Put-Call Parity, we also had a European put option that we used as the ‘fudge’ factor. Here, we only have the stock and the bond. So we have to do better than we did in proving European Put-Call Parity. How can we do better, given that we lost the put option? The answer is that we have been given something we didn’t have for proving European Put-Call Parity. We now get to choose the hedge ratio and make it different from 1.0.

The Stuff of Thought: Language as a Window Into Human Nature
by Steven Pinker
Published 10 Sep 2007

Depending on small differences in how catchy the word is, and on how well-connected, trusted, or charismatic its first adopters are, it may or may not reach the tipping point that would lead it to become entrenched in the community and perpetuated down the generations. 54 This is a way to make sense of the Frequency and Diversity components of the FUDGE factors that Metcalfe suggests are the secrets to a word’s success. So a look at the spread of words and names upends the conventional wisdom of where culture comes from and how it changes. In the twentieth century, a culture came to be thought of as a superorganism that pursues goals, finds meaning, responds to stimuli, and can be the victim of manipulation or the beneficiary of intervention.

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't
by Nate Silver
Published 31 Aug 2012

* Meditation helps people achieve this, in part, by encouraging focus on posture and breathing—things that are within our control but which we normally take for granted. * Some of it also represents market making: certain investment firms, like a well-stocked 7-Eleven, hold a lot of inventory and can be counted on to be open for business when no one else is, hoping to make a few pennies at a time for their trouble. * One could imagine that a small fudge factor might be allowed if our probability estimates were close but not exactly the same, since there is some inconvenience associated with betting. * I met Santorum for my New York Times story on the Iowa vote count dispute after the initial tally had shown Romney ahead. Santorum remembered my bet and jokingly asserted that the bet was my motivation for tracking him down.

Across Realtime
by Vernor Vinge
Published 1 Jan 1986

Now, at fifty megayears, the day was a little over twenty-five hours long. Rather than change the definition of -he second or the hour, the Korolevs had decreed (just another of their decrees) that the standard day should consist of twenty-four hours plus whatever time it took to complete one rotation. Yel‚n called the extra time the Fudge Factor. Everyone else ailed it the Witching Hour. Wil walked through the Witching Hour, looking for some sign of Marta Korolev. He was still on the Robinson estate, that.vas obvious: as advanced travelers, the Robinsons had plenty of robots. Rescue-day ash had been meticulously cleaned from he stone seats, the fountains, the trees, even the ground.

Den of Thieves
by James B. Stewart
Published 14 Oct 1991

Boesky and many arbitrageurs had always viewed the net capital requirements with thinly veiled contempt. His colleagues Conway and especially Mooradian, who had nearly lost his career after being disci- plined for net capital violations, took the law much more seriously and tried to keep Boesky in compliance. They even went so far as to build in what they termed a "fudge factor" that overstated Boesky's actual leverage in order to try to keep him in bounds. In 1985, however, with the pace of merger deals quickening, and the resulting increase in arbitrage opportunities, it was getting harder and harder to keep Boesky in compliance. Finally, that summer, Conway wrote Boesky an angry memo: "You have continued to show very small regard for our net capital position or the debt covenants under our loan agreements. . . .

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

When deaths are expressed as a percentage of total population, World War II is the only twentieth-century event cracking the top ten, behind An Lushan, the Mongol conquests, the Mideast slave trade, the fall of the Ming dynasty, the fall of Rome, the deaths caused by Tamerlane, the annihilation of Native Americans by Europeans, and the Atlantic slave trade. Critics have questioned this—“Hey, stop using fudge factors to somehow make World War II’s 55 million dead less than the fall of Rome’s 8 million.” After all, 9/11’s murders would not have evoked only half as much terror if America had 600 million instead of 300 million citizens. But Pinker’s analysis is appropriate, and analyzing rates of events is how you discover that today’s London is much safer than was Dickens’s or that some hunter-gatherer groups have homicide rates that match Detroit’s.

Networks, Crowds, and Markets: Reasoning About a Highly Connected World
by David Easley and Jon Kleinberg
Published 15 Nov 2010

And indeed, it suffers from exactly this problem — advertisers can sometimes occupy high slots without generating much money for the search engine. The role of ad quality. When Google developed its system for advertising, it addressed this problem as follows. For each ad submitted by an advertiser j, they determine an estimated quality factor qj. This is intended as a “fudge factor” on the clickthrough rate: if advertiser j appears in slot i, then the clickthrough rate is estimated to be not ri but the product qjri. The introduction of ad quality is simply a generalization of the model we’ve been studying all along: in particular, if we assume that all factors qi are equal to 1, then we get back the model that we’ve been using thus far in the chapter.

The Prize: The Epic Quest for Oil, Money & Power
by Daniel Yergin
Published 23 Dec 2008

For instance, depending on the accounting formula that was chosen, 25 percent of the Kuwait Oil Company could be worth anywhere from sixty million to one billion dollars. Finally, in that case, the two sides came together by inventing a new accounting concept, "updated book value," which included inflation and large fudge factors. And in October 1972 a "participation agreement" was finally reached between the Gulf states and the companies. It provided for an immediate 25 percent participation share, rising to 51 percent by 1983. But, despite all the OPEC endorsements, the application of the agreement was less popular in the rest of OPEC than Yamani hoped.

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

As noted in Chapter 12, early probabilistic systems fell out of favor in the early 1970s, leaving a partial vacuum to be filled by alternative methods. These included rule-based expert systems, Dempster-Shafer theory, and (to some extent) fuzzy logic.9 Rule-based approaches to uncertainty hoped to build on the success of logical rule- based systems, but add a sort of “fudge factor”—more politely called a certainty factor—to each rule to accommodate uncertainty. The first such system was MYCIN (Shortliffe, 1976), a medical expert system for bacterial infections. The collection Rule-Based Expert Systems (Buchanan and Shortliffe, 1984) provides a complete overview of MYCIN and its descendants (see also Stefik, 1995).