Christian Bauckhage gives evidence that the SIRS variant of the Kermack-McKendrick compartmental model fits time-series data reasonably well on Internet memes from Google Insights (now Google Trends.)18 He looked at silly recent Internet viruses like the “O RLY?” (Oh, really?) meme that displayed nothing more than a picture of a cute owl with what would appear to be a puzzled facial expression. Because the memes are largely nonsensical, we might expect them to follow a course independent of other ideas and thus to fit the SIRS model well, as Bauckhage found. He found roughly the same hump-shaped pattern of infectives among Internet memes again and again.
Further Reasons to Think That Economic Narratives Have Epidemics as Diseases Do
Even though modern communications media have made direct face-to-face communication of ideas less important, the Kermack-McKendrick three-equation model still remains a workable model for idea epidemics. The core model may apply no matter how people connect with one another.
My colleague John Pound and I conducted a survey in 1985 of both institutional and individual investors to try to learn how systematic they are in their investing decisions. We asked all respondents to recall the latest stock market investment they had made. We asked them if they agreed with the following statement about this investment:
My initial interest was the result of my, or someone else’s, systematic search over a large number of stocks [using a computerized or otherwise similar search procedure] for a stock with certain characteristics.19
Among institutional investors, 67% agreed with this statement, but only 23% of individual investors did. In a separate survey of investors in rapid-price-increase stocks with high price-earnings ratios, we asked the same question. Here, only 25% of institutional investors agreed, and among individuals only 16% agreed.
How, then, do people start to pay attention to an individual stock? The answer: word of mouth. We asked our respondents in the first survey how many people they talked to about the stock. For institutional investors in the random sample, the average answer was seven. For active individual investors, the average answer was even higher: twenty. The conclusion is that people are not generally systematic: they allow their attention to be swayed by unsystematic responses to hearsay. This lesson from the realm of investing likely extends to other economic decisions beyond investing, because it reflects basic patterns of human decision making. Other suggestions that variants of the SIR model might apply to understanding investments in individual assets include evidence that people tend to invest in companies that are nearby geographically, and that epidemics of interest in individual stocks sometimes proceed very swiftly but do not ever infect a high fraction of the population (which the SIR model can accommodate if both c and r are similarly high or confined to a small geographical area).
Such models could help explain the geographical pattern of the spread of economic narratives, including the Bitcoin narrative, which, though contagious in many countries, does have a geographical distribution. Geoffrey Garrett, dean of the Wharton School at the University of Pennsylvania, remarked on attitudes toward Bitcoin upon his return from a visit to Silicon Valley:
Whereas most people on Wall Street remain skeptical, playing a wait-and-see game, Silicon Valley is all in. Literally every meeting I participated in, from the biggest tech companies to the smallest startups, was rich with enthusiastic and creative crypto conversations.20
Idea Epidemics and Information Cascades
Variations of the SIR model can generate chaos. Chaos theory in mathematics shows that many nonlinear differential equation models can be chaotic in a precise mathematical sense. That is, the system can generate seemingly random variations—variations that never repeat themselves, that appear to be generating random numbers even though the system is deterministic. In fact, random number generators on computers are not really invoking chance but are the product of such chaotic deterministic models. Variations of the SEIR epidemic model can be chaotic, as has been shown and studied mathematically and related to actual disease data.21
Chaos theory is associated with the butterfly effect, which refers to the idea that a huge, apparently unpredictable storm might have been generated by a seemingly distant and irrelevant event such as a butterfly flapping its wings on the other side of the planet long ago. Another variation of the SIR model can help explain such butterfly effects by adding information cascades to the basic model.22 If people think they are collecting reliable information by observing the numbers of people who make certain choices, then the equilibrium can move off in random directions, much as in the artificial music-market experiment of Salganik and his colleagues discussed in chapter 4. I recall an experience with Professor Ivo Welch of UCLA, one of the authors of the information cascade theory. While driving me to my hotel, he told me he thought we were near the hotel but that he wasn’t sure exactly where it was. Then he spotted a taxi with no passenger, and he said that he would just follow the taxi, because there was a good chance that the taxi was on its way to the hotel. His guess that the taxi driver had the information we needed worked perfectly, but it could just as well have led us to a different hotel or to any number of random places. If a lot of people were behaving as Ivo was, then one initial taxi could, in principle, start an epidemic that could set off a deluge of taxis to a random place.
Information cascades can explain how speculative bubbles can be perfectly rational, in accordance with the canon of economic theory. In my view, they are interesting because they describe how bubbles or depressions can start from purely random causes, even if people are fairly sensible. George A. Akerlof and Janet L. Yellen coined the term “near-rational” in 1985, and I wish that term had caught on more, that it had gone viral.23 However, information cascades may not be so important a problem. In reality, taxi drivers never seem to follow the leader, at least not in terms of driving to destinations in their city. But, like everyone else, taxi drivers may follow others in terms of remembering “facts” of a more ambiguous nature, such as the best restaurant in a city.24 Ask a taxi driver to take you to the best restaurant: you will likely get laughter in response, and it is unlikely that the destination will be demonstrably the best.25
The movements of taxi drivers, just like changes in behavior of consumers, investors, and entrepreneurs and other economic phenomena, can never be properly understood without some input from narrative economics. Making real progress in narrative economics is a big project for serious research in the future.
Notes
Preface: What Is Narrative Economics?
1. Allen, 1964 [1931], p. 261.
2. Allen, 1964 [1931], p. viii.
3. “Descriptive economics again divides into a formal and narrative branch; of which the former analyzes and classifies the conceptions needed for understanding the science in its widest applications, and the latter investigates historically and comparatively the various forms of economic life exhibited by different communities and at different epochs” (within entry “Method of Economics”), Palgrave, 1894, p. 741.
4. Interest in the role of narratives in driving social movements has been expressed by sociologists in the New Social Movement literature; see Davis, 2002.
5. Fair and Shiller (1989) show evidence that forecasting models have some ability to forecast in the short run, but Lahiri and Wang (2013) show with the Philadelphia Federal Reserve Bank Survey of Professional Forecasters that there is no “significant skill” at attaching a probability to a one-quarter GDP decline in the United States one year in the future. The probability of GDP decline that the professionals as a group have been giving at this forecast horizon is worthless.
6. Andrew Brigden, “The Economist Who Cried Wolf.” Fathom Consulting, February 1, 2019, https://www.fathom-consulting.com/the-economist-who-cried-wolf/#_ftn2. Other studies that have found little success of professional forecasts of recessions at longer horizons include Zarnowitz and Braun, 1992; Abreu, 2011; and An et al. 2018.
7. Koopmans, 1947, p. 166.
8. Boulding, 1969, p. 2. In January 2018, there was a special posthumous session in Boulding’s honor at the annual meeting of the American Economic Association in Philadelphia, “Kenneth Boulding and the Future Direction of Social Science.”
9. Boulding, 1969, p. 3.
10. Irving Kristol, “The Myth of Business Confidence,” Wall Street Journal, November 14, 1977, p. 22.
11. Addams headed an international women’s conference in Zurich in 1919 that issued a statement predicting that the Versailles treaty would create animosities that would lead to future wars. She won the Nobel Peace Prize in 1931.
12. Keynes, 1920 [1919], p. 268.
13. My publications started with my 1972 doctoral dissertation at the Massachusetts Institute of Technology entitled “Rational Expectations and the Structure of Interest Rates.” That dissertation, written under the supervision of Franco Modigliani, who influenced me in trying to find a realistic grounding for economic theories, was not fully comfortable with the economists’ favorite notion that all people are rational and consistent maximizers. Soon thereafter I wrote “Rational Expectations and the Dynamic Structure of Macroeconomic Models: A Critical Review” (1978). I continued with “Stock Prices and Social Dynamics” (1984); Irrational Exuberance (first edition, 2000); and two books coauthored with George Akerlof, Animal Spirits: How Human Psychology Drives the Economy and Why It Matters for Global Capitalism (2009), and Phishing for Phools: The Economics of Manipulation and Deception (2015).
Chapter 1. The Bitcoin Narratives
1. Quoted by Yun Li, “Warren Buffett says bitcoin is a ‘gambling device’ with ‘a lot of frauds connected with it,’ ” CNBC May 4, 2019, https://www.cnbc.com/2019/05/04/warren-buffett-says-bitcoin-is-a-gambling-device-with-a-lot-of-frauds-connected-with-it.html.
2. Paul Vigna and Steven Russolillo, “Bitcoin’s Wildest Rise Yet: 40% in 40 Hours,” Wall Street Journal, December 7, 2017, p. 1.
3. The Merkle tree and the digital signature algorithm are essential elements of the Bitcoin protocol described in the original Bitcoin paper signed by Satoshi Nakamoto in 2008. The equilibrium of the congestion queuing game is described in Huberman et al., 2017.
4. Proudhon 1923 [1840], p. 293.
5. Sterlin Lujan, “Bitcoin Was Built to Incite Peaceful Anarchy,” https://news.bitcoin.com/bitcoin-built-incite-peaceful-anarchy/. Passage is dated January 9, 2016.
6. Ross, 1991, p. 116.
7. Himanen, 2001.
8. Zoë Bernard, “Satoshi Nakamoto was weird, paranoid, and bossy, says early Bitcoin developer who exchanged hundreds of emails with the mysterious crypto creator,” Business Insider, May 30, 2018, http://www.businessinsider.com/satoshi-nakamoto-was-weird-and-bossy-says-bitcoin-developer-2018-5.
Chapter 2. An Adventure in Consilience
1. For example, calls for a broader approach to economic research have asked for the study of “social dynamics” and “popular models” (Shiller, 1984), “culturomics” (Michel et al., 2011), or “humanomics” (McCloskey, 2016); or for more “narrativeness” (Morson and Schapiro, 2017) or for “fictional expectations” (Beckert and Bronk, 2018) or “diagnostic expectations” (Gennaioli and Shleifer, 2018), “policy legends and folklists” (Fine and O’Neill, 2010), or “information-processing difficulty” in responding to news that makes “changes in expectations” into “an independent driver of economic fluctuations” (Beaudry and Portier, 2014).
2. Sarbin, 1986.
3. Berger and Quinney, 2004.
4. Rashkin, 1997.
5. Ganzevoort et al., 2013.
6. Presser and Sandberg, 2015.
7. Bettelheim, 1975.
8. Kozinets et al., 2010.
9. O’Connor, 2000.
10. O’Barr and Conley, 1992.
11. Jung, 1919.
12. Klein, 1921.
13. Klages, 2006, p. 33.
14. Klages, 2006, p. 33.
15. Brooks, 1992, location 74.
16. Brooks, 1992, location 749.
17. For a survey of neurolinguistics, see Kemmerer, 2014.
Chapter 3. Contagion, Constellations, and Confluence
1. World Health Organization, 2015.
2. Wheelis, 2002.
3. Marineli et al., 2013.
4. See also Nagel and Xu, 2018.
5. Vinck et al. 2019, especially table 3.
6. Gerbert et al., 1988.
7. Historians of economic thought (including Dimand, 1988) show that the multiplier accelerator model has even earlier beginnings, via Keynes (1936); before that, Keynes’s student Kahn (1931); before that, a half dozen other multiplier expositors; and before that, even before the 1929 crash, Keynes himself in handwritten notes to himself in preparing for a speech (Kent, 2007). The less pretentious and more metaphoric and visual term “ripple effect” referring to the multiplier (instead of, as formerly, to a pattern or sequence of pleats on clothing) began to go viral around 1970 and by 2000 had surpassed “multiplier effect.”
8. The Samuelson overlapping-generations model was anticipated by Allais (1947), but Allais’s version received little notice; Samuelson does not reference it.
9. These are examples of the “rational ritual” defined by Michael Suk-Young Chwe (2001), rituals undertaken so that people know that other people recognize the narrative, which makes possible a recognizable “common knowledge.”
10. Young, 1987.
11. Writers sometimes describe their craft as looking for stories or vignettes that will serve as “donkeys” for important ideas. See Lawrence Wright, https://www.cjr.org/first_person/longform_podcast_lessons_on_journalism.php.
Chapter 4. Why Do Some Narratives Go Viral?
1. Sartre, 1938, location 952.
2. Pace-Schott, 2013.
3. Polletta, 2002, p. 31.
4. Brown, 1991, location 2852 of 2017 Kindle Edition.
5. Plato, The Republic, bk. 3, trans. Benjamin Jowett, https://www.gutenberg.org/files/1497/1497-h/1497-h.htm.
6. Cicero, 1860 [55 BCE], p. 145.
7. Mineka and Cook, 1988; Curio, 1988.
8. Reeves and Nass, 2003.
9. Brown, 1991; Kirnarskaya, 2009.
10. Jackendoff, 2009.
11. Patel, 2007, p. 324.
12. Newcomb, 1984, p. 234.
13. Hofstadter, 1964.
14. See Fehr and Gächter, 2000.
15. https://www.merriam-webster.com/dictionary/narrative.
16. Kasparov, 2017, p. 138.
17. White, 1981, p. 20.
18. Schank and Abelson, 1977.
19. Shiller, 2002.
20. Thanks to Ryan Larson. It is patent #362,868, 1887, G. I. AP Roberts, https://patents.google.com/patent/US362868A/en.
21. “Come What May: A Wheel of an Idea,” Christian Science Monitor, October 24, 1951, p. 13.
22. Display ad, Los Angeles Times, July 29, 1991, p. A4.
23. Salganik et al., 2016.
Chapter 5: The Laffer Curve and Rubik’s Cube Go Viral
1. Shiller, 1995.
2. Litman, 1983.
3. Jack Valenti, in a speech “Motion Pictures and Their Impact on Society in the Year 2001” (April 25, 1978), quoted in Litman, 1983, p. 159.
4. Goldman, 2012, location 695.
5. For lists of exceptional one-hit wonders, see Wikipedia, https://en.wikipedia.org/wiki/One-hit_wonder.
6. “[A tax] may obstruct the industry of the people, and discourage them from applying to certain branches of business which might give maintenance and employment to great multitudes.” Smith, 1869, p. 416.
7. Cheney was as of 1978 soon to be White House chief of staff, later secretary of defense and vice president of the United States.
8. Rumsfeld was as of 1978 recently secretary of defense.
9. http://americanhistory.si.edu/blog/great-napkins-laffer.
10. Peter Liebhold, “O Say Can You See,” http://americanhistory.si.edu/blog/great-napkins-laffer.
11. Arthur B. Laffer, “The Laffer Curve: Past, Pre
sent and Future,” January 6, 2004, https://www.wiwi.uni-wuerzburg.de/fileadmin/12010500/user_upload/skripte/ss09/FiwiI/LafferCurve.pdf.
12. Paul Blustein, “New Economics: Supply-Side Theories Became Federal Policy with Unusual Speed,” Wall Street Journal, October 8, 1981, p. 1.
13. See Mirowski, 1982.
14. Brill and Hassett, 2007.
15. Cicero, 1860 [55 BCE], pp. 187–88.
16. McDaniel and Einstein, 1986.
17. Lorayne, 2007, p. 18.
18. See Paller and Wagner, 2002.
19. “Second Look in Sweden,” Boston Globe, September 21, 1976, p. 26.
20. In 1989, it was pointed out that it was possible for an elderly person in the United States to pay more in taxes than 100% of an increase in income because of the combined effect of the tax on Social Security benefits and the Medicare surtax. James Kilpatrick, “Elderly Run Faster, Fall Further Behind,” St. Louis Post Dispatch, March 19, 1989, p. B3.
21. US Tax Policy Center, https://www.taxpolicycenter.org/statistics/historical-individual-income-tax-parameters.
22. Patrick Owens, “What’s behind the Tax Revolt?” Newsday, June 2, 1978, p. 75.
23. https://www.gop.gov/9-ronald-reagan-quotes-about-taxes/.
24. Walter Trohan, “Report from Washington,” Chicago Tribune, February 20, 1967, p. 4.
25. Steven V. Roberts, “Washington Talk; Reagan and the Russians: The Joke’s on Them,” New York Times, August 21, 1987, p. A1.
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