Everything Is Obvious (39 page)

  
1.
 See Riding (2005) for the statistic about visitors. See
http://en.wikipedia.org/wiki/Mona_Lisa
for other entertaining details about the
Mona Lisa
.

  
2.
 See Clark (1973, p. 150).

  
3.
 See Sassoon (2001).

  
4.
 See Tucker (1999) for the full article on
Harry Potter
. See (Nielsen 2009)
for details of their Facebook analysis. See Barnes (2009) for the story on movies.

  
5.
 For the story about changes in consumer behavior postrecession, see Goodman (2009). Bruce Mayhew (1980) and Frank Dobbin (1994) have both made a similar argument about circular reasoning.

  
6.
 This argument was made long ago by the physicist Philip Anderson in a famous paper titled “More Is Different” (Anderson 1972).

  
7.
 For Thatcher’s original quote, see Keay (1987).

  
8.
 The definition of “methodological individualism” is typically traced to the early twentieth century in the writings of the Austrian economist Joseph Schumpeter (1909, p. 231); however, the idea goes back much earlier, at least to the writings of Hobbes, and was popular among the thinkers of the Enlightenment, for whom an individualistic view of action fit perfectly with their emerging theories of rational action. See Lukes (1968) and Hodgson (2007) for a discussion of the intellectual origins of methodological individualism, as well as a scathing critique of its logical foundations.

  
9.
 I am oversimplifying here, but not a lot. Although the original models of business cycles did assume a single representative agent, more recent models allow for multiple agents, each of which represents different sectors of the economy (Plosser 1989). Nevertheless, the same essential problem arises in all these models: the agents are not actually real people, or even firms, who pay attention to what other people and firms are doing, but rather are representative agents who make decisions on behalf of a whole population.

10.
 A number of excellent critiques of the representative individual idea have been written, most notably by the economist Alan Kirman (1992). That the criticism is so well known, however, and yet has had so little influence on the actual practice of social science, should demonstrate how difficult a problem it is to expunge.

11.
 Even rational choice theorists—who are as much as anyone the inheritors of methodological individualism—are in practice just as comfortable applying the principle of utility maximization to social actors like households, firms, unions, “elites,” and government bureaus as to individual people. See Becker (1976), Coleman and Fararo (1992), Kiser and Hechter (1998), and Cox (1999) for numerous examples of representative agents employed in rational choice models.

12.
 See Granovetter (1978) for details of the “riot model.”

13.
 For more details on the origins of social influence, see Cialdini (2001) and Cialdini and Goldstein (2004)

14.
 For examples of cumulative advantage models, see Ijiri and Simon (1975), Adler (1985), Arthur (1989), De Vany and Walls (1996), and De Vany (2004).

15.
 For the “army in a lab” quote, see Zelditch (1969). Experiments, it should be noted, are not entirely foreign to sociology. For example, the field of “network exchange” is one area of sociology in which it is common to run lab experiments, but these networks generally comprise only four or five individuals (Cook et al. 1983; Cook et al. 1993). Cooperation studies
in behavioral economics, political science, and sociology also use experiments, but once again the groups involved are small (Fehr and Fischbacher 2003).

16.
 See Salganik, Dodds, and Watts (2006) for a detailed description of the original Music Lab experiment.

17.
 See Salganik and Watts (2009b; 2009a) for more background on Music Lab, and details of follow-up experiments.

  
1.
 The movie
The Social Network
, about the founding of Facebook, was released in 2010. The Fosters beer commercial is available at
http://www.youtube.com/watch?v=nPgSa9djYU8
.

  
2.
 For a history of social network analysis, see Freeman (2004). For summaries of the more recent literature on network science, see Newman (2003), Watts (2004), Jackson (2008), and Kleinberg and Easley (2010). For more popular accounts, see Watts (2003) and Christakis and Fowler (2009).

  
3.
 See Leskovec and Horvitz (2008) for details of the Microsoft instant messenger network study.

  
4.
 See Jacobs (1961, pp. 134–35).

  
5.
 Milgram did not invent the phrase “six degrees of separation,” referring only to the “small world problem.” Instead, it was the playwright John Guare who wrote a play with that title in 1990. Oddly, Guare has credited the origin of the phrase to Guglielmo Marconi, the Italian inventor and developer of radiotelegraphy, who reportedly said that in a world connected by the telegraph, everyone would be connected to everyone else via only six degrees of separation. According to numerous citations on the web (see, e.g.
http://www.megastarmedia.us/mediawiki/index.php/Six_degrees_of_separation
), Marconi is supposed to have made this claim during his Nobel Prize lecture in 1909. Unfortunately, the speech itself (
http://nobelprize.org/nobel_prizes/physics/laureates/1909/marconi-lecture.html
) makes no mention of the concept; nor have I been able to locate the source of Marconi’s quote anywhere else. Regardless of the ultimate origin of the phrase, however Milgram deserves the credit for having been the first to put some evidence behind it.

  
6.
 As a number of critics have noted, Milgram’s results were less conclusive than they have sometimes been portrayed (Kleinfeld 2002). In particular, of the three hundred chains that started out to reach the target, a third began in Boston itself, and another third began with individuals in Omaha who were investors in the stock market—which at the time would have required them to have access to a stockbroker. Seeing as the sole target of the experiment was a Boston stockbroker, it is not so surprising anymore that these chains could reach him. Thus the most compelling evidence for the small-world hypothesis came from the ninety-six chains that began with randomly selected people in Omaha, and only seventeen of these chains actually made it. Given these uncertainties, one has to be careful when placing
too much weight on the role of people like Mr. Jacobs, who could easily have been a statistical fluke. Indeed, Milgram himself noted as much, claiming only that “the convergence of communication chains through common individuals is an important feature of small world nets, and it should be accounted for theoretically.”

  
7.
 See Gladwell (1999).

  
8.
 Naturally, how many friends you count people as having depends a lot on how you define “friendship,” a concept that has always been ambiguous, and is even more so now in the era of social networking sites, where you can “friend” someone you don’t even know. The result is that what we might call “true” friendship has become difficult to distinguish from mere “acquaintanceship,” which in turn has gotten blurred together with the even more ephemeral notion of “one-way acquaintanceship” (i.e., “I’ve heard of you, but you don’t know me from Adam”). Although some people on MySpace have a million “friends,” as soon as we apply even the loosest definition of friendship, such as each person knowing the other on a first-name basis, the number immediately drops to the range of a few hundred to a few thousand. Interestingly, this range has remained surprisingly constant since the first studies were conducted in the late 1980s (McCormick et al. 2008; Bernard et al. 1989, 1991; Zheng et al. 2006).

  
9.
 There are a number of subtleties to the issue of chain lengths in small-world experiments that have led to a certain amount of confusion regarding what can and cannot be concluded from the evidence. For details about the experiment itself, see Dodds, Muhamad, and Watts (2003), and for a clarifying discussion of the evidence, as well as a detailed analysis of chain lengths, see Goel, Muhamad, and Watts (2009).

10.
 See Watts and Strogatz (1998); Kleinberg (2000a; 2000b); Watts, Dodds, and Newman (2002); Watts (2003, ch. 5); Dodds, Muhamad, and Watts (2003); and Adamic and Adar (2005) for details on the searchability of social networks.

11.
 Influencers go by many names. Often they are called opinion leaders or influentials but they are also called e-fluentials, mavens, hubs, connectors, alpha mums, or even passionistas. Not all of these labels are intended to mean exactly the same thing, but they all refer to the same basic idea that a small number of special individuals have an important effect on the opinions, beliefs, and consumption habits of a large number of “ordinary” individuals (see Katz and Lazarsfeld 1955, Merton 1968b, Weimann 1994, Keller and Berry 2003, Rand 2004, Burson-Marsteller 2001, Rosen 2000, and Gladwell 2000 for a range of influentials-related labels). Ed Keller and Michael Berry claim that “One in ten Americans tells the other nine how to vote, where to eat, and what to buy.” They conclude, in fact, that “Few important trends reach the mainstream without passing through the Influentials in the early stages, and the Influentials can stop a would-be trend in its tracks” (Keller and Berry 2003, pp. 21–22); and the market-research firm Burson-Marsteller concurs, claiming that “The far-reaching effect of this powerful group of men and women can make or break a brand, marshal
or dissolve support for business and consumer issues, and provide insight into events as they unfold.” All one needs to do, it seems, is to find these individuals and influence them. As a result, “Influencers have become the ‘holy grail’ for today’s marketers” (Rand 2004).

12.
 For the original quote, see Gladwell (2000, pp. 19–21).

13.
 See Keller and Berry (2003, p. 15).

14.
 See, for example, Christakis and Fowler (2009), Salganik et al. (2006), and Stephen (2009).

15.
 In fact, even then you can’t be sure. If A and B are friends, they are likely to have similar tastes, or watch similar shows on TV and so be exposed to similar information; thus what looks like influence may really just be homophily. So if every time a friend of A’s adopts something that A adopts, we attribute that to A’s influence, we are probably overestimating how influential A is. See Aral (2009), Anagostopoulos et al. (2008), Bakshy et al. (2009), Cohen-Cole and Fletcher (2008b, 2008a) Shuliti and Thomas (2010), and Lyons (2010) for more details on the issue of similarity versus influence.

16.
 See Katz and Lazarsfeld (1955) for a discussion of the difficulty of measuring influence, along with a more general introduction to personal influence and opinion leaders. See Weimann (1994) for a discussion of proxy measures of influence.

17.
 See Watts (2003) and Christakis and Fowler (2009) for discussions of contagion in social networks.

18.
 The connection between influentials and contagion is most explicit in Gladwell’s analogy of “social epidemics,” but a similar connection is implied throughout the literature on influentials. Everett Rogers (1995, p. 281) claims that “The behavior of opinion leaders is important in determining the rate of adoption of an innovation in a system. In fact, the S-shape of the diffusion curve occurs because once opinion leaders adopt and tell others about the innovation, the number of adopters per unit time takes off.” Keller and Berry make a similar point when they claim that influentials are “like the central processing units of the nation. Because they know many people and are in contact with many people in the course of a week, they have a powerful multiplier effect, spreading the word quickly across a broad network when they find something they want others to know about” (Keller and Berry 2003, p. 29).

19.
 For details of the models, see Watts and Dodds (2007).

20.
 The original Bass model is described by Bass (1969).

21.
 See Gladwell (2000, p. 19).

22.
 A number of people interpreted this result as a claim that “influentials don’t exist,” but that’s actually not what we said. To begin with, as I’ve discussed, there are so many different kinds of influentials that it would be impossible to rule them all out even if that was what we intended to do. But we didn’t intend to do that. In fact, the whole point of our models was to assume the existence of influentials and see how much they mattered relative to ordinary individuals. Another misconception regarding our paper was that we had claimed that “influentials don’t matter,” but that’s not what we said
either. Rather, we found only that influentials are unlikely to play the role described by the law of the few. Whether or not influentials, defined somehow, can be reliably identified and exploited in some manner remains an open question.

23.
 See Adar and Adamic (2005); Sun, Rosenn, Marlow, and Lento (2009); Bakshy, Karrer, and Adamic (2009); and Aral et al. (2009) for details.

24.
 For details of the Twitter study see Bakshy et al (2010).

25.
 For the anecdote about Kim Kardashian’s $10,000 Tweets, see Sorkin (2009, b).

  
1.
 A number of sociologists have even argued explicitly that history ought to be a scientific discipline with its own laws and methods for extracting them (Kiser and Hechter 1998). Historians, meanwhile, have been more circumspect regarding the scientific status of their discipline but have nonetheless been tempted to draw analogies between their own practices and those of natural scientists (Gaddis 2002).

  
2.
 See Scott (1998) for a discussion of what he calls
metis
(the Greek word for “skill”), meaning the collection of formal decision procedures, informal rules of thumb, and trained instinct that characterized the performance of experienced professionals.

  
3.
 For more on creeping determinism and hindsight bias, see the classic article by Baruch Fischhoff (1982). Philosophers and psychologists disagree over how strong our psychological bias to think deterministically really is. As Roese and Olson (1996) point out, people frequently do engage in counterfactual thinking—imagining, for example, how things might have worked out “if only” some antecedent event had not taken place—suggesting that commonsense views of causality are more conditional than absolute. A more correct way to state the problem, therefore, is that we systematically overweight the likelihood of what happened relative to the counterfactual outcomes. For the purpose of my argument, however, it is sufficient that we do the latter.

  
4.
 See Dawes (2002, Chapter 7) for the full story of Flight 2605 and analysis.

  
5.
 See Dawes (2002) and Harding et al. (2002) for more on school shootings.

  
6.
 See Gladwell (2000, p. 33)

  
7.
 See Tomlinson and Cockram (2003) for details on the SARS outbreaks in the Prince of Wales Hospital and the Amoy Gardens apartment complex. Various theoretical models (Small et al. 2004; Bassetti et al. 2005; Masuda et al. 2004) have subsequently been proposed to explain the SARS epidemic in terms of superspreaders.

  
8.
 See Berlin (1997, p. 449).

  
9.
 Gaddis (2002), in fact, makes more or less this argument.

10.
 For the full argument, see Danto (1965).

11.
 For the full story of Cisco, see Rosenzweig (2007).

12.
 See Gaddis (2002).

13.
 
See Lombrozo (2007) for details of the study. It should be noted that when told in simple terms the relative probabilities of the different explanations, participants did in fact choose the more complex explanation at a much higher rate. Such explicit information, however, is rarely available in real-world scenarios.

14.
 See Tversky and Kahneman (1983) for details.

15.
 For evidence of confidence afforded by stories, see Lombrozo (2006, 2007) and Dawes (2002, p. 114). Dawes (1999), in fact, makes the stronger argument that human “cognitive capacity shuts down in the absence of a story.”

16.
 For example, a preference for simplicity in explanations is deeply embedded in the philosophy of science. The famous Ockham’s razor—named for the fourteenth-century English logician William of Ockham—posits that “plurality ought never be posited without necessity,” meaning essentially that a complex theory ought never to be adopted where a simpler one would suffice. Most working scientists regard Ockham’s razor with something close to reverence—Albert Einstein, for example, once claimed that a theory “ought to be as simple as possible, and no simpler”—and the history of science would seem to justify this reverence, filled as it is with examples of complex and unwieldy ideas being swept away by simpler, more elegant formulations. What is perhaps less appreciated about the history of science is that it is also filled with examples of initially simple and elegant formulations becoming increasingly more complex and inelegant as they struggle to bear the burden of empirical evidence. Arguably, in fact, it is the capacity of the scientific method to pursue explanatory power, even at the cost of theoretical elegance and parsimony, where its real strength lies.

17.
 For Berlin’s full analysis of the differences between science and history, and the impossibility of remaking the latter in the image of the former, see Berlin (1960).

18.
 See Gaddis (2002) for a warning about the perils of generalizing, and also some examples of doing just that.

19.
 George Santayana (1905).