Conformists and mavericks: the empirics of frequency-dependent cultural transmission☆
Introduction
Social scientists have long recognized the importance of frequency-dependent social learning (Asch, 1956, Bowles, 2004, Boyd & Richerson, 1985, Boyd & Richerson, 2005, Henrich & McElreath, 2003, Lumsden & Wilson, 1980, Richerson & Boyd, 2005, Sherif & Murphy, 1936). Frequency-dependent social learning postulates that individuals adopt a given behavior with a probability that varies in response to how common the behavior is in a relevant social group. Conformity is a type of frequency dependence that has received considerable attention. As formally defined (Boyd & Richerson, 1982, Boyd & Richerson, 1985), conformity is based on the following proposition. In a simple case with two behaviors R and B, where rt is the frequency of R in the population, conformity means that, in the near future, an individual exhibits behavior R with a probability less than rt if rt<1/2, but with a probability greater than rt if rt>1/2. In other words, individuals do not simply follow the majority; rather, they show a disproportionate tendency to follow the majority. They overrespond, so to speak, to frequency information. This feature of conformity is crucial because, as we show below, it homogenizes behavior within social groups. Other types of frequency dependence do not have this effect.
In spite of conformity's acknowledged importance, previous empirical research cannot identify conformity as a disproportionate tendency to follow the majority. Classic research in social psychology (Aronson et al., 2002, Asch, 1955, Asch, 1956), neuroscience experiments (Berns et al., 2005) in the tradition of Asch, and recent experiments with chimpanzees (Whiten, Horner, & de Waal, 2005) show that a focal individual is more likely to adopt a behavior as that behavior becomes more common. A simple model of nonconformity, however, makes exactly the same prediction, as do other hypotheses about positive social influences.
The distinctions, however, among different forms of positive influence are fundamental and not simply matters of definition. Below we present a general model of frequency dependence that includes both conformity and nonconformity as special cases. Although individual psychology is different for conformity and nonconformity, in both cases, the probability that a focal individual adopts a given behavior increases with the frequency of the behavior in the social group. Even so, dynamics at the group level are radically different. Conformity produces multiple steady states and can lead otherwise similar societies to evolve in completely different ways in the wake of small random effects (Bowles, 2004, Efferson & Richerson, 2007). Whatever behavioral variation may exist between groups, however, conformity produces social groups that are internally homogeneous in terms of behavior. Nonconformity, in contrast, increases behavioral variation within groups and decreases variation between groups (Efferson & Richerson, 2007). Whatever names we choose to attach to these forms of social influence, such distinctions are important. To demonstrate conformity as a force that homogenizes behavior within social groups, it is not enough to show simply that individuals adopt common behaviors. Researchers must also show that this inclination is disproportionate in the way described above. In this paper, we present a jointly theoretical and experimental approach to this problem.
Apart from its intrinsic interest, conformity has figured prominently in various discussions in the behavioral and evolutionary social sciences. Theoretically, conformity can be a valuable way to make good decisions in temporally and spatially variable environments (Henrich & Boyd, 1998). Imagine that R and B are two existing technologies. Individuals would like to choose the optimal technology, but returns are stochastic. The environment also varies in space and time, and so identifying the optimal technology is not easy. Assume that individuals experiment from time to time and learn individually as a result, and this produces a slight bias toward the optimal technology. As we show below, conformity exaggerates such a bias by filtering out a lot of the noise at the individual level. A powerful signal pointing toward the optimal technology is the result. By itself, however, conformity implies nothing about the optimality of individual decisions. It only exaggerates existing biases.
In addition to decision making, conformity has the interesting theoretical property that it reduces behavioral variation within populations while potentially increasing variation among populations. All else being equal, this increases the strength of selective pressures at the group level. Thus, in conjunction with the punishment of norm violations and the imitation of success, conformity plays a critical role in the study of how prosocial tendencies could have evolved in humans via cultural group selection (Boyd et al., 2003, Boyd & Richerson, 1982, Fehr & Fischbacher, 2003, Fehr & Fischbacher, 2004, Fehr & Gaechter, 2002, Guererk et al., 2006, Guzmán et al., 2006, Henrich, 2004, Henrich & Boyd, 2001). Conformity also appears to be critical in explaining aggregate patterns that characterize the diffusion of technological innovations (Henrich, 2001, Rogers, 1995).
Section snippets
How conformity works
Theoretically, conformity can be a valuable way to make decisions under uncertainty. Importantly, however, conformity is neither good nor bad by itself. It merely exaggerates existing biases in individual decision making. To illustrate, assume that technology R is optimal. Consider a group of N individuals, each of whom chooses R in a given period with probability rt. The probability that a majority of the individuals in the social group chooses the optimal technology when N is odd is simply,
The empirics of frequency dependence
As suggested in the Introduction, conformity should not be defined simply as any positive social influence. Such an approach neglects important distinctions between different types of frequency dependence, some of which produce internally homogeneous groups, and others of which produce social groups that are maximally heterogeneous. Here we outline the distinctions necessary to integrate theory and empiricism into the study of frequency-dependent social learning.
Experimental methods
With 70 students at the University of Zürich and the Swiss Federal Institute of Technology, we conducted the following experiment. In each period, each player faced a choice between one of two technologies (“red” vs. “blue”). Payoffs followed truncated normal distributions, but one color was optimal in that its payoff distribution had a higher expectation. Specifically, payoffs in experimental currency units for the suboptimal technology were distributed N(30,12), and payoffs for the optimum
Results and discussion
The value of conformity in this experiment depended on the effectiveness of individual learning. Individual learning was highly effective. Individual learners exhibited a roughly uniform distribution over the two colors in Period 1, but the proportion choosing the optimal color increased dramatically as the 25 periods progressed. Specifically, regressing the proportion of individual learners choosing optimally on period, using the method of Newey and West (1987) to correct for
Acknowledgments
The authors would like to thank all the members in the chair of Ernst Fehr at the Institute for Empirical Research in Economics for assistance and comments. Particular thanks go to Urs Fischbacher for his z-Tree program, which was used to produce animated histograms. We would also like to acknowledge two anonymous reviewers for much helpful input.
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C.E. thanks the US Environmental Protection Agency (STAR fellowship no. U-91615601-3) for financial support. This research was funded by a US National Science Foundation grant (award no. 0340148 to P.J.R., R.M., and M.L.).