Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies

Abstract

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behavior, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyze an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behavior, we require statistical methods that do not depend upon tight experimental control. Therefore we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest most participants employ a hierarchical strategy that uses both average observed
payoffs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.