Beyond existence and aiming outside the laboratory: Estimating frequency-dependent and payoﬀ-biased social learning strategies
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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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..
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