It is generally thought that cognition evolved to help us navigate complex environments. Social interactions make up one part of a complex environment, and some have argued that social settings are crucial to the evolution of cognition. This article uses the methods of evolutionary game theory to investigate the effect of social interaction on the evolution of cognition broadly construed as strategic learning or plasticity. I delineate the conditions under which social interaction alone, apart from any additional external environmental variation, (...) can provide the selective pressure necessary for the initial evolution of learning. Furthermore, it is argued that in the context of social interactions we should notexpect traditional learners that ‘best-respond’ to dominate the population. Consequently, it may be important to consider non-traditional learners when modelling social evolution. 1 Introduction2 The Model3 Adapting to the Population3.1 Learning an equlibrium4 Adapting to Individuals4.1 Learning a best response and non-traditional learners5 Conclusion. (shrink)

Biologists and philosophers of biology have argued that learning rules that do not lead organisms to play evolutionarily stable strategies (ESSes) in games will not be stable and thus not evolutionarily successful. This claim, however, stands at odds with the fact that learning generalization---a behavior that cannot lead to ESSes when modeled in games---is observed throughout the animal kingdom. In this paper, I use learning generalization to illustrate how previous analyses of the evolution of learning have gone wrong. It has (...) been widely argued that the function of learning generalization is to allow for swift learning about novel stimuli. I show that in evolutionary game theoretic models learning generalization, despite leading to suboptimal behavior, can indeed speed learning. I further observe that previous analyses of the evolution of learning ignored the short term success of learning rules. If one drops this assumption, I argue, it can be shown that learning generalization will be expected to evolve in these models. I also use this analysis to show how ESS methodology can be misleading, and to reject previous justifications about ESS play derived from analyses of learning. (shrink)

Standard applications of evolutionary game theory look at a single game and focus on the evolution of behavior for that game alone. Instead, this article uses tools from evolutionary game theory to study the competition between choice mechanisms in a rich and variable multigame environment. A choice mechanism is a way of subjectively representing a decision situation, paired with a method for choosing an act based on this subjective representation. We demonstrate the usefulness of this approach by a case study (...) that shows how subjective representations in terms of regret that differ from the actual fitness can be evolutionarily advantageous. (shrink)

Compositionality is a key design feature of human language: the meaning of complex expressions is, for the most part, systematically constructed from the meanings of its parts and their manner of composition. This paper demonstrates that rudimentary forms of compositional communicative behavior can emerge from a variant of reinforcement learning applied to signaling games. This helps explain how compositionality could have emerged gradually: if unsophisticated agents can evolve prevalent dispositions to communicate compositional-like, there is a direct evolutionary benefit for adaptations (...) that exploit the systematicity in form-meaning mappings more rigorously. (shrink)