Human strategy updating in evolutionary games

Abstract

Evolutionary game dynamics describe not only frequency-dependent genetic evolution, but also cultural evolution in humans. In this context, successful strategies spread by imitation. It has been shown that the details of strategy update rules can have a crucial impact on evolutionary dynamics in theoretical models and, for example, can significantly alter the level of cooperation in social dilemmas. What kind of strategy update rules can describe imitation dynamics in humans? Here, we present a way to measure such strategy update rules in a behavioral experiment. We use a setting in which individuals are virtually arranged on a spatial lattice. This produces a large number of different strategic situations from which we can assess strategy updating. Most importantly, spontaneous strategy changes corresponding to mutations or exploration behavior are more frequent than assumed in many models. Our experimental approach to measure properties of the update mechanisms used in theoretical models will be useful for mathematical models of cultural evolution.update mechanisms | cooperation | finite populations | structured populations C lassical game theory assumes that agents make rational decisions, taking into account that they are interdependent with other agents that are also fully rational (1). Although this assumption has proved to be problematic even in humans, evolutionary game theory has been developed to describe the dynamics of genetic or cultural evolution when fitness is not fixed but depends on the interactions with others. Applications of this framework range from the dynamics of microbes (2-4) to animal behavior (5, 6) and human behavior (7-9). Many aspects of evolutionary dynamics hinge on the microscopic rules describing how successful strategies spread. In particular, in structured populations, these rules can crucially alter the evolutionary outcome and, for example, determine whether cooperation evolves or not (10-12). Thus, it is of great importance to infer how strategies are actually adopted. To this end, we have developed a behavioral experiment that mimics typical properties of theoretical models but replaces the computer agents by real human players. Each player interacts only with his or her immediate neighbors. To evaluate his or her performance, each player can compare his or her payoff with the payoff of the neighbors and use this as a basis to adopt new strategies. However, there are some subtle differences between mathematical models and human behavior: Humans may use mixed strategies (i.e., randomize between their options) or even change their strategies over time, whereas most theoretical models consider the simplest case in which a player's strategy is equated with his action. Thus, any change in behavior is equated to a change in strategy. If we aim to apply this simple framework of one-shot games as a first approximation to describe human behavior, we have to infer the details of strategy adoption (e.g., the rate of spontaneous strategy changes). We use a spatial game in which human...

Consistent individual differences in human social learning strategies

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Effects of punishment in a mobile population playing the prisoner's dilemma game

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Heterogeneous networks do not promote cooperation when humans play a Prisoner’s Dilemma

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