Evolutionary Game Theoretic Modeling of Decision Making and Culture

Title of dissertation: EVOLUTIONARY GAME THEORETIC MODELING OF DECISION MAKING AND CULTURE Patrick Roos, Doctor of Philosophy, 2012 Dissertation directed by: Dana Nau Department of Computer Science Evolutionary Game Theory (EGT) has become an attractive framework for modeling human behavior because it provides tools to explicitly model the dynamics of behaviors in populations over time and does not require the strong rationality assumptions of classical game theory. Since the application of EGT to human behavior is still relatively new, many questions about human behavior and culture of interest to social scientists have yet to be examined through an EGT perspective to determine whether explanatory and predictive rather than merely descriptive insights can be gained. In this thesis, informed by social science data and under close collaboration with social scientists, I use EGT-based approaches to model and gain a qualitative understanding of various aspects of the evolution of human decision-making and culture. The specific phenomena I explore are i) risk preferences and their implications on the evolution of cooperation and ii) the relationship between societal threat and the propensity with which agents of societies punish norm-violating behavior. First, inspired by much empirical research that shows human risk-preferences to be state-dependent rather than expected-value-maximizing, I propose a simple sequential lottery game framework to study the evolution of human risk preferences. Using this game model in conjunction with known population dynamics provides the novel insight that for a large range of population dynamics, the interplay between risk-taking and sequentiality of choices allows state-dependent risk behavior to have an evolutionary advantage over expected-value maximization. I then demonstrate how this principle can facilitate the evolution of cooperation in classic game-theoretic games where cooperation entails risk. Next, inspired by striking differences across cultural groups in their willingness to punish norm violators, I develop evolutionary game models based on the Public Goods Game to study punishment behavior. Operationalizing various forms of societal threat and determining the relationship between these threats and evolved punishment propensities, these models show how cross-cultural differences in punishment behavior are at least partially determined by cultures’ exposure to societal threats, providing support for social science theories hypothesizing that higher threat is a causal factor for higher punishment propensities. This work advances the state of the art of EGT and its applications to the social sciences by i) creating novel EGT models to study different phenomena of interest in human decision-making and culture, and ii) using these models to provide insights about the relationships between variables in these models and their impact on evolutionary outcomes. By developing and analyzing these models under close consideration of relevant social science data, this work not only advances our understanding of how to use evolutionary game and multi-agent system models to study social phenomena, but also lays the foundation for more complex explanatory and predictive tools applicable to behaviors in human populations.