Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item An Approximation Framework for Large-Scale Spatial Games(2023) Hsiao, Vincent; Nau, Dana; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Game theoretic modeling paradigms such as Evolutionary Games and Mean Field Games (MFG) are used to model a variety of multi-agent systems in which the agents interact in a game theoretic fashion. These models seek to answer two questions: how to predict the forward dynamics of a population and how to control them. However, both modeling paradigms have unique issues that can make them difficult to analyze in closed form when applied to spatial domains. On one hand, spatial EGT models are difficult to evaluate mathematically and both simulations and approximations run into accuracy and tractability issues. On the other hand, MFG models are not typically formulated to handle domains where agents have strategies and physical locations. Furthermore, any MFG approach for controlling strategy evolution on spatial domains need also address the same accuracy and efficiency challenges in the evaluation of its forward dynamics as those faced by evolutionary game approaches. This dissertation presents a new modeling paradigm and approximation technique termed Bayesian-MFG for large-scale multi-agent games on spatial domains. The new framework lies at an intersection of techniques drawn from spatial evolutionary games, mean field games, and probabilistic reasoning. First, we describe our Bayesian network approximation technique for spatial evolutionary games to address the accuracy issues faced by lower order approximation methods. We introduce additional algorithms used to improve the computational efficiency of Bayesian network approximations. Alongside this, we describe our Pair-MFG model, a method for defining pair level approximate MFG for problems with distinct strategy and spatial components. We combine the pair-MFG model and Bayesian network approximations into a unified Bayesian-MFG framework. Using this framework, we present a method for incorporating Bayesian network approximations into a control problem framework allowing for the derivation of more accurate control policies when compared to existing MFG approaches. We demonstrate the effectiveness of our framework through its application to a variety of domains such as evolutionary game theory, reaction-diffusion equations, and network security.Item EVOLUTIONARY GAME THEORETIC MODELING OF DECISION MAKING AND CULTURE(2012) Roos, Patrick; Nau, Dana S; Gelfand, Michele J; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.Item Generation and Analysis of Strategies in an Evolutionary Social Learning Game(2013) Carr, James Ryan; Nau, Dana; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)An important way to learn new actions and behaviors is by observing others, and several evolutionary games have been developed to investigate what learning strategies work best and how they might have evolved. In this dissertation I present an extensive set of mathematical and simulation results for Cultaptation, which is one of the best-known such games. I derive a formula for measuring a strategy's expected reproductive success, and provide algorithms to compute near-best-response strategies and near-Nash equilibria. Some of these algorithms are too complex to run quickly on larger versions of Cultaptation, so I also show how they can be approximated to be able to handle larger games, while still exhibiting better performance than the current best-known Cultaptation strategy for such games. Experimental studies provide strong evidence for the following hypotheses: 1. The best strategies for Cultaptation and similar games are likely to be conditional ones in which the choice of action at each round is conditioned on the agent's accumulated experience. Such strategies (or close approximations of them) can be computed by doing a lookahead search that predicts how each possible choice of action at the current round is likely to affect future performance. 2. Such strategies are likely to prefer social learning most of the time, but will have ways of quickly detecting structural shocks, so that they can switch quickly to individual learning in order to learn how to respond to such shocks. This conflicts with the conventional wisdom that successful social-learning strategies are characterized by a high frequency of individual learning; and agrees with recent experiments by others on human subjects that also challenge the conventional wisdom.