{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/14378"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/14378","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"Three Essays on the Relationship Between Game Theory and Agent-Based Modeling","abstract":"Game theory (GT) involves formal analysis of the decisions faced by strategic agents in environments in which the payoffs received by the agents depend on the actions of all. Typically, agents in GT are treated as individually rational, that is, they do what’s best for themselves by making rational choices. GT has been widely applied in social science contexts. Agent-based modeling (ABM) is a computational methodology for representing the behavior of heterogeneous boundedly rational agents who interact through networks, away from equilibrium, i.e., more realistically than in rational choice game theory.This dissertation investigates the intersection between GT and ABM. Specifically, I have built three ABMs that relax some of the mathematically-precise but unrealistic assumptions of conventional game theory including rational choice. The first ABM involves combining social network and ‘walk away’ strategies for the Prisoner’s Dilemma. The second involves transitions from inequitable to equitable norms through collaboration in the context of the Nash demand game. The third involves a strategic context—a vehicular intersection with 4 stop signs—in which agents use their own, local ABMs in order to figure out how to best behave. With my first model, I show that high levels of cooperation in the Prisoner’s Dilemma can be sustained through a variety of mechanisms that permit the emergence of self-organized groups of cooperators. By shifting defectors high payoffs are achieved, endogenously. With the second model, I show that collaboration in inequitable regimes can lead to higher payoff for the overall population and the changes in payoff can be understood computationally despite being hard to deduce analytically. In my third model, I show that a wide variety of outcomes are possible when interacting agents build models of one another, in a 4-way stop sign situation. Incorrect perceptions and imperfect models can result in bad outcomes.","abstract_html":"Game theory (GT) involves formal analysis of the decisions faced by strategic agents in environments in which the payoffs received by the agents depend on the actions of all. Typically, agents in GT are treated as individually rational, that is, they do what’s best for themselves by making rational choices. GT has been widely applied in social science contexts. Agent-based modeling (ABM) is a computational methodology for representing the behavior of heterogeneous boundedly rational agents who interact through networks, away from equilibrium, i.e., more realistically than in rational choice game theory.This dissertation investigates the intersection between GT and ABM. Specifically, I have built three ABMs that relax some of the mathematically-precise but unrealistic assumptions of conventional game theory including rational choice. The first ABM involves combining social network and ‘walk away’ strategies for the Prisoner’s Dilemma. The second involves transitions from inequitable to equitable norms through collaboration in the context of the Nash demand game. The third involves a strategic context—a vehicular intersection with 4 stop signs—in which agents use their own, local ABMs in order to figure out how to best behave. With my first model, I show that high levels of cooperation in the Prisoner’s Dilemma can be sustained through a variety of mechanisms that permit the emergence of self-organized groups of cooperators. By shifting defectors high payoffs are achieved, endogenously. With the second model, I show that collaboration in inequitable regimes can lead to higher payoff for the overall population and the changes in payoff can be understood computationally despite being hard to deduce analytically. In my third model, I show that a wide variety of outcomes are possible when interacting agents build models of one another, in a 4-way stop sign situation. Incorrect perceptions and imperfect models can result in bad outcomes.","abstract_has_math":false,"creators":["Escobar, Trang"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-27T19:52:00Z","subjects":["agent based modeling","game theory"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/14378"],"render_values":[{"text":"hdl:1920/14378","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["agent based modeling","game theory"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/14378"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Game theory (GT) involves formal analysis of the decisions faced by strategic agents in environments in which the payoffs received by the agents depend on the actions of all. Typically, agents in GT are treated as individually rational, that is, they do what’s best for themselves by making rational choices. GT has been widely applied in social science contexts. Agent-based modeling (ABM) is a computational methodology for representing the behavior of heterogeneous boundedly rational agents who interact through networks, away from equilibrium, i.e., more realistically than in rational choice game theory.This dissertation investigates the intersection between GT and ABM. Specifically, I have built three ABMs that relax some of the mathematically-precise but unrealistic assumptions of conventional game theory including rational choice. The first ABM involves combining social network and ‘walk away’ strategies for the Prisoner’s Dilemma. The second involves transitions from inequitable to equitable norms through collaboration in the context of the Nash demand game. The third involves a strategic context—a vehicular intersection with 4 stop signs—in which agents use their own, local ABMs in order to figure out how to best behave. With my first model, I show that high levels of cooperation in the Prisoner’s Dilemma can be sustained through a variety of mechanisms that permit the emergence of self-organized groups of cooperators. By shifting defectors high payoffs are achieved, endogenously. With the second model, I show that collaboration in inequitable regimes can lead to higher payoff for the overall population and the changes in payoff can be understood computationally despite being hard to deduce analytically. In my third model, I show that a wide variety of outcomes are possible when interacting agents build models of one another, in a 4-way stop sign situation. Incorrect perceptions and imperfect models can result in bad outcomes."]},{"key":"dc:title","label":"Title","values":["Three Essays on the Relationship Between Game Theory and Agent-Based Modeling"]}]}],"canonical_facts":{"dc:date.issued":["2024"],"dc:description.other":["Game theory (GT) involves formal analysis of the decisions faced by strategic agents in environments in which the payoffs received by the agents depend on the actions of all. Typically, agents in GT are treated as individually rational, that is, they do what’s best for themselves by making rational choices. GT has been widely applied in social science contexts. Agent-based modeling (ABM) is a computational methodology for representing the behavior of heterogeneous boundedly rational agents who interact through networks, away from equilibrium, i.e., more realistically than in rational choice game theory.This dissertation investigates the intersection between GT and ABM. Specifically, I have built three ABMs that relax some of the mathematically-precise but unrealistic assumptions of conventional game theory including rational choice. The first ABM involves combining social network and ‘walk away’ strategies for the Prisoner’s Dilemma. The second involves transitions from inequitable to equitable norms through collaboration in the context of the Nash demand game. The third involves a strategic context—a vehicular intersection with 4 stop signs—in which agents use their own, local ABMs in order to figure out how to best behave. With my first model, I show that high levels of cooperation in the Prisoner’s Dilemma can be sustained through a variety of mechanisms that permit the emergence of self-organized groups of cooperators. By shifting defectors high payoffs are achieved, endogenously. With the second model, I show that collaboration in inequitable regimes can lead to higher payoff for the overall population and the changes in payoff can be understood computationally despite being hard to deduce analytically. In my third model, I show that a wide variety of outcomes are possible when interacting agents build models of one another, in a 4-way stop sign situation. Incorrect perceptions and imperfect models can result in bad outcomes."],"dc:identifier":["hdl:1920/14378"],"dc:subject":["agent based modeling","game theory"],"dc:title":["Three Essays on the Relationship Between Game Theory and Agent-Based Modeling"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T19:52:00Z"}