Noncooperative Human-AI Agent Dynamics
Dylan Waldner, Vyacheslav Kungurtsev, Mitchelle Ashimosi

TL;DR
This paper explores the strategic interactions between humans modeled with Prospect Theory and AI agents with expected utility, revealing emergent behaviors and preference anomalies through extensive simulations in classic games.
Contribution
It introduces a novel framework combining Prospect Theory for humans with expected utility for AI in noncooperative game dynamics, highlighting new behavioral patterns and anomalies.
Findings
Behavioral patterns vary with human and AI knowledge levels
Prospect Theory reproduces known human preference anomalies
Unexpected strategic behaviors emerge in mixed populations
Abstract
This paper investigates the dynamics of noncooperative interactions between artificial intelligence agents and human decision-makers in strategic environments. In particular, motivated by extensive literature in behavioral Economics, human agents are more faithfully modeled with respect to the state of the art using Prospect Theoretic preferences, while AI agents are modeled with standard expected utility maximization. Prospect Theory incorporates known cognitive heuristics employed by humans, including reference dependence and greater loss aversion relative to utility to relative gains. This paper runs different combinations of expected utility and prospect theoretic agents in a number of classic matrix games as well as examples specialized to tease out distinctions in strategic behavior with respect to preference functions, to explore the emergent behaviors from mixed population…
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Taxonomy
TopicsGame Theory and Applications · Complex Systems and Time Series Analysis · Decision-Making and Behavioral Economics
