Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round

Authors Avrim Blum , Melissa Dutz

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Avrim Blum
  • Toyota Technological Institute at Chicago, IL, USA
Melissa Dutz
  • Toyota Technological Institute at Chicago, IL, USA

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Avrim Blum and Melissa Dutz. Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round. In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 18:1-18:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


Gameplay under various forms of uncertainty has been widely studied. Feldman et al. [Michal Feldman et al., 2010] studied a particularly low-information setting in which one observes the opponent’s actions but no payoffs, not even one’s own, and introduced an algorithm which guarantees one’s payoff nonetheless approaches the minimax optimal value (i.e., zero) in a symmetric zero-sum game. Against an opponent playing a minimax-optimal strategy, approaching the value of the game is the best one can hope to guarantee. However, a wealth of research in behavioral economics shows that people often do not make perfectly rational, optimal decisions. Here we consider whether it is possible to actually win in this setting if the opponent is behaviorally biased. We model several deterministic, biased opponents and show that even without knowing the game matrix in advance or observing any payoffs, it is possible to take advantage of each bias in order to win nearly every round (so long as the game has the property that each action beats and is beaten by at least one other action). We also provide a partial characterization of the kinds of biased strategies that can be exploited to win nearly every round, and provide algorithms for beating some kinds of biased strategies even when we don't know which strategy the opponent uses.

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ACM Subject Classification
  • Theory of computation → Algorithmic game theory
  • Game theory
  • Behavioral bias


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