Trait-Augmented Games with Limited-Skill Agents

Michael Gmeiner and Val Lambson
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Michael Gmeiner: Northwestern University, 2211 Campus Drive, 3rd Floor, Evanston, IL, 60208, USA
Val Lambson: ��Brigham Young University, 2146 West View Building, Provo, UT, 84602, USA

International Game Theory Review (IGTR), 2021, vol. 23, issue 03, 1-31

Abstract: We present a framework populated by limited-skill agents whose cognitive abilities fall short of full rationality in two ways. First, the agents are incapable of considering all of the available actions. Second, they do not perceive the actions directly, but only view traits that are related to the actions’ identities. We define skill in terms of the number of action-trait pairs an agent can consider: agents with higher skill can consider more pairs. Limited-skill agents can be grafted into many contexts. We first discuss trends in how agents select which action-trait pairs to view when maximizing expected payoff. We then present two applications: the relationship between skill and expected payoff and product differentiation.

Keywords: Bounded rationality; information; skill; product differentiation; constant-sum games (search for similar items in EconPapers)
JEL-codes: D81 D83 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219198921500043

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