title: Instance-Based Models of Metacognition in the Prisoner's Dilemma.
abstract: In this article, we examine the advantages of simple metacognitive capabilities in a repeated social dilemma. Two types of metacognitive agent were developed and compared with a non-metacognitive agent and two fixed-strategy agents. The first type of metacognitive agent (opponent perspective) takes the perspective of the opponent to anticipate the opponent's future actions and respond accordingly. The other metacognitive agent (modeler) predicts the opponent's next move based on the previous moves of the agent and the opponent. The modeler agent achieves better individual outcomes than a non-metacognitive agent and is more successful at encouraging cooperation. The opponent-perspective agent, by contrast, fails to achieve these outcomes because it lacks important information about the opponent. These simple agents provide insights regarding modeling of metacognition in more complex tasks.
authors.affiliation: Department of Artificial Intelligence, University of Groningen.
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Stevens
authors.affiliation: Department of Artificial Intelligence, University of Groningen.
authors.forename: Niels A
authors.initials: NA
authors.lastname: Taatgen
authors.affiliation: Department of Artificial Intelligence, University of Groningen.
authors.forename: Fokie
authors.initials: F
authors.lastname: Cnossen
mesh_terms: D003657:Decision Making; D005716:Game Theory; D006801:Humans; D007398:Interpersonal Relations; D007858:Learning / Q000502:physiology; D000067489:Metacognition / Q000502:physiology*; D008960:Models, Psychological; D000068956:Prisoner Dilemma; D011340:Problem Solving / Q000502:physiology; D056345:Theory of Mind
keywords: ACT-R; Decision making; Instance-based learning; Metacognition; Prisoner's dilemma; Strategic games; Theory of mind