An Evolutionary Game Theoretic Perspective on Learning in Mult-Agent Systems

Tuyls, Karl; Nowé, Ann; Lenaerts, Tom; Manderick, Bernard

doi:10.1007/1-4020-4094-6_5

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…For a review on evolutionary multiplayer games both in infinitely large populations as well as in finite populations, we refer to Gokhale and Traulsen (2014) [10]. Learning models (of strategic behavior) take a different approach from EGT [23,[36][37][38][39][62][63][64][65]. There is no evolution of strategies in a population necessarily, but a process by which individuals learn strategies dynamically.…”

Section: Introductionmentioning

confidence: 99%

Introspection dynamics in asymmetric multiplayers games

Couto

Pal

2023

Preprint

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Evolutionary game theory and models of learning provide a powerful framework to describe strategic decision-making in social interactions. In the simplest case, these models describe games among two identical players. However, many interactions in everyday life are more complex. They involve more than two players who may differ in their available actions and in their incentives to choose each action. Such interactions can be captured by asymmetric multiplayer games. Recently, introspection dynamics has been introduced to explore such asymmetric games. According to this dynamics, at each time step players compare their current strategy to an alternative strategy. If the alternative strategy results in a payoff advantage, it is more likely adopted. This model provides a simple way to compute the players’ long-run probability of adopting each of their strategies. In this paper, we extend some of the previous results of introspection dynamics for 2-player asymmetric games to games with arbitrarily many players. First, we derive a formula that allows us to numerically compute the stationary distribution of introspection dynamics for any multiplayer asymmetric game. Second, we obtain explicit expressions of the stationary distribution for two special cases. These cases are additive games (where the payoff difference that a player gains by unilaterally switching to a different action is independent of the actions of their co-players), and symmetric multiplayer games with two strategies. To illustrate our results, we revisit several classical games such as the public goods game.

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Section: Introductionmentioning

confidence: 99%

Introspection dynamics in asymmetric multiplayers games

Couto

Pal

2023

Preprint

View full text Add to dashboard Cite

show abstract

Introspection Dynamics in Asymmetric Multiplayer Games

Couto,

Pal

2023

Dyn Games Appl

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Evolutionary game theory and models of learning provide powerful frameworks to describe strategic decision-making in social interactions. In the simplest case, these models describe games among two identical players. However, many interactions in everyday life are more complex. They involve more than two players who may differ in their available actions and in their incentives to choose each action. Such interactions can be captured by asymmetric multiplayer games. Recently, introspection dynamics has been introduced to explore such asymmetric games. According to this dynamics, at each time step players compare their current strategy to an alternative strategy. If the alternative strategy results in a payoff advantage, it is more likely adopted. This model provides a simple way to compute the players’ long-run probability of adopting each of their strategies. In this paper, we extend some of the previous results of introspection dynamics for 2-player asymmetric games to games with arbitrarily many players. First, we derive a formula that allows us to numerically compute the stationary distribution of introspection dynamics for any multiplayer asymmetric game. Second, we obtain explicit expressions of the stationary distribution for two special cases. These cases are additive games (where the payoff difference that a player gains by unilaterally switching to a different action is independent of the actions of their co-players), and symmetric multiplayer games with two strategies. To illustrate our results, we revisit several classical games such as the public goods game.

show abstract

An Evolutionary Game Theoretic Perspective on Learning in Mult-Agent Systems

Cited by 2 publications

References 12 publications

Introspection dynamics in asymmetric multiplayers games

Introspection dynamics in asymmetric multiplayers games

Introspection Dynamics in Asymmetric Multiplayer Games

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