The Selfishness Level of Social Dilemmas

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Abstract

A key contributor to the success of modern societies is humanity's innate ability to meaningfully cooperate. Game-theoretic reasoning shows however, that an individual's amenity to cooperation is directly linked with the mechanics of the scenario at hand. Social dilemmas constitute a subset of such scenarios where players are caught in a dichotomy between the decision to cooperate, prioritising collective welfare, or defect, prioritising their own welfare. In this work, we study such games through the lens of 'the selfishness level', a standard game-theoretic metric which quantifies the extent to which a game's payoffs incentivize self-directed behaviours. Using this framework, we derive the conditions under which SDs can be resolved and, additionally, produce a first-step towards extending this metric to Markov games. Finally, we present an empirical analysis indicating the positive effects of selfishness-level-directed mechanisms in such environments.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Pages2441-2443
Number of pages3
Volume2024-May
Publication statusPublished - 2024
Event23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024 - Auckland, New Zealand
Duration: 6 May 202410 May 2024

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
ISSN (Print)1548-8403

Conference

Conference23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024
Country/TerritoryNew Zealand
CityAuckland
Period6/05/202410/05/2024

Keywords

  • Game Theory
  • Markov Game
  • Multi-agent Reinforcement Learning
  • Reinforcement Learning
  • Social Dilemma

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