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Algorithmic Cooperation

Author

Listed:
  • Bernhard Kasberger
  • Simon Martin
  • Hans-Theo Normann
  • Tobias Werner

Abstract

Algorithms play an increasingly important role in economic situations. These situations are often strategic, where the artificial intelligence may or may not be cooperative. We study the deter-minants and forms of algorithmic cooperation in the infinitely repeated prisoner’s dilemma. We run a sequence of computational experiments, accompanied by additional repeated prisoner’s dilemma games played by humans in the lab. We find that the same factors that increase human cooperation largely also determine the cooperation rates of algorithms. However, algorithms tend to play different strategies than humans. Algorithms cooperate less than humans when cooperation is very risky or not incentive-compatible.

Suggested Citation

  • Bernhard Kasberger & Simon Martin & Hans-Theo Normann & Tobias Werner, 2024. "Algorithmic Cooperation," CESifo Working Paper Series 11124, CESifo.
    • Handle: RePEc:ces:ceswps:_11124
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    File URL: https://www.cesifo.org/DocDL/cesifo1_wp11124.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    artificial intelligence; cooperation; large language models; Q-learning; repeated prisoner’s dilemma;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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