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Do Artificial Agents Reproduce Human Strategies in the Advisers’ Game?

In: Operations Research Proceedings 2022

Author

Listed:
  • Maximilian Moll

    (Universität der Bundeswehr München)

  • Jurgis Karpus

    (Ludwig-Maximilians-Universität München)

  • Bahador Bahrami

    (Ludwig-Maximilians-Universität München)

Abstract

Game theory has been recently used to study optimal advice-giving strategies in settings where multiple advisers compete for a single client’s attention. In the advisers’ game, a client chooses between two well informed advisers to place bets under uncertainty. Experiments have shown that human advisers can learn to play strategically instead of honestly to exploit client behavior. Here, we analyze under which conditions agents trained with Q-learning can adopt similar strategies. To this end, the agent is trained against different heuristics and itself.

Suggested Citation

  • Maximilian Moll & Jurgis Karpus & Bahador Bahrami, 2023. "Do Artificial Agents Reproduce Human Strategies in the Advisers’ Game?," Lecture Notes in Operations Research, in: Oliver Grothe & Stefan Nickel & Steffen Rebennack & Oliver Stein (ed.), Operations Research Proceedings 2022, chapter 0, pages 603-609, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-24907-5_72
    DOI: 10.1007/978-3-031-24907-5_72
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