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.
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Acknowledgements
Jurgis Karpus was supported by LMUexcellent, funded by the Federal Ministry of Education and Research (BMBF) and the Free State of Bavaria under the Excellence Strategy of the Federal Government and the Länder. B. B. was supported by the Humboldt Foundation and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (819040-acronym: rid-O).
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Moll, M., Karpus, J., Bahrami, B. (2023). Do Artificial Agents Reproduce Human Strategies in the Advisers’ Game?. In: Grothe, O., Nickel, S., Rebennack, S., Stein, O. (eds) Operations Research Proceedings 2022. OR 2022. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-24907-5_72
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DOI: https://doi.org/10.1007/978-3-031-24907-5_72
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