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

Maximilian Moll (), Jurgis Karpus and Bahador Bahrami
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Maximilian Moll: Universität der Bundeswehr München
Jurgis Karpus: Ludwig-Maximilians-Universität München
Bahador Bahrami: Ludwig-Maximilians-Universität München

Chapter Chapter 72 in Operations Research Proceedings 2022, 2023, pp 603-609 from Springer

Abstract: 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.

Keywords: Reinforcement learning; Game theory; Decision making (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-24907-5_72

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