Algorithmic Cooperation

Bernhard Kasberger, Simon Martin, Hans-Theo Normann and Tobias Werner

No 11124, CESifo Working Paper Series from CESifo

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.

Keywords: artificial intelligence; cooperation; large language models; Q-learning; repeated prisoner’s dilemma (search for similar items in EconPapers)
JEL-codes: C72 C73 C92 D83 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-ain, nep-cbe, nep-cmp, nep-exp, nep-gth and nep-neu
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_11124

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