Playing Against the Machine: Cooperation, Communication, and Strategy Heterogeneity in Repeated Prisoner's Dilemma

Fuente: arXiv
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Autores principales: Anwar, Chowdhury Mohammad Sakib, Georgalos, Konstantinos
Formato: Preprint
Publicado: 2026
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author Anwar, Chowdhury Mohammad Sakib
Georgalos, Konstantinos
author_facet Anwar, Chowdhury Mohammad Sakib
Georgalos, Konstantinos
contents This paper investigates how natural language communication with an AI agent affects human cooperative behaviour in indefinitely repeated Prisoner's Dilemma games. We conduct a laboratory experiment (n = 126) with two between-subjects treatments varying whether human participants chat with an AI chatbot (GPT-5.2) before every round or only before the first round of each supergame, and benchmark against human-human data from Dvorak and Fehrler (2024) (n = 108). We find four main results. First, cooperation against the AI is high and initially comparable to human-human levels, but unlike in the human-human setting, where cooperation converges to near-complete levels, cooperation against the AI plateaus and never reaches full cooperation. Second, repeated communication, which substantially increases cooperation in human-human interactions, has no detectable effect in the human-AI setting. Third, strategy estimation reveals that human-AI subjects favour Grim Trigger under pre-play communication and remain dispersed under repeated communication, whereas human-human subjects converge to Tit-for-Tat and unconditional cooperation respectively. Fourth, human-AI conversations contain more explicit strategy commitments but fewer emotional and social messages. These results suggest that humans cooperate with AI at high rates but do not develop the trust observed in human-human interactions. Cooperation in the human-AI setting is sustained through conditional rules rather than through the social bonds and mutual understanding that characterise human-human cooperation.
format Preprint
id arxiv_https___arxiv_org_abs_2603_15852
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Playing Against the Machine: Cooperation, Communication, and Strategy Heterogeneity in Repeated Prisoner's Dilemma
Anwar, Chowdhury Mohammad Sakib
Georgalos, Konstantinos
General Economics
Economics
This paper investigates how natural language communication with an AI agent affects human cooperative behaviour in indefinitely repeated Prisoner's Dilemma games. We conduct a laboratory experiment (n = 126) with two between-subjects treatments varying whether human participants chat with an AI chatbot (GPT-5.2) before every round or only before the first round of each supergame, and benchmark against human-human data from Dvorak and Fehrler (2024) (n = 108). We find four main results. First, cooperation against the AI is high and initially comparable to human-human levels, but unlike in the human-human setting, where cooperation converges to near-complete levels, cooperation against the AI plateaus and never reaches full cooperation. Second, repeated communication, which substantially increases cooperation in human-human interactions, has no detectable effect in the human-AI setting. Third, strategy estimation reveals that human-AI subjects favour Grim Trigger under pre-play communication and remain dispersed under repeated communication, whereas human-human subjects converge to Tit-for-Tat and unconditional cooperation respectively. Fourth, human-AI conversations contain more explicit strategy commitments but fewer emotional and social messages. These results suggest that humans cooperate with AI at high rates but do not develop the trust observed in human-human interactions. Cooperation in the human-AI setting is sustained through conditional rules rather than through the social bonds and mutual understanding that characterise human-human cooperation.
title Playing Against the Machine: Cooperation, Communication, and Strategy Heterogeneity in Repeated Prisoner's Dilemma
topic General Economics
Economics
url https://arxiv.org/abs/2603.15852