# Open-source LLMS over-cooperate in prisoner's dilemma, study shows

Digest AI · Research · published 2026-10-20T04:00:00Z

Canonical: https://digestai.news/story/open-source-llms-over-cooperate-in-prisoner-s-dilemma-study-shows

## Summary

The paper reports that open‑source large language models (LLMs) tend to cooperate far more than humans in repeated Prisoner’s Dilemma games. Researchers tested six models—TII Falcon 7B Instruct, Google Gemma 7B, Meta Llama 3.1 8B Instruct, Microsoft Phi Mini MoE Instruct, and Qwen 2.5 7B Instruct—using a standardized prompting framework.

In a shuffled‑partner setting, human cooperation averaged 28.5 % over 100 rounds, while several LLMs exceeded 80 %. Qwen‑2.5‑7B‑Instruct behaved deterministically, whereas Phi‑Mini‑MoE‑Instruct showed greater variability and briefly matched human rates. The study also examined a fixed‑partner setting where both agents retained memory.

These results suggest that current open‑source LLMs are not yet reliable substitutes for human participants in behavioral simulations, highlighting the need for better memory mechanisms and more realistic decision‑making models.

## Key points

- Open‑source llms achieved cooperation rates above 80% in shuffled‑partner setting
- Human cooperation averaged 28.5% in same setting
- Qwen‑2.5‑7B‑Instruct behaved deterministically, Phi‑Mini‑MoE‑Instruct variable

## Why it matters

The findings expose limits of current LLMs for modeling human decision making, underscoring the need for improved memory and realism in AI agents used for social simulations.

## Sources

1. [Modeling Human Behavior in the Prisoner's Dilemma with Large Language Models](https://arxiv.org/abs/2610.23860) (arXiv cs.CL, 2026-10-20, primary source)

## Cite

Digest AI, "Open-source LLMS over-cooperate in prisoner's dilemma, study shows", 20 October 2026, https://digestai.news/story/open-source-llms-over-cooperate-in-prisoner-s-dilemma-study-shows

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