Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918047316443136 |
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| author | Slumbers, Oliver Leibo, Joel Z. Janssen, Marco A. |
| author_facet | Slumbers, Oliver Leibo, Joel Z. Janssen, Marco A. |
| contents | Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as climate change are in this category, and so it is critical to understand their social underpinnings. However, rigorous CRSD methodology often demands large-scale human experiments but it is difficult to guarantee sufficient power and heterogeneity over socio-demographic factors. Generative AI offers a potential complementary approach to address thisproblem. By replacing human participants with large language models (LLM), it allows for a scalable empirical framework. This paper focuses on the validity of this approach and whether it is feasible to represent a large-scale human-like experiment with sufficient diversity using LLM. In particular, where previous literature has focused on political surveys, virtual towns and classical game-theoretic examples, we focus on a complex CRSD used in the institutional economics and sustainability literature known as Port of Mars |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_05555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars Slumbers, Oliver Leibo, Joel Z. Janssen, Marco A. Multiagent Systems Computers and Society Collective risk social dilemmas (CRSD) highlight a trade-off between individual preferences and the need for all to contribute toward achieving a group objective. Problems such as climate change are in this category, and so it is critical to understand their social underpinnings. However, rigorous CRSD methodology often demands large-scale human experiments but it is difficult to guarantee sufficient power and heterogeneity over socio-demographic factors. Generative AI offers a potential complementary approach to address thisproblem. By replacing human participants with large language models (LLM), it allows for a scalable empirical framework. This paper focuses on the validity of this approach and whether it is feasible to represent a large-scale human-like experiment with sufficient diversity using LLM. In particular, where previous literature has focused on political surveys, virtual towns and classical game-theoretic examples, we focus on a complex CRSD used in the institutional economics and sustainability literature known as Port of Mars |
| title | Using Large Language Models to Simulate Human Behavioural Experiments: Port of Mars |
| topic | Multiagent Systems Computers and Society |
| url | https://arxiv.org/abs/2506.05555 |