Simulating Persuasive Dialogues on Meat Reduction with Generative Agents
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917008325476352 |
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| author | Ahnert, Georg Wurth, Elena Strohmaier, Markus Mata, Jutta |
| author_facet | Ahnert, Georg Wurth, Elena Strohmaier, Markus Mata, Jutta |
| contents | Meat reduction benefits human and planetary health, but social norms keep meat central in shared meals. To date, the development of communication strategies that promote meat reduction while minimizing social costs has required the costly involvement of human participants at each stage of the process. We present work in progress on simulating multi-round dialogues on meat reduction between Generative Agents based on large language models (LLMs). We measure our main outcome using established psychological questionnaires based on the Theory of Planned Behavior and additionally investigate Social Costs. We find evidence that our preliminary simulations produce outcomes that are (i) consistent with theoretical expectations; and (ii) valid when compared to data from previous studies with human participants. Generative agent-based models are a promising tool for identifying novel communication strategies on meat reduction -- tailored to highly specific participant groups -- to then be tested in subsequent studies with human participants. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulating Persuasive Dialogues on Meat Reduction with Generative Agents Ahnert, Georg Wurth, Elena Strohmaier, Markus Mata, Jutta Computers and Society Human-Computer Interaction Multiagent Systems Meat reduction benefits human and planetary health, but social norms keep meat central in shared meals. To date, the development of communication strategies that promote meat reduction while minimizing social costs has required the costly involvement of human participants at each stage of the process. We present work in progress on simulating multi-round dialogues on meat reduction between Generative Agents based on large language models (LLMs). We measure our main outcome using established psychological questionnaires based on the Theory of Planned Behavior and additionally investigate Social Costs. We find evidence that our preliminary simulations produce outcomes that are (i) consistent with theoretical expectations; and (ii) valid when compared to data from previous studies with human participants. Generative agent-based models are a promising tool for identifying novel communication strategies on meat reduction -- tailored to highly specific participant groups -- to then be tested in subsequent studies with human participants. |
| title | Simulating Persuasive Dialogues on Meat Reduction with Generative Agents |
| topic | Computers and Society Human-Computer Interaction Multiagent Systems |
| url | https://arxiv.org/abs/2504.04872 |