Steering Prosocial AI Agents: Computational Basis of LLM's Decision Making in Social Simulation

• URL: http://arxiv.org/abs/2504.11671v1
• Date: Wed, 16 Apr 2025 00:02:28 GMT
• Title: Steering Prosocial AI Agents: Computational Basis of LLM's Decision Making in Social Simulation
• Authors: Ji Ma,
• Abstract summary: Large language models (LLMs) increasingly serve as human-like decision-making agents in social science and applied settings.<n>This study proposes and tests methods for probing, quantifying, and modifying an LLM's internal representations in a Dictator Game.<n>Manipulating these vectors during the model's inference can substantially alter how those variables relate to the model's decision-making.
• Score: 7.504095239018173
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) increasingly serve as human-like decision-making agents in social science and applied settings. These LLM-agents are typically assigned human-like characters and placed in real-life contexts. However, how these characters and contexts shape an LLM's behavior remains underexplored. This study proposes and tests methods for probing, quantifying, and modifying an LLM's internal representations in a Dictator Game -- a classic behavioral experiment on fairness and prosocial behavior. We extract ``vectors of variable variations'' (e.g., ``male'' to ``female'') from the LLM's internal state. Manipulating these vectors during the model's inference can substantially alter how those variables relate to the model's decision-making. This approach offers a principled way to study and regulate how social concepts can be encoded and engineered within transformer-based models, with implications for alignment, debiasing, and designing AI agents for social simulations in both academic and commercial applications.

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