Identifying Cooperative Personalities in Multi-agent Contexts through Personality Steering with Representation Engineering

• URL: http://arxiv.org/abs/2503.12722v1
• Date: Mon, 17 Mar 2025 01:21:54 GMT
• Title: Identifying Cooperative Personalities in Multi-agent Contexts through Personality Steering with Representation Engineering
• Authors: Kenneth J. K. Ong, Lye Jia Jun, Hieu Minh "Jord" Nguyen, Seong Hah Cho, Natalia Pérez-Campanero Antolín,
• Abstract summary: Large Language Models (LLMs) gain autonomous capabilities, their coordination in multi-agent settings becomes increasingly important.<n>Inspired by Axelrod's Iterated Prisoner's Dilemma (IPD) tournaments, we explore how personality traits influence LLM cooperation.<n>Using representation engineering, we steer Big Five traits (e.g., Agreeableness, Conscientiousness) in LLMs and analyze their impact on IPD decision-making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models (LLMs) gain autonomous capabilities, their coordination in multi-agent settings becomes increasingly important. However, they often struggle with cooperation, leading to suboptimal outcomes. Inspired by Axelrod's Iterated Prisoner's Dilemma (IPD) tournaments, we explore how personality traits influence LLM cooperation. Using representation engineering, we steer Big Five traits (e.g., Agreeableness, Conscientiousness) in LLMs and analyze their impact on IPD decision-making. Our results show that higher Agreeableness and Conscientiousness improve cooperation but increase susceptibility to exploitation, highlighting both the potential and limitations of personality-based steering for aligning AI agents.

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