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Personas shape LLM agent behavior in strategic games

By PulseAugur Editorial · [1 sources] ·

Researchers investigated how persona prompts influence the strategic behavior of large language model agents in a "Split or Steal" game. Using four open-source models (Ministral-3-3B, phi4:14b, Gemma3:12b, and Gemma4:e4b) interacting with a GPT-4.1 mini-powered virtual human, they found that mutual "Split" outcomes dominated, occurring in approximately 74 percent of rounds. The choice of model significantly impacted agent behavior, with phi4 and Ministral-3-3B consistently cooperative, while Gemma models showed more varied strategies. Persona traits like "Prosocial" and "Principled" were linked to cooperation, whereas "Analytical" personas were more prone to exploitation. AI

IMPACT Demonstrates how persona prompts can influence LLM agent behavior in strategic interactions, offering insights for developing more predictable and controllable AI agents.

RANK_REASON Academic paper detailing experimental results on LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Personas shape LLM agent behavior in strategic games

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Academic paper detailing experimental results on LLM agent behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Carlos Leon, Alexandre Rodrigues, Pedro Gamito, Thomas D. Parsons ·

    How Personas Can Influence Agents to Play Split or Steal

    arXiv:2607.05398v1 Announce Type: new Abstract: Personas are often employed to guide large language model agents, yet their effectiveness in shaping strategic behavior in social dilemma settings remains uncertain. To address this, we examined the impact of persona prompts in an i…

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