When Artificial Minds Negotiate: Dark Personality and the Ultimatum Game in Large Language Models

Vinícius Ferraz, Tamas Olah, Ratin Sazedul, Robert Schmidt and Christiane Schwieren

No 768, Working Papers from University of Heidelberg, Department of Economics

Abstract: We investigate if Large Language Models (LLMs) exhibit personality-driven strategic behavior in the Ultimatum Game by manipulating Dark Factor of Personality (D-Factor) profiles via standardized prompts. Across 400k decisions from 17 open-source models and 4,166 human benchmarks, we test whether LLMs playing the proposer and responder roles exhibit systematic behavioral shifts across five D-Factor levels (from least to most selfish). The proposer role exhibited strong monotonic declines in fair offers from 91% (D1) to 17% (D5), mirroring human patterns but with 34% steeper gradients, indicating hypersensitivity to personality prompts. Responders diverged sharply: where humans became more punitive at higher D-levels, LLMs maintained high acceptance rates (75-92%) with weak or reversed D-Factor sensitivity,failing to reproduce reciprocity-punishment dynamics. These role-specific patterns align with strong-weak situation accounts—personality matters when incentives are ambiguous (proposers) but is muted when contingent (responders). Cross-model heterogeneity was substantial: Models exhibiting the closest alignment with human behavior, according to composite similarity scores (integrating prosocial rates, D-Factor correlations, and odds ratios), were dolphin3, deepseek_1.5b, and llama3.2 (0.74-0.85), while others exhibited extreme or non-variable behavior. Temperature settings (0.2 vs. 0.8) exerted minimal influence. We interpret these patterns as prompt-driven regularities rather than genuine motivational processes, suggesting LLMs can approximate but not fully replicate human strategic behavior in social dilemmas.

Date: 2025-12-16
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