The preprint crosses 20 persona biographies, four local models and two conversation temperatures in 160 Split-or-Steal sessions against a virtual human that is actually GPT-4.1-mini. Each session has 15 rounds. The Zenodo CSVs confirm that 1,768/2,400 rounds (73.67%) end in mutual Split; the agent exploits the VH in 11.08%, the VH exploits the agent in 6.04%, and both Steal in 9.21%. Ministral and Phi4 cooperate more than the two Gemma models, while biographies labeled Prosocial or Principled show more Split than Analytical or Self-Interested biographies. These are descriptive artifact patterns, not causal or psychometric effects. Every condition includes a persona, there is no neutral control, and each persona-model-temperature cell has only one stochastic trajectory. Rounds share history and are not 2,400 independent observations. The paper uses “significantly” without tests, intervals or effect sizes; combined one-sided exploitation is 17.13%, not below 11%. The Big Five groups are Claude Opus 4.6 labels applied to stories that already instruct trust, exploitation or revenge, not scores from an inventory. The annotation audit finds another divergence: Methods claim truncation at 2,000 characters, but the CSV uses exactly the first 300; 27.3% of topic rows and 1.5% of sentiment rows violate the requested sum of 100. Dialogues, decisions and derived tables are public, but code, seeds, model revisions and pipeline are not. The study is useful as an exploratory prompt-following baseline for these simulations, not evidence about human personality or a stable persona effect.
Research question
How do persona biographies, the model, and the dialogue temperature relate to cooperation, exploitation, and apparent strategy in a repeated Split-or-Steal game against an LLM-based VH?