Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel
Yohei Nakajima
cs.AI, cs.CL, cs.GT
Submitted: 2026-08-02
Comments: 19 pages, 5 figures. Project site: https://yoheinakajima.github.io/synthetic-players/ ; code, data, registrations, review record, and zero-call replay capsule: https://github.com/yoheinakajima/synthetic-players
Code: https://github.com/yoheinakajima/synthetic-players
Project page: https://yoheinakajima.github.io/synthetic-players
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Evaluating Counterfactual Strategic Reasoning in Large Language Models
- This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
- Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments
- The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
- Same Game, Different Story: A Minimal Conservative Strategic Robustness Benchmark for Large Language Model Agents
- Strategies of cooperation and defection in five large language models
- LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
- Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
- The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models
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