When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation

This paper investigates how enhanced reasoning in language models can harm the fidelity of behavioral simulations, particularly when the goal is to sample boundedly rational behavior rather than solve a strategic problem. The authors identify a "solver-sampler mismatch" where LLMs over-optimize, collapsing compromise-oriented behavior and leading to diversity without fidelity in outcomes.