MindWorld: Simulating Human Behavior in Strategic Scenarios via Dynamic Cognitive World Model

Large language models (LLMs) are increasingly used as human simulators, yet they excel at mimicking helpful and cooperative assistants rather than authentic, imperfect humans who hesitate, get defensive, or become confused. Existing end-to-end LLM simulators implicitly determine cognitive state changes via next-token distribution, and this uncontrollability often leads to excessive helpfulness. To bridge this gap, we propose MindWorld, a lightweight human simulation framework that models the cognitive world as a deterministic state-space. We anchor the cognitive state in the Big Five personality traits and dynamically simulate state transitions using three deterministic mathematical constraints: continuous damped cognitive dynamics, non-linear cusp-bifurcation stress modeling, and discrete masked Markov stage transitions. These mechanisms provide structural controls on simulated behavior rather than a biological account of human cognition. Across four challenging strategic domains, including clinical communication, bargaining, cooperation, and deception, MindWorld generates responses closer to human references on average than the evaluated baselines across all three LLM backbones, while improving several behavioral risk measures. A sensitivity analysis further characterizes how coefficient scaling influences internal state transitions. Blinded human evaluations show that MindWorld achieves tie-excluded preference rates of 67.4% against the state-of-the-art baseline and 68.3% against human reference responses in overall realism, supporting explicit state control as a useful design principle for strategic human simulation.