Personality-Profiled Virtual Agents Are More Predictable: The Constraint-Entropy Tradeoff for Trustworthy Agent Design

Abstract

Effective human-agent cooperation requires that users form mental models of an agent’s behavioral tendencies. Yet LLM-based virtual agents are inherently stochastic, undermining the behavioral consistency that mental model formation depends on. We introduce the Constraint-Entropy Tradeoff (CET) model, an information-theoretic design framework that quantifies how persona profiles (behavioral constraints specifying an agent’s reasoning style, priorities, and communication patterns) reduce the entropy of a virtual agent’s action distribution. The CET model derives that behavioral entropy decays monotonically under constraint strength and identifies an optimal constraint level balancing predictability against flexibility. We validate the framework using a computational testbed with 80 sessions across five conditions, including intermediate-temperature conditions that reveal a threshold effect in the temperature-consistency relationship. Crucially, at the same high temperature, persona-profiled agents recover substantial behavioral consistency compared to unconstrained agents, demonstrating that persona profiles provide independent behavioral constraint beyond temperature reduction. All participants are LLMs; results establish that persona profiles create measurably distinct behavioral patterns, a necessary precondition for human mental model formation, but human validation is needed. We derive domain-specific design guidelines for applications in education, healthcare, and social simulation.