HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search

Metric aggregation divergence is a hidden confound in agent-based model policy search: when optimization, tournament evaluation, and statistical validation independently implement outcome metric extraction, champion selection reflects aggregation artifact rather than policy quality. We propose Hierarchical Evolutionary Agent Simulation (HEAS), a composable framework that eliminates this confound through a runtime-enforceable metric contract - a uniform metrics_episode() callable shared identically by all pipeline stages. Removing the confound yields robust champion selection: in a controlled experiment (n=30), HEAS reduces rank reversals by 50% relative to ad-hoc aggregation; the HEAS champion wins all 32 held-out ecological scenarios - a null-safety result that would be uninterpretable under aggregation divergence. The contract additionally reduces coupling code by 97% (160 to 5 lines) relative to Mesa 3.3.1. Three case studies validate composability across ecological, enterprise, and mean-field ordinary differential equation dynamics.