AI RESEARCH
HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search
arXiv CS.LG
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ArXi:2508.15555v3 Announce Type: replace-cross 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.