{"ID":2879863,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.15555","arxiv_id":"2508.15555","title":"HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.15555","url_pdf":"https://arxiv.org/pdf/2508.15555v3","authors":"[\"Ruiyu Zhang\",\"Lin Nie\",\"Xin Zhao\"]","published":"2025-08-21T13:35:46Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.CE\",\"cs.LG\",\"cs.NE\",\"cs.SE\"]","methods":"[]","has_code":false}