HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914438425083904 |
|---|---|
| author | Zhang, Ruiyu Nie, Lin Zhao, Xin |
| author_facet | Zhang, Ruiyu Nie, Lin Zhao, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search Zhang, Ruiyu Nie, Lin Zhao, Xin Multiagent Systems Computational Engineering, Finance, and Science Machine Learning Neural and Evolutionary Computing Software Engineering 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. |
| title | HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search |
| topic | Multiagent Systems Computational Engineering, Finance, and Science Machine Learning Neural and Evolutionary Computing Software Engineering |
| url | https://arxiv.org/abs/2508.15555 |