Abstract
In this work, we investigate Markov aggregation for agent-based models (ABMs). Specifically, if the ABM models agent movements on a graph, if its ruleset satisfies certain assumptions, and if the aim is to simulate aggregate statistics such as vertex populations, then the ABM can be replaced by a Markov chain on a comparably small state space. This equivalence between a function of the ABM and a smaller Markov chain allows to reduce the computational complexity of the agent-based simulation from being linear in the number of agents, to being constant in the number of agents and polynomial in the number of locations. We instantiate our theory for a recent ABM for forced migration (Flee). We show that, even though the rulesets of Flee violate some of our necessary assumptions, the aggregated Markov chain-based model, MarkovFlee, achieves comparable accuracy at substantially reduced computational cost. Thus, MarkovFlee can help NGOs and policy makers forecast forced migration in certain conflict scenarios in a cost-effective manner, contributing to fast and efficient delivery of humanitarian relief.
Originalsprache | englisch |
---|---|
Titel | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
Seiten | 1877-1885 |
Seitenumfang | 9 |
Band | 2023-May |
Publikationsstatus | Veröffentlicht - 2023 |
Veranstaltung | 22nd International Conference on Autonomous Agents and Multiagent Systems: AAMAS 2023 - London, Großbritannien / Vereinigtes Königreich Dauer: 29 Mai 2023 → 2 Juni 2023 |
Konferenz
Konferenz | 22nd International Conference on Autonomous Agents and Multiagent Systems |
---|---|
Kurztitel | AAMAS 2023 |
Land/Gebiet | Großbritannien / Vereinigtes Königreich |
Ort | London |
Zeitraum | 29/05/23 → 2/06/23 |
ASJC Scopus subject areas
- Artificial intelligence
- Software
- Steuerungs- und Systemtechnik