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.
Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
Pages | 1877-1885 |
Number of pages | 9 |
Volume | 2023-May |
Publication status | Published - 2023 |
Event | 22nd International Conference on Autonomous Agents and Multiagent Systems: AAMAS 2023 - London, United Kingdom Duration: 29 May 2023 → 2 Jun 2023 |
Conference
Conference | 22nd International Conference on Autonomous Agents and Multiagent Systems |
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Abbreviated title | AAMAS 2023 |
Country/Territory | United Kingdom |
City | London |
Period | 29/05/23 → 2/06/23 |
Keywords
- Agent-Based Model
- Markov Chains
- Model Reduction
- Social Simulation
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering