Agent-based models under uncertainty.

Stepanov, Vladimir and Ferson, Scott ORCID: 0000-0002-2613-0650 (2024) Agent-based models under uncertainty. F1000Research, 12. p. 834.

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Official URL: http://dx.doi.org/10.12688/f1000research.135249.3

Abstract

<h4>Background</h4>Monte Carlo (MC) is often used when trying to assess the consequences of uncertainty in agent-based models (ABMs). However, this approach is not appropriate when the uncertainty is epistemic rather than aleatory, that is, when it represents a lack of knowledge rather than variation. The free-for-all battleship simulation modelled here is inspired by the children's battleship game, where each battleship is an agent.<h4>Methods</h4>The models contrast an MC implementation against an interval implementation for epistemic uncertainty. In this case, our epistemic uncertainty is in the form of an imperfect radar. In the interval method, the approach occludes the status of the agents (ships) and precludes an analyst from making decisions about them in real-time.<h4>Results</h4>In a highly uncertain environment, after many time steps, there can be many ships remaining whose status is unknown. In contrast, any MC simulation invariably tends to conclude with a small number of the remaining ships after many time steps. Thus, the interval approach misses the quantitative conclusion. However, some quantitative results are generated by the interval implementation, e.g. the identities of the surviving ships, which are revealed to be nearly mutual with the MC implementation, though with fewer identities in total compared to MC.<h4>Conclusions</h4>We have demonstrated that it is possible to implement intervals in an ABM, but the results are broad, which may be useful for generating the overall bounds of the system but do not provide insight on the expected outcomes and trends.

Item Type:	Article
Uncontrolled Keywords:	Humans, Monte Carlo Method, Uncertainty, Decision Making, Knowledge, Computer Simulation, Child
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	22 Apr 2024 10:02
Last Modified:	22 Apr 2024 10:02
DOI:	10.12688/f1000research.135249.3
Open Access URL:	https://doi.org/10.12688%2Ff1000research.135249.3
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3180486