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. 2024 Mar 14:12:834.
doi: 10.12688/f1000research.135249.3. eCollection 2023.

Agent-based models under uncertainty

Affiliations

Agent-based models under uncertainty

Vladimir Stepanov et al. F1000Res. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: agent-based modelling; epistemic uncertianty; intervals.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Graphical representation of Monte Carlo realisations (shown in black) against possible outer bounds (blue).
Reproduced with permission from original.
Figure 2.
Figure 2.. Scenarios illustrating the possible ship outcomes.
The full line represents the perfect radar range of the ship, while the dashed line represents the outer edge of its imperfect radar range.
Figure 3.
Figure 3.. Collection of graphs showcasing the number of surviving ships at the end of each simulation (X axis) and their overall frequency (Y axis) under the MC method.

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