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Can Agent-Based Models Assist Decisions on Large-Scale Practical Problems: A Philosophical Analysis

Gross, Dominique, Strand, Roger (2000) Can Agent-Based Models Assist Decisions on Large-Scale Practical Problems: A Philosophical Analysis. Complexity, 5 (5). pp. 26-33. ISSN 1076-2787. (doi:10.1002/1099-0526(200007/08)5:6<26::AID-CPLX6>3.0.CO;2-G) (KAR id:22067)

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Official URL
http://dx.doi.org/10.1002/1099-0526(200007/08)5:6<...

Abstract

The use of predictive agent-based models as decision assisting tools in practical problems has been proposed. This article aims at a theoretical clarification of the conditions for such use under what has been called post-normal problems, characterized by high stakes, high and possibly irreducible uncertainties, and high systemic complexity. Our argument suggests that model validation is often impossible under post-normal conditions; however, predictive models can still be useful as learning devices (heristic purposes, formal Gedanken experiments). In this case, micro-structurally complex models are to be preferred to micro-structurally simple ones; this is illustrated by means of two examples.

Item Type: Article
DOI/Identification number: 10.1002/1099-0526(200007/08)5:6<26::AID-CPLX6>3.0.CO;2-G
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 27 Aug 2009 13:10 UTC
Last Modified: 16 Feb 2021 12:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/22067 (The current URI for this page, for reference purposes)
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