Gross, Dominique and 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:https://doi.org/10.1002/1099-0526(200007/08)5:6<26::AID-CPLX6>3.0.CO;2-G) (Full text available)
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.
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,|
|Divisions:||Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group|
|Depositing User:||Mark Wheadon|
|Date Deposited:||27 Aug 2009 13:10 UTC|
|Last Modified:||14 Jan 2017 04:11 UTC|
|Resource URI:||https://kar.kent.ac.uk/id/eprint/22067 (The current URI for this page, for reference purposes)|