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Can Agent-Based Models Assist Decisions on Large-Scale Practical Problems: A Philosophical Analysis
D Gross and R Strand
Complexity, 5(5):182-196, January 2000.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.
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@article{2688,
author = {D Gross and R Strand},
title = {{C}an {A}gent-{B}ased {M}odels {A}ssist {D}ecisions on {L}arge-{S}cale {P}ractical {P}roblems: {A} {P}hilosophical {A}nalysis},
month = {January},
year = {2000},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2000/2688},
publication_type = {article},
submission_id = {19802_1206628282},
other_year = {2000},
journal = {Complexity},
volume = {5},
number = {5},
}