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espace at MMU > Research Institutes > Manchester Institute of Social and Spatial Transformations (MISST) > Cities, Space and Power Group > Policy analysis from first principles

Please use this identifier to cite or link to this item: http://hdl.handle.net/2173/87194
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Title: Policy analysis from first principles
Authors: Moss, Scott J.
Citation: Proceedings of the National Academy of Sciences, 2002, vol. 99, no. suppl 3, pp. 7267-7274
Publisher: National Academy of Sciences
Issue Date: 14-May-2002
URI: http://hdl.handle.net/2173/87194
DOI: 10.1073/pnas.092080699
Additional Links: http://www.pnas.org
Abstract: The argument of this paper is predicated on the view that social science should start with observation and the specification of a problem to be solved. On that basis, the appropriate properties and conditions of application of relevant tools of analysis should be defined. Evidence is adduced from data for sales volumes and values of a disparate range of goods to show that frequency distributions are commonly fat-tailed. This result implies that any stable population distribution will generally have infinite variance and perhaps undefined mean. Models with agents that reason about their behavior and are influenced by, but do not imitate, other agents known to them will typically generate fat-tailed time series data. A simulation model of intermediated exchange is reported that is populated by such agents and yields the same type of fat-tailed time series and cross-sectional data that is found in data for fast moving consumer goods and for retail outlets. This result supports the proposition that adaptive agent models of markets with agents that reason and are socially embedded have the same statistical signatures as real markets. Whereas this statistical signature precludes any conventional hypothesis testing or forecasting, these models do offer unique opportunities for validation on the basis of domain expertise and qualitative data. Perhaps the most striking conclusion is that neither current social theory nor any similar construct will ever support an effective policy analysis. However, adaptive agent modeling is an effective substitute when embedded in a wider policy analysis procedure.
Type: Article
Language: en
Description: This article was originally published [following peer-review] in National Academy of Sciences. Proceedings, published by and copyright National Academy of Sciences.
Keywords: Social science
Policy analysis
ISSN: 0027-8424
1091-6490
Appears in Collections: Cities, Space and Power Group

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