Policy Analysis from First Principles

CPM Report No.: 01-83
By: Scott Moss
Date: 31 August 2001

Published as: Policy Analysis from First Principles
Publication information: Proceedings of the US National Academy of Sciences (forthcoming) -- an invited paper for the Arthur M. Sackler Colloquium on Adaptive Agents, Intelligence and Emergent Human Organization: Capturing Complexity Through Agent-Based Modeling, October 5 and 6, 2001.


The Sackler Colloquium on on Adaptive Agents, Intelligence and Emergent Human Organization is intended to force a re-examination of current social theory by means of an exploration of adaptive agent models.  The primary issue addressed by this paper is why it should be assumed that adaptive agent models are suitable for that purpose.  The argument is predicated on the view that social science should start with observation and the specification of a problem to be solved and then reason from that basis to define the appropriate properties and conditions of application of relevant tools of analysis.  Accordingly, evidence is adduced from data for sales volumes and values of a disparate range of goods to show that, in all presented cases, frequency distributions are fat tailed.  This result implies that, if there is a suitable and stable population distribution, it will generally have infinite variance and perhaps undefined mean.  Agent based models with agents that reason about their behaviour and specifically do not conform to the rational choice model and are influenced by, but do not imitate, other agents known to them will typically generate fat tailed time series data.  There is no reason to suppose that this data is drawn from any stable distribution.  An agent based social simulation model of intermediated exchange is reported that has the same type 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.  This statistical signature precludes any conventional hypothesis testing or forecasting – a point which pertains both to intermediated (e.g. retail or financial) markets in general and to the models.   However, these agent based social simulation models offer unique opportunities for validation on the basis of domain expertise and qualitative data.  While they cannot be used for prediction or forecasting the specific consequences of any policy or commercial action, they can be used both to identify differences in stakeholders’ perceptions and to investigate the properties of effective responses to unpredictable events.

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