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Towards a Descriptive Model of Agent Strategy Search - Bruce Edmonds

1. Introduction

Complex systems are precisely those for which it is extremely difficult to deduce its behaviour from first principles*1. For example, it is extremely unlikely that one would be able to predict the behaviour of a meercat purely from a priori principles, rather one would have to spend a lot of time observing and describing its actions to get a hold on the intricate contingencies of its actual behaviour. With complex systems observation and description must come first and only much later when the detailed behavioural mechanisms are well understood is it sometimes possible to encapsulate some of these in a predictive model. It seems likely that much economic behaviour is complex in this way. This would not be very surprising since it arises as the consequence of the intricate interactions between members of a species that is characterised by the variety and contingency of its behaviour.

But if we are to give up the chimera of numerical predictive models built from a priori principles, doesn't that mean we have to give up all formal models and rigour? I would say that we do not. What it does mean, however, is that we have to use formal and computational models that are able to reflect the detailed behaviour as it is observed. We need to constrain our models as much as possible using observations of the relevant phenomena, both in terms of the causal processes as well as the outcomes. Pinning down our models using only the verification of predictive outcomes and an insistence on formal simplicity will not be enough. We will need to capture the workings of the processes stage by stage as they are observed and reproduce the known outcomes.

In order to perform this feat we will need formal systems that are up to the task of expressing the qualitative cognitive processes that economic processes are rooted in. These more expressive systems come with a price, they are not simple and they seem to allow for multiple representations of the same outcomes. However there is no need for them to be any less formal or rigorous than a set of differential equations.

In this paper I will exhibit an attempt to construct a more descriptive model of the search for an appropriate strategy by the subjects in a particular experiment. It is, of course, impossible to lose all assumptions in the construction of any model, but the point is to move towards using fewer and less drastic a priori assumptions and use more qualitative and quantitative constraints derived from the processes under study. The purpose of this model is to provide an unambiguous framework for the exploration of the possible processes within these constraints so as to inform the direction of further observation and modelling. This is not merely a static description, for I am not concerned with static phenomena, but a dynamic description of a particular set of observations using the techniques of computational and cognitive modelling. The extent to which this model is generalisable to other phenomena will only become apparent when it is compared with other descriptive models, just as the general characteristics and markings of a species of animal may only become clear when several descriptions of the animals are compared.

To many readers my position will seem too pessimistic. They may be still hoping for some brilliant `short-cut' to a predictive model, that will allow them to miss out the laborious business of observing and describing the underlying processes. However, I would point out that the science of biology has become enormously successful using the methods I am suggesting and, once we have accepted the amount of field work that our subject matter entails, equal success might be achieved in economics.

Towards a Descriptive Model of Agent Strategy Search - Bruce Edmonds - 06 SEP 99
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