Journal Article10.1080/019697298125696
Modeling economic learning as modeling
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TL;DR: This work proposes a framework for model development that uses a combination of measures: the error with respect to past data, the complexity of the model, the cost of finding the model and a measure of themodel's specificity.
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Abstract: Economists tend to represent learning as a procedure for estimating the parameters of the ''correct'' econometric model. We extend this approach by assuming that agents specify as well as estimate models. Learning thus takes the form of a dynamic process of developing models using an internal language of representation where expectations are formed by forecasting with the best current model. This introduces a distinction between the form and content of the internal models that is particularly relevant for boundedly rational agents. We propose a framework for such model development that uses a combination of measures: the error with respect to past data, the complexity of the model, the cost of finding the model, and a measure of the model's specificity. The agent has to make various trade-offs between them. A utility learning agent is given as an example.
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Citations
Modeling socially intelligent agents
TL;DR: An approach to modeling boundedly rational agents using the parallel evolution of a population of mental models for each agent based on the genetic programming paradigm indicates that some of the problems in analyzing human communication will also occur with such models.
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Modelling Bounded Rationality Using Evolutionary Techniques
Bruce Edmonds,Scott Moss +1 more
- 07 Apr 1997
TL;DR: A technique for the credible modelling of economic agents with bounded rationality based on the evolutionary techniques is described and an example application of an agent seeking to maximise its utility by modelling its own utility function is briefly described.
Boundedly versus Procedurally Rational Expectations
Scott Moss,Esther-Mirjam Sent +1 more
TL;DR: In this article, a model of a transition economy is developed with three production sectors and a household sector, and the numerical outputs from that model are broadly in accord with the statistical evidence from the Russian economy.
17
Learning Appropriate Contexts
TL;DR: In this paper, it is argued that for context to be fully learnable, a further step of abstraction is necessary, and some principles of learning to identify useful contexts are proposed.
Learning Appropriate Contexts
TL;DR: It is argued that for context to be fully learnable a further step of abstraction is necessary and some principles of learning to identify useful contexts are proposed.
References
Money as a medium of exchange in an economy with artificially intelligent agents
TL;DR: In this paper, the authors study the exchange economies of Kiyotaki and Wright (1989) in which agents must use a commodity or fiat money as a medium of exchange if trade is to occur.
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•Posted Content
Argumentation as a General Framework for Uncertain Reasoning
TL;DR: In this paper, the authors model argumentation as a labelled deductive system, in which propositions are doubly labelled with the grounds on which they are based and a representation of the confidence attached to the argument.
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Control metaphors in the modelling of economic learning and decision-making behaviour
TL;DR: In this paper two agenda-type algorithms—a genetic algorithm and a production system — are applied to a simple economic model to show that they imply quite different descriptions of learning and decision-making.
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