Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422329
Title: From micro to macro : essays on rationality, bounded rationality, and microfoundations
Author: Salehnejad, Mohammad Reza
Awarding Body: London School of Economics and Political Science
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2005
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Abstract:
This thesis examines some issues at the heart of theoretical macroeconomics, namely the possibility of establishing a predictive theory of individual behaviour and transforming it into a theory of the economy using aggregation. As regards individual behaviour, the basic idea in economics is that homo economicus follows the prescriptions of the expected utility theory. The thesis argues that the expected utility theory takes the agent's view of the economy as given, and is silent about how he models his choice situation and defines his decision problem. As a consequence, it is of only a minor contribution to the analysis of economic phenomena. To explain how the agent learns about the economy and thus models his choice situation, new classical economists have relatively recently proposed that the agent behaves like a statistician. That is, like a statistician, he theorises, estimates, and adapts in attempting to learn about the economy. The usefulness of this hypothesis for modelling the economy depends on the existence of a 'tight enough' theory of statistical inference. To address this issue, the thesis proposes a preliminary conjecture about how a statistician perceives and models a choice situation: the statistician regards measurable features of the environment as realisations of some random variables, with an unknown joint probability distribution. He first uses the data on these quantities to discover the joint probability distribution of the variables and then uses the estimate of the distribution to uncover the causal structure of the variables. If the resulting model turns out to be inadequate, the initial set of variables is modified and the two phases of inference are repeated. This setting allows the separation of probabilistic inference issues from those of causal inference. The thesis studies both stages of learning from data to argue why there cannot be a 'tight enough' theory of statistical learning. As a result, the marriage of the hypothesis that the agent behaves like a decision scientist with the one that he behaves like a statistician is not of much help in predicting behaviour and modelling the economy. The thesis next turns to the other issue relating to the move from a theory of individual behaviour to a theory of the economy. It argues that to explain economic phenomena it is necessary to view the economy as a society of interactive, and heterogeneous, agents. However, the regularities emerging in such a society are not directly related to the laws operating at the micro level. The connection between the individual and the aggregate levels is highly complex.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.422329  DOI: Not available
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