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Econometrics 2015, 3(1), 91-100; doi:10.3390/econometrics3010091

Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems

Graduate School, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA
Academic Editor: Kerry Patterson
Received: 16 December 2014 / Accepted: 5 February 2015 / Published: 16 February 2015
View Full-Text   |   Download PDF [296 KB, uploaded 16 February 2015]

Abstract

As a basis for information recovery in open dynamic microeconomic systems, we emphasize the connection between adaptive intelligent behavior, causal entropy maximization and self-organized equilibrium seeking behavior. This entropy-based causal adaptive behavior framework permits the use of information-theoretic methods as a solution basis for the resulting pure and stochastic inverse economic-econometric problems. We cast the information recovery problem in the form of a binary network and suggest information-theoretic methods to recover estimates of the unknown binary behavioral parameters without explicitly sampling the configuration-arrangement of the sample space. View Full-Text
Keywords: information-theoretic methods; adaptive behavior; causal entropy maximization; pure and stochastic inverse problems; binary network; dynamic economic systems information-theoretic methods; adaptive behavior; causal entropy maximization; pure and stochastic inverse problems; binary network; dynamic economic systems
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Judge, G. Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems. Econometrics 2015, 3, 91-100.

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