Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems
George Judge ()
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George Judge: Graduate School, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA
Econometrics, 2015, vol. 3, issue 1, 1-10
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.
Keywords: information-theoretic methods; adaptive behavior; causal entropy maximization; pure and stochastic inverse problems; binary network; dynamic economic systems (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:3:y:2015:i:1:p:91-100:d:46012
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