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

Econometrics

George Judge¹ 

[1] Graduate School, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA; E-Mail

关键词: information-theoretic methods;    adaptive behavior;    causal entropy maximization;    pure and stochastic inverse problems;    binary network;    dynamic economic systems;   

DOI  :  10.3390/econometrics3010091

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.