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

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  • George Judge

    (Graduate School, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA)

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.

Suggested Citation

  • George Judge, 2015. "Entropy Maximization as a Basis for Information Recovery in Dynamic Economic Behavioral Systems," Econometrics, MDPI, vol. 3(1), pages 1-10, February.
    • Handle: RePEc:gam:jecnmx:v:3:y:2015:i:1:p:91-100:d:46012
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    References listed on IDEAS

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    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, WZB Berlin Social Science Center.
    3. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    4. Wendy K. Tam Cho & George Judge, 2015. "An information theoretic approach to network tomography," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 1-6, January.
    5. Alec Smith & B. Douglas Bernheim & Colin F. Camerer & Antonio Rangel, 2014. "Neural Activity Reveals Preferences without Choices," American Economic Journal: Microeconomics, American Economic Association, vol. 6(2), pages 1-36, May.
    6. Leonardo Bargigli & Andrea Lionetto & Stefano Viaggiu, 2013. "A Statistical Test of Walrasian Equilibrium by Means of Complex Networks Theory," Papers 1307.0817, arXiv.org, revised Sep 2016.
    7. Leonardo Bargigli & Andrea Lionetto & Stefano Viaggiu, 2013. "A Statistical Equilibrium Representation of Markets as Complex Networks," Working Papers - Economics wp2013_23.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    8. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    9. Cho, Wendy K. Tam & Judge, George G., 2007. "Information theoretic solutions for correlated bivariate processes," Economics Letters, Elsevier, vol. 97(3), pages 201-207, December.
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    Cited by:

    1. Sofia B. Villas-Boas & Qiuzi Fu & George Judge, 2015. "Is Benford’s Law a Universal Behavioral Theory?," Econometrics, MDPI, vol. 3(4), pages 1-11, October.
    2. Judge, George, 2023. "Information Recovery in Complex Economic Systems," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt4jj70102, Department of Agricultural & Resource Economics, UC Berkeley.
    3. George Judge, 2018. "Micro-Macro Connected Stochastic Dynamic Economic Behavior Systems," Econometrics, MDPI, vol. 6(4), pages 1-14, December.
    4. Ellis Scharfenaker & Duncan Foley, 2017. "Maximum Entropy Estimation of Statistical Equilibrium in Economic Quantal Response Models," Working Papers 1710, New School for Social Research, Department of Economics, revised May 2017.

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