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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. 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..
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
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    Cited by:

    1. 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.
    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. 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.
    4. George Judge, 2018. "Micro-Macro Connected Stochastic Dynamic Economic Behavior Systems," Econometrics, MDPI, vol. 6(4), pages 1-14, December.

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