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Human and Machine Learning

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  • Ying-Fang Kao
  • Ragupathy Venkatachalam

    (University of London)

Abstract

In this paper, we consider learning by human beings and machines in the light of Herbert Simon’s pioneering contributions to the theory of Human Problem Solving. Using board games of perfect information as a paradigm, we explore differences in human and machine learning in complex strategic environments. In doing so, we contrast theories of learning in classical game theory with computational game theory proposed by Simon. Among theories that invoke computation, we make a further distinction between computable and computational or machine learning theories. We argue that the modern machine learning algorithms, although impressive in terms of their performance, do not necessarily shed enough light on human learning. Instead, they seem to take us further away from Simon’s lifelong quest to understand the mechanics of actual human behaviour.

Suggested Citation

  • Ying-Fang Kao & Ragupathy Venkatachalam, 2021. "Human and Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(3), pages 889-909, March.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:3:d:10.1007_s10614-018-9803-z
    DOI: 10.1007/s10614-018-9803-z
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    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. R.J. Aumann & S. Hart (ed.), 2002. "Handbook of Game Theory with Economic Applications," Handbook of Game Theory with Economic Applications, Elsevier, edition 1, volume 3, number 3.
    3. Smale, Stephen, 1976. "Dynamics in General Equilibrium Theory," American Economic Review, American Economic Association, vol. 66(2), pages 288-294, May.
    4. Sergiu Hart & Andreu Mas-Colell, 2013. "A Simple Adaptive Procedure Leading To Correlated Equilibrium," World Scientific Book Chapters, in: Simple Adaptive Strategies From Regret-Matching to Uncoupled Dynamics, chapter 2, pages 17-46, World Scientific Publishing Co. Pte. Ltd..
    5. Selten, Reinhard & Stoecker, Rolf, 1986. "End behavior in sequences of finite Prisoner's Dilemma supergames A learning theory approach," Journal of Economic Behavior & Organization, Elsevier, vol. 7(1), pages 47-70, March.
    6. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
    7. Prasad, Kislaya, 1997. "On the computability of Nash equilibria," Journal of Economic Dynamics and Control, Elsevier, vol. 21(6), pages 943-953, June.
    8. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    9. Kalai, Ehud & Lehrer, Ehud, 1993. "Rational Learning Leads to Nash Equilibrium," Econometrica, Econometric Society, vol. 61(5), pages 1019-1045, September.
    10. Simon, Herbert A., 2000. "Barriers and bounds to Rationality," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 243-253, July.
    11. Ying-Fang Kao & K. Vela Velupillai, 2015. "Behavioural economics: Classical and modern," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 22(2), pages 236-271, April.
    12. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    13. Spear, Stephen E, 1989. "Learning Rational Expectations under Computability Constraints," Econometrica, Econometric Society, vol. 57(4), pages 889-910, July.
    14. Nachbar, John H & Zame, William R, 1996. "Non-computable Strategies and Discounted Repeated Games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 8(1), pages 103-122, June.
    15. Velupillai, K., 2000. "Computable Economics: The Arne Ryde Memorial Lectures," OUP Catalogue, Oxford University Press, number 9780198295273.
    16. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, April.
    17. Rubinstein, Ariel, 1986. "Finite automata play the repeated prisoner's dilemma," Journal of Economic Theory, Elsevier, vol. 39(1), pages 83-96, June.
    18. Arrow, Kenneth J, 1986. "Rationality of Self and Others in an Economic System," The Journal of Business, University of Chicago Press, vol. 59(4), pages 385-399, October.
    19. M. Euwe, 2016. "Mathematics — Set-Theoretic Considerations on the Game of Chess," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 11-20, March.
    20. Halpern, Joseph Y. & Pass, Rafael, 2015. "Algorithmic rationality: Game theory with costly computation," Journal of Economic Theory, Elsevier, vol. 156(C), pages 246-268.
    21. Simon, Herbert A. & Schaeffer, Jonathan, 1992. "The game of chess," Handbook of Game Theory with Economic Applications, in: R.J. Aumann & S. Hart (ed.), Handbook of Game Theory with Economic Applications, edition 1, volume 1, chapter 1, pages 1-17, Elsevier.
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