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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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