Human and Machine Learning

Ying-Fang Kao () and Ragupathy Venkatachalam ()
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Ragupathy Venkatachalam: University of London

Computational Economics, 2021, vol. 57, issue 3, No 8, 889-909

Abstract: 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.

Keywords: Machine learning; Human problem solving; Herbert Simon; Learning; Artificial intelligence; Go (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-018-9803-z

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