Human and Machine Learning

Kao, Ying-Fang and Venkatachalam, Ragupathy. 2018. Human and Machine Learning. Computational Economics, pp. 889-909. ISSN 0927-7099 [Article]

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Abstract or Description

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.

Item Type:


Identification Number (DOI):


Machine learning · Human problem solving · Herbert Simon · Learning · Artificial intelligence · Go

Departments, Centres and Research Units:

Institute of Management Studies


15 February 2018Accepted
21 February 2018Published Online

Item ID:


Date Deposited:

22 Feb 2018 11:01

Last Modified:

26 Apr 2021 11:46

Peer Reviewed:

Yes, this version has been peer-reviewed.


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