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.
Highlights
How do human beings make decisions? By his own admission, this question provided Herbert Simon the impetus to shape his unparalleled research pursuits (Feigenbaum 2001)
We considered the problem of learning by humans and machines with a particular focus on complex board games like chess and Go
We surveyed the approaches to learning in classical game theory which resorts to substantive rationality and optimization
Summary
How do human beings make decisions? By his own admission, this question provided Herbert Simon the impetus to shape his unparalleled research pursuits (Feigenbaum 2001). The reasons behind this choice are partly historical, given their prominent place in the development of AI over the years because of their amenability to investigation via computational methods These games are complex and the impressive ability displayed by human beings in playing them was seen as providing an ideal ground for understanding the ingenuity of human intelligence. Our central argument in this paper is the following: unlike Simon’s approach, the focus and contents of most contemporary machine learning algorithms render them inadequate to explain human learning despite their impressive performance. 2, we outline different approaches to learning in classical game theory (another prominent area of research on games) and contrast it with Simon’s approach Unlike the former approach, the latter focuses on procedural rationality exhibited by boundedly rational agents, for whom optimization in such complex environments is out of reach.
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