Modelling Analogical Reasoning: One-Size-Fits-All?

Votsis, Ioannis (2024) Modelling Analogical Reasoning: One-Size-Fits-All? In: Wittgenstein and AI. Anthem Press. ISBN 9781839991363 (In Press)

Abstract

A key type of reasoning in everyday life and science is reasoning by analogy. Roughly speaking, such reasoning involves the transposition of solutions that work well in one domain to another, on the basis of pre-existing shared properties between the two domains. If we are to automate scientific reasoning with artificial intelligence (AI), then we need adequate models of analogical reasoning that clearly specify the conditions under which good analogical inferences can be made and bad ones avoided. Two general approaches to such modelling exist: universal and local. In this chapter, we assess the merits and demerits of both approaches. We concede that there are substantial obstacles standing in the way of the universal model view, but that these may be mitigated to some extent by supplementing existing models with additional criteria. One such criterion is defended, particularly against a challenge due to Wittgenstein. We argue that this challenge can be met and thus that there is hope for a one-size-fits-all model in the study of analogical reasoning.

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