Introduction

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 analogous 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.

The structure of the chapter is as follows. The section titled ‘Philosophical Models of Analogical Reasoning’ provides an overview of the main philosophical models of analogical reasoning, identifying some of their strengths and weaknesses. The next section, titled ‘AI Models of Analogical Reasoning’, briefly looks at one model of analogical reasoning that originates in the symbolic AI tradition, and offers some very general remarks about the prospects of modelling analogical reasoning with neural AI. Following that, the section titled ‘Norton's Material Challenge’ sets out the key issue of concern for this chapter, namely whether a universal model of analogical reasoning can be constructed. In the section titled ‘Relevant Conceptual Uniformity’, we consider one promising route towards a universal model via the supplementary criterion that the concepts involved are relevantly uniform. The subsequent section, titled ‘A Wittgensteinian Spanner in the Works?’, presents a challenge to this route that can be found in Wittgenstein's family resemblance metaphor, whose ultimate target is the rejection of concept uniformity. An attempt is made to meet this challenge by arguing that some concepts in natural science are uniform, or at least more uniform than others, but also that scientific inquiry strives towards, and manages to increase, uniformity.