† Email: f.picinali@lse.ac.uk. I am indebted to Ron Allen, Jim Franklin, Dan Kahan, Mike Redmayne and Giulio Ubertis for the inestimable exchanges I had with them during the research for this article and for their valuable comments and criticisms on earlier versions thereof. One such version was presented in the workshop on AI and Evidence at the ICAIL 2011; I am grateful to all of its participants for their consideration and valuable comments. Special thanks go to Peter Tillers, without whose teachings and stimulus this research would not have begun.
The article proposes a normative theory of inferential reasoning for criminal fact finding, centred on the concept of ‘analogy’. While evidence law scholars have devoted little attention to the topic, the article maintains that analogy deserves more consideration. In particular, it argues that an analogical theory of inferential reasoning has three main advantages. First, the theory makes it possible to incorporate within a single coherent framework the important insights of different approaches to ‘reasoning under uncertainty’; indeed, it welcomes both the Pascalian notion of ‘relevance’ based on the Bayesian likelihood ratio and the Baconian concept of ‘weight’. Secondly, it helps advance the conventional understanding of the reference class problem, an evidential conundrum widely discussed in the recent legal scholarship. Finally, the theory allows for a functional taxonomy of reasonable doubts.