Abstract

This paper presents the results of training an artificial neural network (ANN) to classify moral situations. The ANN produces a similarity space in the process of solving its classification problem. The state space is subjected to analysis that suggests that holistic approaches to interpreting its functioning are problematic. The idea of a contributory or pro tanto standard, as discussed in debates between moral particularists and generalists, is used to understand the structure of the similarity space generated by the ANN. A spectrum of possibilities for reasons, from atomistic to holistic, is discussed. Reasons are understood as increasing in nonlocality as they move away from atomism. It is argued that contributory standards could be used to understand forms of nonlocality that need not go all the way to holism. It is also argued that contributory standards may help us to understand the kind of similarity at work in analogical reasoning and argument in ethics. Some objections to using state space approaches to similarity are dealt with, as are objections to using empirical and computational work in philosophy