A Theory of Similarity and Uncertainty

Uncertainty may entail at the same time lack of determinacy and imprecise knowledge. Lack of determinacy is an ontological property of the universe we are considering. Imprecise knowledge is an epistemic property of the agents in that universe. A desirable feature of a theory of uncertainty is that both properties should be taken into account and integrated within a unifying framework. A possible route to identifying such a framework is suggested by Henry Kyburg’s conception of objective (ontological) probability (see Kyburg, Chapter 2, this volume) and Isaac Levi’s view concerning the relative autonomy of cognitive objectives (see Levi, Chapter 3, this volume). Kyburg maintains that ‘many people think that the evidence renders certain beliefs irrational’ (s. 2.3). He also maintains that ‘the issue is important in artificial intelligence for the same reason: are there constraints that degrees of beliefs should satisfy? Or is one coherent distribution as good as another?’ (s. 2.3). Finally, Kyburg calls attention to the issue of objectivity in statistical inference: ‘if there are no objective constraints, it is hard to know how differences of opinion regarding statistical conclusions can be resolved’ (s. 2.3). From the point of view of cognitive commitment, Kyburg’s claim about the effectiveness of objectivity constraints is close to Isaac Levi’s view concerning the autonomy of cognitive values.