A Structuralist Theory of Belief Revision

The present paper aims at a synthesis of belief revision theory with the Sneed formalism known as the structuralist theory of science. This synthesis is brought about by a dynamisation of classical structuralism, with an abductive inference rule and base generated revisions in the style of Rott (2001). The formalism of prioritised default logic (PDL) serves as the medium of the synthesis. Why seek to integrate the Sneed formalism into belief revision theory? With the hybrid system of the present investigation, a substantial simplification of the ranking information that is necessary to define revisions and contractions uniquely is achieved. This system is, furthermore, expressive enough to capture complex and non-trivial scientific examples. It is thus closely related to a novel research area within belief revision theory which addresses the dynamics of scientific knowledge

Simulation Validation from a Bayesian Perspective

Bayesian epistemology offers a powerful framework for characterizing scientific inference. Its basic idea is that rational belief comes in degrees that can be measured in terms of probabilities. The axioms of the probability calculus and a rule for updating (e.g., Bayesian conditionalization) emerge as constraints on the formation of rational belief. Bayesian epistemology has led to useful explications of notions such as confirmation. It thus is natural to ask whether Bayesian epistemology offers a useful framework for thinking about the inferences implicit in the validation of computer simulations. The aim of this chapter is to answer this question. Bayesian epistemology is briefly summarized and then applied to validation. Updating is shown to form a viable method for data-driven validation. Bayesians can also express how a simulation obtains prior credibility because the underlying conceptual model is credible. But the impact of this prior credibility is indirect since simulations at best provide partial and approximate solutions to the conceptual model. Fortunately, this gap between the simulations and the conceptual model can be addressed using what we call Bayesian verification. The final part of the chapter systematically evaluates the use of Bayesian epistemology in validation, e.g., by comparing it to a falsificationist approach. It is argued that Bayesian epistemology goes beyond mere calibration and that it can provide the foundations for a sound evaluation of computer simulations