This chapter explains how to apply the Certainty-Loss Framework (CLF) to stories involving self-locating belief and context-sensitivity. This formal modeling framework and its distinctive updating rules (Generalized Conditionalization and the Proper Expansion Principle) were defined and defended in previous chapters. Here the framework is used to model rational requirements in a number of stories, most importantly Adam Elga’s highly controversial Sleeping Beauty Problem. After analyzing solutions to that problem offered by Elga and by David Lewis, the chapter shows two different ways of constructing CLF models that refute Lewis’s solution (without invoking indifference principles or even Lewis’s Principal Principle). Objections to the CLF models are considered and rebutted.
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