This paper develops a Multidimensional Decision Theory and argues that it better captures ordinary intuitions about fair distribution of chances than classical decision theory. The theory is an extension of Richard Jeffrey’s decision theory to counterfactual prospects and is a form of Modal Consequentialism, according to which the value of actual outcomes often depends on what could have been. Unlike existing versions of modal consequentialism, the multidimensional decision theory allows us to explicitly model the desirabilistic dependencies between actual and counterfactual (...) outcomes that, I contend, are at the heart of common intuitions about fair distribution of chances. (shrink)

Chance Neutrality is the thesis that, conditional on some proposition being true, its chance of being true should be a matter of practical indifference. The aim of this article is to examine whether Chance Neutrality is a requirement of rationality. We prove that given Chance Neutrality, the Principal Principle entails a thesis called Linearity; the centerpiece of von Neumann and Morgenstern’s expected utility theory. With this in mind, we argue that the Principal Principle is a requirement of practical rationality but (...) that Linearity is not and, hence, that Chance Neutrality is not rationally required. (shrink)