Abstract

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.