Descriptive Uncertainty and Maximizing Expected Choice-Worthiness

Abstract

A popular model of normative decision-making under uncertainty suggests choosing the option with the maximum expected moral choice-worthiness (MEC), where the choice-worthiness values from each moral theory, which are assumed commensurable, are weighted by credence and combined. This study adds descriptive uncertainty about the non-moral facts of a situation into the model by treating choice-worthiness as a random variable. When agents face greater descriptive uncertainty, the choice-worthiness random variable will have a greater spread and a larger standard deviation. MEC, as a decision rule, is sensitive only to the expected value of the random variable and not to its standard deviation. For example, MEC is insensitive to cases where an option with a large degree of descriptive uncertainty may have a higher probability of being below some threshold of impermissibility than does an option with less dispersion, even though the latter has a higher expected choice-worthiness. When applied to the same situation, similar moral theories will have statistically correlated choice-worthiness values. This correlation affects the dispersion of the credence-weighted sum of the random variables but not its expected value. Thus, MEC is insensitive to aspects of the normative situation to which a good decision-rule should be sensitive.