Decisions with Uncertain Consequences-A Total Ordering on Loss-Distributions.

ABSTRACT: Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action can be modeled by a random variable, then the decision problem boils down to comparing different effects (random variables) by comparing their distribution functions. Although the full space of probability distributions cannot be ordered, a properly restricted subset of distributions can be totally ordered in a practically meaningful way. We call these loss-distributions, since they provide a substitute for the concept of loss-functions in decision theory. This article introduces the theory behind the necessary restrictions and the hereby constructible

SUBMITTER: Rass S 

PROVIDER: S-EPMC5193423 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature