Elements of Multi-Bayesian Decision Theory

Bayesian Decision Making Theory

Multiple utility constrained multi-objective programs using Bayesian theory

A utility function is an important tool for representing a DM’s preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utili...

Bayesian Decision Theory and Psychophysics

We argue that Bayesian decision theory provides a good theoretical framework for visual perception. Such a theory involves a likelihood function specifying how the scene generates the image(s), a prior assumption about the scene, and a decision rule to determine the scene interpretation. This is illustrated by describing Bayesian theories for individual visual cues and showing that perceptual b...

A Unied Bayesian Decision Theory

This paper provides new foundations for Bayesian Decision Theory based on a representation theorem for preferences de…ned on a set of prospects containing both factual and conditional possibilities. This use of a rich set of prospects not only provides a framework within which the main theoretical claims of Savage, Ramsey, Je¤rey and others can be stated and compared, but also allows for the po...

A Minimal Extension of Bayesian Decision Theory

Savage denied that Bayesian decision theory applies in large worlds. This paper proposes a minimal extension of Bayesian decision theory to a large-world context that evaluates an event E by assigning it a number π(E) that reduces to an orthodox probability for a class of measurable events. The Hurwicz criterion evaluates π(E) as a weighted arithmetic mean of its upper and lower probabilities, ...