The Effects of Perfect and Sample Information on Fuzzy Utilities in Decision-Making
This work addresses decision-making under uncertainty for AI applications, but it appears incremental as it extends existing fuzzy and Bayesian concepts without introducing major new methods.
The paper tackles the problem of how sample information affects expected utility in decision-making by modeling utility functions as fuzzy random variables within a Bayesian framework, concluding that sample information increases expected utility on average, with perfect information providing an upper bound.
In this paper, we first consider a Bayesian framework and model the "utility function" in terms of fuzzy random variables. On the basis of this model, we define the "prior (fuzzy) expected utility" associated with each action, and the corresponding "posterior (fuzzy) expected utility given sample information from a random experiment". The aim of this paper is to analyze how sample information can affect the expected utility. In this way, by using some fuzzy preference relations, we conclude that sample information allows a decision maker to increase the expected utility on the average. The upper bound on the value of the expected utility is when the decision maker has perfect information. Applications of this work to the field of artificial intelligence are presented through two examples.