A Markov Decision Process (MDP) is given by (i) a finite set of states S and an initial state s1 ¥ S, (ii) a finite set of actions A, (iii) a cost function c: S×AQ R, and (iv) a transition rule p: S×AQ D(S), where D(S) is the space of probability distributions over S. At every stage n ¥ N, where N is the set of positive integers, the process is in some state sn ¥ S. The decision maker chooses a...

This paper provides a case study of a method to estimate the value of additional information, before its acquisition, to aid decision making in the face of uncertainty. The approach employs conditional simulation in a Monte Carlo framework to conduct a Bayesian assessment of the value of information in an explicitly spatial setting. This paper demonstrates the procedure as applied by a hypothet...

We present a new model and a new approach for solving fuzzylinear programming (FLP) problems with various utilities for the satisfactionof the fuzzy constraints. The model, constructed as a multi-objective linearprogramming problem, provides flexibility for the decision maker (DM), andallows for the assignment of distinct weights to the constraints and the objectivefunction. The desired solutio...

In this paper I analyze how careerist decision makers aggregate and use information provided by others. I find that decision makers who are motivated by reputation concerns tend to ‘anti-herding’, i.e., they excessively contradict public information such as the prior or others’ recommendations. I also find that some decision makers may deliberately act unilaterally and not consult advisers alth...

Getting the right data to the right decision-maker is a significant problem for many industrial companies. One of the main reasons is an overload of data. With the increasing amounts of industrial data this problem is becoming a bigger problem in the future. In order to address this challenge we propose the use of an Industrial Data Recommender System (IDRS). An IDRS recommends additional data ...

We develop an axiomatic approach to decision under uncertainty that explicitly takes into account the information available to the decision maker. The information is described by a set of priors and a reference prior. We define a notion of imprecision for this informational setting and show that a decision maker who is averse to information imprecision maximizes the minimum expected utility com...

Decision theory focuses on the problem of making decisions under uncertainty. This uncertainty arises from the unknown aspects of the state of the world the decision maker is in or the unknown utility function of performing actions. The uncertainty can be modeled as a probability distribution capturing our belief about the world the decision maker is in. Upon making new observations, the decisi...