In this article, we discuss problems with probability theory in representing uncertainties encountered in the "real world" and show how belief functions can overcome these difficulties. Also, we discuss an expected utility approach of decision making under ambiguity using the belief function framework. In particular, we develop a proposition for decision making under ambiguity using the expecte...

We explore how individuals make decisions in an operations management setting when there is information asymmetry between the firm and an outside investor. A common assumption in the signaling game literature is that beliefs among the participants in the game are refined using the Intuitive Criterion refinement. Our experimental results provide evidence that the predictive power of this refinem...

Decision theory is used to define a notion of ‘relevance’ in terms of decision problems. Decision problems are also used to explain why attitude attributions are made. Assuming that belief attributions are made to explain unexpected actions, and that assertions have to be relevant, it is shown that potentially ambiguous, or underspecified, de re belief attributions can be disambiguated by conte...

This paper investigates a multi-criteria decision making method in an uncertain environment, where the uncertainty is represented using the belief function framework. Indeed, we suggest a novel methodology that tackles the challenge of introducing uncertainty in both the criterion and the alternative levels. On the one hand and in order to judge the criteria weights, our proposed approach sugge...

Decision making is still an open issue in the application of Dempster-Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability assignments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the p...

A participatory integrated (social, economic, environmental) approach based on causal loop diagram, Bayesian belief networks and evolutionary multiobjective optimisation is proposed for efficient water resources management. The proposed methodology incorporates all the conflicting objectives in the decision making process. Causal loop diagram allows a range of different factors to be considered...

This article discusses a design pattern for building belief networks for application domains in which causal models are hard to construct. In this approach we pursue a modular belief network structure that is easily extended by the users themselves, while remaining reliable for decision support. The Hypothesis Management Framework proposed here is a pragmatic attempt to enable analysts and doma...

There are many conceptualizations and formalizations of decision making. In this paper we compare classical decision theory with qualitative decision theory, knowledge-based systems and belief-desire-intention models developed in artificial intelligence and agent theory. They all contain representations of information and motivation. Examples of informational attitudes are probability distribut...

In this paper, we present a decision support system based on belief functions and the pignistic transformation. The system is an integration of an evidential system for belief function propagation and a valuation-based system for Bayesian decision analysis. The two subsystems are connected through the pignistic transformation. The system takes as inputs the user's "gut feelings" about a situati...

We introduce the Bayes-Adaptive Interactive Partially Observable Markov Decision Process (BA-IPOMDP), the first multiagent decision model that explicitly incorporates model learning. As in I-POMDPs, the BA-IPOMDP agent maintains beliefs over interactive states, which include the physical states as well as the other agents’ models. The BA-IPOMDP assumes that the state transition and observation ...