We derive qualitative relationships about the informational relevance of variables in graph­ ical decision models based on a considera­ tion of the topology of the models. Specifi­ cally, we identify dominance relations for the expected value of information on chance vari­ ables in terms of their position and relation­ ships in influence diagrams. The qualitative relationships can be harnessed ...

This paper proposes a partial implementation of an integrated Quality, Security and Environment management system [1] to deal with the definition of an appropriate global management plan. This implementation is based on the multi-objective influence diagrams [12] which are one of the most commonly used graphical decision models for reasoning under uncertainty with multiple objectives.

The aim of the article is to show a stochastic approach for both modelling and optimizing the statistical agent belief in a probability model. Two networks are defined: a decision network D of the agent belief state and a utility network U, presenting the utility structure of the agent belief problem. The agent belief is presented via the following three items (B,D,U), where B is a Bayesian net...

Caste based endogamy is one of the main characteristics of traditional caste system practiced in rural Pakistan. Members of different quoms do not inter-marry or at least do not prefer to marry out of their quom. While social acceptance for inter quom marriages among different landowning quoms is increasing in Punjabi villages, inter marriages between landowning quoms and service providing quom...

We study the diffusion of a product in two customer segments where the acceptance level in one segment affects the diffusion rate not only in that same segment, but also in the other. The inter-segment influence can be positive or negative, i.e., the acceptance level of the product in one segment can reinforce or impede its diffusion in the other. The model set-up also applies to the diffusion ...

Imprecision in probability values (represented through credal sets) allows a more realistic modelling of uncertainty in decision problems. Uncertainties often lead to partially ordered preferences and, consequently, different criteria of choice may be taken into account. In this paper, we present an algorithm that evaluates an extended version of influence diagrams, where chance variables repre...

Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assume no-forgetting or regularity, which makes the class of problems we address ...