Humans know how to reason based on cause and effect, but cause and effect is not enough to draw conclusions due to the problem of imperfect information and uncertainty. To resolve these problems, humans reason combining causal models with probabilistic information. The theory that attempts to model both causality and probability is called probabilistic causation, better known as Causal Bayes Ne...

The usefulness of the Choquet integral for modelling decision under risk and uncertainty is shown. It is shown that some paradoxes of expected utility theory are solved using Choquet integral. It is shown that Choquet expected utility model for decision under uncertainty and rank dependent utility model for decision under risk are respectively same as their simplified version.

Modelling A decision analytic model (decision tree) was used to examine the costs and outcomes associated with the three antidepressants over a period of 6 months, for each country. The model was constructed using assumptions derived from the experts' panel (decision nodes). After construction, the model was populated with the data obtained from the review of the relevant literature (chance nod...

This paper reviews research in relation with modelling uncertainty within Decision Support Systems (DSS) from 2000 to 2011. It specifically addresses software that has been built or prototyped with the purpose of supporting actual decision making, which is able to explicitly deal with uncertainty (widely understood) on the corresponding model parameters and/or data. The main DSS features analys...

We present the Unified Policy Framework (UPF), a generic framework for modelling security (access-control) policies; in Isabelle/HOL. UPF emphasizes the view that a policy is a policy decision function that grants or denies access to resources, permissions, etc. In other words, instead of modelling the relations of permitted or prohibited requests directly, we model the concrete function that i...

Agent-based modelling of human crowds has now become an important and active research field, with a wide range of applications such as military training, evacuation analysis and digital game. One of the significant and challenging tasks in agent-based crowd modelling is the design of decision rules for agents, so as to reproduce desired emergent phenomena behaviors. The common approach in agent...

Decision support systems are often not empirically evaluated, especially the underlying modelling components. This can be attributed to such systems necessarily being designed to handle complex and poorly structured problems and decision making. Nonetheless, evaluation is critical and should be focused on empirical testing whenever possible. Verification and validation, in combination, comprise...

When planning to change operations at ports there are two key stake holders with very different interests involved in the decision making processes. Port operators are attentive to their standards, a smooth service flow and economic viability while border agencies are concerned about national security. The time taken for security checks often interferes with the compliance to service standards ...

BACKGROUND Despite significant research efforts in Canada, real application of modelling in public health decision making and practice has not yet met its full potential. There is still room to better address the diversity of the Canadian population and ensure that research outcomes are translated for use within their relevant contexts. OBJECTIVES To strengthen connections to public health pr...

In this paper a special higher order neuron, the hypersphere neuron, is introduced. By embedding Euclidean space in a conformal space, hyperspheres can be expressed as vectors. The scalar product of points and spheres in conformal space, gives a measure for how far a point lies inside or outside a hypersphere. It will be shown that a hypersphere neuron may be implemented as a perceptron with tw...