Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Evidential support logic programming
Fuzzy Sets and Systems
Quantifying judgmental uncertainty: Methodology, experiences, and insights
IEEE Transactions on Systems, Man and Cybernetics
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Readings in uncertain reasoning
Readings in uncertain reasoning
An Introduction to the IPSE 2.5 project
Proceedings of the international workshop on environments on Software engineering environments
Random sets and fuzzy interval analysis
Fuzzy Sets and Systems - Special issue on mathematical aspects of fuzzy sets
DSS research and practice in perspective
Proceedings of the conference on First specialized conference on decision support systems
An introduction to process-centred environments
Software process modelling and technology
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Aris-Business Process Frameworks
Aris-Business Process Frameworks
Representing Uncertain Knowledge: An Artificial Intelligence Approach
Representing Uncertain Knowledge: An Artificial Intelligence Approach
Decision support for risk analysis on dynamic alliance
Decision Support Systems
Building a Multiple-Criteria Negotiation Support System
IEEE Transactions on Knowledge and Data Engineering
Decision maps: A framework for multi-criteria decision support under severe uncertainty
Decision Support Systems
Incorporating utility and cloud theories for owner evaluation in tendering
Expert Systems with Applications: An International Journal
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Complex socio-technical decisions, such as infrastructure investment decisions, are based on large quantities of evidence assembled and manipulated by multi-disciplinary teams. Information about decision options and future states of nature will often be ambiguous, incomplete or conflicting. In this article, a software-supported approach to assembling, structuring and representing evidence in a decision, based on hierarchical modelling of the processes leading up to a decision, is presented. Uncertainty in the available evidence is represented and propagated through the evidence hierarchy using Interval Probability Theory (IPT), providing a commentary on sources and implications of uncertainty in the decision. Case studies in the oil and civil engineering industries demonstrate how the approach has helped to develop shared understanding of the implications of uncertainty. It has enabled experts to externalise their knowledge and has facilitated discussion and negotiation.