Using graphical models and multi-attribute utility theory for probabilistic uncertainty handling in large systems, with application to nuclear emergency management

Although many decision-making problems involve uncertainty, uncertainty handling within large decision support systems (DSSs) is challenging. One domain where uncertainty handling is critical is emergency response management, in particular nuclear emergency response, where decision making takes place in an uncertain, dynamically changing environment. Assimilation and analysis of data can help to reduce these uncertainties, but it is critical to do this in an efficient and defensible way. After briefly introducing the structure of a typical DSS for nuclear emergencies, the paper sets up a theoretical structure that enables a formal Bayesian decision analysis to be performed for environments like this within a DSS architecture. In such probabilistic DSSs many input conditional probability distributions are provided by different sets of experts overseeing different aspects of the emergency. These probabilities are then used by the decision maker (DM) to find her optimal decision. We demonstrate in this paper that unless due care is taken in such a composite framework, coherence and rationality may be compromised in a sense made explicit below. The technology we describe here builds a framework around which Bayesian data updating can be performed in a modular way, ensuring both coherence and efficiency, and provides sufficient unambiguous information to enable the DM to discover her expected utility maximizing policy

Probabilistic analysis of cost-effectiveness models: choosing between treatment strategies for gastroesophageal reflux disease

When choosing between mutually exclusive treatment options, it is common to construct a cost-effectiveness frontier on the cost-effectiveness plane that represents efficient points from among the treatment choices. Treatment options internal to the frontier are considered inefficient and are excluded either by strict dominance or by appealing to the principle of extended dominance. However, when uncertainty is considered, options excluded under the baseline analysis may form part of the cost-effectiveness frontier. By adopting a Bayesian approach, where distributions for model parameters are specified, uncertainty in the decision concerning which treatment option should be implemented is addressed directly. The approach is illustrated using an example from a recently published cost-effectiveness analysis of different possible treatment strategies for gastroesophageal reflux disease.It is argued that probabilistic analyses should be encouraged because they have potential to quantify the strength of evidence in favor of particular treatment choices

Modeling payback from research into the efficacy of left-ventricular assist devices as destination therapy

Objectives: Ongoing developments in design have improved the outlook for left-ventricular assist device (LVAD) implantation as a therapy in end-stage heart failure. Nevertheless, early cost-effectiveness assessments, based on first-generation devices, have not been encouraging. Against this background, we set out (i) to examine the survival benefit that LVADs would need to generate before they could be deemed cost-effective; (ii) to provide insight into the likelihood that this benefit will be achieved; and (iii) from the perspective of a healthcare provider, to assess the value of discovering the actual size of this benefit by means of a Bayesian value of information analysis. Methods: Cost-effectiveness assessments are made from the perspective of the healthcare provider, using current UK norms for the value of a quality-adjusted life-year (QALY). The treatment model is grounded in published analyses of the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) trial of first-generation LVADs, translated into a UK cost setting. The prospects for patient survival with second-generation devices is assessed using Bayesian prior distributions, elicited from a group of leading clinicians in the field. Results: Using established thresholds, cost-effectiveness probabilities under these priors are found to be low (.2 percent) for devices costing as much as £60,000. Sensitivity of the conclusions to both device cost and QALY valuation is examined. Conclusions: In the event that the price of the device in use would reduce to £40,000, the value of the survival information can readily justify investment in further trials

An Empirical Comparison of Three Inference Methods

In this paper, an empirical evaluation of three inference methods for uncertain reasoning is presented in the context of Pathfinder, a large expert system for the diagnosis of lymph-node pathology. The inference procedures evaluated are (1) Bayes' theorem, assuming evidence is conditionally independent given each hypothesis; (2) odds-likelihood updating, assuming evidence is conditionally independent given each hypothesis and given the negation of each hypothesis; and (3) a inference method related to the Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic metrics are used to compare the diagnostic accuracy of the inference methods.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (UAI1988

Children and young people’s experiences and perceptions of self-management of type 1 diabetes: A qualitative meta-synthesis

The aim of this review was to conduct a meta-synthesis of the experiences and perceptions of self-management of type 1 diabetes of children and young people living with type 1 diabetes (CYPDs). Six databases were systematically searched for studies with qualitative findings relevant to CYPDs’ (aged 8–18 years) experiences of self-management. A thematic synthesis approach was used to combine articles and identify analytical themes. Forty articles met the inclusion criteria. Two analytical themes important to CYPDs’ experiences and perceptions of self-management were identified: (1) negotiating independence and (2) feeling in control. The synthesis contributes to knowledge on contextual factors underpinning self-management and what facilitates or impedes transition towards autonomous self-management for CYPDs

A Pilot Study of Value of Information Analysis to Support Research Recommendations for the National Institute for Health and Clinical Excellence

Background - This project developed as a result of the activities of the Research Teams at the Centre for Health Economics, University of York, and ScHARR at the University of Sheffield in the methods and application of decision analysis and value of information analysis as a means of informing the research recommendations made by NICE, as part of its Guidance to the NHS in England and Wales, and informing the deliberations of the NICE Research and Development Committee. Objectives - The specific objectives of the pilot study were to: • Demonstrate the benefits of using appropriate decision analytic methods and value of information analysis to inform research recommendations. • Establish the feasibility and resource implications of applying these methods in a timely way, to inform NICE. • Identify critical issues and methodological challenges to the use of value of information methods for research recommendations (with particular regard to the new reference case as a suitable basis for this type of analysis).

The Premises of Condorcet's Jury Theorem Are Not Simultaneously Justified

Condorcet''s famous jury theorem reaches an optimistic conclusion on the correctness of majority decisions, based on two controversial premises about voters: they are competent and vote independently, in a technical sense. I carefully analyse these premises and show that: (i) whether a premise is justified depends on the notion of probability considered; (ii) none of the notions renders both premises simultaneously justified. Under the perhaps most interesting notions, the independence assumption should be weakened.mathematical economics;

Updating beliefs with incomplete observations

Currently, there is renewed interest in the problem, raised by Shafer in 1985, of updating probabilities when observations are incomplete. This is a fundamental problem in general, and of particular interest for Bayesian networks. Recently, Grunwald and Halpern have shown that commonly used updating strategies fail in this case, except under very special assumptions. In this paper we propose a new method for updating probabilities with incomplete observations. Our approach is deliberately conservative: we make no assumptions about the so-called incompleteness mechanism that associates complete with incomplete observations. We model our ignorance about this mechanism by a vacuous lower prevision, a tool from the theory of imprecise probabilities, and we use only coherence arguments to turn prior into posterior probabilities. In general, this new approach to updating produces lower and upper posterior probabilities and expectations, as well as partially determinate decisions. This is a logical consequence of the existing ignorance about the incompleteness mechanism. We apply the new approach to the problem of classification of new evidence in probabilistic expert systems, where it leads to a new, so-called conservative updating rule. In the special case of Bayesian networks constructed using expert knowledge, we provide an exact algorithm for classification based on our updating rule, which has linear-time complexity for a class of networks wider than polytrees. This result is then extended to the more general framework of credal networks, where computations are often much harder than with Bayesian nets. Using an example, we show that our rule appears to provide a solid basis for reliable updating with incomplete observations, when no strong assumptions about the incompleteness mechanism are justified.Comment: Replaced with extended versio