Bayesian decision-makers reaching consensus using expert information

Published ArticleThe paper is concerned with the problem of Bayesian decision-makers seeking consensus about the decision that should be taken from a decision space. Each decision-maker has his own utility function and it is assumed that the parameter space has two points, Θ = {θ1,θ2 }. The initial probabilities of the decision-makers for Θ can be updated by information provided by an expert. The decision-makers have an opinion about the expert and this opinion is formed by the observation of the expert's performance in the past. It is shown how the decision-makers can decide beforehand, on the basis of this opinion, whether the consultation of an expert will result in consensus

A literature review on the use of expert opinion in probabilistic risk analysis

Risk assessment is part of the decision making process in many fields of discipline, such as engineering, public health, environment, program management, regulatory policy, and finance. There has been considerable debate over the philosophical and methodological treatment of risk in the past few decades, ranging from its definition and classification to methods of its assessment. Probabilistic risk analysis (PRA) specifically deals with events represented by low probabilities of occurring with high levels of unfavorable consequences. Expert judgment is often a critical source of information in PRA, since empirical data on the variables of interest are rarely available. The author reviews the literature on the use of expert opinion in PRA, in particular on the approaches to eliciting and aggregating experts'assessments. The literature suggests that the methods by which expert opinions are collected and combined have a significant effect on the resulting estimates. The author discusses two types of approaches to eliciting and aggregating expert judgments-behavioral and mathematical approaches, with the emphasis on the latter. It is generally agreed that mathematical approaches tend to yield more accurate estimates than behavioral approaches. After a short description of behavioral approaches, the author discusses mathematical approaches in detail, presenting three aggregation models: non-Bayesian axiomatic models, Bayesian models, andpsychological scaling models. She also discusses issues of stochastic dependence.Health Monitoring&Evaluation,ICT Policy and Strategies,Public Health Promotion,Enterprise Development&Reform,Statistical&Mathematical Sciences,ICT Policy and Strategies,Health Monitoring&Evaluation,Statistical&Mathematical Sciences,Science Education,Scientific Research&Science Parks