Epidemiology and litigation—the Sellafield childhood leukaemia cases

Statisticians and scientists are often required to provide information outside the scientific community. One important example is as an expert witness in the law courts. The civil litigation cases of Reay versus British Nuclear Fuels plc and Hope versus British Nuclear Fuels plc are a vivid illustration of how science and scientists interact with the law and lawyers. The personal injury cases were decided on the basis of how a specific epidemiological association should be properly interpreted, and this involved many experts. It is desirable that statisticians and scientists understand the nature of expert evidence, and these two cases are used to illuminate the role of the scientist in civil litigation.     

Exploration of associations between governance and economics and country level foot-and-mouth disease status by using Bayesian model averaging

Summary.  Using Bayesian model averaging, we quantify associations of governance and economic health with country level presence of foot-and-mouth disease (FMD) and estimate the probability of the presence of FMD in each country from 1997 to 2005. The Bayesian model averaging accounted for countries' previous FMD status and other possible confounders, as well as uncertainty about the 'true' model, and provided accurate predictions (90% specificity and 80% sensitivity). This model represents a novel approach to predicting FMD, and other conditions, on a global scale and in identifying important risk factors that can be applied to global policy and allocation of resources for disease control.     

基于BP神经网络专家系统的财产保险公司全面风险预警系统研究

为了克服传统的偿付能力监管所隐藏的风险因子考虑不周以及保险统计数据匮乏等的弊端,借鉴国内外权威保险监管机构和专家学者的最新研究成果,尝试提出了我国财产保险公司全面风险预警指标体系;利用BP神经网络和专家系统相结合的方法,构建了我国财产保险公司全面风险预警机制并进行了实证分析。结果表明,利用BP神经网络专家系统对财产保险公司进行全面风险预警,具有极高的预测精度和较强的鲁棒性。     

面向对象的自动工艺设计系统

本文介绍一种面向对象的自动工艺设计系统.它采用分层分类方法搜集知识,以产生式规划和与或树结构完整地表达知识,利用规则匹配和深度优先搜索机制进行搜索推理,从而自动进行工艺设计,得到满意工艺规程.该系统设置的外部数据库和规则库,可由用户进行数据及规则的扩充与修改,因而具有较高柔性,可适用于各种环境下的工艺决策.     

SUPRA-BAYESIAN POOLING OF PRIORS LINKED BY A DETERMINISTIC SIMULATION MODEL

Deterministic simulation models are used to guide decision-making and enhance understanding of complex systems such as disease transmission, population dynamics, and tree plantation growth. Bayesian inference about parameters in deterministic simulation models can require the pooling of expert opinion. One class of approaches to pooling expert opinion in this context is supra-Bayesian pooling, in which expert opinion is treated as data for an ultimate decision maker. This article details and compares two supra-Bayesian approaches—“event updating” and “parameter updating.” The suitability of each approach in the context of deterministic simulation models is assessed based on theoretical properties, performance on examples, and the selection and sensitivity of required hyperparameters. In general, we favor a parameter updating approach because it uses more intuitive hyperparameters, it performs sensibly on examples, and because the alternative event updating approach fails to exhibit a desirable property (relative propensity consistency) in all cases. Inference in deterministic simulation models is an increasingly important statistical and practical problem, and supra-Bayesian methods represent one viable option for achieving a sensible pooling of expert opinion.     

Single and Multiple Period Decision Models for Analysis of Quality and Quantity of Validation*

This paper investigates a single period model for the analysis of the impact of the quality of the validation effort. The single period model uses a Bayesian approach to find that validation is a critical point process. That model is then extended to allow for the uncertainty of the validation process to determine the quality of the underlying model. Some monotonicity results are developed for the model and investigated in light of the process being a critical point process. The model indicates that, consistent with comments from real world settings, the impact of the quality of the validation effort can be substantial. The paper also presents two multiperiod models of the impact of the quantity of the validation effort. In practice, the development of an expert system may follow a recurring multiperiod life cycle, where a prototype is built, the system is validated to determine how well it performs, and based on that performance, is either funded or not funded. The first multiple period model assumes that validation and funding occurs at each point in the PVF budget cycle. The model employs Bayesian revision of probabilities to update the prior probability of obtaining a model with an appropriate level of success. It is found that the critical point for multiperiod problems is different than that for single period problems. This model forms the basis of the second model. The second multiple period model extends the first by assuming that the quantity of validation can be varied. The more validation, the more likely that flaws in the model will be found. Thus, the more validation, the better the understanding of the level of performance of the model.     

The Emergence of Artificial Intelligence as a Reference Discipline for Decision Support Systems Research

It is timely and appropriate to examine both philosophical and pragmatical issues associated with formalizing the adoption of artificial intelligence as a reference discipline for decision support systems research. This paper reflects on where we were when the first special issue of Decision Sciences on expert systems and decision support systems was published, addresses the dynamics of what has taken place subsequent to the publication of that first special issue, sets forth a proposition to stimulate ongoing dialog with respect to synergies between the decision support system research agenda and the research agenda of the artificial intelligence discipline, and demonstrates how the papers appearing in this follow-up special issue of Decision Sciences are representative of an emerging, challenging, and exciting new decision support systems era.