Risk-informed decision-making in the presence of epistemic uncertainty

An important issue in risk analysis is the distinction between epistemic and aleatory uncertainties. In this paper, the use of distinct representation formats for aleatory and epistemic uncertainties is advocated, the latter being modelled by sets of possible values. Modern uncertainty theories based on convex sets of probabilities are known to be instrumental for hybrid representations where aleatory and epistemic components of uncertainty remain distinct. Simple uncertainty representation techniques based on fuzzy intervals and p-boxes are used in practice. This paper outlines a risk analysis methodology from elicitation of knowledge about parameters to decision. It proposes an elicitation methodology where the chosen representation format depends on the nature and the amount of available information. Uncertainty propagation methods then blend Monte Carlo simulation and interval analysis techniques. Nevertheless, results provided by these techniques, often in terms of probability intervals, may be too complex to interpret for a decision-maker and we, therefore, propose to compute a unique indicator of the likelihood of risk, called confidence index. It explicitly accounts for the decisionmaker’s attitude in the face of ambiguity. This step takes place at the end of the risk analysis process, when no further collection of evidence is possible that might reduce the ambiguity due to epistemic uncertainty. This last feature stands in contrast with the Bayesian methodology, where epistemic uncertainties on input parameters are modelled by single subjective probabilities at the beginning of the risk analysis process.

Risky decision making in younger and older adults:The role of learning.

Are older adults risk seeking or risk averse? Answering this question might depend on both the task used and the analysis performed. By modeling responses to the Balloon Analogue Risk Task (BART), our results illustrate the value of modeling as compared to relying on common analysis techniques. While analysis of overall measures suggested initially that older and younger adults do not differ in their risky decisions, our modeling results indicated that younger adults were at first more willing to take greater risks. Furthermore, older adults may be more cautious when their decision making is based on initial perceptions of risk, rather than learning following some experience with a task.

Self-disclosure decision making based on intimacy and privacy

Autonomous agents may encapsulate their principals' personal data attributes. These attributes may be disclosed to other agents during agent interactions, producing a loss of privacy. Thus, agents need self-disclosure decision-making mechanisms to autonomously decide whether disclosing personal data attributes to other agents is acceptable or not. Current self-disclosure decision-making mechanisms consider the direct benefit and the privacy loss of disclosing an attribute. However, there are many situations in which the direct benefit of disclosing an attribute is a priori unknown. This is the case in human relationships, where the disclosure of personal data attributes plays a crucial role in their development. In this paper, we present self-disclosure decision-making mechanisms based on psychological findings regarding how humans disclose personal information in the building of their relationships. We experimentally demonstrate that, in most situations, agents following these decision-making mechanisms lose less privacy than agents that do not use them. (C) 2012 Elsevier Inc. All rights reserved.

Integrating textual analysis and evidential reasoning for decision making in Engineering design

Decision making is an important element throughout the life-cycle of large-scale projects. Decisions are critical as they have a direct impact upon the success/outcome of a project and are affected by many factors including the certainty and precision of information. In this paper we present an evidential reasoning framework which applies Dempster-Shafer Theory and its variant Dezert-Smarandache Theory to aid decision makers in making decisions where the knowledge available may be imprecise, conflicting and uncertain. This conceptual framework is novel as natural language based information extraction techniques are utilized in the extraction and estimation of beliefs from diverse textual information sources, rather than assuming these estimations as already given. Furthermore we describe an algorithm to define a set of maximal consistent subsets before fusion occurs in the reasoning framework. This is important as inconsistencies between subsets may produce results which are incorrect/adverse in the decision making process. The proposed framework can be applied to problems involving material selection and a Use Case based in the Engineering domain is presented to illustrate the approach. © 2013 Elsevier B.V. All rights reserved.

Assessment of factors that influence physician decision making regarding medication use in patients with dementia at the end of life

Objective
This study aimed to evaluate the extent to which patient-related factors and physicians' country of practice (Northern Ireland [NI] and the Republic of Ireland [RoI]) influenced decision making regarding medication use in patients with end-stage dementia.

Methods
The study utilised a factorial survey design comprising four vignettes to evaluate initiating/withholding or continuing/discontinuing specific medications in patients with dementia nearing death. Questionnaires and vignettes were mailed to all hospital physicians in geriatric medicine and to all general practitioners (GPs) in NI (November 2010) and RoI (December 2010), with a second copy provided 3 weeks after the first mailing. Logistic regression models were constructed to examine the impact of patient-related factors and physicians' country of practice on decision making. Significance was set a priori at p ≤ 0.05. Free text responses to open questions were analysed qualitatively using content analysis.

Results
The response rate was 20.6% (N = 662) [21.1% (N = 245) for GPs and 52.1% (N = 38) for hospital physicians in NI, 18.3% (N = 348) for GPs and 36.0% (N = 31) for hospital physicians in RoI]. There was considerable variability in decision making about initiating/withholding antibiotics and continuing/discontinuing the acetylcholinesterase inhibitor and memantine hydrochloride, and less variability in decision making regarding statins and antipsychotics. Patient place of residence and physician's country of practice had the strongest and most consistent effects on decision making although effect sizes were small.

Conclusions
Further research is required into other factors that may impact upon physicians' prescribing decisions for these vulnerable patients and to clarify how the factors examined in this study influence prescribing decisions.