Understanding dietary decision-making in patients attending a secondary prevention clinic following myocardial infarction

Aims and objectives. This study aimed to explore the issues that influence the dietary choices made by patients attending a secondary prevention clinic following a myocardial infarction.

Individual and couple decision behavior under risk: evidence on the dynamics of power balance

This article reports results of an experiment designed to analyze the link between risky decisions made by couples and risky decisions made separately by each spouse. We estimate both the spouses and the couples' degrees of risk aversion, we assess how the risk preferences of the two spouses aggregate when they make risky decisions, and we shed light on the dynamics of the decision process that takes place when couples make risky decisions. We find that, far from being fixed, the balance of power within the household is malleable. In most couples, men have, initially, more decision-making power than women but women who ultimately implement the joint decisions gain more and more power over the course of decision making.

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.

Intra-operative decision making by ophthalmic surgeons

Research on surgical decision making and risk management usually focuses on peri-operative care, despite the magnitude and frequency of intra-operative risks. The aim of this study was to examine ophthalmic surgeons' intra-operative decisions and risk management strategies in order to explore differences in cognitive processes.

Unravelling the migration decision-making process: English early retirees moving to rural mid-Wales

There are established migrant reasons to explain rural in-migration. These include quality of life, rural idyll and lifestyle motivations. However, such one-dimensional sound bites portray rural in-migration in overly simplistic and stereotypical terms. In contrast, this paper distinguishes the decision to move from the reason for moving and in doing so sheds new light on the interconnections between different domains (family, work, finance, health) of the migrant's life which contribute to migration behaviour. Focussing on early retirees to mid-Wales and adopting a life course perspective the overall decision to move is disaggregated into a series of decisions. Giving voices to the migrants themselves demonstrates the combination of life events necessary to lead to migration behaviour, the variable factors (and often economic dominance) considered in the choice of destination (including that many are reluctant migrants to Wales), and the perceived 'accidental' choice of location and/or property. It is argued that quality of life, rural idyll and lifestyle sound bites offer an inadequate understanding of rural in-migration and associated decision-making processes. Moreover, they disguise the true nature of migrant decision making.

Regret and adaptive decision making in young children

In line with the claim that regret plays a role in decision making, O’Connor, McCormack, and Feeney (2014) found that children who reported feeling sadder on discovering they had made a non-optimal choice were more likely to make a different choice next time round. We examined two issues of interpretation regarding this finding: whether the emotion measured was indeed regret, and whether it was the experience of this emotion rather than the ability to anticipate it that impacted on decision making. To address the first issue, we varied the degree to which children aged 6-7 were responsible for an outcome, assuming that responsibility is a necessary condition for regret. The second was addressed by examining whether children could accurately anticipate that they would feel worse on discovering they had made a non-optimal choice. Children were more likely to feel sad if they were responsible for the outcome; however even if they were not responsible, children were more likely than chance to report feeling sadder. Moreover, across all conditions feeling sadder was associated with making a better subsequent choice. In a separate task, we demonstrated that children of this age cannot accurately anticipate feeling sadder on discovering that they had not made the best choice. These findings suggest that although children may feel regret following a non-optimal choice, even if they were not responsible for an outcome they may experience another negative emotion such as frustration. Experiencing either of these emotions seems to be sufficient to support better decision making.

Approximate credal network updating by linear programming with applications to decision making

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.

Decision Making by Credal Nets

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. This feature makes the model particularly suited for the implementation of classifiers and knowledge-based systems. When working with sets of (instead of single) probability distributions, the identification of the optimal option can be based on different criteria, some of them eventually leading to multiple choices. Yet, most of the inference algorithms for credal nets are designed to compute only the bounds of the posterior probabilities. This prevents some of the existing criteria from being used. To overcome this limitation, we present two simple transformations for credal nets which make it possible to compute decisions based on the maximality and E-admissibility criteria without any modification in the inference algorithms. We also prove that these decision problems have the same complexity of standard inference, being NP^PP-hard for general credal nets and NP-hard for polytrees.