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.

‘All illness is personal to that individual’: a qualitative study of patients’ perspectives on treatment adherence in bronchiectasis

Background: Adherence to treatment is low in bronchiectasis and is associated with poorer health outcomes. Factors affecting adherence decisions have not been explored in patients with bronchiectasis. 

Objective: We aimed to explore patients' perspectives on adherence, factors affecting adherence decision making and to develop a conceptual model explaining this decision-making process in adults with bronchiectasis. 

Methods: Adults with bronchiectasis participated in one-to-one semi-structured interviews. Interviews were audio-recorded, transcribed verbatim and analysed independently by two researchers using thematic analysis. Data from core themes were extracted, categorized into factors affecting adherence decision making and used to develop the conceptual model. 

Results: Participants' beliefs about treatment, the practical aspects of managing treatment, their trust in health-care professionals and acceptance of disease and treatment were important aspects of treatment adherence. The conceptual model demonstrated that adherence decisions were influenced by participants' individual balance of barriers and motivating factors (treatment-related, disease-related, health-care-related, personal and social factors). 

Conclusion: Adherence decision-making in bronchiectasis is complex, but there is the potential to enhance adherence by understanding patients' specific barriers and motivators to adherence and using this to tailor adherence strategies to individual patients and treatments. © 2014 John Wiley & Sons Ltd.