New Importance Measures Based on Failure Probability in Global Sensitivity Analysis of Reliability

This article presents new sensitivity measures in reliability-oriented global sensitivity analysis. The obtained results show that the contrast and the newly proposed sensitivity measures (entropy and two others) effectively describe the influence of input random variables on the probability of failure Pf. The contrast sensitivity measure builds on Sobol, using the variance of the binary outcome as either a success (0) or a failure (1). In Bernoulli distribution, variance Pf(1 − Pf) and discrete entropy—Pfln(Pf) − (1 − Pf)ln(1 − Pf) are similar to dome functions. By replacing the variance with discrete entropy, a new alternative sensitivity measure is obtained, and then two additional new alternative measures are derived. It is shown that the desired property of all the measures is a dome shape; the rise is not important. Although the decomposition of sensitivity indices with alternative measures is not proven, the case studies suggest a rationale structure of all the indices in the sensitivity analysis of small Pf. The sensitivity ranking of input variables based on the total indices is approximately the same, but the proportions of the first-order and the higher-order indices are very different. Discrete entropy gives significantly higher proportions of first-order sensitivity indices than the other sensitivity measures, presenting entropy as an interesting new sensitivity measure of engineering reliability.

Global Sensitivity Analysis Based on Entropy: From Differential Entropy to Alternative Measures

Differential entropy can be negative, while discrete entropy is always non-negative. This article shows that negative entropy is a significant flaw when entropy is used as a sensitivity measure in global sensitivity analysis. Global sensitivity analysis based on differential entropy cannot have negative entropy, just as Sobol sensitivity analysis does not have negative variance. Entropy is similar to variance but does not have the same properties. An alternative sensitivity measure based on the approximation of the differential entropy using dome-shaped functionals with non-negative values is proposed in the article. Case studies have shown that new sensitivity measures lead to a rational structure of sensitivity indices with a significantly lower proportion of higher-order sensitivity indices compared to other types of distributional sensitivity analysis. In terms of the concept of sensitivity analysis, a decrease in variance to zero means a transition from the differential to discrete entropy. The form of this transition is an open question, which can be studied using other scientific disciplines. The search for new functionals for distributional sensitivity analysis is not closed, and other suitable sensitivity measures may be found.

Responsiveness to Food-Related Conflict Predicts Healthy Eating

One reason why people fail to eat healthily is that they lack control over unwanted actions. Another largely ignored reason might be that they fail to engage control when encountering temptations. Accordingly, we tested (N=511) if how responsive people are to conflict between healthy and unhealthy food is an important part of eating regulation. We developed a conflict sensitivity measure that indicates responsiveness to conflict between healthy and unhealthy food via post-conflict slowing. We then show that the stronger participants are committed to healthy eating, the more they slowed down after relevant conflict (Study 1, 2) but not after irrelevant conflict (Study 2). Furthermore, increasing commitment to healthy eating increased post-conflict slowing compared to when the goal was not activated (Study 3). Importantly, post-conflict slowing predicted subsequent healthy eating in participants’ everyday life (Study 2). Our findings suggest that conflict responses might be an important part of healthy eating.

A Novel Global Sensitivity Measure Based on Probability Weighted Moments

Global sensitivity analysis (GSA) is a useful tool to evaluate the influence of input variables in the whole distribution range. Variance-based methods and moment-independent methods are widely studied and popular GSA techniques despite their several shortcomings. Since probability weighted moments (PWMs) include more information than classical moments and can be accurately estimated from small samples, a novel global sensitivity measure based on PWMs is proposed. Then, two methods are introduced to estimate the proposed measure, i.e., double-loop-repeated-set numerical estimation and double-loop-single-set numerical estimation. Several numerical and engineering examples are used to show its advantages.

Psychometric Properties of the Russian Three-Factor Interpersonal Sensitivity Measure

We present the results of a study conducted to validate the Interpersonal Sensitivity measure in the Russian sample. Interpersonal sensitivity is a personality trait encompassing preoccupation with the behavior and emotions of other people and fear of their criticism and rejection (Boyce, Parker, 1989). The sample consisted of 645 participants (100 men) aged 18—35 (M=22.92±5.01). A three-factor structure of the measure was revealed in the Russian sample; the factors named Dependence on the Appraisal by Others, Fear of Rejection and Interpersonal Worry converged in the higher-order factor of Interpersonal Sensitivity. The scales yielded good internal consistency and test-retest reliability. Interpersonal sensitivity was higher in women and decreased with age. It was significantly positively related to rejection sensitivity, loneliness, state anxiety, and emotional dysregulation, and negatively related to emotional stability. Conclusions: Interpersonal Sensitivity measure can be used in psychological studies to assess personal factors of distress.