Implicit and explicit COVID-19-vaccine harmfulness/helpfulness associations predict vaccine beliefs,intentions, and behaviors

We investigated the role of implicit and explicit associations between harm and COVID-19 vaccines using a large sample (N = 4668) of online volunteers. The participants completed a brief implicit association test and explicit measures to evaluate the extent to which they associated COVID-19 vaccines with concepts of harmfulness or helpfulness. We examined the relationship between these harmfulness/helpfulness COVID-19 vaccine associations and vaccination status, intentions, beliefs, and behavior. We found that stronger implicit and explicit associations that COVID-19 vaccines are helpful relate to vaccination status and beliefs about the COVID-19 vaccine. That is, stronger pro-helpful COVID-19 vaccine associations, both implicitly and explicitly, related to greater intentions to be vaccinated, more positive beliefs about the vaccine, and greater vaccine uptake.     

Toward a new conceptualization and operationalization of risk perception within the genetic counseling domain

The purpose of this paper is twofold. First, it provides an historical overview of studies of risk, risk perception, and decision making under risk within the genetic counseling domain. Second, it proposes an alternative conceptualization and operationalization for the study of risk perception. The conceptualization involves probability, adversity, incompleteness, and ambiguity. Prior studies of risk perception focus on the recurrence risk and operationalize risk perception by asking for interpretations of the magnitude of the probability of the outcome. Their focus is on the probability of a particular outcome. We formulate the problem in terms of a gamble and suggest that risk perception be operationalized in terms of the riskiness of the gamble. Our focus is on the riskiness of a decision option which entails two or more outcomes.     

Exploring the set-theoretical structure of objects by additive trees

Arrangements of feature sets that have been proposed to represent qualitative and quantitative variation among objects are shown to generate identical sets of set-symmetric distances. The set-symmetric distances for these feature arrangements can be represented by path lengths in an additive linear tree. Imperfect versions of these feature arrangements are proposed, which also are indistinguishable by the set-symmetric distance model. The distances for the imperfect versions can be represented by path lengths in an additive imperfectly linear tree. When dissimilarities are defined by the more general contrast model and a constant may be added to proximity data, then for both the perfect and imperfect arrangements an additive tree analysis obtains a perfect fit with an imperfectly linear tree. However, in the case of the contrast model also the distinction between the perfect and imperfect arrangements disappears in that also for the perfect arrangements the resulting tree need no longer be linear.The author is grateful to Mathieu Koppen for detailed comments on an earlier version of this article.     

A compensatory approach to optimal selection with mastery scores

A Bayesian approach for simultaneous optimization of test-based decisions is presented using the example of a selection decision for a treatment followed by a mastery decision. A distinction is made between weak and strong rules where, as opposed to strong rules, weak rules use prior test scores as collateral data. Conditions for monotonicity of optimal weak and strong rules are presented. It is shown that under mild conditions on the test score distributions and utility functions, weak rules are always compensatory by nature. The authors are indebted to Wilbert Kallenberg for his valuable comments and to Jan Gulmans for providing the data for the empirical example. The names of the authors are alphabetical; they are equally responsible for the contents of this paper.     

A study of the foundations of ethical decision making of clinical medical ethicists

A study of clinical medical ethicists was conducted to determine the various philosophical positions they hold with respect to ethical decision making in medicine and their various positions' relationship to the subjective-objective controversy in value theory. The study consisted of analyzing and interpreting data gathered from questionnaires from 52 clinical medical ethicists at 28 major health care centers in the United States. The study revealed that most clinical medical ethicists tend to be objectivists in value theory, i.e., believe that value judgments are knowledge claims capable of being true or false and therefore expressions of moral requirements and normative imperatives emanating from an external value structure or moral order in the world. In addition, the study revealed that most clinical medical ethicists are consistent in the philosophical foundations of their ethical decision making, i.e., in decision making regarding values they tend not to hold beliefs which are incompatible with other beliefs they hold about values.     

Simulation of Preference Information in an Interactive Reference Point‐Based Method for the Bi‐Objective Inventory Routing Problem

In the research area of multiple criteria decision making, very few publications exist that explicitly design the simulation of a decision maker (DM) in an interactive approach. For this reason, we outline some methods widely used in the literature to identify common assumptions of simulating the DM's responses and the required input preference information. Our paper aims at covering the identified gap by introducing experimental concepts. Such concepts are used for theoretical analyses of a combined search‐and‐decision‐making procedure. Simulating the DM is a fruitful idea because the algorithm can be tested without integrating a human decision maker. Finally, we conduct experiments based on the proposed settings for a multiobjective inventory routing problem, which is a relevant and challenging logistic problem. Copyright © 2014 John Wiley & Sons, Ltd.     

Adversary effects and the tactics of violent offenders

We examine the implication of adversary effects for target choice, lethal intent, and the use of weapons and allies in violent incidents. Adversary effects refer to the tendency of offenders to make tactical decisions based on the coercive power of victims and potential victims. Using the victim's gender as a proxy for coercive power, we analyzed violent incidents from the National Incident‐Based Reporting System (2005–2014). The sample included over six million assaults, robberies, and homicides. Consistent with adversary effects, offenders who attack males (vs. females) are more likely to (a) kill victims; (b) use guns, knives, blunt objects, poison, and automobiles; (c) use male (but not female) allies; and (d) use multiple allies. The evidence for target choice is mixed: unarmed female offenders, but not unarmed male offenders, are more likely to target females than males. The evidence shows how a simple theoretical principle can parsimoniously account for basic patterns of violence in society related to gender, weapons, and group violence.     

Choosing what to choose from: Preference for inclusion over exclusion when constructing consideration sets from large choice sets

Decision making is a two‐stage process, consisting of, first, consideration set construction and then final choice. Decision makers can form a consideration set from a choice set using one of two strategies: including the options they wish to further consider or excluding those they do not wish to further consider. The authors propose that decision makers have a relative preference for an inclusion (vs. exclusion) strategy when choosing from large choice sets and that this preference is driven primarily by a lay belief that inclusion requires less effort than exclusion, particularly in large choice sets. Study 1 demonstrates that decision makers prefer using an inclusion (vs. exclusion) strategy when faced with large choice sets. Study 2 replicates the effect of choice set size on preference for consideration set construction strategy and demonstrates that the belief that exclusion is more effortful mediates the relative preference for inclusion in large choice sets. Studies 3 and 4 further support the importance of perceived effort, demonstrating a greater preference for inclusion in large choice sets when decision makers are primed to think about effort (vs. accuracy; Study 3) and when the choice set is perceived as requiring more effort because of more information being presented about each alternative (vs. more alternatives in the choice set; Study 4). Finally, Study 5 manipulates consideration set construction strategy, showing that using inclusion (vs. exclusion) in large choice sets leads to smaller consideration sets, greater confidence in the decision process, and a higher quality consideration set.     

What could be My Next Job? Using Flat Information Structures to Generate Creative Future Career Ideas

In a dynamic labor market, it is important to help people combine information and generate creative solutions to cope with complex career challenges. In the present research, we apply the theory of information structure to creative career idea generation and hypothesize that flat information structures—that is, structures in which the information is disorganized—are more conducive to creativity than hierarchical information structures—that is, structures in which information is organized in higher‐order categories. In two experimental studies, participants had to combine career information related to self and work preferences that was presented either in flat or hierarchical structures. We found that flat information structures, compared with hierarchical information structures, led to future career ideas that were more creative on average. Our results suggest that cognitive flexibility explains the effect of information structure on the creativity of career ideas. Theoretical implications and suggestions for career management practices are discussed.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.