Information search,coherence effects,and their interplay in legal decision making

Legal judgments require one to make sense of a complex set of typically contradicting pieces of information that can easily be interpreted in a biased manner. This systematic judgment bias can be caused by biased information search (i.e., confirmatory search) as well as biased information processing (i.e., coherence effects) in which the interpretation of information is changed to fit the emerging favored option. In four studies, we investigate the complex interplay between both kinds of influences. Participants completed three legal cases in which they could freely search for information. We manipulated between subjects whether systematic search was possible or not and measured the assessment of each selected piece of information. In line with previous studies, we observe strong coherence effects in each study, in that the evidence interpretation strongly depended on the current tendency towards acquittal or conviction. In contrast to our expectation, however, people searched for information that was contrary to their current belief in the given case (i.e., disconfirmatory information search). We also observed a trend towards an interaction between both factors, in that coherence effects were slightly stronger for neutral and pro-guilty evidence when systematic information search was possible. Our results underline an unconscious striving for coherence when making complex judgments that is not easily corrected.     

Estimation of geodesic tortuosity and constrictivity in stationary random closed sets

We investigate the problem of estimating geodesic tortuosity and constrictivity as two structural characteristics of stationary random closed sets. They are of central importance for the analysis of effective transport properties in porous or composite materials. Loosely speaking, geodesic tortuosity measures the windedness of paths, whereas the notion of constrictivity captures the appearance of bottlenecks resulting from narrow passages within a given materials phase. We first provide mathematically precise definitions of these quantities and introduce appropriate estimators. Then, we show strong consistency of these estimators for unboundedly growing sampling windows. In order to apply our estimators to real data sets, the extent of edge effects needs to be controlled. This is illustrated using a model for a multiphase material that is incorporated in solid oxide fuel cells.     

Linear scalar-on-surface random effects regression models

Many research fields increasingly involve analyzing data of a complex structure. Models investigating the dependence of a response on a predictor have moved beyond the ordinary scalar-on-vector regression. We propose a regression model for a scalar response and a surface (or a bivariate function) predictor. The predictor has a random component and the regression model falls in the framework of linear random effects models. We estimate the model parameters via maximizing the log-likelihood with the ECME (Expectation/Conditional Maximization Either) algorithm. We use the approach to analyze a data set where the response is the neuroticism score and the predictor is the resting-state brain function image. In the simulations we tried, the approach has better performance than two other approaches, a functional principal component regression approach and a smooth scalar-on-image regression approach.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.     

Properties of the weighted log‐rank test in the design of confirmatory studies with delayed effects

Proportional hazards are a common assumption when designing confirmatory clinical trials in oncology. This assumption not only affects the analysis part but also the sample size calculation. The presence of delayed effects causes a change in the hazard ratio while the trial is ongoing since at the beginning we do not observe any difference between treatment arms, and after some unknown time point, the differences between treatment arms will start to appear. Hence, the proportional hazards assumption no longer holds, and both sample size calculation and analysis methods to be used should be reconsidered. The weighted log‐rank test allows a weighting for early, middle, and late differences through the Fleming and Harrington class of weights and is proven to be more efficient when the proportional hazards assumption does not hold. The Fleming and Harrington class of weights, along with the estimated delay, can be incorporated into the sample size calculation in order to maintain the desired power once the treatment arm differences start to appear. In this article, we explore the impact of delayed effects in group sequential and adaptive group sequential designs and make an empirical evaluation in terms of power and type‐I error rate of the of the weighted log‐rank test in a simulated scenario with fixed values of the Fleming and Harrington class of weights. We also give some practical recommendations regarding which methodology should be used in the presence of delayed effects depending on certain characteristics of the trial.     

Managing the Risk of Aggressive Dog Behavior: Investigating the Influence of Owner Threat and Efficacy Perceptions

Aggressive behavior in pet dogs is a serious problem for dog owners across the globe, with bite injuries representing a serious risk to both people and other dogs. The effective management of aggressive behavior in dogs represents a challenging and controversial issue. Although positive reinforcement training methods are now considered to be the most effective and humane technique to manage the risk of aggression, punishment‐based methods continue to be used. Unfortunately, there has been little scientific study into the various factors influencing whether dog owners choose to use positive reinforcement techniques to manage aggression in their dogs. As such, current understanding of how best to encourage and support dog owners to use these methods remains extremely limited. This article uses a survey methodology based on protection motivation theory (PMT) to investigate the factors that influence owner use of positive reinforcement methods to manage aggressive behavior, in an attempt to understand potential barriers and drivers of use. In addition, the article provides an initial exploration of the potential role of wider psychological factors, including owner emotional state, social influence, and cognitive bias. Findings show that the perceived efficacy of positive reinforcement methods and the perceived ability of owners to effectively implement the technique are both key factors predicting future intentions and current reported use. Future interventions should focus on enhancing owner confidence in the effective use of positive reinforcement techniques across multiple scenarios, as well as helping owners manage their own emotional responses when they encounter challenging situations and setbacks.     

The contagion of democracy through international networks

Work on democratization typically considers the diffusion of democracy through interstate partnerships. However, such partnerships constitute complex networks that scholars have yet to fully explore as vectors for the spread of democracy. We develop a network theory of democratization which characterizes these networks as epistemic communities that influence elites’ attitudes towards favorable regime types. Our theory predicts, and our empirical strategy confirms, that direct and indirect ties in the alliance network are vectors for democratization. In contrast to conventional wisdom, we find that direct influence is only transmitted through the defensive alliance network and find evidence of higher-order effects.     

Gender differences in risk behavior and the link to household effects and individual wealth

This study examined the differences in risk behavior between men and women using a household survey that captured the risk preferences of two members in a household and recorded wealth at the individual level instead of the usual approach of representing wealth at the household level. After controlling for commonly used explanatory variables, such as gender, education, age, and wealth, household fixed effects explain about 15% of the variation in risk behavior. This highlights the magnitude of household effects in shaping one’s risk behavior. In general, females in the study area are more risk averse than males based on a risk game with real payout. The gender differences disappear when focusing on only the top land owners. However, even in those cases, females consider themselves more risk averse, supporting results from previous studies that link culture and societal norms to the gender differences in risk behavior.     

Psychometric validation of the PERMA-profiler as a well-being measure for student veterans

Abstract

Objective: The current study aims to validate the PERMA-Profiler, a well-known well-being measure, among a sample of student veterans. Participants: A sample of 205 student veterans were recruited from universities across the United States. Method: Cross-sectional research design was used in this study. Measurement structure of the PERMA-Profiler was evaluated using exploratory factor analysis (EFA). Convergent, divergent, and criterion-related validity was tested using Pearson correlation coefficients and Kruskal-Wallis test. Results: The EFA results yielded a two-factor solution for student veterans. Factors are named as emotional character strengths and performance character strengths. Conclusions: The PERMA-Profiler is a multidimensional scale with good reliability and acceptable levels of convergent, divergent, and criterion-related validity. The PERMA-Profiler can help researchers and practitioners better gauge well-being in student veterans. Implications will be discussed.     

航线网络中需求无约束估计的非参数选择模型

传统的航空客运需求无约束估计方法仅针对平行直达航班中的顾客需求“溢出”和“再 现”问题，未能考虑航空网络中直达和中转联程航班之间的网络替代效应． 基于顾客偏好排序列表定义了航空网络顾客类型集合，建立了考虑顾客策略行为的网络型非参数离散选择模型．考虑到网络环境下历史预售数据的不完备性，站在线上和线下交易平台的角度，分别建立了非截尾和截尾需求情况下的完备数据对数似然函数． 采用 EM 算法对顾客到达率和概率质量函数进行联合估计，并提出了网络环境下的顾客“初始需求”、“再现需求”和“溢出需求”无约束估计计算方法．通过数值模拟验证了所提方法的可行性和有效性，相较于现有方法能准确反映产品间网络替代效应对顾客选择行为影响，从而更加有效地避免对历史顾客“初始需求”的高估问题．