组织公民行为研究的新视角

对2000年以后组织公民行为领域出现的最新研究动态进行讨论和分析,主要包括基于社会资本和社会网络的组织公民行为研究、社会规范对组织公民行为的影响、群体层次的组织公民行为研究以及对组织公民行为的负面作用的探讨。作者分别讨论了上述4个最近研究方向在我国研究的理论和实践意义,并提出了一些具体的研究建议。最后,作者对这4个研究方向的相互关系和整合提出了自己的观点     

Network structure reveals clusters of associations between childhood adversities and development outcomes

Exposure to childhood adversity is common and associated with a host of negative developmental outcomes. The most common approach used to examine the consequences of adversity exposure is a cumulative risk model. Recently, we have proposed a novel approach, the dimensional model of adversity and psychopathology (DMAP), where different dimensions of adversity are hypothesized to impact health and well‐being through different pathways. We expect deprivation to primarily disrupt cognitive processing, whereas we expect threat to primarily alter emotional reactivity and automatic regulation. Recent hypothesis‐driven approaches provide support for these differential associations of deprivation and threat on developmental outcomes. However, it is not clear whether these patterns would emerge using data‐driven approaches. Here we use a network analytic approach to identify clusters of related adversity exposures and outcomes in an initial study (Study 1: N = 277 adolescents aged 16–17 years; 55.1% female) and a replication (Study 2: N = 262 children aged 8–16 years; 45.4% female). We statistically compare our observed clusters with our hypothesized DMAP model and a clustering we hypothesize would be the result of a cumulative stress model. In both samples we observed a network structure consistent with the DMAP model and statistically different than the hypothesized cumulative stress model. Future work seeking to identify in the pathways through which adversity impacts development should consider multiple dimensions of adversity.     

Neuroevolution-based autonomous robot navigation: A comparative study

The field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained and optimized using four advanced bio-inspired evolutionary algorithms (EA). The algorithms are Multi-Verse Optimizer (MVO), Moth-flame optimization (MFO), Cuckoo Search (CS) and Particle Swarm Optimization (PSO). Each algorithm is equipped with two operators: evolutionary population dynamics and mutation, which impact on exploration and exploitation. Optimized MLPs are then used for the navigation of an autonomous robot. Accuracy and area under the curve metrics are used for the evaluation and comparison metrics. Moreover, two well-regarded gradient descent algorithms including Back propagation (BP) and Levenberg Marquardt (LM) are utilized to validate the results obtained by evolutionary-based MLP trainers. It is observed that MLPs developed using MFO are the most robust ones among MLPs trained using other evolutionary and gradient descent algorithms.     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

A comprehensive analysis of the integration of team research between sport psychology and management

Both sports and organizations rely on teams. As such, the sport psychology and management literatures have contributed greatly to our understanding of team functioning. Despite this, previous reviews based on subsets of articles in these literatures indicate a lack of communication between them. In this article, we assess the state of integration between the entirety of the sport psychology and management literatures on teams by considering the full set of interconnected team articles in the SCOPUS database (6974 articles over 69 years). We use this data to conduct a combination of citation network analysis and content analysis via topic modeling to evaluate conceptual integration. The data show that interdisciplinary discussion between these two fields is lacking, particularly regarding the integration of sport psychology into management research. Whereas 7% of references to team articles in sport psychology come from management journals, only 0.6% of team references in management journals come from sport psychology. Despite this, longitudinal analysis indicates that in the last 10 years the rate of integration between these fields is increasing. We identify specific topics that have accounted for this integration and suggest topics ripe for future integration.     

Key players in sport teams. An exploratory study on the effects of attachment styles on intra-team relational networks

Relationship issues in sport are considered vital for the overall wellbeing of athletes (Jowett, 2005). The way athletes interact, create and maintain relationships is thought to be powerfully influenced by the athletes' attachment states of mind (Thomson & Jaque, 2018). To our knowledge, no study so far has investigated the impact players' attachment styles, as personality dispositions, have on overall intra-team relationship dynamics. This paper examines intra-team social relational networks from the perspective of adult attachment theory. Cross-sectional data were collected from 10 sport teams (158 players) from different sports. Self-report data were collected to assess attachment style and social relationships with teammates. Social network analyses (SNA) were used to calculate players' centrality scores based on incoming and outgoing relational nominations. Multilevel linear analysis was further performed to check inferential relationships between the study variables. Data showed that attachment styles of players, as personality dispositions, affected partly centrality degree positions within the various team social relational networks. The study offers an innovative perspective on the interactional and dynamic world of social relationships in sports teams and confirms that attachment style differences among players predispose them to particular positions within the team social relational networks. Although the current research has an exploratory character, it shows a new direction in the study of groups and sport team dynamics.     

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice

We introduce a general response model that allows for several simple restrictions, resulting in other models such as the extended Rasch model. For the extended Rasch model, a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values. To ensure comparability over time, a data augmentation method is used, which provides an augmented person-by-item data matrix and reproduces the sufficient statistics of the complete data matrix. Hence, longitudinal comparisons can be easily made based on simple summaries, such as proportion correct, sum score, etc. As an illustration of the method, an example is provided using data from a computer-adaptive practice mathematical environment.     

Personality,Resilience, and Psychopathology: A Model for the Interaction between Slow and Fast Network Processes in the Context of Mental Health

Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast-evolving psychopathology network (e.g. a depressive episode) can influence the slow-evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

Conformity of two sociometric relations

The problem of comparing two sociometric relations or measurements (A andB) recorded in distinct sociomatrices was originally discussed by Katz and Powell in the early 1950's and Hubert and Baker in the late 1970's. The problem is considered again using a probabilistic model designed specifically for discrete-valued network measurements. The model allows for the presence of various structural tendencies, such as reciprocity and differential popularity. A parameter that isolates the tendency for actors to choose other actors on both relations simultaneously is introduced, and estimated conditional on the presence of other parameters that reflect additional important network properties. The parameter is presented as a symmetric index but is also generalized to the predictive (A onB orB onA) situation. This approach to the problem is illustrated with the same data used by the earlier solutions, and the unique nature of the two relations in the data set (A = received choices,B = perceived choices), as it affects the modeling, is discussed. Significance tests for the parameter and related parameters are described, as well as an extension to more than two relations.Research support provided by National Science Grant #SES84-08626 to the University of Illinois at Urbana-Champaign. I am grateful to Dawn Iacobucci and Sheila Weaver for assistance with the research reported here, and to Carolyn Anderson, James Green, David Holtgrave, and four anonymous referees for comments on the paper.     

从人脑到计算机─—鸿沟能否逾越

人类对人脑及其精神活动过程的彻底了解,意味着哲学上的根本性事件的发生。所谓彻底的了解,标志就是人类对这种精神活动的成功模拟和再现。作为神经科学的分支和广延的人工智能的兴起,使人类有理由希望：存在于人脑与计算机之间的鸿沟终将被人类逾越。