Does the β-measure make empirical sense? A vignette experiment on humans’ judgment of social dominance

Dominance hierarchies play an important role in governing the social interactions of humans and other species of social animals. In a social group, dominance relations can be inferred from the directed network of matchups between actors. Methodologists have proposed different ways to measure social dominance in directed networks. One such measure, the “β-measure” (van den Brink and Gilles, 2000), emphasizes the quality of defeated opponents in a way that an actor is seen as being more dominant when s/he defeats opponents who are more rarely defeated. While insightful in theory, the validity of the measure in people’s perception remains questionable, considering the cognitive complexity imposed by this measure, compared to a simpler measure that merely counts the number of defeated opponents. We conducted a vignette experiment with human subjects (professional athletes) to test their judgments of the dominance relation in a hypothetical tournament. Fitting our parametric model to peoples’ evaluations in the experiment, we found strong evidence in support of the β-measure: Although, in general, contestants who win more in the tournament are regarded as being more dominant, the contents of the winning records matter, such that those who beat more victorious opponents are further regarded as more dominant than those who defeat less victorious opponents. We also found a gender difference, in that men have a stronger propensity than women to adopt the β-measure when judging social dominance.     

信息不对称下价格随机的应急数量折扣契约研究

以市场需求和市场价格随机波动的二级供应链为研究对象，寻找其在信息不对称条件下绩效优化的路径。通过显示原理分别构建生产成本和销售成本信息不对称时应急供应链的数量折扣契约模型，得到最优订货量与最佳批发价策略。通过具体的算例加以验证，分析了信息不对称程度对供应链上成员及整个供应链绩效的影响。研究结果表明：当突发事件造成市场价格随机波动和市场需求变化时，供应链上的跟随者能从隐瞒私人信息中获利。但当市场需求增大时，整个供应链绩效受损，反之则反是。这与以往研究在市场需求随机、市场价格固定和信息不对称条件下的结论迥异。     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Parametrizing the Kepler exoplanet period-radius distribution with the bivariate normal inverse Gaussian distribution

This paper presents a simple and robust method for obtaining a comprehensive understanding of the joint period and radius distribution in Kepler exoplanets. The proposed method is based on particle swarm optimization and bivariate Normal Inverse Gaussian distribution. Furthermore, in the construction of the probability density function, this study selects planet-host stars with the GK-type. The injecting approach is also employed to solve the survey completeness of sample. The resulting occurrence rate of Earth analogs is 0.025 with a 95% bootstrap confidence interval between 0.023 and 0.032.     

基于理想解和灰关联度的动态评价方法及其应用研究

为了对多个多属性（指标）待评价对象（方案）在多个时间点的发展状态和该时间段内的总体发展水平进行比较分析，根据理想解法和灰关联度法优缺点，提出基于理想解和灰关联度的动态评价方法。该方法基于三维数据，将欧氏距离和灰色关联度相结合，提出一种新贴近度，同时反映了位置关系和数据曲线的相似性差异，兼顾评价指标值差异程度和增长程度。最后将该方法应用于"十二五"期间省域循环经济生态效益评价，通过实例验证该方法实际应用上的有效性。     

政府动态奖惩机制下绿色建筑供给侧演化博弈研究

从绿色建筑供给侧角度出发，构建消费者效用函数、开发商利润函数和政府与开发商的演化博弈模型，分析消费者绿色偏好、建筑绿色度、成本等因素对建筑价格、需求和利润的影响以及政府动态奖惩政策下系统的演化稳定策略。结果表明：消费者绿色偏好支付系数和建筑绿色度的增大有利于绿色建筑价格、需求和利润的提高，但高成本却使需求和利润降低；政府采用静态补贴与静态税收政策时，博弈系统不存在演化稳定策略，采用动态补贴与静态税收、静态补贴与动态税收、动态补贴与动态税收三种政策组合时，系统存在演化稳定策略；动态补贴与静态税收的政策组合在推动绿色建筑发展方面优于其他政策；开发商开发绿色建筑的概率与补贴上限值呈负相关，与税收上限值呈正相关。     

考虑储存成本的集装箱公铁联运路径优化

联运企业的效益受运输路径和运输方式的影响。如何降低联运成本、提高联运企业的效益,已成为学术研究的重中之重。针对集装箱公铁联运路径优化问题,以运输成本、换装成本、储存成本、时间惩罚成本构成的运输总成本为目标函数,建立集装箱公铁联运路径优化模型,考虑运输时间限制、班期限制、节点储存成本,得出使运输总成本最低的运输路径及运输方式,并利用算例验证模型的有效性。     

集体行动视角下的制度逻辑演化机制研究

通过对我国专车服务市场制度变革过程的纵向案例研究，探讨了组织场域中的集体行动如何推动制度逻辑演化的内在机制问题。研究发现：制度逻辑的演化过程主要经历了分离、冲突和共存三种状态，不同的制度逻辑的演化是由行动主体能动性策略和资源利用所构建的集体行动模式差异性驱动的。制度变革机会的涌现促使场域中的行动主体采用协调性的集体行动模式，促成了新制度逻辑的生成，并导致了新、旧两种制度逻辑的分离；在制度变革机会变得模糊的场域中，行动主体非协调的集体行动促使新、旧两种制度逻辑之间的激烈冲突；而在制度变革机会沉没的情境下，行动主体再协调性的集体行动最终促成了新、旧两种制度逻辑的共存。研究结论对我国当前复杂制度环境下新兴业态的发展以及传统业态的协同治理提供了重要的借鉴和启示。     

Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

面向服务的复杂成形装备产品架构设计与优化

复杂成形装备作为高端制造产业的基础装备，是各产业转型升级和技术进步的重要保障。同时快速发展的互联网与大数据等新兴信息技术已经成为装备制造业创新不可或缺的组成部分，极其深刻地影响着复杂成形装备产业的制造模式、发展战略以及产品的开发方式。本文研究了面向服务的复杂成形装备产品架构设计与优化方法。采用公理化设计的域结构思想，建立需求域、功能域和架构域之间的映射关系；提出了基于QFD的功能生成方法以及功能模型约简方法；利用DMM建立产品架构和服务架构之间的关联关系；利用模块化理论方法，并考虑成本因素对产品架构进行优化。