Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Fast and rigorous: Configurational determinants of strategic decision-making balance

A longstanding debate in the strategic decision-making literature has focused on whether top management teams (TMTs) can effectively balance speed and comprehensiveness when making important decisions. In our research, we build on early insights and pivot from considering whether TMTs can engage indecision-making that balances these tensions to focus instead on when certain types of TMTs are able to achieve such balance. We employ a novel configurational analytical approach and a theoretical framework built from role theory to examine the CEO-TMT interface in a new way. In so doing, we are able to identify specific CEO-TMT constellations that support decision-making that is both fast and rigorous. Using a unique primary dataset and an abductive, configurational approach grounded in fuzzy set qualitative comparative analysis (fsQCA), we identify six specific leader-team configurations that each facilitate decision processes characterized by rigorous intra-team debate, meaningful reconciliation of divergent ideas, and fast decision speed (which we describe as strategic decision-making balance). The range of CEO-TMT configurations that emerge from our analyses contribute new theory and findings for the strategic decision-making and interface literatures more broadly, as well as the specific research streams on executive gender, humility, and TMT structure.     

The Need to Reconcile Concepts that Characterize Systems Facing Threats

Desirable system performance in the face of threats has been characterized by various management concepts. Through semistructured interviews with editors of journals in the fields of emergency response and systems management, a literature review, and professional judgment, we identified nine related and often interchangeably used system performance concepts: adaptability, agility, reliability, resilience, resistance, robustness, safety, security, and sustainability. A better understanding of these concepts will allow system planners to pursue management strategies best suited to their unique system dynamics and specific objectives of good performance. We analyze expert responses and review the linguistic definitions and mathematical framing of these concepts to understand their applications. We find a lack of consensus on their usage between interview subjects, but by using the mathematical framing to enrich the linguistic definitions, we formulate comparative visualizations and propose distinct definitions for the nine concepts. We present a conceptual framing to relate the concepts for management purposes.     

On quantile residuals in beta regression

Beta regression is often used to model the relationship between a dependent variable that assumes values on the open interval (0, 1) and a set of predictor variables. An important challenge in beta regression is to find residuals whose distribution is well approximated by the standard normal distribution. Two previous works compared residuals in beta regression, but the authors did not include the quantile residual. Using Monte Carlo simulation techniques, this article studies the behavior of certain residuals in beta regression in several scenarios. Overall, the results suggest that the distribution of the quantile residual is better approximated by the standard normal distribution than that of the other residuals in most scenarios. Three applications illustrate the effectiveness of the quantile residual.     

Checking validity of monotone domain mean estimators

Estimates of population characteristics such as domain means are often expected to follow monotonicity assumptions. Recently, a method to adaptively pool neighbouring domains was proposed, which ensures that the resulting domain mean estimates follow monotone constraints. The method leads to asymptotically valid estimation and inference, and can lead to substantial improvements in efficiency, in comparison with unconstrained domain estimators. However, assuming incorrect shape constraints may lead to biased estimators. Here, we develop the Cone Information Criterion for Survey Data as a diagnostic method to measure monotonicity departures on population domain means. We show that the criterion leads to a consistent methodology that makes an asymptotically correct decision choosing between unconstrained and constrained domain mean estimators. The Canadian Journal of Statistics 47: 315–331; 2019 © 2019 Statistical Society of Canada     

Effects of mediator competence and mediation process on user satisfaction with child custody,finances, and property outcomes

This study draws on survey data from 205 family mediation service users concerning their satisfaction with the outcomes of mediation to address two research questions: (a) how satisfied are service users? and (b) what factors account for users' satisfaction with the outcome of child custody arrangements and the allocation of finances and property? Overall, users' level of satisfaction with outcomes was high. While the mediation process explained a significant variance in satisfaction with the outcomes for child custody and for finances and property, the mediator's perceived professional competence did not predict user satisfaction with the outcomes for either issue. This study contributes to our knowledge on the significance of mediators' process skill, specifically their calm and clear responses to the socioemotional needs of the mediated parties, on the service outcomes for both types of dispute. The implications for mediation training are discussed.     

Assessing the market values of soccer players – a robust analysis of data from German 1. and 2. Bundesliga

The paper examines to what extent a player's market value depends on his skills. Therefore, a data set covering 28 performance measures and the market values of about 493 players from 1. and 2. German Bundesliga is analysed. Applying robust analysis techniques, we are able to robustly estimate market values of soccer players. The results show (1) that there are significantly underrated and overrated players and (2) that a player's affiliation to a certain team may contribute to his market value. We conclude that a club's reputation affects the market values of its players and that star players are in tendency overrated.     

中介机构参与下联盟企业知识转移的三方利益博弈分析

知识转移是企业保存知识、提升创新能力的重要途径，中介机构在转移过程中发挥了重要作用。本文以联盟企业间知识转移为研究对象，考虑了企业的不同心理压力，首先建立了联盟企业双方的讨价还价博弈模型；其次构建了考虑中介机构参与知识转移的博弈模型，分析和讨论三方收益变化的影响因素及策略选择。结果表明，在直接知识转移博弈中，转移主体的收益大小与自身的心理压力呈负相关，而与对方的心理压力呈正相关，同等心理压力下知识转出方占优；中介机构参与博弈情形下，中介机构作为协作角色的博弈收益仅受博弈双方心理压力影响，且与知识接收方协作可获得更大收益，而主导角色博弈情形下收益受到三方主体心理压力的综合影响，并在主导知识接收博弈时获得收益更大；中介机构的角色选择与知识转出方心理压力变化相关性不明显，主要受到知识接收方及自身心理压力影响。     

融合内容分析和关联分析的短生命周期体验品需求特征模式挖掘方法研究

挖掘特定产品的需求模式无法从整体掌握该类产品的市场特征；短生命周期体验品因缺乏历史销售数据，并且销售总量波动性极大，尤其需要从整体掌握销售总量与产品属性间关系的需求特征规律，但又难以挖掘，亟待提出适用于该类产品的需求特征模式挖掘方法。基于按销售总量分区后各区的需求特征的规律性，提出了一种按销售总量分区、以已有产品介绍集和销售总量为源信息、适用于新产品开发前使用、融合内容分析和关联分析的短生命周期体验品需求特征模式挖掘方法。该方法包括基于内容分析法的产品属性挖掘方法和基于关联分析的产品属性关系模式挖掘方法。前者可以得到较全面的产品属性；后者能够构建不同销售总量区间内产品集的属性关系模式，得到各区间的产品属性关系网，获得高销售总量区间具备，但中、低区间不具备的属性关系模式，从而获得需求特征模式。通过不断更新产品介绍集和销售总量并迭代挖掘，该方法能够动态挖掘需求特征模式。最后利用2013至2016年国产犯罪和爱情类电影数据验证了该方法的可行性，并得到了这两类电影的产品属性及近年的需求特征模式，可用于指导这两类电影的创作。