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.     

Impression management in corporate annual reports during the global financial crisis

This paper analyses impression management (IM) during the global financial crisis (GFC). It examines the differences in multiple textual characteristics and attributions between a highly positive performance period (2002–2007) and the GFC period (2008–2012), within the setting of Spain, where these two economic cycles were extreme. In contrast to previous research, companies' extreme poor performance in our sample is driven by an exogenous event. The findings do not show clear evidence of IM based on textual characteristics specifically linked to the GFC. Companies tried not to use overt IM and, to some extent, tried to clarify the impact of the crisis on performance. They were under great scrutiny and probably preferred to tell a more careful story. However, a general pattern of IM was still present during the GFC in the form of consistent positive attributions, favourable benchmarks and enhancement practices. In essence, the crisis did not fully stop IM practices, but rather influenced the way IM was produced. Overall, our results show that IM was lower during the GFC than in the case of poor performance in normal macroeconomic conditions found by previous literature. The results also show that the narratives of firms in the finance and real estate sectors were the most reactive to the GFC, probably linked to their key role in the crisis.     

Local mobilisations and the formation of environmental networks in a democratizing Tunisia

ABSTRACT

This study examines the environmental protests that occurred in Tunisia after the 2011 uprisings. It analyses the factors underpinning the rise of the environmental networks during the period of transition (2011–2014). It details the mobilising strategies that were crucial for the networks’ growth or survival during this period of institutional instability. The study shows how networks leaders were able to bring together social and political actors from different backgrounds and ideological orientations. It is argued that the ability of networks to develop new distinctive collective identities was crucial for network sustainability. Those networks and actors who did not develop new clearly defined environmental identities and continued to rely importantly on pre-existing (authoritarian) structures and practices were more negatively impacted by ideological cleavages and political calculations. Empirically, the contribution builds on interviews and observations, as well as documents collected from Tunisian municipalities between 2013 and 2015. Conceptually, the research proposes a bottom-up perspective that highlights the interplay between micro- and macro-dynamics and strategies during a political transition. The analysis details the actors’ capacity to build alliances via interpersonal relations at the micro level, and their strategies to engage with institutional actors and processes.     

客户集中度、市场环境与存货管理效率

客户处在供应链的下游,对企业存货的采购、生产和销售都会产生重要影响,而多变的市场环境也在无形当中影响着客户与企业之间的关系。从企业所处行业、地区产品市场和客户三个角度所带来的市场环境变化入手,采用2011—2017年我国沪深两市A股制造业上市公司的数据,实证分析客户集中度与企业存货管理效率之间的关系。研究发现:(1)客户集中度越高,企业存货管理效率越高；(2)产品市场竞争力度越强,客户集中度对企业存货管理效率的影响越大；(3)产品市场发育越好,客户集中度对企业存货管理效率的影响越大。进一步研究发现,当企业因客户生产经营原因导致需求发生较大变化时,客户集中度对企业存货管理效率的正向影响更加显著。研究结论表明,政府应当注重市场竞争力的提升和良好经营环境的建设,使得企业在维护客户关系的同时充分发挥客户集中度对企业存货管理效率的积极作用。     

双循环发展的改革开放空间试验场——我国开发区的缘起、演进和趋势

开发区是我国改革开放进程中重要的"制度安排",在制度转型、市场机制培育、产业结构升级和带动地区经济增长等方面发挥着引领示范作用,但同时也存在政策效应边际递减、土地资源浪费、创新不足等诸多问题.梳理我国开发区过去三十多年的发展经验和不足,并展望其未来定位是一项重要的研究课题.在详细讨论我国开发区的缘起、动力机制、发展阶段、功能演变、面临问题的基础上,文章从制度安排、比较优势、产业集聚和商业环境四个方面总结了我国开发区取得的成功经验.未来,开发区的发展应实现从生产到生活、从效率到公平、从地的繁荣到人的繁荣、从外围到中心、从专业化到多样化、从高速增长到高质量发展、从对外开放到国内国际双循环等方面的转变.     

影子银行、地方政府债务及金融发展动态关系研究——基于省级面板VAR模型

基于2004—2017年中国省级面板数据,运用面板向量自回归(PVAR)模型,使用系统GMM估计、脉冲响应函数、方差分解以及格兰杰因果关系检验等方法分析了影子银行、地方政府债务及金融发展之间的动态关系.结果表明:影子银行、地方政府债务与金融发展水平三者之间存在动态耦合关系.在地方政府融资能力受到约束的情况下,影子银行为地方政府提供了多元的融资方式,在增加政府融资能力的同时提升了政府债务水平;而地方政府债务需求显著推动了影子银行规模的快速发展.同时,影子银行过度扩张危害了金融市场的健康发展,降低金融发展水平,继而使地方政府的融资渠道受到约束.但金融发展并不能有效约束影子银行规模,原因在于,政府融资需求是影子银行的主要动力,若不能控制地方政府的借贷行为则无法从源头解决问题.监管机构在去杠杆的过程中,应该综合考虑影子银行与地方政府债务、金融发展之间的动态关系,如此才能够实现预期的政策效果.     

Impact of competitive conditions on supplier evaluation: a construction supply chain case study

This research proposes a multi-criteria decision-making (MCDM) framework and demonstrates the impact of competitive conditions on supplier evaluation process for construction supply chains. The paper focuses on the supply chain of a large-scale housing project in order to illustrate the role of competitive capability and suppliers’ profile and its influence on supplier evaluation based on prevailing supply/market conditions. Various scenarios are investigated to demonstrate the impact of competition on supplier evaluation. The contribution of the study lies in highlighting the impact of supply/market conditions on MCDM decisions causing supplier evaluation ‘imbalance’ and MCDM usage. It is expected that the study will be useful for project management, construction, supply chain management, sourcing professionals. The findings of the study are generalisable to projects-based situations such as petroleum refinery and ship building where bill of materials typically consists of thousands of items and a large number of suppliers are involved.     

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.     

On the Compatibility of Organizational Behavior Management and BACB Certification

The number of practicing behavior analysts who hold Behavior Analyst Certification Board (BACB) certification has substantially increased in the past decade. Some have mistakenly interpreted the BACB’s certification requirements as being specific to the autism and intellectual disabilities practice area. We present key BACB requirements, describe how they are practice-area neutral, and provide specific examples of their relevance to organizational behavior management.     

大数据驱动下的政府治理机制研究——基于2020年后精准扶贫领域的返贫阻断分析

大数据时代已经到来，大数据驱动下的政府治理发生何种改变，目前相关研究较少。当前政府治理的考验集中在2020年实现全面脱贫后精准扶贫领域的返贫阻断。文章以该领域为切入点，探索大数据驱动下政府治理的内在机制。研究发现：大数据背景下，政府治理能力催生出新的核心能力，即数据治理能力。数据治理能力驱动下，政府治理主体多元化有利于实现政府治理资源的宽范围、精准化动员；政府治理方式实现经验式决策向数据化决策转变，有利于实现政府资源的高效率和公平配置；政府治理客体的精准识别，有利于政府资源的精准化和最优化运用。但是客观上必须具备数据治理能力，主观上必须按照大数据驱动的要求重构政府治理体系（包括治理主体、治理方式和治理客体），才能真正实现以数据治理驱动政府治理，进而提高政府治理能力的目的。而数据治理能力客观上也加速了政府治理体系的重构，进而加快了政府治理能力的提升，并最终提高政府治理绩效。在对标大数据应用不同阶段分析常规式返贫阻断和大数据返贫阻断的不足和差距的基础上，笔者提出重构政府治理体系和提升数据治理能力等针对性建议。