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.     

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     

基于技术多样性的专利申请人与审查员的引证行为差异

近年来，专利引文越来越多地被用于技术路径研究、技术会聚、技术扩散等技术领域间的知识活动研究，但并未有效区分申请人引用和审查员引用，现有技术领域层面对申请人和审查员引用差异的对比研究还停留在较为浅显的统计层面。将技术类别间的差异性引入到申请人引用和审查员引用的对比研究中，在技术类别分布基础上，探究专利技术类别多样性视角下申请人引用和审查员引用的知识来源差异，并对其开展相关性分析。研究结果表明:专利申请人引用涉及的技术类别范围更广，而审查员引用则更集中于本技术类别；对大部分技术类别，申请人引用的技术类别多样性要高于审查员引用，但两者呈现出显著的正相关性。     

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。     

External knowledge search,absorptive capacity and radical innovation in high-technology firms

Open innovation and absorptive capacity are two concepts based on the idea that companies can leverage the knowledge generated externally to improve their innovation performance. The aim of this paper is to analyse the joint effect of open innovation and absorptive capacity on a firm's radical innovation. Open innovation is expressed in terms of external search breadth and depth strategies and absorptive capacity is described by distinguishing between potential and realized absorptive capacity. In order to test our hypotheses, we carried out empirical research in firms operating in high-technology industries. The results indicate that internal routines and processes for absorbing external knowledge help explain radical innovation as they show a significant effect of potential and realized absorptive capacity. Also, there is a moderating effect of absorptive capacity on open innovation. Specifically, potential absorptive capacity exerts a positive effect on the relationship between external search breadth and depth and radical innovation. Realized absorptive capacity moderates the influence of external search breadth. These findings confirm the complementary nature of absorptive capacity and open innovation search strategies on radical innovation.     

The need for knowledge modification in technology change: a framework to consider changes in domain complexity,knowledge and productivity

This paper researches productivity in relation to domain complexity and the present knowledge capacity in an organisational context. The study is based on five very different case studies. Three studies are conducted in Denmark, Germany, Mexico and China and are related to knowledge transfer in the relocation of manufacturing facilities. Two studies investigate operation and automation of oil and gas production in the North Sea. The case study method involves semi-structured interviews, surveys, an analysis of historical production data and observations. Based on the findings from the field studies, the paper develops a conceptual framework that management can use for discussions of productivity, development of knowledge and design of learning programmes when considering changes in the complexity of a domain or a change in knowledge.     

知识分布、衰减程度与产业集群创新模式的内在机制研究

以知识流动为研究视角，将集群创新过程中的知识溢出、吸收能力内生化，构建包含三种创新模式下的产业集群演化模型，重点分析集群知识分布和知识衰减程度与集群创新模式的内在机制及其对创新产出的影响，并进行仿真实验。研究表明：不同知识分布下，混合创新模式的创新产出表现最好，外部吸收创新模式与内部自主创新模式呈现出不同的创新产出演化路径；在外部吸收创新和混合创新模式下，均匀分布型产业集群的创新产出表现最好，集中分布型表现次之，异质分布型表现最差，而在内部自主创新模式下，集中分布型则表现最好；不同创新模式对知识衰减的敏感程度呈现阶段性差异特征。这些结论对产业集群创新和可持续发展具有很好的理论和实践指导意义。     

基于SECI模型的实习教师隐性知识显性化探究

实习教师隐性知识显性化能有效促进实习教师个体教学经验的积累和理论知识的增长，增加教育实习的价值。当下，我国学者对教师隐性知识的研究对象更多集中于资深教师团体。由于实习教师隐性知识外显化缺少必要的机制与条件，所以学者们往往忽略了对实习教师隐性知识显性化的挖掘。SECI模型指知识转化的四种模式，即社会化、外部化、组合化和内部化。与之相对应的有四种场所，分别是创始场、对话场、系统化场和练习场。在此基础上，提出SECI知识转换扩展模型，并从知识创生的四个不同阶段探究实习教师对SECI模型的应用情况。据此，提出相应的教育实习策略，如建立学习共同体、引导实习教师深入反思教学实践、建立有效的学校ICT环境、形成高效且自主的实习教师授课模式等，以促进隐性知识的共享与应用。     

When standardization becomes the lens of professional practice in child welfare services

This paper examines the relationship between professional work and standardization. There has been an increase in the use of standardized programmes in child welfare services (CWS) in Western society. Some researchers have criticized standardized programmes suggesting that they undermine professionals expertise and threaten their position, whereas others argue that such programmes strengthen professional practice. In this paper, we examine how standardized tools, in this case, a standardized parenting programme and a standardized Norwegian assessment tool, influence professional roles as experienced by child welfare workers (CWS professionals) in Norway. Semistructured individual and group interviews were conducted with 31 frontline workers in two CWS agencies. Our findings suggest that standardized tools increase the social workers experienced professional competence but challenge their professional knowledge base, reflective practice, and professional accountability. Professional and practical implications for CWS work are discussed in the light of these findings.     

日语“空间性状”的时间映射制约研究

日语空间性状的时间映射具有局部映射性和选择性，映射具有负向语义选择偏好；形成“水平线性空间>垂直线性空间>立体三维空间”维度制约等级。上述制约特质是时空隐喻概念相似性与语言类型特质共同驱动促成的。概念相似性是其内在驱动力，相似者易于映射，反之则受制约性强；日语的时间性特质是其负向语义选择偏好的外在驱动力。