批判恩斯特·布洛赫的"马克思主义"乌托邦

乌托邦社会的物质产品最大化必然要无限制地掠夺自然资源和能源,然而资源是有限的,对能源的无尽开采又会产生无法消除的污染.这些都会加速人类和地球的毁灭,因而恩斯特·布洛赫武的乌托邦是不可能的.与布洛赫勾勒的"积极闲暇的人间天堂"相反,人类自古就已达到了人性可能达到的高度,人性从来都是具有两面性的.     

Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Statistics and Computing - We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with nonlinear...     

Understanding innovation performance and its antecedents: A socio-cognitive model

Little attention in the previous literature has been paid to understanding employees’ factors that drive customer development knowledge and performance from the perspective of social psychology. Drawing on social cognitive theory, this study validates a research model that examines the above issue. In the setting of new product development across high-tech firms in Taiwan, this study postulates that innovation self-efficacy, role conflict, and role ambiguity influence innovation performance directly and indirectly via the mediation of customer knowledge development and innovation outcome expectation. This study contributes to the social science literature by applying social cognitive theory to the rarely explored area of innovation performance and by presenting an operationalization of role stressors (i.e., role ambiguity and role conflict) in the area. Lastly, managerial implications and limitations from the empirical findings are provided.     

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.