考虑项目关联关系的R&D网络风险传播建模与仿真

考虑同一企业内不同项目间关联关系的情况下,基于研发项目的数量将研发网络分为不同子网络。借鉴SIS模型构建了R&D网络风险传播动力学模型,并对该模型进行了数理解析与仿真分析。结果表明：R&D网络风险传播阈值是由不同子网络的拓扑结构以及企业内不同项目间的关联程度共同决定的;考虑项目关联的研发网络风险传播阈值小于不考虑关联关系的研发网络风险传播阈值;项目间关联关系的企业个数及项目间的关联程度对风险在不同子网络上的传播速度与传播范围均具有影响。该研究成果对于提高研发网络抗风险能力具有重要意义。     

基于共同持有资产的银行间接关联网络研究

金融市场是一个复杂系统,银行之间不仅有直接的拆借关系,还能够通过投资市场建立间接关联。本文分析了资产内生相关性、资产降价出售以及银行投资行为等因素对银行间接关联程度的影响,基于这些因素通过持有共同资产构建银行系统的间接关联,并以此为基础通过平面极大过滤图方法生成银行间接关联网络,论证了该网络中的系统重要性银行,并通过对该网络结构特征的分析,发现间接关联网络具有小世界和无标度等特征,这些特征与银行通过直接拆借形成的网络相似。本文对于银行间接关联网络的研究不仅能够更加清晰地了解银行之间的关系,也为银行系统性风险的监管提供了新的视角。     

考虑重复购买因素的品牌竞争对新产品扩散的影响研究——基于小世界网络的仿真环境

在对品牌竞争影响要素分析的基础上,考虑重复购买在新产品扩散过程中的调节效应,构建以消费者微观采纳机制为核心的新产品扩散的阈值模型,在小世界网络仿真情境下,运用多智能仿真方法对品牌竞争与新产品扩散之间的影响关系进行分析,研究结果表明：转换成本对新产品扩散的速度和深度的影响存在一定的"条件区域",在该区域之外两者不存在显著的影响关系;进入时间与两种竞争性品牌的扩散速度存在一致的负向的影响关系,而对两种竞争性品牌的扩散深度的影响方向则相反;在此过程中,重复购买系数起着一定的调节效应,会在一定程度上放大或缩小转换成本及进入时间对新产品扩散的影响效应。通过本文的研究,不仅可以进一步丰富和完善竞争性产品创新扩散的相关理论,也可以为企业的新产品推广实践提供决策支持。     

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness,Planning, and Response for Severe Nuclear Power Plant Accidents

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system‐wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.     

连带责任治理在供应商集群中的有效性研究

在核心企业与供应商集群的渠道关系情境下探讨连带责任治理对机会主义行为的作用,将网络中心性和使用渠道权力纳入连带责任治理研究框架,考察二者对机会主义行为的影响以及对连带责任与机会主义行为之间关系的调节效应。以一家出口企业的供应商集群为研究对象,获得82个连带责任小组样本共246个被调查者的调查数据,应用SPSS 19．0软件和层级回归法对研究假设进行检验。研究结果表明,供应商之间的连带责任对其机会主义行为有显著的抑制作用;供应商领导者的网络中心性负向影响其他供应商成员的机会主义行为,并强化连带责任对机会主义行为的抑制作用;核心企业通过使用渠道权力负向影响供应商集群的机会主义行为,但会弱化连带责任对机会主义行为的抑制作用。     

双边不确定性条件下制造商质量投资与零售商销售努力决策

已有研究较少同时考察供应链中制造商产品质量信号传递和零售商销售努力调整的问题。本文考虑零售商通过努力水平改变市场需求,且制造商分摊一定比例销售努力成本的情形,制造商在决定进行质量投资后,双方对制造商能否成为高质量类型各有不同的信念,零售商只能通过观察制造商提供的契约来修正关于制造商类型的信念,并确定产品销售价格与销售努力水平。构建博弈模型分析了双方的质量投资决策、销售努力决策、定价决策以及成本分摊比例。结果表明,在不考虑质量投资成本的情形下,制造商的质量投资能够达到帕累托最优。为了激励零售商付出最优促销努力水平,销售契约中规定销售成本分摊比例与惩罚措施,由于讨价还价的存在,实际的销售成本分摊比例小于对制造商最有利的成本分摊比例。对零售商而言,制造商承担的销售成本分摊比例并非越高越好,这意味着如果超过一定范围,制造商的成本分摊反而会损害零售商的利益。     

The Effect of Demand–Supply Mismatches on Firm Risk

A supply chain management (SCM) system comprises many subsystems, including forecasting, order management, supplier management, procurement, production planning and control, warehousing and distribution, and product development. Demand–supply mismatches (DSMs) could indicate that some or all of these subsystems are not working as expected, creating uncertainties about the overall capabilities and effectiveness of the SCM system, which can increase firm risk. This article documents the effect of DSMs on firm risk as measured by equity volatility. Our sample consists of three different types of DSMs announced by publicly traded firms: production disruptions, excess inventory, and product introduction delays. We find that all three types of DSMs result in equity volatility increases. Over a 2‐year period around the announcement date, we observe mean abnormal equity volatility increases of 5.62% for production disruptions, 11.19% for excess inventory, and 6.28% for product introduction delays. Volatility increases associated with excess inventory are significantly higher than the increases associated with production disruptions and product introduction delays. Across all three types of DSMs, volatility changes are positively correlated with changes in information asymmetry. The results provide some support that volatility changes are also correlated with changes in financial and operating leverage.     

Information Acquisition and Voluntary Disclosure in an Export‐Processing System

We study a strategic information management problem in the export‐processing trade, where the buyer controls the raw material input and sales and the producer is responsible for production. The production is vulnerable to random yield risk. The producer can exert a costly effort to acquire the private yield rate information and discretionarily share it with the buyer. We develop a sequential Bayesian game model that captures three key features of the system—endogenous information endowment, voluntary disclosure, and ex post information sharing—a significant departure from the literature. The optimal disclosure strategy is driven by the trade‐off between the gains from Pareto efficiency improvement and self‐interested overproduction. It is specified by two thresholds on yield rate: only the middle‐yield producers (with yield rate between these two thresholds) share private information to improve supply‐demand match; the low‐ and high‐yield producers withhold information to extract excess input from the buyer. The buyer in response penalizes nondisclosure with reduced input and rewards information sharing with a larger order. This strategic interaction is further exacerbated by the double marginalization effect from decentralization, resulting in severe efficiency loss. We examine the effectiveness of three corrective mechanisms—vertical integration, mandatory disclosure, and production restriction—and reveal the costs of information suppressive effect and overinvestment incentive and the benefit from concessions on the processing fee. Our study endogenizes the asymmetric supply risk and provides the first attempt to rationalize the strategic interactions of informational and operational incentives in the export‐processing system.