Some Limitations of “Risk = Threat × Vulnerability × Consequence” for Risk Analysis of Terrorist Attacks

Several important risk analysis methods now used in setting priorities for protecting U.S. infrastructures against terrorist attacks are based on the formula: Risk=Threat×Vulnerability×Consequence. This article identifies potential limitations in such methods that can undermine their ability to guide resource allocations to effectively optimize risk reductions. After considering specific examples for the Risk Analysis and Management for Critical Asset Protection (RAMCAP?) framework used by the Department of Homeland Security, we address more fundamental limitations of the product formula. These include its failure to adjust for correlations among its components, nonadditivity of risks estimated using the formula, inability to use risk‐scoring results to optimally allocate defensive resources, and intrinsic subjectivity and ambiguity of Threat, Vulnerability, and Consequence numbers. Trying to directly assess probabilities for the actions of intelligent antagonists instead of modeling how they adaptively pursue their goals in light of available information and experience can produce ambiguous or mistaken risk estimates. Recent work demonstrates that two‐level (or few‐level) hierarchical optimization models can provide a useful alternative to Risk=Threat×Vulnerability×Consequence scoring rules, and also to probabilistic risk assessment (PRA) techniques that ignore rational planning and adaptation. In such two‐level optimization models, defender predicts attacker's best response to defender's own actions, and then chooses his or her own actions taking into account these best responses. Such models appear valuable as practical approaches to antiterrorism risk analysis.     

Outsourcing to a Powerful Contract Manufacturer: The Effect of Learning‐by‐Doing

The contract manufacturing industry has grown rapidly in recent years as firms have increasingly outsourced production to reduce costs. This growth has created powerful contract manufacturers (CMs) in several industries. Achieving a competitive cost position is often a primary motive for outsourcing. Outsourcing influences both the original equipment manufacturer's (OEM) and the CM's production levels, and, therefore, through learning‐by‐doing renders future costs dependent on past outsourcing decisions. As such, outsourcing should not be viewed as a static decision that, once made, is not revisited. We address these considerations by analyzing a two‐period game between an OEM and a powerful CM wherein both firms can reduce their production costs through learning‐by‐doing. We find that partial outsourcing, wherein the OEM simultaneously outsources and produces in‐house, can be an optimal strategy. Also, we find that the OEM's outsourcing strategy may be dynamic—i.e., change from period to period. In addition, we find both that the OEM may engage in production for leverage (i.e., produce internally when at a cost disadvantage) and that the CM may engage in low balling. These and other findings in this paper demonstrate the importance of considering learning, the power of the CM, and future periods when making outsourcing decisions.     

Decision Making in Cross‐Functional Teams: The Role of Decision Power*

Through a series of game‐theoretical models, this study systematically examines decision making in cross‐functional teams. It provides a framework for the design of an organization‐specific decision‐making process and for the alignment of a team's microdecision with the “optimal” decision that maximizes the firm's payoff. This study finds that even without changing the team leader, firms could change and even dictate the team's microdecision outcome via adjusting the team member's seniority, empowering team members with veto power or involving a supervisor as a threat to overrule the team decision. This finding implies that to reposition products in the marketplace, structuring cross‐functional teams’ microdecision‐making processes is essential.     

Adversarial Risk Analysis with Incomplete Information: A Level‐k Approach

This article proposes, develops, and illustrates the application of level‐k game theory to adversarial risk analysis. Level‐k reasoning, which assumes that players play strategically but have bounded rationality, is useful for operationalizing a Bayesian approach to adversarial risk analysis. It can be applied in a broad class of settings, including settings with asynchronous play and partial but incomplete revelation of early moves. Its computational and elicitation requirements are modest. We illustrate the approach with an application to a simple defend‐attack model in which the defender's countermeasures are revealed with a probability less than one to the attacker before he decides on how or whether to attack.     

Secondary Risk Theory: Validation of a Novel Model of Protection Motivation

Protection motivation theory states individuals conduct threat and coping appraisals when deciding how to respond to perceived risks. However, that model does not adequately explain today's risk culture, where engaging in recommended behaviors may create a separate set of real or perceived secondary risks. We argue for and then demonstrate the need for a new model accounting for a secondary threat appraisal, which we call secondary risk theory. In an online experiment, 1,246 participants indicated their intention to take a vaccine after reading about the likelihood and severity of side effects. We manipulated likelihood and severity in a 2 × 2 between‐subjects design and examined how well secondary risk theory predicts vaccination intention compared to protection motivation theory. Protection motivation theory performed better when the likelihood and severity of side effects were both low (R² = 0.30) versus high (R² = 0.15). In contrast, secondary risk theory performed similarly when the likelihood and severity of side effects were both low (R² = 0.42) or high (R² = 0.45). But the latter figure is a large improvement over protection motivation theory, suggesting the usefulness of secondary risk theory when individuals perceive a high secondary threat.     

Out of the Frying Pan into the Fire: Behavioral Reactions to Terrorist Attacks

A low-probability, high-damage event in which many people are killed at one point of time is called a dread risk. Dread risks can cause direct damage and, in addition, indirect damage mediated though the minds of citizens. I analyze the behavioral reactions of Americans to the terrorist attacks on September 11, 2001, and provide evidence for the dread hypothesis: (i) Americans reduced their air travel after the attack; (ii) for a period of one year following the attacks, interstate highway travel increased, suggesting that a proportion of those who did not fly instead drove to their destination; and (iii) for the same period, in each month the number of fatal highway crashes exceeded the base line of the previous years. An estimated 1,500 Americans died on the road in the attempt to avoid the fate of the passengers who were killed in the four fatal flights.     

TOT项目融资中国有资产转让定价的博弈分析

本文以TOT项目融资方式为对象,运用博弈的方法,从合理分配国有资产经营期内增值收入的角度出发,得出这样的结论,在国有资产转让过程中,政府与私营资本之间的谈判是一系列复杂的博弈过程,在博弈中政府的最优行为是对国有资产转让进行招标,同时给予某一招标企业一定的中标优先权,这样政府能够较现行的国有资产转让方式获得更多的收益.     

Entry Deterrence of Capacitated Competition Using Price and Non‐Price Strategies

We analyze the role of pricing and branding in an incumbent firm's decision when facing competition from an entrant firm with limited capacity. We do so by studying two price competition models (Stackelberg and Nash), where we consider the incumbent's entry‐deterrence pricing strategy based on a potential entrant's capacity size. In an extension, we also study a branding model, where the incumbent firm, in addition to pricing, can also invest in influencing market preference for its product. With these models, we study conditions under which the incumbent firm may block the entrant (i.e., prevent entry without any market actions), deter the entrant (i.e., stop entry with suitable market actions) or accommodate the entrant (i.e., allow entry and compete), and how the entrant will allocate its limited capacity across its own and the new market, if entry occurs. We also study the timing difference between the two different dynamics of the price competition models and find that the incumbent's first‐mover advantage benefits both the incumbent and the entrant. Interestingly, the entrant firm's profits are not monotonically increasing in its capacity even when it is costless to build capacity. In the branding model, we show that in some cases, the incumbent may even increase its price and successfully deter entry by investing in consumer's preference for its product. Finally, we incorporate demand uncertainty into our model and show that the incumbent benefits from demand uncertainty while the entrant may be worse off depending on the magnitude of demand uncertainty and its capacity.     

经理人股票期权无效性的动态非合作博弈分析

大量学者从不同角度对经理人股票期权的有效性提出了质疑.本文基于现有理论和经验研究成果,分析股票期权收益的重要因素--继任经理人员的努力类型,对股票期权激励效果的影响,构建了经理人员股票期权激励合约的新框架,并建立经理人员之间的非合作动态博弈模型,有效解释了股票期权激励无效的内在原因.     

博弈结构对VMI模式下供应链性能的影响分析

通过建立VMI模式下供应链的多阶段博弈模型,研究分析了VMI中零售商和供应商的不同博弈结构对供应链性能的影响,结果表明,当零售商和供应商之间进行的是双方同时决策的Nash静态博弈时,供应链的性能是最低的,而在一方先决策的动态博弈结构下,供应链的性能会得到明显的改善。最后通过具体的算例分析证实了上述结论。     

Risk‐Mitigating Policies and Adversarial Behavior: Case of Backlash

A probabilistic game‐theoretic model is developed within both a static and a dynamic framework to capture adversary–defender conflict in the presence of backlash. I find that not accounting for backlash in counteradversary policies may be costly to the target government. But to minimize adversarial backlash requires understanding how backlash emerges and if, and how, adversaries strategize to goad target governments into policies that induce backlash. The dynamic version of the model shows that when backlash occurs with a time lag, an escalation of the conflict is likely to occur.     

Efficient Allocation of Resources for Defense of Spatially Distributed Networks Using Agent‐Based Simulation

This article presents ongoing research that focuses on efficient allocation of defense resources to minimize the damage inflicted on a spatially distributed physical network such as a pipeline, water system, or power distribution system from an attack by an active adversary, recognizing the fundamental difference between preparing for natural disasters such as hurricanes, earthquakes, or even accidental systems failures and the problem of allocating resources to defend against an opponent who is aware of, and anticipating, the defender's efforts to mitigate the threat. Our approach is to utilize a combination of integer programming and agent‐based modeling to allocate the defensive resources. We conceptualize the problem as a Stackelberg “leader follower” game where the defender first places his assets to defend key areas of the network, and the attacker then seeks to inflict the maximum damage possible within the constraints of resources and network structure. The criticality of arcs in the network is estimated by a deterministic network interdiction formulation, which then informs an evolutionary agent‐based simulation. The evolutionary agent‐based simulation is used to determine the allocation of resources for attackers and defenders that results in evolutionary stable strategies, where actions by either side alone cannot increase its share of victories. We demonstrate these techniques on an example network, comparing the evolutionary agent‐based results to a more traditional, probabilistic risk analysis (PRA) approach. Our results show that the agent‐based approach results in a greater percentage of defender victories than does the PRA‐based approach.     

Risk Perception and Information Processing: The Development and Validation of a Questionnaire to Assess Self‐Reported Information Processing

The role of information processing in understanding people's responses to risk information has recently received substantial attention. One limitation of this research concerns the unavailability of a validated questionnaire of information processing. This article presents two studies in which we describe the development and validation of the Information‐Processing Questionnaire to meet that need. Study 1 describes the development and initial validation of the questionnaire. Participants were randomized to either a systematic processing or a heuristic processing condition after which they completed a manipulation check and the initial 15‐item questionnaire and again two weeks later. The questionnaire was subjected to factor reliability and validity analyses on both measurement times for purposes of cross‐validation of the results. A two‐factor solution was observed representing a systematic processing and a heuristic processing subscale. The resulting scale showed good reliability and validity, with the systematic condition scoring significantly higher on the systematic subscale and the heuristic processing condition significantly higher on the heuristic subscale. Study 2 sought to further validate the questionnaire in a field study. Results of the second study corresponded with those of Study 1 and provided further evidence of the validity of the Information‐Processing Questionnaire. The availability of this information‐processing scale will be a valuable asset for future research and may provide researchers with new research opportunities.     

同侪激励机制下政府补贴对制造商绿色研发影响的演化博弈分析

针对地方财政支付压力过大的问题,制定合理的补贴机制激励制造商绿色研发,是实现政企合作双赢的关键。在同侪激励视角下建立政府补贴和企业研发的演化博弈模型,结合静态激励和动态激励两种方案,分析双方博弈中的局部稳定性,探讨同侪激励基金、补贴系数等因素对群体演化路径的影响,从合作机制层面验证了同侪激励的可行性:较低的政府补贴支出仍能激励制造商绿色研发,较高的政府补贴支出可能导致补贴困局,动态调节的同侪激励机制可以协调政企双方不稳定的合作关系。     

网络群体事件政府治理的演化博弈分析

利用演化博弈模型预测舆论的发展趋势,对政府应对网络群体事件采取的治理模式进行了研究.在一般化复制动态模型基础上建立网络群体事件舆论的传播方程,将地方政府部门与弱势群体的策略互动和行为演化融入到传染病传播模型中,用以模拟舆论的扩散和收敛过程.结果表明:在网络群体事件初期,如果弱势群体通过抗争获得政府补偿的概率不断增大,将造成事态的扩大.此时,地方政府应控制信息的交流程度与初始抗争人数,减缓舆论的扩散;在网络群体事件舆论大范围扩散阶段,上级政府介入并采取惩罚措施,及时披露信息,促使网络舆论的收敛平息,实现社会福利最大化.     

DAMS: A Model to Assess Domino Effects by Using Agent‐Based Modeling and Simulation

Historical data analysis shows that escalation accidents, so‐called domino effects, have an important role in disastrous accidents in the chemical and process industries. In this study, an agent‐based modeling and simulation approach is proposed to study the propagation of domino effects in the chemical and process industries. Different from the analytical or Monte Carlo simulation approaches, which normally study the domino effect at probabilistic network levels, the agent‐based modeling technique explains the domino effects from a bottom‐up perspective. In this approach, the installations involved in a domino effect are modeled as agents whereas the interactions among the installations (e.g., by means of heat radiation) are modeled via the basic rules of the agents. Application of the developed model to several case studies demonstrates the ability of the model not only in modeling higher‐level domino effects and synergistic effects but also in accounting for temporal dependencies. The model can readily be applied to large‐scale complicated cases.     

Impact of Take‐Back Regulation on the Remanufacturing Industry

As waste from used electronic products grows steadily, manufacturers face take‐back regulations mandating its collection and proper treatment through recycling, or remanufacturing. Environmentalists greet such regulation with enthusiasm, but its effect on remanufacturing activity and industry competition remains unclear. We research these questions, using a stylized model with an original equipment manufacturer (OEM) facing competition from an independent remanufacturer (IR). We examine the effects of regulation on three key factors: remanufacturing levels, consumer surplus, and the OEM profit. First, we find that total OEM remanufacturing actually may decrease under high collection and/or reuse targets, meaning more stringent targets do not imply more remanufacturing. Consumer surplus and the OEM profit, meanwhile, may increase when OEM‐IR competition exists in a regulated market. Finally, through a numerical study, we investigate how total welfare changes in the collection target, what happens when the cost of collection is not linear, and what happens when IR products are valued differently by consumers.     

A Cross‐Cultural Study of Perceived Benefit Versus Risk as Mediators in the Trust‐Acceptance Relationship

Several recent studies have identified the significant role social trust in regulatory organizations plays in the public acceptance of various technologies and activities. In a cross‐cultural investigation, the current work explores empirically the relationship between social trust in management authorities and the degree of public acceptability of hazards for individuals residing in either developed or emerging Latin American economies using confirmatory rather than exploratory techniques. Undergraduates in Mexico, Brazil, and Chile and the United States and Spain assessed trust in regulatory authorities, public acceptance, personal knowledge, and the risks and benefits for 23 activities and technological hazards. Four findings were encountered. (i) In Latin American nations trust in regulatory entities was strongly and significantly (directly as well as indirectly) linked with the public's acceptance of any activity or technology. In developed countries trust and acceptability are essentially linked indirectly (through perceived risk and perceived benefit). (ii) Lack of knowledge strengthened the magnitude and statistical significance of the trust‐acceptability relationship in both developed and developing countries. (iii) For high levels of claimed knowledge, the impact on the trust‐acceptability relationship varied depending upon the origin of the sample. (iv) Confirmatory analysis revealed the relative importance of perceived benefit over perceived risk in meditating the trust‐acceptability causal chain.     

Towards a Theory of Competitive Progression: Evidence from High‐Tech Manufacturing

This study replicates and extends Ferdows and De Meyers' observed ‘sand cone’ model of cumulative competitive capabilities by means of Roth's related competitive progression theory (CPT). Using path analysis, we model and test the relationships among the generic competitive capability constructs of conformance quality, delivery reliability, volume flexibility, and low cost as predicted by CPT. Our results, drawn from a sample of high‐tech manufacturers, provide further evidence that on average, these four capabilities are acquired both cumulatively and in that sequence. We also find that each generic capability increases operational know‐how and reduces non‐value‐added directly and/or indirectly through the enhancement of successive capabilities in the progression, which in turn improves profitability. The paper contributes a theoretical rationale for the observed sand cone effect, describes how the competitive progression acts to influence accelerated organizational learning over an innovation cycle, and offers evidence that combinative capabilities have strategic value for high‐tech manufacturers.     

Using Prior Risk‐Related Knowledge to Support Risk Management Decisions: Lessons Learnt from a Tunneling Project

The authors of this article have developed six probabilistic causal models for critical risks in tunnel works. The details of the models' development and evaluation were reported in two earlier publications of this journal. Accordingly, as a remaining step, this article is focused on the investigation into the use of these models in a real case study project. The use of the models is challenging given the need to provide information on risks that usually are both project and context dependent. The latter is of particular concern in underground construction projects. Tunnel risks are the consequences of interactions between site‐ and project‐ specific factors. Large variations and uncertainties in ground conditions as well as project singularities give rise to particular risk factors with very specific impacts. These circumstances mean that existing risk information, gathered from previous projects, is extremely difficult to use in other projects. This article considers these issues and addresses the extent to which prior risk‐related knowledge, in the form of causal models, as the models developed for the investigation, can be used to provide useful risk information for the case study project. The identification and characterization of the causes and conditions that lead to failures and their interactions as well as their associated probabilistic information is assumed to be risk‐related knowledge in this article. It is shown that, irrespective of existing constraints on using information and knowledge from past experiences, construction risk‐related knowledge can be transferred and used from project to project in the form of comprehensive models based on probabilistic‐causal relationships. The article also shows that the developed models provide guidance as to the use of specific remedial measures by means of the identification of critical risk factors, and therefore they support risk management decisions. Similarly, a number of limitations of the models are discussed.