AbSRiM: An Agent‐Based Security Risk Management Approach for Airport Operations

Security risk management is essential for ensuring effective airport operations. This article introduces AbSRiM, a novel agent‐based modeling and simulation approach to perform security risk management for airport operations that uses formal sociotechnical models that include temporal and spatial aspects. The approach contains four main steps: scope selection, agent‐based model definition, risk assessment, and risk mitigation. The approach is based on traditional security risk management methodologies, but uses agent‐based modeling and Monte Carlo simulation at its core. Agent‐based modeling is used to model threat scenarios, and Monte Carlo simulations are then performed with this model to estimate security risks. The use of the AbSRiM approach is demonstrated with an illustrative case study. This case study includes a threat scenario in which an adversary attacks an airport terminal with an improvised explosive device. The approach provides a promising way to include important elements, such as human aspects and spatiotemporal aspects, in the assessment of risk. More research is still needed to better identify the strengths and weaknesses of the AbSRiM approach in different case studies, but results demonstrate the feasibility of the approach and its potential.     

Reverse Auctions with Multiple Reinforcement Learning Agents*

Reverse auctions in business‐to‐business (B2B) exchanges provide numerous benefits to participants. Arguably the most notable benefit is that of lowered prices driven by increased competition in such auctions. The competition between sellers in reverse auctions has been analyzed using a game‐theoretic framework and equilibria have been established for several scenarios. One finding of note is that, in a setting in which sellers can meet total demand with the highest‐bidding seller being able to sell only a fraction of the total capacity, the sellers resort to a mixed‐strategy equilibrium. Although price randomization in industrial bidding is an accepted norm, one might argue that in reality managers do not utilize advanced game theory calculations in placing bids. More likely, managers adopt simple learning strategies. In this situation, it remains an open question as to whether the bid prices converge to the theoretical equilibrium over time. To address this question, we model reverse‐auction bidding behavior by artificial agents as both two‐player and n‐player games in a simulation environment. The agents begin the game with a minimal understanding of the environment but over time analyze wins and losses for use in determining future bids. To test for convergence, the agents explore the price space and exploit prices where profits are higher, given varying cost and capacity scenarios. In the two‐player case, the agents do indeed converge toward the theoretical equilibrium. The n‐player case provides results that reinforce our understanding of the theoretical equilibria. These results are promising enough to further consider the use of artificial learning mechanisms in reverse auctions and other electronic market transactions, especially as more sophisticated mechanisms are developed to tackle real‐life complexities. We also develop the analytical results when one agent does not behave strategically while the other agent does and show that our simulations for this environment also result in convergence toward the theoretical equilibrium. Because the nature of the best response in the new setting is very different (pure strategy as opposed to mixed), it indicates the robustness of the devised algorithm. The use of artificial agents can also overcome the limitations in rationality demonstrated by human managers. The results thus have interesting implications for designing artificial agents in automating bid responses for large numbers of bids where human intervention might not always be possible.     

Security Investment in Contagious Networks

Security of the systems is normally interdependent in such a way that security risks of one part affect other parts and threats spread through the vulnerable links in the network. So, the risks of the systems can be mitigated through investments in the security of interconnecting links. This article takes an innovative look at the problem of security investment of nodes on their vulnerable links in a given contagious network as a game‐theoretic model that can be applied to a variety of applications including information systems. In the proposed game model, each node computes its corresponding risk based on the value of its assets, vulnerabilities, and threats to determine the optimum level of security investments on its external links respecting its limited budget. Furthermore, direct and indirect nonlinear influences of a node's security investment on the risks of other nodes are considered. The existence and uniqueness of the game's Nash equilibrium in the proposed game are also proved. Further analysis of the model in a practical case revealed that taking advantage of the investment effects of other players, perfectly rational players (i.e., those who use the utility function of the proposed game model) make more cost‐effective decisions than selfish nonrational or semirational players.     

Whither Risk Assessment: New Challenges and Opportunities a Third of a Century After the Red Book

Six multi‐decade‐long members of SRA reflect on the 1983 Red Book in order to examine the evolving relationship between risk assessment and risk management; the diffusion of risk assessment practice to risk areas such as homeland security and transportation; the quality of chemical risk databases; challenges from other groups to elements at the core of risk assessment practice; and our collective efforts to communicate risk assessment to a diverse set of critical groups that do not understand risk, risk assessment, or many other risk‐related issues. The authors reflect on the 10 recommendations in the Red Book and present several pressing challenges for risk assessment practitioners.     

Risk Management of Domino Effects Considering Dynamic Consequence Analysis

Domino effects are low‐probability high‐consequence accidents causing severe damage to humans, process plants, and the environment. Because domino effects affect large areas and are difficult to control, preventive safety measures have been given priority over mitigative measures. As a result, safety distances and safety inventories have been used as preventive safety measures to reduce the escalation probability of domino effects. However, these safety measures are usually designed considering static accident scenarios. In this study, we show that compared to a static worst‐case accident analysis, a dynamic consequence analysis provides a more rational approach for risk assessment and management of domino effects. This study also presents the application of Bayesian networks and conflict analysis to risk‐based allocation of chemical inventories to minimize the consequences and thus to reduce the escalation probability. It emphasizes the risk management of chemical inventories as an inherent safety measure, particularly in existing process plants where the applicability of other safety measures such as safety distances is limited.     

On Forward Induction

A player's pure strategy is called relevant for an outcome of a game in extensive form with perfect recall if there exists a weakly sequential equilibrium with that outcome for which the strategy is an optimal reply at every information set it does not exclude. The outcome satisfies forward induction if it results from a weakly sequential equilibrium in which players' beliefs assign positive probability only to relevant strategies at each information set reached by a profile of relevant strategies. We prove that if there are two players and payoffs are generic, then an outcome satisfies forward induction if every game with the same reduced normal form after eliminating redundant pure strategies has a sequential equilibrium with an equivalent outcome. Thus in this case forward induction is implied by decision‐theoretic criteria.     

A Methodology for Modeling Regional Terrorism Risk

Over the past decade, terrorism risk has become a prominent consideration in protecting the well‐being of individuals and organizations. More recently, there has been interest in not only quantifying terrorism risk, but also placing it in the context of an all‐hazards environment in which consideration is given to accidents and natural hazards, as well as intentional acts. This article discusses the development of a regional terrorism risk assessment model designed for this purpose. The approach taken is to model terrorism risk as a dependent variable, expressed in expected annual monetary terms, as a function of attributes of population concentration and critical infrastructure. This allows for an assessment of regional terrorism risk in and of itself, as well as in relation to man‐made accident and natural hazard risks, so that mitigation resources can be allocated in an effective manner. The adopted methodology incorporates elements of two terrorism risk modeling approaches (event‐based models and risk indicators), producing results that can be utilized at various jurisdictional levels. The validity, strengths, and limitations of the model are discussed in the context of a case study application within the United States.     

On the Existence of Pure and Mixed Strategy Nash Equilibria in Discontinuous Games

A game is better-reply secure if for every nonequilibrium strategy x^* and every payoff vector limit u^* resulting from strategies approaching x^*, some player i has a strategy yielding a payoff strictly above u_i^* even if the others deviate slightly from x^*. If strategy spaces are compact and convex, payoffs are quasiconcave in the owner's strategy, and the game is better-reply secure, then a pure strategy Nash equilibrium exists. Better-reply security holds in many economic games. It also permits new results on the existence of symmetric and mixed strategy Nash equilibria.     

Should the Model for Risk‐Informed Regulation be Game Theory Rather than Decision Theory?

Risk analysts frequently view the regulation of risks as being largely a matter of decision theory. According to this view, risk analysis methods provide information on the likelihood and severity of various possible outcomes; this information should then be assessed using a decision‐theoretic approach (such as cost/benefit analysis) to determine whether the risks are acceptable, and whether additional regulation is warranted. However, this view ignores the fact that in many industries (particularly industries that are technologically sophisticated and employ specialized risk and safety experts), risk analyses may be done by regulated firms, not by the regulator. Moreover, those firms may have more knowledge about the levels of safety at their own facilities than the regulator does. This creates a situation in which the regulated firm has both the opportunity—and often also the motive—to provide inaccurate (in particular, favorably biased) risk information to the regulator, and hence the regulator has reason to doubt the accuracy of the risk information provided by regulated parties. Researchers have argued that decision theory is capable of dealing with many such strategic interactions as well as game theory can. This is especially true in two‐player, two‐stage games in which the follower has a unique best strategy in response to the leader's strategy, as appears to be the case in the situation analyzed in this article. However, even in such cases, we agree with Cox that game‐theoretic methods and concepts can still be useful. In particular, the tools of mechanism design, and especially the revelation principle, can simplify the analysis of such games because the revelation principle provides rigorous assurance that it is sufficient to analyze only games in which licensees truthfully report their risk levels, making the problem more manageable. Without that, it would generally be necessary to consider much more complicated forms of strategic behavior (including deception), to identify optimal regulatory strategies. Therefore, we believe that the types of regulatory interactions analyzed in this article are better modeled using game theory rather than decision theory. In particular, the goals of this article are to review the relevant literature in game theory and regulatory economics (to stimulate interest in this area among risk analysts), and to present illustrative results showing how the application of game theory can provide useful insights into the theory and practice of risk‐informed regulation.     

A Sales Forecast Model for Short‐Life‐Cycle Products: New Releases at Blockbuster

We develop, in this article, a sales model for movie and game products at Blockbuster. The model assumes that there are three sales components: the first is from consumers who have already committed to purchasing (or renting) a product (e.g., based on promotion of, or exposure to, the product prior to its launch); the second comes from consumers who are potential buyers of the product; and the third comes from either a networking effect on closely tied (as in a social group) potential buyers from previous buyers (in the case of movie rental and all retail products) or re‐rents (in the case of game rental). In addition, we explicitly formulate into our model dynamic interactions between these sales components, both within and across sales periods. This important feature is motivated by realism, and it significantly contributes to the accuracy of our model. The model is thoroughly tested against sales data for rental and retail products from Blockbuster. Our empirical results show that the model offers excellent fit to actual sales activity. We also demonstrate that the model is capable of delivering reasonable sales forecasts based solely on environmental data (e.g., theatrical sales, studio, genre, MPAA ratings, etc.) and actual first‐period sales. Accurate sales forecasts can lead to significant cost savings. In particular, it can improve the retail operations at Blockbuster by determining appropriate order quantities of products, which is critical in effective inventory management (i.e., it can reduce the extent of over‐stocking and under‐stocking). While our model is developed specifically for product sales at Blockbuster, we believe that with context‐dependent modifications, our modeling approach could also provide a reasonable basis for the study of sales for other short‐Life‐Cycle products.     

引入人脸抓拍系统还是升级安检设备？——有限资源下的地铁暴恐防御序贯博弈模型

在有限防御资源约束下,地铁安防部门面临着引进人脸抓拍系统构建二级地铁暴恐防御体系,还是升级原安检系统构建增强型一级地铁暴恐防御系统的选择。本文基于暴恐分子决策的目标价值依赖性和安防部门的接警反应时间不对称性,构建了有限资源下地铁暴恐防御策略选择的序贯博弈模型。得出并分析了双方的四种均衡策略,最后结合北京地铁积水潭站早高峰案例,给出了考虑高峰大客流冲击效应的暴恐防御决策建议。研究发现,当增强型安检系统的准确率过高,或接警反应时间有效率较高时,引进人脸抓拍系统策略占优,且人脸抓拍系统的社会价值对安防部门决策无直接影响。当增强型安检系统的准确率适中,或接警反应时间有效率较低时,第一种情况,在安防部门侧重效率的情况下,人脸抓拍系统的社会价值与安防效率正相关,引进人脸抓拍策略在其社会价值较高时占优;第二种情况,在侧重安全情况下,升级加强原安检系统策略占优,此时,人脸抓拍系统的社会价值对安防部门决策无影响。另外,较高的安检准确率将提升安检厅的乘客淤滞水平、强化高峰大客流对安检厅的冲击效应、削弱高峰大客流对候车厅的冲击效应,并最终拉升暴恐分子直接袭击安检厅的风险。     

政府监管与企业加入第三方国际环境审计的模糊博弈分析

针对政府环境监管与企业实施环境行为博弈分析中无法对双方的损益情况做出判断的问题,文章结合三角模糊数构建了政府监管和企业加入第三方国际环境审计的博弈模型,分别指出了纯策略纳什均衡和混合策略均衡情况需要满足的条件,讨论了政府与污染环境企业博弈策略的影响因素及作用机制,为政府监管与企业加入第三方国际环境审计顺利实施提供了相关建议。然后通过三角结构元方法对算例进行求解,验证了结论的正确性和可行性,引入三角模糊概念更加符合实际应用情况,拓展了第三方国际环境审计的应用背景。研究结果表明,政府对消费者消费的引导和对污染企业的惩罚对企业是否决定加入第三方国际环境审计起着举足轻重的作用。     

Game Theory and Risk Analysis

Risk analysts often analyze adversarial risks from terrorists or other intelligent attackers without mentioning game theory. Why? One reason is that many adversarial situations—those that can be represented as attacker‐defender games, in which the defender first chooses an allocation of defensive resources to protect potential targets, and the attacker, knowing what the defender has done, then decides which targets to attack—can be modeled and analyzed successfully without using most of the concepts and terminology of game theory. However, risk analysis and game theory are also deeply complementary. Game‐theoretic analyses of conflicts require modeling the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis methods are well suited to accomplish these tasks. Conversely, game‐theoretic formulations of attack‐defense conflicts (and other adversarial risks) can greatly improve upon some current risk analyses that attempt to model attacker decisions as random variables or uncertain attributes of targets (“threats”) and that seek to elicit their values from the defender's own experts. Game theory models that clarify the nature of the interacting decisions made by attackers and defenders and that distinguish clearly between strategic choices (decision nodes in a game tree) and random variables (chance nodes, not controlled by either attacker or defender) can produce more sensible and effective risk management recommendations for allocating defensive resources than current risk scoring models. Thus, risk analysis and game theory are (or should be) mutually reinforcing.     

A Simplified Approach to Risk Assessment Based on System Dynamics: An Industrial Case Study

Seveso plants are complex sociotechnical systems, which makes it appropriate to support any risk assessment with a model of the system. However, more often than not, this step is only partially addressed, simplified, or avoided in safety reports. At the same time, investigations have shown that the complexity of industrial systems is frequently a factor in accidents, due to interactions between their technical, human, and organizational dimensions. In order to handle both this complexity and changes in the system over time, this article proposes an original and simplified qualitative risk evaluation method based on the system dynamics theory developed by Forrester in the early 1960s. The methodology supports the development of a dynamic risk assessment framework dedicated to industrial activities. It consists of 10 complementary steps grouped into two main activities: system dynamics modeling of the sociotechnical system and risk analysis. This system dynamics risk analysis is applied to a case study of a chemical plant and provides a way to assess the technological and organizational components of safety.     

沪市知情交易概率(PIN)特征与风险定价能力

本文以EKOP模型为基础,研究了沪市股票知情交易概率的特征,并检验了知情交易概率的风险定价能力。结果显示,在我国股票市场上,知情交易概率作为定价因子是有解释力的,但是知情交易概率对收益产生的却是负效应。这一结果不仅与理论上信息不对称需要风险补偿相反,而且与美国市场上的实证结果也相反。本文结合案例分析,提出该实证结果出现的原因在于我国股市所特有的庄家与散户博弈的赢利模式。此外,本文的实证结果也不同于之前对我国市场的研究,产生差异的原因在于模型估计方法、回归方法和样本区间的不同。     

基于有限知识和理性的双寡头战略定产纳什均衡问题研究

经典古诺寡头模型以及目前相关的寡头定产竞争模型最致命的缺陷是对寡头博弈目标的假设、时序假设、有限理性与知识的假设与现实严重不符.本文依据新的博弈条件假设,构建了一种对现实决策情形具有较强的普适性的描述性博弈结构模型.该模型能够对现实中的主导与从属型厂商之间、存在先知先觉博弈者的战略定产决策问题进行很好地描述,且经证明经典古诺博弈模型是该模型的一种特例.在此基础上,本文证明了:一般情况下,斗争策略是基于战略利益的双寡头定产竞争的纳什均衡策略,并找到了双寡头战略调整的聚点均衡;发现了:存在战略扩展阻尼条件的先期决策寡头的阻尼纳什均衡,并给出了该问题的构造性的证明和仿真算例.     

Examining Public Trust in Risk‐Managing Organizations After a Major Disaster

This research investigates the public's trust in risk‐managing organizations after suffering serious damage from a major disaster. It is natural for public trust to decrease in organizations responsible for mitigating the damage. However, what about trust in organizations that address hazards not directly related to the disaster? Based on the results of surveys conducted by a national institute, the Japanese government concluded, in a White Paper on Science and Technology, that the public's trust in scientists declined overall after the 2011 Tohoku Earthquake. Because scientists play a key role in risk assessment and risk management in most areas, one could predict that trust in risk‐managing organizations overall would decrease after a major disaster. The methodology of that survey, however, had limitations that prevented such conclusions. For this research, two surveys were conducted to measure the public's trust in risk‐managing organizations regarding various hazards, before and after the Tohoku Earthquake (n = 1,192 in 2008 and n = 1,138 in 2012). The results showed that trust decreased in risk‐managing organizations that deal with earthquakes and nuclear accidents, whereas trust levels related to many other hazards, especially in areas not touched by the Tohoku Earthquake, remained steady or even increased. These results reject the assertion that distrust rippled through all risk‐managing organizations. The implications of this research are discussed, with the observation that this result is not necessarily gratifying for risk managers because high trust sometimes reduces public preparedness for disasters.     

A Risk Assessment Scheme of Infection Transmission Indoors Incorporating the Impact of Resuspension

A new risk assessment scheme was developed to quantify the impact of resuspension to infection transmission indoors. Airborne and surface pathogenic particle concentration models including the effect of two major resuspension scenarios (airflow‐induced particle resuspension [AIPR] and walking‐induced particle resuspension [WIPR]) were derived based on two‐compartment mass balance models and validated against experimental data found in the literature. The inhalation exposure to pathogenic particles was estimated using the derived airborne concentration model, and subsequently incorporated into a dose‐response model to assess the infection risk. Using the proposed risk assessment scheme, the influences of resuspension towards indoor infection transmission were examined by two hypothetical case studies. In the case of AIPR, the infection risk increased from 0 to 0.54 during 0–0.5 hours and from 0.54 to 0.57 during 0.5–4 hours. In the case of WIPR, the infection risk increased from 0 to 0.87 during 0–0.5 hours and from 0.87 to 1 during 0.5–4 hours. Sensitivity analysis was conducted based on the design‐of‐experiments method and showed that the factors that are related to the inspiratory rate of viable pathogens and pathogen virulence have the most significant effect on the infection probability under the occurrence of AIPR and WIPR. The risk assessment scheme could serve as an effective tool for the risk assessment of infection transmission indoors.     

The Expected Number of Nash Equilibria of a Normal Form Game

Fix finite pure strategy sets _S1,…,_Sn , and let S=_S1×⋯×_Sn . In our model of a random game the agents' payoffs are statistically independent, with each agent's payoff uniformly distributed on the unit sphere in ℝ^S. For given nonempty _T1⊂_S1,…,_Tn⊂_Sn we give a computationally implementable formula for the mean number of Nash equilibria in which each agent i's mixed strategy has support T_i. The formula is the product of two expressions. The first is the expected number of totally mixed equilibria for the truncated game obtained by eliminating pure strategies outside the sets T_i. The second may be construed as the “probability” that such an equilibrium remains an equilibrium when the strategies in the sets _Si∖_Ti become available.