ETHNIC DIVERSITY AND THE SPREAD OF CIVIL WAR

This paper theoretically and empirically investigates the relationship between local‐level ethnic composition and the spread of conflict. Cross‐country literature on conflict finds that ethnic diversity, and ethnic polarization in particular, are associated with greater incidence of conflict. However, the question remains as to where within ethnically diverse countries conflict begins and where and how it spreads. To study this question, I present a model in which local ethnic groups' decision to attack depends on three key variables: ethnic population shares, ethnic groups' weapons ratio, and the share of co‐ethnic successes in the battles that took place in the previous period. The model generates three predictions: conflict starts in ethnically homogeneous areas and only later spreads to ethnically heterogeneous areas; neighbor co‐ethnics' success increases subsequent probability of winning and may lead to attack; and greater ethnic diversity is associated with costlier conflict. I find strong support for these predictions using detailed municipal‐level data on attacks and ethnic polarization during the initial spread of the Bosnian Civil War. Moreover, my conflict model is able to predict the sequence of actual conflict outcomes with reasonably high accuracy. (JEL: D39, D74, J15, R12)     

Perspectives on Cybersecurity Information Sharing among Multiple Stakeholders Using a Decision‐Theoretic Approach

The government, private sectors, and others users of the Internet are increasingly faced with the risk of cyber incidents. Damage to computer systems and theft of sensitive data caused by cyber attacks have the potential to result in lasting harm to entities under attack, or to society as a whole. The effects of cyber attacks are not always obvious, and detecting them is not a simple proposition. As the U.S. federal government believes that information sharing on cybersecurity issues among organizations is essential to safety, security, and resilience, the importance of trusted information exchange has been emphasized to support public and private decision making by encouraging the creation of the Information Sharing and Analysis Center (ISAC). Through a decision‐theoretic approach, this article provides new perspectives on ISAC, and the advent of the new Information Sharing and Analysis Organizations (ISAOs), which are intended to provide similar benefits to organizations that cannot fit easily into the ISAC structure. To help understand the processes of information sharing against cyber threats, this article illustrates 15 representative information sharing structures between ISAC, government, and other participating entities, and provide discussions on the strategic interactions between different stakeholders. This article also identifies the costs of information sharing and information security borne by different parties in this public‐private partnership both before and after cyber attacks, as well as the two main benefits. This article provides perspectives on the mechanism of information sharing and some detailed cost–benefit analysis.     

Resource Distribution in Multiple Attacks with Imperfect Detection of the Attack Outcome

This article extends the previous research of consecutive attacks strategy by assuming that an attacker observes the outcome of each attack imperfectly. With given probabilities it may wrongly identify a destroyed target as undestroyed, and wrongly identify an undestroyed target as destroyed. The outcome of each attack is determined by a contest success function that depends on the amount of resources allocated by the defender and the attacker to each attack. The article suggests a probabilistic model of the multiple attacks and analyzes how the target destruction probability and the attacker's relative resource expenditure are impacted by the two probabilities of incorrect observation, the attacker's and defender's resource ratio, the contest intensity, the number of attacks, and the resource distribution across attacks. We analyze how the attacker chooses the number of attacks, the attack stopping rule, and the optimal resource distribution across attacks to maximize its utility.     

Terrorism Risks and Cost‐Benefit Analysis of Aviation Security

We evaluate, for the U.S. case, the costs and benefits of three security measures designed to reduce the likelihood of a direct replication of the 9/11 terrorist attacks. To do so, we assess risk reduction, losses, and security costs in the context of the full set of security layers. The three measures evaluated are installed physical secondary barriers (IPSB) to restrict access to the hardened cockpit door during door transitions, the Federal Air Marshal Service (FAMS), and the Federal Flight Deck Officer (FFDO) Program. In the process, we examine an alternate policy measure: doubling the budget of the FFDO program to $44 million per year, installing IPSBs in all U.S. aircraft at a cost of $13.5 million per year, and reducing funding for FAMS by 75% to $300 million per year. A break‐even cost‐benefit analysis then finds the minimum probability of an otherwise successful attack required for the benefit of each security measures to equal its cost. We find that the IPSB is costeffective if the annual attack probability of an otherwise successful attack exceeds 0.5% or one attack every 200 years. The FFDO program is costeffective if the annual attack probability exceeds 2%. On the other hand, more than two otherwise successful attacks per year are required for FAMS to be costeffective. A policy that includes IPSBs, an increased budget for FFDOs, and a reduced budget for FAMS may be a viable policy alternative, potentially saving hundreds of millions of dollars per year with consequences for security that are, at most, negligible.     

A Macro-Economic Framework for Evaluation of Cyber Security Risks Related to Protection of Intellectual Property

The article is based on the premise that, from a macro-economic viewpoint, cyber attacks with long-lasting effects are the most economically significant, and as a result require more attention than attacks with short-lasting effects that have historically been more represented in literature. In particular, the article deals with evaluation of cyber security risks related to one type of attack with long-lasting effects, namely, theft of intellectual property (IP) by foreign perpetrators. An International Consequence Analysis Framework is presented to determine (1) the potential macro-economic consequences of cyber attacks that result in stolen IP from companies in the United States, and (2) the likely sources of such attacks. The framework presented focuses on IP theft that enables foreign companies to make economic gains that would have otherwise benefited the U.S. economy. Initial results are presented.     

De‐Biasing the Reporting Bias in Social Media Analytics

User‐generated contents (UGC) in social media such as online reviews are inherently incomplete since we do not capture the opinions of users who do not write a review. These silent users may be systematically different than those who speak up. Such differences can be driven by users’ differing sentiments toward their shopping experiences as well as their disposition to generate UGC. Overlooking silent users’ opinions can result in a reporting bias. We develop a method to model users’ UGC generating process and then rectify this bias through an inverse probability weighting (IPW) approach. In the context of users’ movie review activities at Blockbuster.com, our results show that the average probability for a customer to post a review is 0.06 when the customer is unsatisfied with a movie, 0.23 when indifferent, and 0.32 when satisfied. The distribution of user's reporting probability with positive experience first‐order stochastically dominates the one with negative experience. Our approach provides a realistic solution for business managers to properly utilize incomplete UGC.     

Dynamic Forecasting Conditional Probability of Bombing Attacks Based on Time‐Series and Intervention Analysis

In recent years, various types of terrorist attacks occurred, causing worldwide catastrophes. According to the Global Terrorism Database (GTD), among all attack tactics, bombing attacks happened most frequently, followed by armed assaults. In this article, a model for analyzing and forecasting the conditional probability of bombing attacks (CPBAs) based on time‐series methods is developed. In addition, intervention analysis is used to analyze the sudden increase in the time‐series process. The results show that the CPBA increased dramatically at the end of 2011. During that time, the CPBA increased by 16.0% in a two‐month period to reach the peak value, but still stays 9.0% greater than the predicted level after the temporary effect gradually decays. By contrast, no significant fluctuation can be found in the conditional probability process of armed assault. It can be inferred that some social unrest, such as America's troop withdrawal from Afghanistan and Iraq, could have led to the increase of the CPBA in Afghanistan, Iraq, and Pakistan. The integrated time‐series and intervention model is used to forecast the monthly CPBA in 2014 and through 2064. The average relative error compared with the real data in 2014 is 3.5%. The model is also applied to the total number of attacks recorded by the GTD between 2004 and 2014.     

Efficiency of Even Separation of Parallel Elements with Variable Contest Intensity

The article considers strategic defense and attack of a system that can be separated into parallel elements. The defender distributes its resource between separation and protecting the elements from outside attacks. The vulnerability of each element is determined by an attacker‐defender contest success function, which depends on a contest intensity that may increase or decrease through the separation process. The article determines criteria of separation efficiency for systems without performance redundancy and 1‐out‐of‐N and Q‐out‐of‐N systems with performance redundancy. For the systems with performance redundancy the cases of expected damage proportional to the probability that the demand is not met, and expected damage proportional to the unsupplied demand, are considered.     

Behavioral Determinants of Target Shifting and Deterrence in an Analog Cyber-Attack Game

This study examines how exploiting biases in probability judgment can enhance deterrence using a fixed allocation of defensive resources. We investigate attacker anchoring heuristics for conjunctive events with missing information to distort attacker estimates of success for targets with equal defensive resources. We designed and conducted a behavioral experiment functioning as an analog cyber attack with multiple targets requiring three stages of attack to successfully acquire a target. Each stage is associated with a probability of successfully attacking a layer of defense, reflecting the allocation of resources for each layer. There are four types of targets that have nearly equal likelihood of being successfully attacked, including one type with equally distributed success probabilities over every layer and three types with success probabilities that are concentrated to be lowest in the first, second, or third layer. Players are incentivized by a payoff system that offers a reward for successfully attacked targets and a penalty for failed attacks. We collected data from a total of 1,600 separate target selections from 80 players and discovered that the target type with the lowest probability of success on the first layer was least preferred among attackers, providing the greatest deterrent. Targets with equally distributed success probabilities across layers were the next least preferred among attackers, indicating greater deterrence for uniform-layered defenses compared to defenses that are concentrated at the inner (second or third) levels. This finding is consistent with both attacker anchoring and ambiguity biases and an interpretation of failed attacks as near misses.     

Defender–Attacker Decision Tree Analysis to Combat Terrorism

We propose a methodology, called defender–attacker decision tree analysis, to evaluate defensive actions against terrorist attacks in a dynamic and hostile environment. Like most game‐theoretic formulations of this problem, we assume that the defenders act rationally by maximizing their expected utility or minimizing their expected costs. However, we do not assume that attackers maximize their expected utilities. Instead, we encode the defender's limited knowledge about the attacker's motivations and capabilities as a conditional probability distribution over the attacker's decisions. We apply this methodology to the problem of defending against possible terrorist attacks on commercial airplanes, using one of three weapons: infrared‐guided MANPADS (man‐portable air defense systems), laser‐guided MANPADS, or visually targeted RPGs (rocket propelled grenades). We also evaluate three countermeasures against these weapons: DIRCMs (directional infrared countermeasures), perimeter control around the airport, and hardening airplanes. The model includes deterrence effects, the effectiveness of the countermeasures, and the substitution of weapons and targets once a specific countermeasure is selected. It also includes a second stage of defensive decisions after an attack occurs. Key findings are: (1) due to the high cost of the countermeasures, not implementing countermeasures is the preferred defensive alternative for a large range of parameters; (2) if the probability of an attack and the associated consequences are large, a combination of DIRCMs and ground perimeter control are preferred over any single countermeasure.     

Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies

Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system‐based for high‐consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward‐looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high‐consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents.     

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.     

THE CHOICE OF EXCHANGE‐RATE REGIME AND SPECULATIVE ATTACKS

We develop a framework that makes it possible to study, for the first time, the strategic interaction between the ex ante choice of exchange‐rate regime and the likelihood of ex post currency attacks. The optimal regime is determined by a policymaker who trades off the loss from nominal exchange‐rate uncertainty against the cost of adopting a given regime. This cost increases, in turn, with the fraction of speculators who attack the local currency. Searching for the optimal regime within the class of exchange‐rate bands, we show that the optimal regime can be either a peg (a zero‐width band), a free float (an infinite‐width band), or a nondegenerate band of finite width. We study the effect of several factors on the optimal regime and on the probability of currency attacks. In particular, we show that a Tobin tax induces policymakers to set less flexible regimes. In our model, this generates an increase in the probability of currency attacks. (JEL: F31, D84)     

How Incentives Increase Inequality

Abstract. We determine the optimal level of bonuses in the case of Co‐production between an agent and multiusers. There is Co‐production when the outcome is the result of both the agent's effort and the user's effort and type. Paying bonuses to the agent encourages him to vary his effort according to the user he meets. This variety in the agent's effort results in an increase in inequality. Hence, the optimal level of bonuses is muted when the principal cares about equality among users. This is typically the case in education or health.     

An Improved Security Requirement for Data Perturbation with Implications for E‐Commerce

With the rapid increase in the ability to store and analyze large amounts of data, organizations are gathering extensive data regarding their customers, vendors, and other entities. There has been a concurrent increase in the demand for preserving the privacy of confidential data that may be collected. The rapid growth of e‐commerce has also increased calls for maintaining privacy and confidentiality of data. For numerical data, data perturbation methods offer an easy yet effective solution to the dilemma of providing access to legitimate users while protecting the data from snoopers (legitimate users who perform illegitimate analysis). In this study, we define a new security requirement that achieves the objective of providing access to legitimate users without an increase in the ability of a snooper to predict confidential information. We also derive the specifications under which perturbation methods can achieve this objective. Numerical examples are provided to show that the use of the new specification achieves the objective of no additional information to the snooper. Implications of the new specification for e‐commerce are discussed.     

信息共享环境的数据与技术模块研究

信息共享环境与国家安全和国际安全密切相关,近几年才受到国内外政府与学者的广泛关注和高度重视.数据模块与技术模块是构建信息共享环境的核心.数据模块探讨了功能标准、模块构架、数据描述的抽象模型、数据环境的抽象模型和数据共享的抽象模型等.技术模块探讨了模块架构、服务接入与输出模型、服务平台与基础设施模型、框架模型、以及服务界面与集成模型等.     

Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data

Prediction of natural disasters and their consequences is difficult due to the uncertainties and complexity of multiple related factors. This article explores the use of domain knowledge and spatial data to construct a Bayesian network (BN) that facilitates the integration of multiple factors and quantification of uncertainties within a consistent system for assessment of catastrophic risk. A BN is chosen due to its advantages such as merging multiple source data and domain knowledge in a consistent system, learning from the data set, inference with missing data, and support of decision making. A key advantage of our methodology is the combination of domain knowledge and learning from the data to construct a robust network. To improve the assessment, we employ spatial data analysis and data mining to extend the training data set, select risk factors, and fine‐tune the network. Another major advantage of our methodology is the integration of an optimal discretizer, informative feature selector, learners, search strategies for local topologies, and Bayesian model averaging. These techniques all contribute to a robust prediction of risk probability of natural disasters. In the flood disaster's study, our methodology achieved a better probability of detection of high risk, a better precision, and a better ROC area compared with other methods, using both cross‐validation and prediction of catastrophic risk based on historic data. Our results suggest that BN is a good alternative for risk assessment and as a decision tool in the management of catastrophic risk.     

Using Probabilistic Terrorism Risk Modeling for Regulatory Benefit-Cost Analysis: Application to the Western Hemisphere Travel Initiative in the Land Environment

This article presents a framework for using probabilistic terrorism risk modeling in regulatory analysis. We demonstrate the framework with an example application involving a regulation under consideration, the Western Hemisphere Travel Initiative for the Land Environment, (WHTI‐L). First, we estimate annualized loss from terrorist attacks with the Risk Management Solutions (RMS) Probabilistic Terrorism Model. We then estimate the critical risk reduction, which is the risk‐reducing effectiveness of WHTI‐L needed for its benefit, in terms of reduced terrorism loss in the United States, to exceed its cost. Our analysis indicates that the critical risk reduction depends strongly not only on uncertainties in the terrorism risk level, but also on uncertainty in the cost of regulation and how casualties are monetized. For a terrorism risk level based on the RMS standard risk estimate, the baseline regulatory cost estimate for WHTI‐L, and a range of casualty cost estimates based on the willingness‐to‐pay approach, our estimate for the expected annualized loss from terrorism ranges from $2.7 billion to $5.2 billion. For this range in annualized loss, the critical risk reduction for WHTI‐L ranges from 7% to 13%. Basing results on a lower risk level that results in halving the annualized terrorism loss would double the critical risk reduction (14–26%), and basing the results on a higher risk level that results in a doubling of the annualized terrorism loss would cut the critical risk reduction in half (3.5–6.6%). Ideally, decisions about terrorism security regulations and policies would be informed by true benefit‐cost analyses in which the estimated benefits are compared to costs. Such analyses for terrorism security efforts face substantial impediments stemming from the great uncertainty in the terrorist threat and the very low recurrence interval for large attacks. Several approaches can be used to estimate how a terrorism security program or regulation reduces the distribution of risks it is intended to manage. But, continued research to develop additional tools and data is necessary to support application of these approaches. These include refinement of models and simulations, engagement of subject matter experts, implementation of program evaluation, and estimating the costs of casualties from terrorism events.     

Values and Risk Perceptions: A Cross‐Cultural Examination

This article examines the relationship between values and risk perceptions regarding terror attacks. The participants in the study are university students from Turkey (n = 536) and Israel (n = 298). Schwartz value theory (1992, 1994) is applied to conceptualize and measure values. Cognitive (perceived likelihood and perceived severity) and emotional (fear, helplessness, anger, distress, insecurity, hopelessness, sadness, and anxiety) responses about the potential of (i) being personally exposed to a terror attack, and (ii) a terror attack that may occur in one's country are assessed to measure risk perceptions. Comparison of the two groups suggests that the Turkish participants are significantly more emotional about terror risks than the Israeli respondents. Both groups perceive the risk of a terror attack that may occur in their country more likely than the risk of being personally exposed to a terror attack. No significant differences are found in emotional representations and perceived severity ratings regarding these risks. Results provide support for the existence of a link between values and risk perceptions of terror attacks. In both countries, self‐direction values are negatively related to emotional representations, whereas security values are positively correlated with emotions; hedonism and stimulation values are negatively related to perceived likelihood. Current findings are discussed in relation to previous results, theoretical approaches (the social amplification of risk framework and cultural theory of risk), and practical implications (increasing community support for a course of action, training programs for risk communicators).     

Quantifying Cyberinfrastructure Resilience against Multi‐Event Attacks

This article introduces a general approach for characterizing cyberinfrastructure resilience in the face of multiple malicious cyberattacks, such as when a sequence of denial‐of‐service attacks progressively target an already weakened information system. Although loss assessment frequently focuses on a single overall measure such as cost or downtime, the proposed technique considers both the timing and the amount of loss associated with each individual attack, as well as whether this loss is incurred suddenly or is “slow‐onset.” In support of this, an underlying mathematical model is developed to represent the relative impact of each attack and the corresponding length of time that its effects persist within the system, as well as to illustrate the trade‐offs between these two factors. The model is extended to represent uncertainty in its parameters and thus to support comparative analyses among various security configurations with respect to a baseline estimate of resilience. Monte Carlo simulation is then used to illustrate the model's capabilities and to support a discussion of its ability to provide for more effective decision making in the context of disaster planning and mitigation. [Submitted: March 21, 2011. Revised: July 14, 2011; November 4, 2011. Accepted: December 19, 2011.]