On the definition of vulnerabilities in measuring risks to infrastructures.
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Biomedical subjects
Yacov Y Haimes
Publications and source records attributed to Yacov Y Haimes.
Applying the partitioned multiobjective risk method (PMRM) to portfolio selection.
The analysis of risk-return tradeoffs and their practical applications to portfolio analysis paved the way for Modern Portfolio Theory (MPT), which won Harry Markowitz a 1992 Nobel Prize in Economics. A typical approach in measuring a portfolio's expected return is based on the historical returns of the assets included in a portfolio. On the other hand, portfolio risk is usually measured using volatility, which is derived from the historical variance-covariance relationships among the portfolio assets. This article focuses on assessing portfolio risk, with emphasis on extreme risks. To date, volatility is a major measure of risk owing to its simplicity and validity for relatively small asset price fluctuations. Volatility is a justified measure for stable market performance, but it is weak in addressing portfolio risk under aberrant market fluctuations. Extreme market crashes such as that on October 19, 1987 ("Black Monday") and catastrophic events such as the terrorist attack of September 11, 2001 that led to a four-day suspension of trading on the New York Stock Exchange (NYSE) are a few examples where measuring risk via volatility can lead to inaccurate predictions. Thus, there is a need for a more robust metric of risk. By invoking the principles of the extreme-risk-analysis method through the partitioned multiobjective risk method (PMRM), this article contributes to the modeling of extreme risks in portfolio performance. A measure of an extreme portfolio risk, denoted by f(4), is defined as the conditional expectation for a lower-tail region of the distribution of the possible portfolio returns. This article presents a multiobjective problem formulation consisting of optimizing expected return and f(4), whose solution is determined using Evolver-a software that implements a genetic algorithm. Under business-as-usual market scenarios, the results of the proposed PMRM portfolio selection model are found to be compatible with those of the volatility-based model. However, under extremely unfavorable market conditions, results indicate that f(4) can be a more valid measure of risk than volatility.
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Risk management for Leontief-based interdependent systems.
When stricken by a terrorist attack, a war, or a natural disaster, an economic unit or a critical infrastructure may suffer significant loss of productivity. More importantly, due to interdependency or interconnectedness, this initial loss may propagate into other systems and eventually lead to much greater derivative loss. This belongs to what is known as a cascading effect. It is demonstrated in this article that the cascading effect and the derivative loss can be significantly reduced by effective risk management. This is accomplished by deliberately distributing the initial inoperability to other systems so that the total loss (or inoperability) is minimized. The optimal distribution strategy is found by a linear programming technique. The same risk management can also be applied to situations where objectives need to be prioritized. A case study featuring 12 economic sectors illustrates the theory. The result suggests that using the same amount of resources, minimizing risk (inoperability) of infrastructures will generally give rise to highest payoff, whereas overlooking it may result in greatest total loss. The framework developed in this work uses a steady-state approach that applies primarily to managing situations where the attack is catastrophic resulting in very long recovery time.
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Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures.
Interdependency analysis in the context of this article is a process of assessing and managing risks inherent in a system of interconnected entities (e.g., infrastructures or industry sectors). Invoking the principles of input-output (I-O) and decomposition analysis, the article offers a framework for describing how terrorism-induced perturbations can propagate due to interconnectedness. Data published by the Bureau of Economic Analysis Division of the U.S. Department of Commerce is utilized to present applications to serve as test beds for the proposed framework. Specifically, a case study estimating the economic impact of airline demand perturbations to national-level U.S. sectors is made possible using I-O matrices. A ranking of the affected sectors according to their vulnerability to perturbations originating from a primary sector (e.g., air transportation) can serve as important input to risk management. For example, limited resources can be prioritized for the "top-n" sectors that are perceived to suffer the greatest economic losses due to terrorism. In addition, regional decomposition via location quotients enables the analysis of local-level terrorism events. The Regional I-O Multiplier System II (RIMS II) Division of the U.S. Department of Commerce is the agency responsible for releasing the regional multipliers for various geographical resolutions (economic areas, states, and counties). A regional-level case study demonstrates a process of estimating the economic impact of transportation-related scenarios on industry sectors within Economic Area 010 (the New York metropolitan region and vicinities).
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Strategic alternative responses to risks of terrorism.
The terrorist acts of September 11, 2001 were a wake-up call for changing our traditional response to risks of terrorism. Given that government and worldwide think-tank organizations maintain that risks of terrorism will continue for the indefinite future, the following questions deserve strategic answers. How long can we respond to terrorism with tactical measures only, sustain current curtailments of some of our freedoms, travel, and quality of life, and absorb losses in human life and properties? Should not underlying strategic motivation lead to the tactical measures? Why do so many groups and individuals in some developing countries hate us? Is it because they fear that the ideas we export through television, movies, literature, and music have a corrupting influence on their cultures? Is it because of past operations that we conducted in such countries as Iran, Nicaragua, El Salvador, and Granada? Can the genesis of the risks of terrorism to the homeland be traced to the unfavorable socioeconomic conditions in less-privileged and developing countries, where civil and religious freedoms are close to nonexistent, and sanitary conditions, health and education, and critical infrastructures of essential utilities are almost at the same level that existed in the United States almost a century ago? If we could make progress at improving the quality of life of the billions of people in the developing countries and become more sensitive to their needs, cultures, and heritage, would their hatred subside? What other measures can we take to reduce their hatred, without compromising our basic cultural and democratic principles or their cultural and social heritage?
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Risk filtering, ranking, and management framework using hierarchical holographic modeling.
This paper contributes a methodological framework to identify, prioritize, assess, and manage risk scenarios of a large-scale system. Qualitative screening of scenarios and classes of scenarios is appropriate initially, while quantitative assessments may be applied once the set of all scenarios (hundreds) has been prioritized in several phases. The eight-phase methodology is described in detail and is applied to operations other than war. The eight phases are as follows: Phase I, Scenario Identification-A hierarchical holographic model (HHM) is developed to describe the system's "as planned" or "success" scenario. Phase II, Scenario Filtering-The risk scenarios identified in Phase I are filtered according to the responsibilities and interests of the current system user. Phase III, Bi-Criteria Filtering and Ranking. Phase IV, Multi-Criteria Evaluation. Phase V, Quantitative Ranking-We continue to filter and rank scenarios based on quantitative and qualitative matrix scales of likelihood and consequence; and ordinal response to system resiliency, robustness, redundancy. Phase VI, Risk Management is performed, involving identification of management options for dealing with the filtered scenarios, and estimating the cost, performance benefits, and risk reduction of each. Phase VII, Safeguarding Against Missing Critical Items--We examine the performance of the options selected in Phase VI against the scenarios previously filtered out during Phases II to V. Phase VIII, Operational Feedback-We use the experience and information gained during application to refine the scenario filtering and decision processes in earlier phases. These eight phases reflect a philosophical approach rather than a mechanical methodology. In this philosophy, the filtering and ranking of discrete scenarios is viewed as a precursor to, rather than a substitute for, consideration of the totality of all risk scenarios.
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The role of risk analysis in the protection of critical infrastructures against terrorism.
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Are we forgetting the risk of COTS products in wireless communications?
This paper addresses the risks associated with the exponential increase in the use of wireless communications and the implementation of wireless as a set of COTS technologies available in most large-scale computer-based systems. It describes the critical impact of these trends on information assurance, as well as on the trustworthiness of the infrastructures that are interdependent on and interconnected with wireless communications. A holistic process of risk assessment and management is suggested to prevent future problems.
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