User-model interaction in decision support systems for risk management

This paper describes and evaluates three different approaches to building decision support systems: the Operations Research/Management Science approach, the Decision Analysis/Multiattribute Utility approach, and the Artificial Intelligence/Expert Systems approach. It evaluates the usefulness of the three approaches for risk management. In particular, it defines evaluation objectives of risk analysts, risk managers, and laypeople and provides a subjective assessment how the three approaches stack up against their objectives. The paper concludes that for most risk management applications a combination of the three approaches would be most desirable.This paper was written under contract No. 2709-85-05 ED ISP D of the European Atomic Energy Community, Commission of the European Communities, Joint Research Centre, Ispra Establishment, Ispra, Italy to the Gemeinschaft für Entscheidungs- und Risikoanalyse, Berlin, West Germany. It was prepared for presentation at the Conference on Operations Research and Multiattribute Decision Analysis held in Passau, April 20–26, 1986. The views and opinions expressed in this paper are solely those of the author.     

A fuzzy multiobjective programming algorithm in decision support systems

Multicriteria analysis is one of the analytical functions in the problem processing system of decision support systems (DSS). In this paper, an interactive and iterative fuzzy programming method for solving a quasi-optimization problem in complex decisions under constraints involving a multiple objective function is proposed. Comparing with an adapted gradient search method, a surrogate worth tradeoff method, and a Zionts—Wallenius method, an approximate preference structure is emphasized in the proposed method.     

Designing man–machine interactions for mobile clinical systems: MET triage support using Palm handhelds

The Mobile Emergency Triage (MET) system is a clinical triage support system that aids physicians in making triage decisions as to whether a child presenting in the Emergency Department of a hospital with a specific pain complaint should be discharged to a family physician, needs to be admitted for further investigation/observation, or requires an urgent specialist consult. The system’s mobile component is designed to work on handheld computers. This paper presents our experience and discusses our original solutions with regard to designing man–machine interactions for mobile clinical support systems. The specific interactions adopted in the MET design that are discussed in the paper were created in consultation with potential end-users and tested at the Children’s Hospital of Eastern Ontario in Ottawa.     

Stable modeling in energy risk management

High price volatility in energy markets compels the companies to adopt and implement policies for measurement and management of the energy risk. A popular measure of risk exposure is the Value at Risk (VaR). Traditional methods of estimation of VaR used by major energy companies fail to capture the heavy tails and asymmetry of energy returns distributions. We suggest the use of stable distributions for modeling energy return distributions. The results of our study demonstrate that stable modeling captures asymmetry and heavy-tails of returns, and, therefore, provides more accurate estimates of energy VaR.     

A risk management system for sustainable fleet replacement

This article analyzes the fleet management problem faced by a firm when deciding which vehicles to add to its fleet. Such a decision depends not only on the expected mileage and tasks to be assigned to the vehicle but also on the evolution of fuel and CO₂ emission prices and on fuel efficiency. This article contributes to the literature on fleet replacement and sustainable operations by proposing a general decision support system for the fleet replacement problem using stochastic programming and conditional value at risk (CVaR) to account for uncertainty in the decision process. The article analyzes how the CVaR associated with different types of vehicle is affected by the parameters in the model by reporting on the results of a real-world case study.     

Robust risk management

Estimating the probabilities by which different events might occur is usually a delicate task, subject to many sources of inaccuracies. Moreover, these probabilities can change over time, leading to a very difficult evaluation of the risk induced by any particular decision. Given a set of probability measures and a set of nominal risk measures, we define in this paper the concept of robust risk measure as the worst possible of our risks when each of our probability measures is likely to occur. We study how some properties of this new object can be related with those of our nominal risk measures, such as convexity or coherence. We introduce a robust version of the Conditional Value-at-Risk (CVaR) and of entropy-based risk measures. We show how to compute and optimize the Robust CVaR using convex duality methods and illustrate its behavior using data from the New York Stock Exchange and from the NASDAQ between 2005 and 2010.     

Portfolio optimization under entropic risk management

framework in the risk uniqueness In this paper, properties of the entropic risk measure are examined rigorously in a general This risk measure is then applied in a dynamic portfolio optimization problem, appearing management constraint. By considering the dual problem, we prove the existence and of the solution and obtain an analytic expression for the solution.     

Pricing and risk management of interest rate swaps

This paper reformulates the valuation of interest rate swaps, swap leg payments and swap risk measures, all under stochastic interest rates, as a problem of solving a system of linear equations with random perturbations. A sequence of uniform approximations which solves this system is developed and allows for fast and accurate computation. The proposed method provides a computationally efficient alternative to Monte Carlo based valuations and risk measurement of swaps. This is demonstrated by conducting numerical experiments and so our method provides a potentially important real-time application for analysis and calculation in markets.     

Data analysis and decision support

In this paper, we consider developmental lines of computer-assisted decision support (with consideration of knowledge-based approaches) for data analysis problems. First, we discuss some situations where it is obviously appropriate to apply computer-assisted decision support in connection with data analysis tasks. Then, a brief historical retrospect is given viewing the development of this area of research and its interfaces to knowledge-based approaches. Against this background we illustrate two prototypes of knowledge-based decision support systems for specific data-analysis problems related to fields of interest of our own. Finally, we indicate possible progress and future activities in this area.     

The design of a database for multiperson decision support

The MEDIATOR approach to implementing negotiation support systems views collaborative decisions as complex, evolving data objects to be managed by an extended database management system. The paper outlines the requirements and a rough design for such a DBMS. Specifically, our approach represents decision problems as multispatial mappings that eventually lead from real-world observations to group utilities. Group decision processes are represented as nested transactions which collaboratively design this mapping data structure, controlled by group communication norms represented as integrity constraints. The paper concludes with some implementation considerations, using a conceptual model base management approach.     

油田A类物资采购决策支持系统研究

结合大庆油田物资公司在重要供应链管理环节,即采购、需求和库存中所面临的实际问题(物资需求增加、仓储压力增大、采购成本增多)及三者之间的相互作用,基于预测和优化理论,构建了针对油田A类物资的采购优化和库存管理决策支持系统原型,包括：预测模块、优化模块和方案调整评估模块,为相关部门制定合理物资采购方案提供决策支持.进一步,以银浪仓库中的4种A类物资为例,运用该原型系统进行数值模拟. 结果表明,2009年和2010年4种物资的总成本节省率分别为10.35%和8.07%,效益可观. 考虑到油田物资数据结构不完备及优化模型的复杂性,该原型系统在大庆油田大规模推广方面仍需进一步完善.     

Computerized support systems for emergency decision making

In all fields of human society, occasional emergencies are almost inevitable. Once an emergency occurs, rapid and proper decision making is required. The purpose of this paper is to explore the design and development of computerized support systems for emergency decision making (EDM). First the characteristics of EDM problems are examined. Then, in view of limited human computer rationality, requirements for a computerized support system for EDM are determined. A conceptual structure for knowledge-based distributed emergency decision support systems is proposed. Finally, a prototype system for safety protection and disaster response in coal mines, developed using the proposed structure, is briefly described.This work is partly supported by the State Science and Technology Commission of China, the National Key Laboratory on Industrial Control of China and Fok Ying Tung Education Foundation.     

Integrated risk management for an electricity producer

Electricity suppliers face two sources of risk: uncertainty of spot prices and uncertainty of production costs. Uncertainty in selling the output is usually “solved” by signing forwards and the two sources of risk are dealt with separately. However, managing the integrated risk optimally is the direction we will suggest. We intend to analyse the problem a power producer is confronted to upon acting in a market where spot and forward agreements are available. Since forwards allow to sell production in advance at a given price but do not hedge against cost volatility, the total risk can be reduced by selling also in the spot market. The analysis is further detailed to encompass the spread option inherent in electricity production. We find a benchmark value for forwards to sign and the optimal spot/forward combinations. The analysis is carried out by accounting for market figures for input and output variables in the German market EEX.     

Consistent modeling of risk averse behavior with spectral risk measures

This paper clarifies the relation between decisions of a risk-averse decision maker, based on expected utility theory on the one hand, and spectral risk measures on the other.     

An integrated operation module for individual risk management

In this study, we used the cumulative prospect theory to propose the individual risk management process (IRM) which includes risk analysis and risk response stages. According to an individual’s preferential structure, the process has been developed into an operational module which includes two sub-modules. From this, the individual’s risk level for the confronted risk can be identified from the risk analysis, while the response strategies can be assessed at the risk response stage. Therefore, optimal response strategies can be recommended based on individual risk tolerance levels.     

Cost/risk balanced management of scarce resources using stochastic programming

We consider the situation when a scarce renewable resource should be periodically distributed between different users by a Resource Management Authority (RMA). The replenishment of this resource as well as users demand is subject to considerable uncertainty. We develop cost optimization and risk management models that can assist the RMA in its decision about striking the balance between the level of target delivery to the users and the level of risk that this delivery will not be met. These models are based on utilization and further development of the general methodology of stochastic programming for scenario optimization, taking into account appropriate risk management approaches. By a scenario optimization model we obtain a target barycentric value with respect to selected decision variables. A successive reoptimization of deterministic model for the worst case scenarios allows the reduction of the risk of negative consequences derived from unmet resources demand. Our reference case study is the distribution of scarce water resources. We show results of some numerical experiments in real physical systems.     

What are the actual costs of cyber risk events?

Cyber risks are high on the business agenda of every company, but they are difficult to assess due to the absence of reliable data and thorough analyses. This paper is the first to consider a broad range of cyber risk events and actual cost data. For this purpose, we identify cyber losses from an operational risk database and analyze these with methods from statistics and actuarial science. We use the peaks-over-threshold method from extreme value theory to identify “cyber risks of daily life” and “extreme cyber risks”. Human behavior is the main source of cyber risk and cyber risks are very different compared with other risk categories. Our models can be used to yield consistent risk estimates, depending on country, industry, size, and other variables. The findings of the paper are also useful for practitioners, policymakers and regulators in improving the understanding of this new type of risk.     

Risk management strategies via minimax portfolio optimization

Recent extreme economic developments nearing a worst-case scenario motivate further examination of minimax linear programming approaches for portfolio optimization. Risk measured as the worst-case return is employed and a portfolio from maximizing returns subject to a risk threshold is constructed. Minimax model properties are developed and parametric analysis of the risk threshold connects this model to expected value along a continuum, revealing an efficient frontier segmenting investors by risk preference. Divergence of minimax model results from expected value is quantified and a set of possible prior distributions expressing a degree of Knightian uncertainty corresponding to risk preference determined. The minimax model will maximize return with respect to one of these prior distributions providing valuable insight regarding an investor’s risk attitude and decision behavior. Linear programming models for financial firms to assist individual investors to hedge against losses by buying insurance and a model for designing variable annuities are proposed.     

Systemic risk elicitation: Using causal maps to engage stakeholders and build a comprehensive view of risks

As evidenced through both a historical and contemporary number of reported over-runs, managing projects can be a risky business. Managers are faced with the need to effectively work with a multitude of parties and deal with a wealth of interlocking uncertainties. This paper describes a modelling process developed to assist managers facing such situations. The process helps managers to develop a comprehensive appreciation of risks and gain an understanding of the impact of the interactions between these risks through explicitly engaging a wide stakeholder base using a group support system and causal mapping process. Using a real case the paper describes the modelling process and outcomes along with its implications, before reflecting on the insights, limitations and future research.