From the special issue editor.(Editorial)

This is a special issue organized to showcase a range of interesting and successful applications in the field of Decision Sciences for the Review of Business. The Decision Sciences discipline applies a scientific approach, and mathematical modeling methodology on computers, to yield solutions in supporting a wide spectrum of decision-making activities. In the eyes of professionals, it is a field that is connected almost interchangeably with the fields of Management Science, Operations research, Information Science and Quantitative Analysis. The growing popularity of the Decision Sciences discipline may be evidenced by an article, "Higher Math Delivers Formula for Success--Businesses Turn to Algorithms to Solve Complex Problems," that was headlined in the Money section of USA Today on December 31, 1977. According to U.S. Labor Department projections, the demand for Decision Sciences and Information Systems professionals is expected to grow dramatically in the next 10 years.

Given the recent and ongoing sub-prime mortgage debacle, it is imperative that the conscientious and conservative investor use robust mathematical models instead of investing in financial instruments using a "seat of the pants" approach. In their timely article "The Sub-prime Mortgage Debacle and What We Can Learn from Mathematical Programs," Robert Fireworker, Gavriel Yarmish and Harry Nagel discuss an approach to value a mortgage-backed security, and methods for using these programs for asset allocation. The authors first describe the difference between a deterministic and stochastic instrument, and then describe a linear programming model for allocating bonds deterministically and then, as in the case of mortgage-backed securities, stochastically. These models are suitable for the conservative investors who would like to take advantage of securities that offer decent returns, but who at the same time need to be sure that their obligations are met. The paper also provides a number of real-world business examples, illustrating the applicability of stochastic linear programming techniques to both past and current real-world practices.

For an organization to survive in today's complex international business environment, it must constantly evaluate how well it is functioning, and make whatever changes are necessary in response to new challenges. These tasks must be carried out with no disruption in corporate productivity. In "Strategic Analysis: Approaching Continuous Improvement Proactively," Lisa B. Ncube and Mara H. Wasburn discuss the need for an ongoing assessment process to identify and clarify the challenges facing an organization. Using Strategic Analysis, the company can then determine what courses of action are available to it, to confront these challenges most effectively. This is an ongoing process for continuous corporate improvement.

Reliability is defined as the ability of a system or component to perform its required functions under stated conditions for a specified period of time, and it is often reported or expressed in terms of a probability. In his paper "Assessing Redundancy's Impact on the Reliability of Microcontroller/ Processor-Based Systems in Mission Critical Applications," Michael I. Liechenstein analyzes the absolute and relative mission reliability of the microcontroller/processor-based systems as a function of redundancy level for linked, parallel units operating in either inactive or active standby modes under constant hazard rates. The paper studies two dynamical models--inactive standby units and active redundant units--and it reveals noteworthy insights regarding the impact of redundancy on overall system mission reliability.

In 1977, the U.S. Congress passed the Community Reinvestment Act (CRA) with an aim intended to reduce discriminatory credit practices against low- and moderate-income neighborhoods, a practice known as 'redlining.' CRA is politically motivated and is designed to encourage banking institutions to meet the needs of borrowers in all segments of their communities. Specifically, federal regulatory bodies can use the degree of CRA compliance to approve or disapprove the applications for new branches, or for mergers or acquisitions of banks. In his paper "Problems with the Application of a Metric for CRA Compliance in Banking," Andrew Russakoff discusses some of the ways that the government has attempted to implement CRA compliance, what this reveals about the implicit model for investment, and how the metric for CRA compliance might be modified to better address the issue. He reveals that the creation of a metric for CRA compliance is not an easy task due to intriguing political factors or influences. In particular, he uses the two models (Treatment Effects and Probit Model) suggested by Dahl, Evanoff, and Spivey (for the Federal Reserve Bank) for detecting the sensitivity of bank mortgage loans to CRA down grading. This is an important example because it may reveal the weakness of introducing measurement standards in a situation which straddles two very different sets of values: the economic and the political.

Faced with the unprecedented debacles of large financial institutions, the severe freezing of credit markets and huge losses of investors' pensions and investment portfolios, Manuel Russon's paper on measuring the Value at Risk (VaR) of an investment portfolio is timely and valuable. Value at Risk (VaR) was introduced in the 1980's and is defined as the largest portfolio loss that could be sustained in any given period of time for a given level of confidence. The purpose of VaR is to provide an analyst, an executive, a risk manager, or a regulator with a guide to appraise the risk of an enterprise at any particular moment, as well as the trends in the risk of an entire enterprise. In his paper, "The Intuition and Methodology of Value at Risk," Russon presents four different methods used to compute the magnitude of VaR for a given of confidence, each with varying degrees of complexity. The four methods are: Historical VaR and Historical Parametric, Parametric VaR, Simulated Historical VaR and Simulated Parametric VaR. Each method relies on a set of historical data as inputs for the computations and the difference between the methods lies in what is done with the historical data to arrive at a VaR. It is evident that VaR is an attempt by management to model human behavior as instigated by real economic, geopolitical, environmental phenomena affecting markets and it amounts to sophisticated guesswork. The author cautions about the use of applying VaR to measure a portfolio or an enterprise as it has limitations due to its heuristic nature, and is subject to attendant assumptions and complications as represented by skewness, leptokurtosis and outliers (especially), and conditional volatility and conditional correlation.

Input-output analysis investigates inter- or cross-industry relations in an economy by studying how the output of one industry is used by another industry where it serves as an input. In essence, industries interact with each other, with each industry acting both as customer of output and as supplier of inputs. In his paper "Input-Output Methodology Used for Forecasting Purposes--A Cost Analysis," A. Vasilopoulos proposes a methodology for extending the static (open-loop) model to a dynamic and recursive model that are suitable for forecasting. A cost function is defined and derived for the basic and extended models and shown to be "relatively reasonable" when compared to the cost of the static model, thus making the Input-Output methodology a useful forecasting tool.

Friedman, Friedman and Pollack investigate what scientists in each discipline can learn from models of other fields. They create a framework that will go a long way towards providing a structure for further interdisciplinary scholarly work; a way for scholars from different areas of study to establish common language and thereby develop new paradigms for research in this age of disciplinary convergence. "The Role of Modeling in Scientific Disciplines: A Taxonomy" provides an examination of the various models, and reveals that there is indeed some commonality in the way very different fields process information. Each model is a view of reality; it has a purpose; and it employs abstraction, structure and information hiding. In addition, each model alters reality to some degree. Scholars working in one discipline, by understanding how models are used in other areas of study, will be able to develop new types of models and thus ultimately gain a better understanding of their own discipline.

Dr. F. Victor Lu Special Editor Chair of CIS/DS Department

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