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Decision Science (DS) originated in Great Britain during World War II, when mathematical or quantitative approaches were used to solve logistic problems during military operations. Since then, it has evolved to be applicable to the management of all aspects of a system, product, or service. It is now considered an important input to decision-making in a wide variety of applications in business, industry, and government. The growing complexity of management since the 1940s has necessitated the development of sophisticated mathematical techniques for planning and decisionmaking. DS involves the quantitative evaluation of alternative policies, plans, and decisions and has become centered in the structured decision-making process cycle. It may also be called Operations Research (OR, American), Operational Research (OR, United Kingdom), Systems Science, Mathematical Modeling, Industrial Engineering, Critical Systems Strategic Thinking, Success Science (SS), and Systems Analysis and Design.
The study of DS involves the application of mathematical methods and tools for solving problems relating to the allocation of scarce resources subject to certain constraints. It contributes to the understanding of human decision-making as well as the development of methods and tools of analysis. Usually the problems deal with determining the least cost or greatest profit, subject to constraints such as some required quantities, capacity to manufacture or store, and available resources over a large number of variables.
The fundamental part of DS modeling is the “systems approach” to problem solving that indicates that the context of organizational problems is as important as the stated problem. The modeling process helps to improve operations through the use of scientific methods and the development of specialized techniques. It often involves defining a problem, collecting information, making decisions based on them, taking action, monitoring and evaluating the results of the implementation, and checking for new problems iteratively. There are two approaches to the decision process-sequential model or nonsequential model. The sequential model requires following certain linear steps, while the nonsequential model has certain phases that have a circular relationship. Orville G. Brim and others proposed one of the early sequential models in Personality and Decision Processes, Studies in the Social Psychology of Thinking (1962). They proposed following six steps as part of the methodology: Identification of the problem, obtaining necessary information, production of possible solutions, evaluation of such solutions, selection of a strategy for performance, and implementation of the decision.
However, a more realistic model should allow the various parts of the decision process to vary in order. One of the most accepted nonsequential models was proposed by Mintzberg, Raisinghani, and Theoret in 1976 in “The Structure of ‘Unstructured’ Decision Processes,” Administrative Sciences Quarterly. They identified the decision process to have three phases: identification, development, and selection. In this model, one may cycle through one or all of the phases in any order until an acceptable solution is found.
Identification and Diagnosis
The identification phase consists of identifying the problems or opportunities (decision recognition routine) and diagnosis routine-using the existing information and identifying new information to clarify and define issues. The development phase defines and clarifies the options and involves two steps-a search routine to find ready-made solutions and a design routine at developing new solutions or modifying existing ones. The selection phase involves three routines: a screen routine, to eliminate subobtimal alternatives; an evaluation-choice routine, to evaluate different alternatives and use judgment, bargaining, and analysis; and an authorization routine, to gain approval for the solution selected.
Most of the environmental problems are complex in nature; this makes the DS approach appear the most suitable for achieving suitable decisions. In the context of the environment, DS involves methodical procedures for integrating information about physical and social phenomenon, environmental processes, available options, the effects of different options on environmental and social conditions, and human values.
Decision science often helps to improve the decision process as it helps in making explicit judgments about information (environmental policy) that involves diverse, conflicting, and changing values with scientific uncertainty. It involves participants in making the decisions, and therefore makes it potentially more acceptable and causes less contention among the participants.
Conversely, however, since many or most environmental decisions explicitly or implicitly involve reallocation of control or rights to environmental goods or services, or the control or shifting of externalities, benefits, or risks, critics charge that DS merely creates a depoliticized gloss for inherently political decisions. So too, as the science of risk analysis and perception has evolved, the importance of effective or emotional components of decision-making have become better understood. Their incorporation into DS remains somewhat unclear. For these reasons, while DS remains a potentially important component for environmental decision making, its role is not without concern or controversy.
- Garry D. Brewer and Paul Stern, eds., “Decision Making for the Environment: Social and Behavioral Science Research Priorities,” Panel on Social and Behavioral Science Research Priorities for Environmental Decision Making, Committee on the Human Dimensions of Global Change (National Research Council, 2005);
- Robert T. Clemen, Making Hard Decisions: An Introduction to Decision Analysis (PWS Co., 1991);
- John Lawrence, Jr. and Barry A. Pasternack, Applied Management Science: A Computer-Integrated Approach for Decision-Making (John Wiley & Sons, 2002).