Decision Analysis
Decision analysis is a systematic, quantitative approach to decision-making under uncertainty. It involves identifying and evaluating alternative courses of action, assessing the potential outcomes and their probabilities, and selecting the best option based on a rational and logical framework. Decision analysis utilizes mathematical models and tools such as decision trees, influence diagrams, and probability distributions to support decision-making processes.
6 Key excerpts on "Decision Analysis"
eBook - ePubEnterprise Project Portfolio Management
Building Competencies for R&D and IT Investment Success
- Richard Bayney, Ram Chakravarti(Authors)
- 2012(Publication Date)
- J. Ross Publishing(Publisher)
...Appendix A A Primer in Decision Analysis A.1 Introduction to Decision Analysis and Its Utility in Decision Making Decision Analysis is a quantitative discipline that follows a structured process to enable the identification and assessment of a range of alternative solutions to a problem after which a dominant solution may emerge. It is best utilized under the following conditions: • The problem has several potential solutions, none of which may appear to be readily dominant. • The problem can be decomposed into a series of sequential investments during which time information may be gathered to enable better informed downstream decisions. • The entire problem requires relatively high investments over a protracted time horizon for which there may be significant uncontrollable risks and uncertain outcomes. In many organizations, traditional decision making has been based largely on a combination of experience, judgment, heuristics, advocacy, and of course analysis. The extent to which quantitative analysis has shaped informed decision making has ranged from its use as the principal guide to its relegation to the role of no more than a supporting data point. Far too often, qualitative decision criteria (e.g., brand image, strategic positioning) are commingled with quantitative attributes (e.g., gross profit, ROI), creating the perception of a holistic decision when, in fact, quantitative value is foregone. A decision that leads to acceptable value creation tends to be made and all parties feel satisfied that, after an appropriate investment of time and energy for discussion and deliberation, a good decision has been rendered. Because intuition is difficult to audit for completeness and logical consistency and defensibility of thought (Skinner, 1999), it is not evident that better decisions created by superior intuition could have emerged from the existing information...
eBook - ePub- Gregory S. Parnell, Terry Bresnick, Steven N. Tani, Eric R. Johnson(Authors)
- 2013(Publication Date)
- Wiley(Publisher)
...Ralph Keeney of Duke University (Keeney, 1982) provides an intuitive and a technical definition. Keeney’s intuitive definition is “a formalization of common sense for decision problems that are too complex for informal use of common sense.” His technical definition is “a philosophy, articulated by a set of logical axioms, and a methodology and collection of systematic procedures, based on those axioms, for responsibly analyzing the complexities inherent in decision problems.” Professor Larry Phillips of the London School of Economics emphasizes that Decision Analysis is a socio-technical process to provide insights to decision makers in organizations (Phillips et al., 1990) and (Phillips, 2005). In a popular Decision Analysis textbook, Clemen and Reilly state that “Decision Analysis provides effective methods for organizing a problem into a structure that can be analyzed. In particular, elements of a decision’s structure include the possible courses of action, the possible outcomes that could result, the likelihood of those outcomes, and eventual consequences (e.g., costs and benefits) to be derived from the different outcomes” (Clemen & Reilly, 2001). We will use the following definition of Decision Analysis: Decision Analysis is a philosophy and a social-technical process to create value for decision makers and stakeholders facing difficult decisions involving multiple stakeholders, multiple (possibly conflicting) objectives, complex alternatives, important uncertainties, and significant consequences. Decision Analysis is founded on an axiomatic decision theory and uses insights from the study of decision making. In Decision Analysis, we distinguish between a good decision and a good outcome. A good decision is one that is logically consistent with our preferences for the potential outcomes, our alternatives, and our assessment of the uncertainties. A good outcome is the occurrence of a favorable event—one that we like...
eBook - ePub- Andrew P. Sage, William B. Rouse(Authors)
- 2011(Publication Date)
- Wiley-Interscience(Publisher)
...28 Decision Analysis CRAIG W. KIRKWOOD 28.1 INTRODUCTION Over the last several decades, a philosophy and a body of techniques have been developed to assist in analyzing decisions, and this approach has been used successfully in a wide variety of systems engineering and management situations. The underlying assumption governing the approach is that many significant decisions in complex systems are made through a process involving technical staff and management as well as interested outsiders. Therefore, a key to good decision making is to provide structured methods for incorporating the information, opinions, and preferences of the various relevant people into the decision-making process. A systematic approach to quantitative Decision Analysis includes the following steps: 1. Specify objectives and scales for measuring achievement with respect to these objectives. 2. Develop alternatives that potentially might achieve the objectives. 3. Determine how well each alternative achieves each objective. 4. Consider trade-offs among the objectives. 5. Select the alternative that, on balance, best achieves the objectives, taking into account uncertainties. In the remainder of this chapter we consider these steps in more detail. 28.2 STRUCTURING OBJECTIVES This section reviews procedures for developing and organizing objectives to use in analyzing decisions. Methods are first presented that can be used to develop a qualitative structure for objectives, and then ways are presented to develop quantitative scales that measure the degree of attainment of these objectives. To address these issues, it is useful to define some terminology: Evaluation Consideration. Evaluation considerations are significant enough to be taken into account while evaluating alternatives...
- Reinette Biggs, Alta de Vos, Rika Preiser, Hayley Clements, Kristine Maciejewski, Maja Schlüter, Reinette Biggs, Alta de Vos, Rika Preiser, Hayley Clements, Kristine Maciejewski, Maja Schlüter(Authors)
- 2021(Publication Date)
- Routledge(Publisher)
...Methods for analysing systems – directly informing decision-making 29 Decision Analysis based on optimisation Anne-Sophie Crépin 1 and Stephen Polasky 2 1 BEIJER INSTITUTE OF ECOLOGICAL ECONOMICS, THE ROYAL SWEDISH ACADEMY OF SCIENCES, STOCKHOLM, SWEDEN 2 UNIVERSITY OF MINNESOTA, MINNEAPOLIS, MINNESOTA, USA DOI: 10.4324/9781003021339-35 Key methods discussed in this chapter Mathematical programming, optimal control theory, game theory, decision theory, cost-benefit analysis, multi-criteria Decision Analysis Connections to other chapters Methods for data generation and systems scoping (Chapters 5 – 8), futures analysis (Chapter 10), scenario development (Chapter 11), dynamical systems modelling (Chapter 26), state-and-transition modelling (Chapter 27), agent-based modelling (Chapter 28) and other methods for analysing systems (Chapters 30 – 32) can be used to inform decision processes. Controlled behavioural experiments (Chapter 21) can help to evaluate potential impacts of decisions. The methods in this chapter can help to model people’s behaviour in futures analysis, scenario development, dynamical systems modelling and agent-based modelling. Introduction Decision Analysis is a systematic approach to evaluating information about alternative choices, when multiple options are possible, with many possible outcomes and different trade-offs. In social-ecological systems (SES), multiple types of decisions (policy, management, private, other) – all with different objectives – influence the social, economic and ecological dimensions, making it hard to compare across alternatives. Decision Analysis can analyse these situations and their impacts on individual actors, society and the rest of the system. The objective of a decision can, for example, be related to maximising measures of human well-being (‘utility’) or reaching a particular target, such as remaining below a maximum level of pollution, reducing inequality or conserving biodiversity...
eBook - ePub- D.J. White(Author)
- 2018(Publication Date)
- Routledge(Publisher)
...CHAPTER 7 Mathematical Models and Decision From our point of view any model which can add any decidability content to selection processes comes within the area of mathematical models. Thus, for example, as is the case in queueing theory, any analysis which allows one to deduce the probabilities of certain events from other probabilities is allowable, even though this may not be carried any further in the analysis of the final selection. We would include simulation studies because they analyze the effect of various decision rules even though the final choice may be left to the person himself without any prior analysis of his values. The simulation procedure is simply a physical means of solving a given mathematical problem, which has first of all to be developed from the real system before simulation can be carried out. It will not be the purpose of this report to give a detailed coverage of the mathematical models involved. These can be found in the appropriate references. The purpose is simply to ascertain the manner in which decision is introduced into selective processes by the use of mathematical models. The essential mathematical content of our analysis lies in the manner in which the mathematical model, once derived, is processed to get the solution. We have mentioned simulation. However, the real power of mathematics lies in the ability to derive computational algorithms for characteristic models, which are as effective as possible. These algorithms effectively allow many more alternatives to be considered than would be possible by direct enumeration in the absence of a mathematical model. Optimization is always relative to the problem specified. Thus, although there may exist alternatives which are better than the one eventually selected, this does not invalidate its optimality relative to the initial problem. If higher level optima are required then their attainment must be allowed for in the original problem...
eBook - ePub- Adil H. Mouhammed(Author)
- 2015(Publication Date)
- Routledge(Publisher)
...CHAPTER NINE Decision Analysis In the real world, economists, managers, and other individuals confront many problems that force them to make decisions. For example, an individual with $1000 must decide whether to invest this money in government bonds or in shares issued by IBM. Another individual may have to decide whether to go to graduate school or to find a job. Given these problems, a decision maker also wishes to know the conditions under which one must make a decision. Is the situation under condition of risk, uncertainty, certainty, or conflict? Knowing the type of condition is important, because there are criteria that decision makers use in these particular circumstances. In this chapter we shall discuss various decision criteria under several conditions, except under condition of conflicts, which will be discussed in chapter 10 : Game Theory. Before discussing these criteria, students should know the difference between a risky and an uncertain condition. A risky condition indicates that a decision maker is able to know the probability distribution of events prevailing under this condition. An uncertain condition reflects a situation in which the probability distribution and hence the probability of event(s) cannot be obtained. In other words, the decision maker just does not know. Formulation of Decision Problems To formulate a decision problem, three components must be specified. First, the decision maker should list all the alternative courses of action of which one decision or strategy (course of action) will be selected. For example, a producer may have to decide whether to come out with a new product, say a car, or to continue manufacturing a truck—the producer does not face any other decision...
