Decision Analysis

9 Key excerpts on "Decision Analysis"

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  eBook - ePub

  Operations Research

  A Practical Introduction

  • Michael Carter, Camille C. Price, Ghaith Rabadi(Authors)
  • 2018(Publication Date)
  • Chapman and Hall/CRC

    (Publisher)

  9 Decision Analysis 9.1      The Decision-Making Process

  Decision Analysis is as much of an art as a science. Mathematical Decision Analysis must be considered in the context of an individual decision-maker. The techniques that have been developed in this area can be described as tools that encourage and assist people in making rational decisions. They are not intended as substitutes for the individual. Most of the techniques incorporate some interactive dialogue with the decision-maker to try to determine personal preferences and attitudes.

  To truly appreciate this interaction, it is useful to try to imagine actually being faced with a particular problem. To illustrate this idea, consider the decision to buy a new car. We can easily develop a set of criteria that define a good car (price, mileage, maintenance, horsepower, etc.), and then we can devise a system of weights that measures the relative importance of each criterion. The car with the highest score is clearly the one to buy. Most people would agree that this sounds like a reasonable model. They might even be willing to recommend this selection to someone else. But imagine for a moment that you are making a decision concerning your own car. Would you be willing to accept the advice of this model without question? In fact, the majority of intelligent decision-makers tend to have reservations about accepting a strict mathematical interpretation and recommendation for their problem.

  Decision Analysis differs from the mathematical structure of many other areas of Operations Research in that it contains a high degree of uncertainty. The uncertainty is, in part, a by-product of any long range planning function. Traditional Operations Research problems in production planning and inventory analysis, for example, are concerned with a monthly sales forecast that may vary according to some probability distribution. In Decision Analysis, we may be deciding whether to develop and market a new product, build a new plant, or create a new government agency, or diversify our business interests. For example, the demand for an existing product next month is relatively predictable in most industries, but the demand for a new and unfamiliar product in five years’ time is virtually impossible to estimate. Such issues as these can have a major impact, and an analysis of the effect of any current decision will not be fully appreciated for five or ten years into the future. The factors that must be considered in the decision process often involve a dramatic degree of uncertainty simply by virtue of the extended time frame.

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  Operations and Supply Chain Management

  • Roberta S. Russell, Bernard W. Taylor(Authors)
  • 2023(Publication Date)
  • Wiley

    (Publisher)

  For example, the demand for a product may not be 100 units next week but may vary between 0 and 200 units, depending on the state of the market, which is uncertain. Decision Analysis is a set of quantitative decision-making techniques to aid the decision maker in dealing with a deci- sion situation in which there is uncertainty. However, the usefulness of Decision Analysis for decision making is also a beneficial topic to study because it reflects a structured, system- atic approach to decision making that many decision makers follow intuitively without ever consciously thinking about it. Decision Analysis represents not only a collection of decision- making techniques but also an analysis of logic underlying decision making. A decision-making situation includes several components, the decisions themselves and the events that may occur in the future, known as states of nature. Future states of nature may be high or low demand for a product or good or bad economic conditions. At the time a decision is made, the decision maker is uncertain which state of nature will occur in the future and has no control over these states of nature. When probabilities can be assigned to the occurrence of states of nature in the future, the situation is referred to as decision making under risk. When probabilities cannot be assigned to the occurrence of future events, the situation is called decision making under uncertainty. We discuss the latter case next. To facilitate the analysis of decision situations, they are organized into payoff tables. A payoff table is a means of organizing and illustrating the payoffs from the different decisions, given the various states of nature, and has the general form shown in Table S1.1. Each decision, 1 or 2, in Table S1.1 will result in an outcome, or payoff, for each state of nature that will occur in the future.

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  Decision Based Design

  • Vijitashwa Pandey(Author)
  • 2013(Publication Date)
  • CRC Press

    (Publisher)

  More importantly, he or she should not be overwhelmed by the many decision variables, attributes, and sources of uncertainty but try to make a structured decision founded on a defensible set of rules. Decision Analysis helps us do just that. Recall that there is an emphasis placed on the term normative in Decision Analysis. It qualifies the field as being normative or prescriptive, as in what a decision maker should do, as opposed to what a decision maker would do. This distinction is important because being able to make a rational decision has little to do with our instincts, which most people use to make everyday decisions. For everyday decisions it is not critical to be able to make the best possible decision; it may not even be advisable to pursue it, considering that the effort may not be justified. However, when decisions involve extreme outcomes, such as possible loss or gain of large amounts of money, loss of life, and impact on many people, we need methods that can help us make good defensible decisions under uncertainty. Uncertainty in engineering and sciences has a specific meaning. It refers to a physically discernible mathematical quantity that does not always take a fixed value, but one from a range of values called realizations. We generally do not talk about what the weather is going to be like tomorrow; instead, ask “What will be the temperature in degrees Celsius at 3 p.m.?” or “What will be the relative humidity at noon?” This helps in clearly defining what aspect of the uncertainty we are dealing with. It also breaks down a complicated input like weather into its constituents, whose effects can be measured on an engineering system. Furthermore, having a mathematically measureable number allows us to use sophisticated tools from probability theory. Generally, uncertainty about a quantity is expressed in terms of statis-tics, for example, mean value and standard deviation. But to get a complete

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  Making Hard Decisions with DecisionTools

  • Robert Clemen, Terence Reilly(Authors)
  • 2013(Publication Date)
  • Cengage Learning EMEA

    (Publisher)

  Much of this book will focus on decomposing problems to understand their struc-tures and to measure uncertainty and value; indeed, decomposition is the key to Decision Analysis. The approach is to “ divide and conquer. ” The first level of decomposition calls for structuring the problem in smaller and more manageable pieces. Subsequent decomposition by the decision maker may entail careful consideration of elements of uncertainty in different parts of the problem or careful thought about different aspects of the objectives. The idea of modeling is critical in Decision Analysis, as it is in most quan-titative or analytical approaches to problems. As indicated in Figure 1.1, we will use models in several ways. We will use influence diagrams or decision trees to create a representation or model of the decision problem. Probability will be used to build models of the uncertainty inherent in the problem. Hier-archical and network models will be used to understand the relationships among multiple objectives, and we will assess utility functions in order to model the way in which decision makers value different outcomes and trade off competing objectives. These models are mathematical and graphical in nature, allowing one to find insights that may not be apparent on the surface. Of course, a key advantage from a decision-making perspective is that the mathematical representation of a decision can be subjected to analysis, which can help us understand key drivers in the problem, for example, or identify a preferred alternative, Decision Analysis is typically an iterative process. Once a model has been built, sensitivity analysis is performed. Such analysis answers “ what if ” ques-tions: “ If we make a slight change in one or more aspects of the model, does the optimal decision change? ” If so, the decision is said to be sensitive to these small changes, and the decision maker may wish to reconsider more carefully those aspects to which the decision is sensitive.

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  Value of Information in the Earth Sciences

  Integrating Spatial Modeling and Decision Analysis

  • Jo Eidsvik, Tapan Mukerji, Debarun Bhattacharjya(Authors)
  • 2015(Publication Date)
  • Cambridge University Press

    (Publisher)

  Finally, we outline some bibliographic notes in Section 3.5. 3.1 Background Decision Analysis stands on the solid foundation built over hundreds of years of thought regarding decision making under uncertainty – its fundamental notions can be traced back to some of the pioneers of probability theory – for instance, Bernoulli, Bayes, and Laplace. The term “Decision Analysis” was coined by Ronald Howard (1964) to define the field of study that logically evaluates available alternatives in a decision situation once the uncer- tainties involved and the preferences of the decision maker have been recognized. The first book that used the phrase “Decision Analysis” in the title is by Raiffa (1968). Figure 3.2 shows a fundamental distinction between various fields of study pertaining to decision making, categorizing them into one of three types: (a) descriptive domains are those that effectively describe behavior around how people make decisions, (b) norma- tive ones are those that lay down a set of norms (or axioms), and (c) prescriptive fields of study regarding decision making are concerned with methods and tools for helping people make better decisions. Decision Analysis is a prescriptive approach with the intent of guid- ing a decision maker through a potentially opaque decision situation, leading him or her to clarity of action through a process of assessing his or her beliefs and preferences. It is based on the normative principles of decision theory.

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  Quantitative Methods for Business

  • David Anderson, Dennis Sweeney, Thomas Williams, Jeffrey Camm(Authors)
  • 2015(Publication Date)
  • Cengage Learning EMEA

    (Publisher)

  Decision Analysis CONTENTS 4.1 PROBLEM FORMULATION Influence Diagrams Payoff Tables Decision Trees 4.2 DECISION MAKING WITHOUT PROBABILITIES Optimistic Approach Conservative Approach Minimax Regret Approach 4.3 DECISION MAKING WITH PROBABILITIES Expected Value of Perfect Information 4.4 RISK ANALYSIS AND SENSITIVITY ANALYSIS Risk Analysis Sensitivity Analysis 4.5 Decision Analysis WITH SAMPLE INFORMATION Influence Diagram Decision Tree Decision Strategy Risk Profile Expected Value of Sample Information Efficiency of Sample Information 4.6 COMPUTING BRANCH PROBABILITIES WITH BAYES’ THEOREM APPENDIX 4.1 USING ANALYTIC SOLVER PLATFORM TO CREATE DECISION TREES CHAPTER 4 Copyright 2016 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 104 Chapter 4 Decision Analysis Decision Analysis can be used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain or risk-filled pattern of future events. For example, Ohio Edison used Decision Analysis to choose the best type of particulate control equipment for coal-fired generating units when it faced future uncertainties concerning sul- fur content restrictions, construction costs, and so on. The State of North Carolina used deci- sion analysis in evaluating whether to implement a medical screening test to detect metabolic disorders in newborns. The Q.M. in Action, Natural Resource Management, discusses the use of Decision Analysis to evaluate alternative actions to protect endangered species.

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  Spreadsheet Modeling and Decision Analysis

  A Practical Introduction to Business Analytics

  • Cliff Ragsdale(Author)
  • 2021(Publication Date)
  • Cengage Learning EMEA

    (Publisher)

  30, pp. 1487–1497, 2003. Clemen, R. Making Hard Decisions: An Introduction to Decision Analysis. Mason, OH: Cengage Learning, 2001. Corner, J. and C. Kirkwood. “Decision Analysis Applications in the Operations Research Literature 1970–1989.” Operations Research, vol. 39, no. 2, 1991. Coyle, R. Decision Analysis. London: Nelson, 1972. Hastie, R. and R. Dawes. Rational Choice in an Uncertain World: The Psychology of Judgment and Decision Making. Thou- sand Oaks, CA: Sage, 2001. Heian, B. and J. Gale. “Mortgage Selection Using a Decision-Tree Approach: An Extension.” Interfaces , vol. 18, July–August 1988. Howard, R.A., “Decision Analysis: Practice and Promise.” Management Science, vol. 34, pp. 679–695, 1988. Keeney, R. Value-Focused Thinking. Cambridge, MA: Harvard University Press, 1992. Keeney, R. and H. Raiffa. Decision with Multiple Objectives. New York: Wiley, 1976. Merkhofer, M.W. “Quantifying Judgmental Uncertainty: Methodology, Experiences & Insights.” IEEE Transactions on Systems, Man, and Cybernetics, vol. 17, pp. 741–752, 1987. Skinner, D. Introduction to Decision Analysis: A Practitioner’s Guide to Improving Decision Quality. Gainesville, FL: Probabilistic Publishing, 2001. Wenstop, F. and A. Carlsen. “Ranking Hydroelectric Power Projects with Multicriteria Decision Analysis.” Interfaces, vol. 18, no. 4, 1988. Zahedi, F. “The Analytic Hierarchy Process—A Survey of the Method and Its Applications.” Interfaces, vol. 16, no. 4, 1986. THE WORLD OF BUSINESS ANALYTICS Decision Theory Helps Hallmark Trim Discards Many items distributed by Hallmark Cards, Incorporated can be sold only during a single season. Leftovers, or discards, must then be disposed of outside normal dealer channels. For example, table items such as napkins can be used in the company’s cafe- teria, donated to charity, or sold without the brand name to volume discounters. Other items have no salvage value at all. (Continued) Copyright 2022 Cengage Learning.

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  Probability, Statistics, And Decision Making In The Atmospheric Sciences

  • Allan Murphy, Richard W. Katz(Authors)
  • 2019(Publication Date)
  • CRC Press

    (Publisher)

  The model can also be used to investigate the advisability of purchasing additional information (e.g., additional forecasts) before making a final deci-sion, and the sensitivity of the results to changes in various inputs (e.g., probabilities, outcomes) can be investigated. Although not all decision-making models involve the use of Bayesian techniques, it is often quite important in such models to revise probabilities as new information is obtained. For example, 493 494 situations involving sequences of decisions to be made over time, with information being received between decisions, necessitate proba-bility revision. As a result, the development of methods for modeling decision-making problems under uncertainty has to some degree paralleled the development of Bayesian methods. The book by Raiffa and Schlaifer (1961) provides an example of such parallel development. Modeling of decision-making problems under uncertainty has often been labeled statistical decision theory, or simply deci-sion theory. In recent years, the term Decision Analysis, which is sometimes taken to suggest a more applied orientation than decision theory, but which deals essentially with the same problems and methods, has gained favor. Thus, Decision Analysis is used in this chapter to connote the modeling of decision-making problems under uncertainty. The purpose of this chapter is to provide an introduction to Decision Analysis. For more extensive developments of decision anal-ysis, see Raiffa (1968), Schlaifer (1969), Halter and Dean (1971), Lindley (1971), Winkler (1972). Brown et al. (1974), Moore and Thomas (1976). Anderson et al. (1977). LaValle!~78). Holloway (1979). and Keeney (1982). --The outline of this chapter is as follows. The elements of Decision Analysis (actions. events. probabilities, consequences. utilities) and the structuring of these elements in the form of deci-sion tables and decision trees are presented in Section 2.

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  Business Analytics

  Data Analysis & Decision Making

  • S. Albright, Wayne Winston, S. Albright, Wayne Winston, , (Authors)
  • 2019(Publication Date)
  • Cengage Learning EMEA

    (Publisher)

  Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. 6-2 Elements of Decision Analysis 2 4 5 4. There is uncertainty about which outcome will occur, and probabilities of the possible outcomes are assessed. 5. For each decision and each possible outcome, a payoff is received or a cost is incurred. 6. A “best” decision must be chosen using an appropriate decision criterion. We now discuss these elements in some generality. 1 Identifying the Problem When something triggers the need to solve a problem, you should think carefully about the problem that needs to be solved before diving in. Perhaps you are just finishing your undergrad-uate degree (the trigger), and you want to choose the Business School where you should get your MBA degree. You could define the problem as which MBA program you should attend, but maybe you should define it more generally as what you should do next now that you have your undergraduate degree. You don’t necessarily have to enter an MBA program right away. You could get a job and then get an MBA degree later, or you could enter a graduate program in some area other than Business. Maybe you could even open your own business and forget about graduate school. The point is that by changing the problem from deciding which MBA program to attend to deciding what to do next, you change the decision problem in a fundamental way. Possible Decisions The possible decisions depend on the previous step: how the problem is specified. But after you identify the problem, all possible decisions for this problem should be listed.

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