Information about Decision Analysis
Decision Analysis (DA) is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing the important aspects of a decision situation, for prescribing the recommended course of action by applying the maximum expected utility action axiom to a well-formed representation of the decision, and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker and other stakeholders.
The term decision analysis was coined in 1964 by Ronald A. Howard, who since then, as a professor at Stanford University, has been instrumental in developing much of the practice and professional application of DA.
Graphical representation of decision analysis problems commonly use influence diagrams and decision trees. Both of these tools represent the alternatives available to the decision maker, the uncertainty they face, and evaluation measures representing how well they achieve their objectives in the final outcome. Uncertainties are represented through probabilities and probability distributions. The decision maker's attitude to risk is represented by utility functions and their attitude to trade-offs between conflicting objectives can be made using multi-attribute value functions or multi-attribute utility functions (if there is risk involved).
Decision analytic methods are used in a wide variety of fields, including business (planning, marketing, and negotiation), environmental remediation, health care research and management, energy exploration, litigation and dispute resolution, etc. However, there is growing concern that these tools do not lead to real improvement in decision making. Some authors [Klien G, 2003. The Power of Intuition. Doubleday, New York.] point out that people don't make decisions this way and that the intuitive style of decision making needs to replace the disaggregated approaches commonly used by most decision analysts. Decision analysts point out that their approach is prescriptive, providing a prescription of what actions to take based on sound logic, rather than a descriptive approach, describing the flaws in the way people do make decisions. Overall a good decision maker should understand both approaches, understanding how people go wrong in making decisions and providing a sound basis for them to make better decisions. Furthermore, several studies conclusively show how even the simplest decision analysis methods are superior to "unaided intuition".[1][2]
It should also be noted that several areas within decision analysis deal with normative results that are provably optimal for specific quantifiable decisions, and for which human intuition alone will almost never be correct or even close to correct. For example, the optimal order scheduling in a manufacturing facility or optimal hedging strategies are purely mathematical and their results are necessarily provable. The term "decision analytic" has often been reserved for "softer" issues that, according to some, may not appear to lend themselves to mathematical optimization methods. Methods like applied information economics, however, attempt to apply more rigorous quantitative methods even to these types of decisions.
The term decision analysis was coined in 1964 by Ronald A. Howard, who since then, as a professor at Stanford University, has been instrumental in developing much of the practice and professional application of DA.
Graphical representation of decision analysis problems commonly use influence diagrams and decision trees. Both of these tools represent the alternatives available to the decision maker, the uncertainty they face, and evaluation measures representing how well they achieve their objectives in the final outcome. Uncertainties are represented through probabilities and probability distributions. The decision maker's attitude to risk is represented by utility functions and their attitude to trade-offs between conflicting objectives can be made using multi-attribute value functions or multi-attribute utility functions (if there is risk involved).
Decision analytic methods are used in a wide variety of fields, including business (planning, marketing, and negotiation), environmental remediation, health care research and management, energy exploration, litigation and dispute resolution, etc. However, there is growing concern that these tools do not lead to real improvement in decision making. Some authors [Klien G, 2003. The Power of Intuition. Doubleday, New York.] point out that people don't make decisions this way and that the intuitive style of decision making needs to replace the disaggregated approaches commonly used by most decision analysts. Decision analysts point out that their approach is prescriptive, providing a prescription of what actions to take based on sound logic, rather than a descriptive approach, describing the flaws in the way people do make decisions. Overall a good decision maker should understand both approaches, understanding how people go wrong in making decisions and providing a sound basis for them to make better decisions. Furthermore, several studies conclusively show how even the simplest decision analysis methods are superior to "unaided intuition".[1][2]
It should also be noted that several areas within decision analysis deal with normative results that are provably optimal for specific quantifiable decisions, and for which human intuition alone will almost never be correct or even close to correct. For example, the optimal order scheduling in a manufacturing facility or optimal hedging strategies are purely mathematical and their results are necessarily provable. The term "decision analytic" has often been reserved for "softer" issues that, according to some, may not appear to lend themselves to mathematical optimization methods. Methods like applied information economics, however, attempt to apply more rigorous quantitative methods even to these types of decisions.
Bibliography
1. ^ Robyn M. Dawes and Bernard Corrigan, ‘‘Linear Models in Decision Making’? Psychological Bulletin 81, no. 2 (1974): 93–106.
2. ^ B. Fischhoff, L. D. Phillips, and S. Lichtenstein, ‘‘Calibration of Probabilities: The State of the Art to 1980,’’ in Judgement under Uncertainty: Heuristics and Biases, ed. D. Kahneman and A. Tversky, (Cambridge University Press, 1982).
2. ^ B. Fischhoff, L. D. Phillips, and S. Lichtenstein, ‘‘Calibration of Probabilities: The State of the Art to 1980,’’ in Judgement under Uncertainty: Heuristics and Biases, ed. D. Kahneman and A. Tversky, (Cambridge University Press, 1982).
- Clemen, Robert, Making Hard Decisions: An Introduction to Decision Analysis, 2nd edition (1996), Belmont CA: Duxbury Press, 1996.
- Goodwin, P., and G. Wright, Decision Analysis for Management Judgment, 3rd edition (2004). Wiley, Chichester. ISBN 0-470-86108-8
- Hammond, J.S. and Keeney, R.L. and Raiffa, H., Smart Choices: A Practical Guide to Making Better Decisions (1999). Harvard Business School Press
- Holtzman, Samuel, Intelligent Decision Systems (1989), Addison-Wesley.
- Howard, R.A., and J.E. Matheson (editors), Readings on the Principles and Applications of Decision Analysis, 2 volumes (1984), Menlo Park CA: Strategic Decisions Group.
- Keeney, R.L.,Value-focused thinking -- A Path to Creative Decisionmaking (1992). Harvard University Press. ISBN 0-674-93197-1
- Matheson, David, and Matheson, Jim, The Smart Organization: Creating Value through Strategic R&D (1998). Harvard Business School Press. ISBN 0-87584-765-X
- Raiffa, Howard, Decision Analysis: Introductory Readings on Choices Under Uncertainty (1997). McGraw Hill. ISBN 0-07-052579-X
- Skinner, David, Introduction to Decision Analysis, 2nd Edition (1999). Probabilistic. ISBN 0-9647938-3-0
- Smith, J.Q., Decision Analysis: A Bayesian Approach (1988), Chapman and Hall. ISBN 0-412-27520-1
- Virine, L. and Trumper M., Project Decisions: The Art and Science (2007). Management Concepts. Vienna, VA, ISBN-13: 978-1-56726-217-0
- Winkler, Robert L, Introduction to Bayesian Inference and Decision, 2nd Edition (2003). Probabilistic. ISBN 0-9647938-4-9
See also
- Decision tree
- Choice
- Decision analysis cycle
- Decision model
- Decision theory
- Decision support
- Influence diagram
- Maximum expected utility
- MORS Military Operations Research Society
- Multiscale decision making
- Multi-criteria decision analysis (MCDA)
- Morphological Analysis
- INFORMS Institute for Operations Research and the Management Sciences
External links
- Decision Analysis, a journal of the Institute for Operations Research and the Management Sciences
- Decision Analysis Society, a subdivision of the Institute for Operations Research and the Management Sciences specializing in Decision Analysis
- Decision Analysis in Health Care Online course from George Mason University providing free lectures and tools for decision analysis modeling in health care settings.
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Philosophy is the discipline concerned with questions of how one should live (ethics); what sorts of things exist and what are their essential natures (metaphysics); what counts as genuine knowledge (epistemology); and what are the correct principles of reasoning (logic).
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The word theory has a number of distinct meanings in different fields of knowledge, depending on their methodologies and the context of discussion.
In common usage, people often use the word theory to signify a conjecture, an opinion, or a speculation.
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In common usage, people often use the word theory to signify a conjecture, an opinion, or a speculation.
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Methodology is defined as
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- "the analysis of the principles of methods, rules, and postulates employed by a discipline" or
- "the development of methods, to be applied within a discipline"
- "a particular procedure or set of procedures". [1].
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professional can be either a person in a profession (certain types of skilled work requiring formal training/education) or in sports (a sportsman/sportwoman doing sports for payment).
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A decision is a final product of a specific mental/cognitive process by an individual or group, which is called decision making, or in more detail, Inactive decision making, Reactive decision making, and Proactive decision making. Therefore it is a subjective concept.
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A procedure is a specification of series of actions, acts or operations which have to be executed in the same manner in order to always obtain the same result in the same circumstances (for example, emergency procedures).
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In economics, utility is a measure of the relative satisfaction or desiredness from consumption of goods. Given this measure, one may speak meaningfully of increasing or decreasing utility, and thereby explain economic behavior in terms of attempts to increase one's utility.
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An action axiom is an axiom that embodies a criterion for recommending action. Action axioms are of the form "IF a condition holds, THEN the following should be done.". Decision theory and, hence, decision analysis are based on the maximum expected utility (MEU) action axiom.
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Decision making is the cognitive process leading to the selection of a course of action among variations. Every decision making process produces a final choice. It can be an action or an opinion. It begins when we need to do something but know not what.
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Stakeholder may refer to:
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- Stakeholder (corporate), a party who affects, or can be affected by, the company's actions
- Stakeholder theory, identifies and models the groups which are stakeholders of a corporation
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Ronald A. Howard has been a professor at Stanford University since 1965. In 1964 He defined the profession of decision analysis, and since then has been developing the field as professor in the Department of Engineering-Economic Systems (now the Department of Management Science and
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Leland Stanford Junior University, commonly known as Stanford University or simply Stanford, is a private university located approximately 37 miles (60 kilometers) southeast of San Francisco and approximately 20 miles (32 km) northwest of San Jose in Stanford,
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Graph may refer to:
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- A chart, a graphic representing tabular data or functions
- A graph used in visualising scientific data, representing the relationship between two or more variables
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An influence diagram (ID) (also called a decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of Bayesian network where not only probabilistic inference problems but also
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In operations research, specifically in decision analysis, a decision tree (or tree diagram) is a decision support tool that uses a graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
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Decision making is the cognitive process leading to the selection of a course of action among variations. Every decision making process produces a final choice. It can be an action or an opinion. It begins when we need to do something but know not what.
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Uncertainty is a term used in subtly different ways in a number of fields, including philosophy, statistics, economics, finance, insurance, psychology, engineering and science.
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Strategic planning is an organization's process of defining its strategy, or direction, and making decisions on allocating its resources to pursue this strategy, including its capital and people.
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Probability is the likelihood that something is the case or will happen. Probability theory is used extensively in areas such as statistics, mathematics, science and philosophy to draw conclusions about the likelihood of potential events and the underlying mechanics of
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probability distribution that assigns a probability to every subset (more precisely every measurable subset) of its state space in such a way that the probability axioms are satisfied.
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Decision making is the cognitive process leading to the selection of a course of action among variations. Every decision making process produces a final choice. It can be an action or an opinion. It begins when we need to do something but know not what.
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In economics, utility is a measure of the relative satisfaction or desiredness from consumption of goods. Given this measure, one may speak meaningfully of increasing or decreasing utility, and thereby explain economic behavior in terms of attempts to increase one's utility.
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Strategic planning is an organization's process of defining its strategy, or direction, and making decisions on allocating its resources to pursue this strategy, including its capital and people.
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In economics, utility is a measure of the relative satisfaction or desiredness from consumption of goods. Given this measure, one may speak meaningfully of increasing or decreasing utility, and thereby explain economic behavior in terms of attempts to increase one's utility.
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Business law
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Business organizations
Basic forms:
Sole proprietorship
Corporation
Partnership
(General · Limited · LLP)
Cooperative
USA:
Business trust · LLC · LLLP
Delaware corporation
Nevada corporation
UK/Commonwealth:
Limited company
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Planning is both the organizational process of creating and maintaining a plan; and the psychological process of thinking about the activities required to create a desired future on some scale.
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For planning in AI, see .
Planning is both the organizational process of creating and maintaining a plan; and the psychological process of thinking about the activities required to create a desired future on some scale.
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Marketing is a social process which satisfies consumers' wants. The term includes advertising, distribution and selling of a product or service. It is also concerned with anticipating the customers' future needs and wants, often through market research.
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Generally, remediation means providing a remedy, so environmental remediation deals with the removal of pollution or contaminants from environmental media such as soil, groundwater, sediment, or surface water for the general protection of human health and the environment or
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