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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. 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.
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