Editing Decision analysis (section)

==Methodology==

Framing is the front end of decision analysis, which focuses on developing an opportunity statement (what and why), boundary conditions, success measures, a decision hierarchy, strategy table, and action items.  It is sometimes believed that the application of decision analysis always requires the use of quantitative methods.  In reality, however, many decisions can be made using qualitative tools that are part of the decision-analysis toolbox, such as value-focused thinking,<ref name=":0">{{cite book|author=Keeney R|title=Value Focused Thinking: A Path to Creative Decisionmaking|year=2002|publisher=Harvard University Press |isbn=0-674-93197-1}}</ref> without the need for quantitative methods.

The framing process may lead to the development of an [[influence diagram]] or [[decision tree]].  These are commonly used [[Diagram|graphical]] representations of decision-analysis problems. These graphical tools are used to represent the alternatives available to the [[Decision-making|decision maker]], the [[uncertainty|uncertainties]] they involve, and how well the decision maker's [[goal|objectives]] would be achieved by various final outcomes. They can also form the basis of a quantitative model when needed.  For example, quantitative methods of conducting [[Bayesian inference]] and identifying [[Optimal_decision |optimal decisions]] using influence diagrams were developed in the 1980s,<ref>{{cite journal |last=Shachter |first=R.D. |title=Evaluating influence diagrams |journal=Operations Research |volume=34 |issue=6 |pages=871–882 |date=November–December 1986 |doi=10.1287/opre.34.6.871 |url=http://or.journal.informs.org/content/34/6/871.full.pdf+html |format=PDF |url-access=subscription }}{{Dead link|date=January 2024 |bot=InternetArchiveBot |fix-attempted=yes }}</ref><ref>{{cite journal |last=Shachter |first=R.D. |title=Probabilistic inference and influence diagrams |journal=Operations Research |volume=36 |issue=4 |pages=589–604 |date=July–August 1988 |doi=10.1287/opre.36.4.589 |url=http://or.journal.informs.org/content/36/4/589.full.pdf+html |format=PDF |hdl=10338.dmlcz/135724 |hdl-access=free }}{{Dead link|date=January 2024 |bot=InternetArchiveBot |fix-attempted=yes }}</ref> and are now incorporated in software.

In a quantitative decision-analysis model, uncertainties are represented through [[probability|probabilities]] -- specifically, [[Probability_interpretations#Subjectivism|subjective probabilities]]. The decision maker's attitude to risk is represented by [[utility|utility functions]], and the attitude to trade-offs between conflicting [[goal|objectives]] can be expressed using multi-attribute value functions or [[Multi-attribute_utility|multi-attribute utility functions]] (if there is risk involved). (In some cases, utility functions can be replaced by the probability of achieving an uncertain aspiration level or "target".)<ref>{{cite journal |last1=Bordley |first1=R. |first2=M. |last2=LiCalzi |title=Decision Analysis Using Targets Instead of Utility Functions |journal=Decisions in Economics and Finance |year=2000 |volume=23 |issue=1 |pages=53–74 |doi=10.1007/s102030050005 |hdl=10278/3610 |s2cid=11162758 |hdl-access=free }}</ref><ref>{{cite journal |last1=Bordley |first1=R. |first2=C. |last2=Kirkwood |title=Multiattribute preference analysis with Performance Targets |journal=Operations Research |year=2004 |volume=52 |issue=6 |pages=823–835 |doi=10.1287/opre.1030.0093 }}</ref> Based on the axioms of decision analysis, the best decision to choose is the one whose consequences have the maximum expected utility (or that maximizes the probability of achieving the uncertain aspiration level).

It is sometimes assumed that quantitative decision analysis can be applied only to factors that lend themselves easily to measurement (e.g., in natural units such as dollars).  However, quantitative decision analysis and related methods, such as applied information economics, can also be applied even to seemingly intangible factors.