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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.

Contents

History and Methodology

The term decision analysis was coined in 1964 by Ronald A. Howard[1], 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). In some cases, utility functions can be replaced by the probability of achieving uncertain aspiration levels. Decision analysis advocates choosing that decision whose consequences have the maximum expected utility (or which maximize the probability of achieving the uncertain aspiration level). Such 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.

Controversy

There is growing concern that these tools do not lead to real improvement in decision making. Some authors[2] 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".[3][4]

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.

See also

References

  1. ^ Howard, Ronald A., "Decision Analysis: Applied Decision Theory," Proceedings of the 4th International Conference on Operational Research (1966) 55-77 PDF
  2. ^ Klein G, 2003. The Power of Intuition. Doubleday, New York.
  3. ^ Robyn M. Dawes and Bernard Corrigan, ‘‘Linear Models in Decision Making’’ Psychological Bulletin 81, no. 2 (1974): 93–106.
  4. ^ 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).

Further reading

  • 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
  • Leach, Patrick, Why Can't You Just Give Me the Number? An Executive's Guide to Using Probabilistic Thinking to Manage Risk and to Make Better Decisions (2006). Probabilistic. ISBN 0-964-79385-7.
  • 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
  • Shi H, Lyons-Weiler J. 2007. Clinical decision modeling system. BMC Med Inform Decis Mak. 2007 Aug 13;7(1):23
  • 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 978-1-56726-217-0
  • Winkler, Robert L, Introduction to Bayesian Inference and Decision, 2nd Edition (2003). Probabilistic. ISBN 0-9647938-4-9
  • Alemi F, Gustafson D, Decision Analysis for Healthcare Managers (2006) Health Administration Press. ISBN 978-1567932560
  • Fineberg, Harvey V.; Weinstein, Milton C. (1980). Clinical decision analysis. Philadelphia: Saunders. ISBN 0-7216-9166-8.  

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.
  • Decision Analysis Affinity Group, DAAG, and informal group of DA practitioners who have a Conference annually to discuss ideas, best practices, etc. DAAG was started in 1995 by Tom Spradlin, John Palmer, and David Skinner.

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 important aspects of a decision, for prescribing a 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.

Contents

History and Methodology

The term decision analysis was coined in 1964 by Ronald A. Howard[1], 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). In some cases, utility functions can be replaced by the probability of achieving uncertain aspiration levels. Decision analysis advocates choosing that decision whose consequences have the maximum expected utility (or which maximize the probability of achieving the uncertain aspiration level). Such 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.

Controversy

Decision researchers studying how individuals research decisions have found that decision analysis is rarely used [2]. High-stakes decisions, made under time pressure, are not well described by decision analysis.[3] Some decision analysts, in turn,[4] argue 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. Critics cite the phenomenon of paralysis by analysis as one possible consequence of over-reliance on decision analysis in organizations.

Studies have demonstrated the utility of decision analysis in creating decision-making algorithms that are superior to "unaided intuition".[5][6]

Some areas within decision analysis deal with normative results that are provably optimal for specific quantifiable decisions[citation needed]. 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 decisions that do 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.

See also

References

  1. ^ Howard, Ronald A. (1966). "Decision Analysis: Applied Decision Theory" (PDF). Proceedings of the 4th International Conference on Operational Research. pp. 55–77. http://decision.stanford.edu/library/ronald-a.-howard/Decision%20Analysis-%20Applied%20Decision%20Theory.pdf/view. 
  2. ^ Klein G (2003). The Power of Intuition. New York: Doubleday. ISBN 0385502893. 
  3. ^ Klein G (1999). Sources of Power. Boston, MA: MIT Press. ISBN 0262112272. 
  4. ^ Keeney R (2002). Value Focused Thinking: A Path to Creative Decisionmaking. ISBN 0674931971. 
  5. ^ Robyn M. Dawes and Bernard Corrigan (1974). [Expression error: Unexpected < operator "Linear Models in Decision Making"]. Psychological Bulletin 81 (2): 93–106. 
  6. ^ B. Fischhoff, L. D. Phillips, and S. Lichtenstein (1982). "Calibration of Probabilities: The State of the Art to 1980". In D. Kahneman and A. Tversky. Judgement under Uncertainty: Heuristics and Biases. Cambridge University Press. 

Further reading

  • Clemen, Robert (1996). Making Hard Decisions: An Introduction to Decision Analysis (2nd ed.). Belmont CA: Duxbury Press. ISBN 0534260357. 
  • Goodwin, P., and G. Wright (2004). Decision Analysis for Management Judgment (3rd ed.). Chichester: Wiley. ISBN 0-470-86108-8. 
  • Hammond, J.S., Keeney, R.L. and Raiffa, H. (1999). Smart Choices: A Practical Guide to Making Better Decisions. Harvard Business School Press. ISBN 0585310750. 
  • Holtzman, Samuel (1989). Intelligent Decision Systems. Addison-Wesley. ISBN 0201116022. 
  • Howard, R.A., and J.E. Matheson, ed (1984). Readings on the Principles and Applications of Decision Analysis. Menlo Park CA: Strategic Decisions Group. ISBN 0962307408. 
  • Keeney, R.L. (1992). Value-focused thinking—A Path to Creative Decisionmaking. Harvard University Press. ISBN 0-674-93197-1. 
  • Leach, Patrick (2006). Why Can't You Just Give Me the Number? An Executive's Guide to Using Probabilistic Thinking to Manage Risk and to Make Better Decisions. Probabilistic. ISBN 0-964-79385-7. 
  • Matheson, David, and Matheson, Jim (1998). The Smart Organization: Creating Value through Strategic R&D. Harvard Business School Press. ISBN 0-87584-765-X. 
  • Raiffa, Howard (1997). Decision Analysis: Introductory Readings on Choices Under Uncertainty. McGraw Hill. ISBN 0-07-052579-X. 
  • Shi H, Lyons-Weiler J (2007). "Clinical decision modeling system". BMC Med Inform Decis Mak 7: 23. doi:10.1186/1472-6947-7-23. PMID 17697328. PMC 2131745. http://www.biomedcentral.com/1472-6947/7/23. 
  • Skinner, David (1999). Introduction to Decision Analysis (2nd ed.). Probabilistic. ISBN 0-9647938-3-0. 
  • Smith, J.Q. (1988). Decision Analysis: A Bayesian Approach. Chapman and Hall. ISBN 0-412-27520-1. 
  • Virine, L. and Trumper M. (2007). Project Decisions: The Art and Science. Vienna, VA: Management Concepts. ISBN 978-1-56726-217-0. 
  • Winkler, Robert L (2003). Introduction to Bayesian Inference and Decision (2nd ed.). Probabilistic. ISBN 0-9647938-4-9. 
  • Alemi F, Gustafson D (2006). Decision Analysis for Healthcare Managers. Health Administration Press. ISBN 978-1567932560. 
  • Fineberg, Harvey V.; Weinstein, Milton C. (1980). Clinical decision analysis. Philadelphia: Saunders. ISBN 0-7216-9166-8. 

External links

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