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Decision theory is an interdisciplinary area of study, related to and of interest to practitioners in mathematics, statistics, economics, philosophy, management and psychology. It is concerned with how real decision-makers make decisions, and with how optimal decisions can be reached.

Normative and descriptive decision theoryEdit

Most of decision theory is normative or prescriptive, i.e. it is concerned with identifying the best decision to take, assuming an ideal decision taker who is fully informed, able to compute with perfect accuracy, and fully rational. The practical application of this prescriptive approach (how people should make decisions) is called decision analysis, and aimed at finding tools, methodologies and software to help people make better decisions. The most systematic and comprehensive software tools developed in this way are called decision support systems.

Since it is obvious that people do not typically behave in optimal ways, there is also a related area of study, which is a positive or descriptive discipline, attempting to describe what people will actually do. Since the normative, optimal decision often creates hypotheses for testing against actual behaviour, the two fields are closely linked. Furthermore it is possible to relax the assumptions of perfect information, rationality and so forth in various ways, and produce a series of different prescriptions or predictions about behaviour, allowing for further tests of the kind of decision-making that occurs in practice.

What kinds of decisions need a theory?Edit

Decision theory is only relevant in decisions that are difficult for some reason. A few types of decision have attracted particular attention:

  • riskless choice between incommensurable commodities (commodities that cannot be measured in the same units)
  • choice under uncertainty
  • intertemporal choice - study of the relative value people assign to two or more payoffs at different points in time
  • social decisions

Choice between incommensurable commoditiesEdit

This area is concerned with the decision whether to have, say, one ton of guns and 3 tons of butter, or 2 tons of guns and 1 ton of butter. This is the classic subject of study of microeconomics and is rarely considered under the heading of decision theory, but such choices are often in fact part of the issues that are considered within decision theory. (editor's note: this is not an accepted use of the term 'commensurable'.)

Choice under uncertaintyEdit

This area represents the heartland of decision theory. The procedure now referred to as Expected value was known from the 17th century. Blaise Pascal invoked it in his famous wager (see below), which is contained in his Pensées, published in 1670. The idea of expected value is that, when faced with a number of actions each of which could give rise to more than one possible outcome with different probabilities, the rational procedure is to identify all possible outcomes, determine their values (positive or negative) and the probabilities that they will result from each course of action, and multiply the two to give an expected value. The action to be chosen should be the one that gives rise to the highest total expected value. In 1738 Daniel Bernoulli published an influential paper entitled Exposition of a New Theory on the Measurement of Risk in which he uses the St. Petersburg paradox to show that expected value theory must be normatively wrong. He also gives an example in which a Dutch merchant is trying to decide whether to insure a cargo being sent from Amsterdam to St Petersburg in winter, when it is known that there is a 5% chance that the ship and cargo will be lost. In his solution he defines a utility function and computes expected utility rather than expected financial value.

In the 20th century, interest was reignited by Abraham Wald's 1939 paper pointing out that the two central concerns of orthodox statistical theory at that time - statistical hypothesis testing and statistical estimation theory - could both be regarded as particular special cases of the more general decision problem. This paper introduced much of the mental landscape of modern decision theory, including loss functions, risk functions, admissible decision rules, a priori distributions, Bayes decision rules, and minimax decision rules. The phrase "decision theory" itself was first used in 1950 by E. L. Lehmann.

The rise of subjective probability theory, from the work of Frank Ramsey, Bruno de Finetti, Leonard Savage and others, extended the scope of expected utility theory to situations where only subjective probabilities are available. At this time it was generally assumed in economics that that people behave as rational agents and thus expected utility theory also provided a theory of actual human decision-making behaviour under risk. The work of Maurice Allais and Daniel Ellsberg showed that this was clearly not so. The prospect theory of Daniel Kahneman and Amos Tversky placed behavioural economics on a more evidence-based footing. It emphasised that in actual human (as opposed to normatively correct) decision-making "losses loom larger than gains", people are more focused on changes in their utility states than the states themselves and estimation of subjective probabilities is severely biased by anchoring.

Alternatives to ProbabilityEdit

A highly controversial issue is whether one can replace the use of probability in decision theory by other alternatives. The proponents of fuzzy logic, possibility theory and Dempster-Shafer theory maintain that probability is only one of many alternatives and point to many examples where non-standard alternatives have been implemented with apparent success. Advocates of probability theory point to

  • the Dutch book paradoxes of Bruno de Finetti as illustrative of the theoretical difficulties that can arise from departures from the probability axioms and to
  • the complete class theorems which show that all admissible decision rules are equivalent to a Bayesian decision rule with some prior distribution (possibly improper) and some utility function. Thus, for any decision rule generated by non-probabilistic methods either there is an equivalent rule derivable by Bayesian means, or there is a rule derivable by Bayesian means which is never worse and (at least) sometimes better.

Intertemporal choiceEdit

This area is concerned with the kind of choice where different actions lead to outcomes that are realised at different points in time. If I receive a windfall of several thousand dollars, I could spend it on an expensive holiday, giving me immediate pleasure, or I could invest it in a pension scheme, giving me an income at some time in the future. What is the optimal thing to do? The answer depends partly on factors such as the expected rates of interest and inflation, my life expectancy, and my confidence in the pensions industry. However even with all those factors taken into account, human behaviour again deviates greatly from the predictions of prescriptive decision theory, leading to alternative models in which, for example, objective interest rates are replaced by subjective discount rates.

Social decisionsEdit

Some decisions are difficult because of the need to take into account how other people in the situation will respond to the decision that is taken. The analysis of such social decisions is the business of game theory, and is not normally considered part of decision theory, though it is closely related.

Complex decisionsEdit

Other areas of decision theory are concerned with decisions that are difficult simply because of their complexity, or the complexity of the organization that has to make them. In such cases the issue is not the deviation between real and optimal behaviour, but the difficulty of determining the optimal behaviour in the first place. The Club of Rome, for example, developed a model of economic growth and resource usage that helps politicians make real-life decisons in complex situations.

Paradox of choiceEdit

Observed in many cases is the paradox that more choices may lead to a poorer decision or a failure to make a decision at all. It is sometimes theorized to be caused by analysis paralysis, real or perceived, or perhaps from rational ignorance. A number of researchers including Dr. Sheena S. Iyengar, now of Columbia, and Dr. Mark R. Lepper, of Stanford have published studies on this phenomenon. (Goode, 2001)

See alsoEdit


  • Paul Goodwin and George Wright, Decision Analysis for Management Judgment, 3rd edition. Chichester: Wiley, 2004 ISBN 0-470-86108-8 (covers both normative and descriptive theory)
  • Robert Clemen. Making Hard Decisions: An Introduction to Decision Analysis, 2nd edition. Belmont CA: Duxbury Press, 1996. (covers normative decision theory)
  • D.W. North. "A tutorial introduction to decision theory". IEEE Trans. Systems Science and Cybernetics, 4(3), 1968. Reprinted in Shafer & Pearl. (also about normative decision theory)
  • Glenn Shafer and Judea Pearl, editors. Readings in uncertain reasoning. Morgan Kaufmann, San Mateo, CA, 1990.
  • Howard Raiffa Decision Analysis: Introductory Readings on Choices Under Uncertainty. McGraw Hill. 1997. ISBN 0-07-052579-X
  • Morris De Groot Optimal Statistical Decisions. Wiley Classics Library. 2004. (Originally published 1970.) ISBN 0-471-68029-X.
  • Khemani , Karan, Ignorance is Bliss: A study on how and why humans depend on recognition heuristics in social relationships, the equity markets and the brand market-place, thereby making successful decisions, 2005.
  • J.Q. Smith Decision Analysis: A Bayesian Approach. Chapman and Hall. 1988. ISBN 0-412-27520-1
  • Akerlof, George A. and Janet L. YELLEN, Rational Models of Irrational Behavior
  • Arthur, W. Brian, Designing Economic Agents that Act like Human Agents: A Behavioral Approach to Bounded Rationality
  • James O. Berger Statistical Decision Theory and Bayesian Analysis. Second Edition. 1980. Springer Series in Statistics. ISBN 0-387-96098-8.
  • Goode, Erica. (2001) In Weird Math of Choices, 6 Choices Can Beat 600. The New York Times. Retrieved May 16, 2005.
  • Anderson, Barry F. The Three Secrets of Wise Decision Making. Single Reef Press. 2002. ISBN 0-9722177-0-3.
  • Sentient Insight: a behavioral psychology blog A blog on a broad range of psychological issues including:

consumer subconscious, neuromarketing, human motivation & emotion, and decision science.

Further readingEdit

  • Kenrick, D. T., Li, N. P., & Butner, J. (2003). Dynamical evolutionary psychology: Individual decision rules and emergent social norms. Psychological Review, 110, 3-28. Full text
Edit General subfields and scientists in Cybernetics
K1 Polycontexturality, Second-order cybernetics
K2 Catastrophe theory, Connectionism, Control theory, Decision theory, Information theory, Semiotics, Synergetics, Sociosynergetics, Systems theory
K3 Biological cybernetics, Biomedical cybernetics, Biorobotics, Computational neuroscience, Homeostasis, Medical cybernetics, Neuro cybernetics, Sociocybernetics
Cyberneticians William Ross Ashby, Claude Bernard, Valentin Braitenberg, Ludwig von Bertalanffy, George S. Chandy, Joseph J. DiStefano III, Heinz von Foerster, Charles François, Jay Forrester, Buckminster Fuller, Ernst von Glasersfeld, Francis Heylighen, Erich von Holst, Stuart Kauffman, Sergei P. Kurdyumov, Niklas Luhmann, Warren McCulloch, Humberto Maturana, Horst Mittelstaedt, Talcott Parsons, Walter Pitts, Alfred Radcliffe-Brown, Robert Trappl, Valentin Turchin, Francisco Varela, Frederic Vester, John N. Warfield, Kevin Warwick, Norbert Wiener
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