Decision Theory

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## Normative and descriptive

## Types of decisions

### Choice under uncertainty

### Intertemporal choice

### Interaction of decision makers

### Complex decisions

## Heuristics

## Alternatives

### Probability theory

### Alternatives to probability theory

### Ludic fallacy

## See also

## References

## Further reading

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

Decision Theory

**Decision theory** (or the **theory of choice** not to be confused with choice theory) is the study of an agent's choices.^{[1]} Decision theory can be broken into two branches: normative decision theory, which analyzes the outcomes of decisions or determines the optimal decisions given constraints and assumptions, and descriptive decision theory, which analyzes *how* agents actually make the decisions they do.

Decision theory is closely related to the field of game theory^{[2]} and is an interdisciplinary topic, studied by economists,
statisticians, psychologists, biologists,^{[3]} political and other social scientists, philosophers,^{[4]} and computer scientists.

Empirical applications of this rich theory are usually done with the help of statistical and econometric methods.

Normative decision theory is concerned with identification of optimal decisions where optimality is often determined by considering an ideal decision maker who is able to compute with perfect accuracy and is in some sense fully rational. The practical application of this prescriptive approach (how people *ought to* make decisions) is called decision analysis and is aimed at finding tools, methodologies, and software (decision support systems) to help people make better decisions.^{[5]}^{[6]}

In contrast, positive or descriptive decision theory is concerned with describing observed behaviors often under the assumption that the decision-making agents are behaving under some consistent rules. These rules may, for instance, have a procedural framework (e.g. Amos Tversky's elimination by aspects model) or an axiomatic framework (e.g. stochastic transitivity axioms), reconciling the Von Neumann-Morgenstern axioms with behavioral violations of the expected utility hypothesis, or they may explicitly give a functional form for time-inconsistent utility functions (e.g. Laibson's quasi-hyperbolic discounting).^{[5]}^{[6]}

The prescriptions or predictions about behavior that positive decision theory produces allow for further tests of the kind of decision-making that occurs in practice. In recent decades, there has also been increasing interest in what is sometimes called "behavioral decision theory" and contributing to a re-evaluation of what useful decision-making requires.^{[7]}^{[8]}

The area of choice under uncertainty represents the heart of decision theory. Known from the 17th century (Blaise Pascal invoked it in his famous wager, 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 will result from each course of action, and multiply the two to give an "expected value", or the average expectation for an outcome; 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 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. In his solution, he defines a utility function and computes expected utility rather than expected financial value (see^{[9]} for a review).

In the 20th century, interest was reignited by Abraham Wald's 1939 paper^{[10]} pointing out that the two central procedures of sampling-distribution-based statistical-theory, namely hypothesis testing and parameter estimation, are special cases of the general decision problem. Wald's paper renewed and synthesized many concepts of statistical theory, including loss functions, risk functions, admissible decision rules, antecedent distributions, Bayesian procedures, and minimax procedures. The phrase "decision theory" itself was used in 1950 by E. L. Lehmann.^{[11]}

The revival 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 subjective probabilities can be used. At the time, von Neumann and Morgenstern's theory of expected utility^{[12]} proved that expected utility maximization followed from basic postulates about rational behavior.

The work of Maurice Allais and Daniel Ellsberg showed that human behavior has systematic and sometimes important departures from expected-utility maximization. The prospect theory of Daniel Kahneman and Amos Tversky renewed the empirical study of economic behavior with less emphasis on rationality presuppositions. Kahneman and Tversky found three regularities - in actual human decision-making, "losses loom larger than gains"; persons focus more on *changes* in their utility-states than they focus on absolute utilities; and the estimation of subjective probabilities is severely biased by anchoring.

Intertemporal choice is concerned with the kind of choice where different actions lead to outcomes that are realised at different points in time. If someone received a windfall of several thousand dollars, they could spend it on an expensive holiday, giving them immediate pleasure, or they could invest it in a pension scheme, giving them 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, the person's life expectancy, and their confidence in the pensions industry. However even with all those factors taken into account, human behavior 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.

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 more often treated under the label of game theory, rather than decision theory, though it involves the same mathematical methods. From the standpoint of game theory, most of the problems treated in decision theory are one-player games (or the one player is viewed as playing against an impersonal background situation). In the emerging field of socio-cognitive engineering, the research is especially focused on the different types of distributed decision-making in human organizations, in normal and abnormal/emergency/crisis situations.^{[13]}

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. Individuals making decisions may be limited in resources or are boundedly rational (have finite time or intelligence); in such cases the issue, more than the deviation between real and optimal behaviour, is the difficulty of determining the optimal behaviour in the first place. One example is the model of economic growth and resource usage developed by the Club of Rome to help politicians make real-life decisions in complex situations^{[]}. Decisions are also affected by whether options are framed together or separately; this is known as the distinction bias. In 2011, Dwayne Rosenburgh explored and showed how decision theory can be applied to complex decisions that arise in areas such as wireless communications.^{[14]}

Heuristics in decision-making is the ability of making decisions based on unjustified or routine thinking. While quicker than step-by-step processing, heuristic thinking is also more likely to involve fallacies or inaccuracies.^{[15]} The main use for heuristics in our daily routines is to decrease the amount of evaluative thinking we perform when making simple decisions, making them instead based on unconscious rules and focusing on some aspects of the decision, while ignoring others.^{[16]} One example of a common and erroneous thought process that arises through heuristic thinking is the Gambler's Fallacy -- believing that an isolated random event is affected by previous isolated random events. For example, if a coin is flipped to tails for a couple of turns, it still has the same probability of doing so; however it seems more likely, intuitively, for it to roll heads soon.^{[17]} This happens because, due to routine thinking, one disregards the probability and concentrates on the ratio of the outcomes, meaning that one expects that in the long run the ratio of flips should be half for each outcome.^{[18]} Another example is that decision-makers may be biased towards preferring moderate alternatives to extreme ones; the *Compromise Effect* operates under a mindset that the most moderate option carries the most benefit. In an incomplete information scenario, as in most daily decisions, the moderate option will look more appealing than either extreme, independent of the context, based only on the fact that it has characteristics that can be found at either extreme.^{[19]}

A highly controversial issue is whether one can replace the use of probability in decision theory by other alternatives.

Advocates for the use of probability theory point to:

- the work of Richard Threlkeld Cox for justification of the probability axioms,
- the Dutch book paradoxes of Bruno de Finetti as illustrative of the theoretical difficulties that can arise from departures from the probability axioms, and
- the complete class theorems, which show that all admissible decision rules are equivalent to the Bayesian decision rule for some utility function and some prior distribution (or for the limit of a sequence of prior distributions). Thus, for every decision rule, either the rule may be reformulated as a Bayesian procedure (or a limit of a sequence of such), or there is a rule that is sometimes better and never worse.

The proponents of fuzzy logic, possibility theory, quantum cognition, Dempster-Shafer theory, and info-gap decision 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; notably, probabilistic decision theory is sensitive to assumptions about the probabilities of various events, while non-probabilistic rules such as minimax are robust, in that they do not make such assumptions.

A general criticism of decision theory based on a fixed universe of possibilities is that it considers the "known unknowns", not the "unknown unknowns"^{[]}: it focuses on expected variations, not on unforeseen events, which some argue have outsized impact and must be considered - significant events may be "outside model". This line of argument, called the ludic fallacy, is that there are inevitable imperfections in modeling the real world by particular models, and that unquestioning reliance on models blinds one to their limits.

- Bayesian statistics
- Causal decision theory
- Choice modelling
- Constraint satisfaction
- Decision making
- Evidential decision theory
- Game theory
- Multi-criteria decision making
- Operations research
- Optimal decision
- Decision quality
- Preference (economics)
- Quantum cognition
- Rationality
- Secretary problem
- Signal detection theory
- Small-numbers game
- Stochastic dominance
- TOTREP
- Two envelopes problem
- Daniel Kahneman
- Prospect theory

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^{a}^{b}MacCrimmon, Kenneth R. "Descriptive and normative implications of the decision-theory postulates."*Risk and uncertainty*. Palgrave Macmillan, London, 1968. 3-32. - ^
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- Akerlof, George A.; Yellen, Janet L. (May 1987). "Rational Models of Irrational Behavior" (PDF).
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(help) - Anand, Paul (1993).
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*The American Economic Review*.**81**(2): 353-9. - Berger, James O. (1985).
*Statistical decision theory and Bayesian Analysis*(2nd ed.). New York: Springer-Verlag. ISBN 978-0-387-96098-2. MR 0804611. - Bernardo JM, Smith AF (1994).
*Bayesian Theory*. Wiley. ISBN 978-0-471-92416-6. MR 1274699. - Clemen, Robert; Reilly, Terence (2014).
*Making Hard Decisions with DecisionTools: An Introduction to Decision Analysis*(3rd ed.). Stamford CT: Cengage. ISBN 978-0-538-79757-3.*(covers normative decision theory)* - De Groot, Morris,
*Optimal Statistical Decisions*. Wiley Classics Library. 2004. (Originally published 1970.) ISBN 0-471-68029-X. - Goodwin, Paul; Wright, George (2004).
*Decision Analysis for Management Judgment*(3rd ed.). Chichester: Wiley. ISBN 978-0-470-86108-0.*(covers both normative and descriptive theory)* - Hansson, Sven Ove. "Decision Theory: A Brief Introduction" (PDF). Archived from the original (PDF) on July 5, 2006.
- 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.
- 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 978-0-9647938-5-9. A rational presentation of probabilistic analysis. - Miller L (1985). "Cognitive risk-taking after frontal or temporal lobectomy--I. The synthesis of fragmented visual information".
*Neuropsychologia*.**23**(3): 359-69. doi:10.1016/0028-3932(85)90022-3. PMID 4022303. - Miller L, Milner B (1985). "Cognitive risk-taking after frontal or temporal lobectomy--II. The synthesis of phonemic and semantic information".
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*An Introduction to Decision Theory*. Cambridge University Press. ISBN 978-0-521-71654-3. - Raiffa, Howard (1997).
*Decision Analysis: Introductory Lectures on Choices Under Uncertainty*. McGraw Hill. ISBN 978-0-07-052579-5. - Robert, Christian (2007).
*The Bayesian Choice*. Springer Texts in Statistics (2nd ed.). New York: Springer. doi:10.1007/0-387-71599-1. ISBN 978-0-387-95231-4. MR 1835885. - Shafer, Glenn; Pearl, Judea, eds. (1990).
*Readings in uncertain reasoning*. San Mateo, CA: Morgan Kaufmann. - Smith, J.Q. (1988).
*Decision Analysis: A Bayesian Approach*. Chapman and Hall. ISBN 978-0-412-27520-3. - Charles Sanders Peirce and Joseph Jastrow (1885). "On Small Differences in Sensation".
*Memoirs of the National Academy of Sciences*.**3**: 73-83.http://psychclassics.yorku.ca/Peirce/small-diffs.htm - Ramsey, Frank Plumpton; "Truth and Probability" (PDF), Chapter VII in
*The Foundations of Mathematics and other Logical Essays*(1931). - de Finetti, Bruno (September 1989). "Probabilism: A Critical Essay on the Theory of Probability and on the Value of Science".
*Erkenntnis*.**31**. (translation of 1931 article) - de Finetti, Bruno (1937). "La Prévision: ses lois logiques, ses sources subjectives".
*Annales de l'Institut Henri Poincaré*.

- de Finetti, Bruno. "Foresight: its Logical Laws, Its Subjective Sources," (translation of the 1937 article in French) in H. E. Kyburg and H. E. Smokler (eds),
*Studies in Subjective Probability,*New York: Wiley, 1964.

- de Finetti, Bruno.
*Theory of Probability*, (translation by AFM Smith of 1970 book) 2 volumes, New York: Wiley, 1974-5. - Donald Davidson, Patrick Suppes and Sidney Siegel (1957).
*Decision-Making: An Experimental Approach*. Stanford University Press. - Pfanzagl, J (1967). "Subjective Probability Derived from the Morgenstern-von Neumann Utility Theory". In Martin Shubik (ed.).
*Essays in Mathematical Economics In Honor of Oskar Morgenstern*. Princeton University Press. pp. 237-251. - Pfanzagl, J. in cooperation with V. Baumann and H. Huber (1968). "Events, Utility and Subjective Probability".
*Theory of Measurement*. Wiley. pp. 195-220. - Morgenstern, Oskar (1976). "Some Reflections on Utility". In Andrew Schotter (ed.).
*Selected Economic Writings of Oskar Morgenstern*. New York University Press. pp. 65-70. ISBN 978-0-8147-7771-8. - Non-Robust Models in Statistics by Lev B. Klebanov, Svetlozat T. Rachev and Frank J. Fabozzi, Nova Scientific Publishers, Inc. New York, 2009.

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