Decision theory

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Decision theory (or the theory of choice) is a branch of applied

probabilities to various factors and assigning numerical consequences to the outcome.[1]

There are three branches of decision theory:

  1. Normative decision theory: Concerned with the identification of optimal decisions, where optimality is often determined by considering an ideal decision-maker who is able to calculate with perfect accuracy and is in some sense fully rational.
  2. Prescriptive decision theory: Concerned with describing observed behaviors through the use of conceptual models, under the assumption that those making the decisions are behaving under some consistent rules.
  3. Descriptive decision theory: Analyzes how individuals actually make the decisions that they do.

Decision theory is a broad field from management sciences and is an interdisciplinary topic, studied by management scientists, medical researchers, mathematicians, data scientists, psychologists, biologists,[2] social scientists, philosophers[3] and computer scientists.

Empirical applications of this theory are usually done with the help of statistical and discrete mathematical approaches from computer science.

Normative and descriptive

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 calculate 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.[4][5]

In contrast, descriptive decision theory is concerned with describing observed behaviors often under the assumption that those making decisions are behaving under some consistent rules. These rules may, for instance, have a procedural framework (e.g.

utility functions (e.g. Laibson's quasi-hyperbolic discounting).[4][5]

Prescriptive decision theory is concerned with predictions about behavior that positive decision theory produces to allow for further tests of the kind of decision-making that occurs in practice. In recent decades, there has also been increasing interest in "behavioral decision theory", contributing to a re-evaluation of what useful decision-making requires.[6][7]

Types of decisions

Choice under uncertainty

Further information: Expected utility hypothesis

The area of choice under uncertainty represents the heart of decision theory. Known from the 17th century (

expected utility rather than expected financial value.[8]

In the 20th century, interest was reignited by

E. L. Lehmann.[10]

The revival of

expected utility[11]
proved that expected utility maximization followed from basic postulates about rational behavior.

The work of

anchoring
.

Intertemporal choice

Main article: Intertemporal choice

Intertemporal choice is concerned with the kind of choice where different actions lead to outcomes that are realised at different stages over time.[14] It is also described as cost-benefit decision making since it involves the choices between rewards that vary according to magnitude and time of arrival.[15] 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.

Interaction of decision makers

Military planners often conduct extensive simulations
to help predict the decision-making of relevant actors.

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 often treated under decision theory, though it involves mathematical methods. 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.[16]

Complex decisions

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 are limited in resources (i.e. time and intelligence) and are therefore boundedly rational; the issue is thus, more than the deviation between real and optimal behaviour, the difficulty of determining the optimal behaviour in the first place. Decisions are also affected by whether options are framed together or separately; this is known as the distinction bias.

Heuristics

Main article:
Heuristics in judgment and decision-making
A ball inside a spinning roulette wheel
The gambler's fallacy: even when the roulette ball repeatedly lands on red, it is no more likely to land on black the next time.

Heuristics are procedures for making a decision without working out the consequences of every option. Heuristics decrease the amount of evaluative thinking required for decisions, focusing on some aspects of the decision while ignoring others.[17] While quicker than step-by-step processing, heuristic thinking is also more likely to involve fallacies or inaccuracies.[18]

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 flips of a fair coin give repeated tails, the coin still has the same probability (i.e., 0.5) of tails in future turns, though intuitively it might seems that heads becomes more likely.[19] In the long run, heads and tails should occur equally often; people commit the gambler's fallacy when they use this heuristic to predict that a result of heads is "due" after a run of tails.[20] 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.[21]

Alternatives

A highly controversial issue is whether one can replace the use of probability in decision theory with something else.

Probability theory

Advocates for the use of probability theory point to:

Alternatives to probability theory

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, whereas non-probabilistic rules, such as minimax, are robust in that they do not make such assumptions.

Ludic fallacy

Main article: Ludic fallacy

A general criticism of decision theory based on a fixed universe of possibilities is that it considers the "known unknowns", not the "

unknown unknowns":[22] 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.

See also

Wikiquote has quotations related to Decision theory.

References

  1. ^ "Decision theory Definition and meaning". Dictionary.com. Retrieved 2022-04-02.
  2. PMID 28379950
    . Retrieved 2022-04-02.
  3. ^ Hansson, Sven Ove. "Decision theory: A brief introduction." (2005) Section 1.2: A truly interdisciplinary subject.
  4. ^
    OCLC 231114
    .
  5. ^
    hdl:1794/22385
    .
  6. ISBN 0-19-823303-5
    .
  7. doi:10.1037/0096-3445.114.2.133
    .
  8. JSTOR 2724488
    .
  9. MR 0000932
    .
  10. JSTOR 2236552
    .
  11. ^ Neumann Jv, Morgenstern O (1953) [1944]. Theory of Games and Economic Behavior (third ed.). Princeton, NJ: Princeton University Press.
  12. ISBN 9789048183548
    .
  13. ISBN 9781912303687
    .
  14. ISBN 9783642644993
    .
  15. ISBN 9780124171558
    .
  16. ^ Crozier, M. & Friedberg, E. (1995). "Organization and Collective Action. Our Contribution to Organizational Analysis" in Bacharach S.B, Gagliardi P. & Mundell P. (Eds). Research in the Sociology of Organizations. Vol. XIII, Special Issue on European Perspectives of Organizational Theory, Greenwich, CT: JAI Press.
  17. PMID 28557503
    .
  18. doi:10.1287/mnsc.31.4.395
    .
  19. PMID 11381834
    .
  20. PMID 24549140
    .
  21. S2CID 9432630
    .
  22. ^ Feduzi, A. (2014). "Uncovering unknown unknowns: Towards a Baconian approach to management decision-making". Decision Processes. 124 (2): 268–283.

Further reading

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.
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