Debiasing

Debiasing ( German for "equalizing") describes a bundle of measures aimed at reducing negative effects caused by cognitive distortions . The term debiasing originally comes from psychology and is composed of the Latin prefix de- (for down, away, down) and English cognitive bias or just bias for cognitive distortion .

Scientific relevance

In addition to psychology (especially decision psychology ), debiasing is also playing an increasingly important role in other scientific areas. This includes in particular those areas of business administration and economics that deal with issues of human behavior and decision-making (see also behavioral accounting, behavioral economics , behavior-oriented financial market theory , marketing ). Debiasing can take place on the level of individuals or groups as well as on the level of organizations or companies. Since cognitive distortions ( English cognitive biases ) in all areas where people make decisions may be relevant, that are in the science discussed approaches to reduce the potentially negative effects of cognitive distortions very diverse. Basically, the debiasing measure chosen should be based on the cognitive distortion to be reduced. For example, while simple mistake by training can be reduced, it has been shown that the hubris (also overconfidence bias mentioned) is not reduced by expertise, but even increased in some cases.

Systematic debiasing in the corporate context

For companies that want to use debiasing to improve the quality of their decisions and the efficiency of their processes, a systematic approach is therefore a good option . Decisions in companies that lead to bad investments due to cognitive distortions can be associated with high direct costs for the company as well as lead to indirect costs through the loss of reputation with customers or employees . Systematic debiasing can help to keep such costs as low as possible, which is why the topic of debiasing is gaining in importance in both science and business practice. In the case of the healthcare industry, where debiaising is also playing an increasingly important role, avoiding misdiagnosis can even save lives. The classic instruments of the principal-agent theory , (monetary) incentives and controls , can be used against cognitive distortions in certain situations, but they quickly reach their limits. In particular, poorly designed incentive and control mechanisms can lead to cognitive distortions that are harmful to the company being amplified or created. This is why further measures are constantly being developed to reduce cognitive distortions. The development of measures should be based on the cognitive distortions relevant to the situation and should be developed in a countercurrent process. For the implementation of debiasing it is particularly important that, in addition to a systematic approach, the specific business context is also considered.

Examples of these measures that companies can use as part of systematic debiasing are:

Devil's advocate (also known as Devil's Advocate ( English Devil's Advocate ))
Mathematical models
Pre-mortem analysis
Justification (result justification or litigation justification)
training
Wisdom of the crowd (compare wisdom of the many )

If a cognitive distortion is used to reduce the effect of another cognitive distortion, one speaks of rebiasing (from the Latin prefix re for back and English cognitive bias or just bias for cognitive distortion).

Individual evidence

↑ ^a ^b Niklas Kreilkamp, Maximilian Schmidt, Arnt Wöhrmann: Increase efficiency by debiasing: recommendations for practice. Retrieved June 24, 2019 .
↑ Jack B. Soll, Katherine L. Milkman, John W. Payne: A User's Guide to Debiasing . In Gideon Keren George Wu (ed.): The Wiley Blackwell Handbook of Judgment and Decision Making, . John Wiley & Sons, 2015, ISBN 978-1-118-46839-5 , pp. 924-951 .
^ Max H. Bazerman, Don A. Moore: Judgment in Managerial Decision Making . 8th ed. Wiley, New Jersey 2013, ISBN 978-1-118-06570-9 .
↑ Peter Scherpereel, Julian Gaul, Martin Muhr: Controlling decision-making behavior in investments . In: Controlling & Management Review . Special issue 2, 2015.
↑ Pat Croskerry, Geeta Singhal, Sílvia Mamede: Cognitive Debiasing 1: Origins of Bias and Theory of Debiasing . In: BMJ Qual Saf Published Online . 2013.
↑ Richard P. Larrick: debiasing . In: Derek J. Koehler, Nigel Harvey (Eds.): Blackwell Handbook of Judgment and Decision Making . Blackwell Publishing, 2004, ISBN 1-4051-0746-4 , pp. 316-338 .

[:0-1] Niklas Kreilkamp, Maximilian Schmidt, Arnt Wöhrmann: Increase efficiency by debiasing: recommendations for practice. Retrieved June 24, 2019 .

[2] Jack B. Soll, Katherine L. Milkman, John W. Payne: A User's Guide to Debiasing . In Gideon Keren George Wu (ed.): The Wiley Blackwell Handbook of Judgment and Decision Making, . John Wiley & Sons, 2015, ISBN 978-1-118-46839-5 , pp. 924-951 .

[3] Max H. Bazerman, Don A. Moore: Judgment in Managerial Decision Making . 8th ed. Wiley, New Jersey 2013, ISBN 978-1-118-06570-9 .

[4] Peter Scherpereel, Julian Gaul, Martin Muhr: Controlling decision-making behavior in investments . In: Controlling & Management Review . Special issue 2, 2015.

[5] Pat Croskerry, Geeta Singhal, Sílvia Mamede: Cognitive Debiasing 1: Origins of Bias and Theory of Debiasing . In: BMJ Qual Saf Published Online . 2013.

[6] Richard P. Larrick: debiasing . In: Derek J. Koehler, Nigel Harvey (Eds.): Blackwell Handbook of Judgment and Decision Making . Blackwell Publishing, 2004, ISBN 1-4051-0746-4 , pp. 316-338 .