Introduction Theory Heuristics Biases Conclusion
Enhanced Decision Making (Heuristics and Biases)
Dr Markus Weinmann
University of Liechtenstein [email protected]
October 9, 2015
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 1 [Source: http://bernews.com/2011/04/locals-urged-to-become-organ-donors/] Introduction Theory Heuristics Biases Conclusion
Organ Donation
Figure 1: Effective consent rates by country. Explicit consent (opt-in, gold) and presumed consent (opt- out, blue).
[Johnson and Goldstein, 2003] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 3 Introduction Theory Heuristics Biases Conclusion
Choice Architecture
I ”what is chosen often depends upon how the choice is presented” [Johnson, et al., 2012], p. 488.
I ”choice architects [deliberately organize] the context in which people make decisions” [Thaler et al., 2010], p. 2.
I people act in a boundedly rational way [Simon, 1955]
I decision making is influenced by various heuristics and biases [Tversky and Kahneman, 1974]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 4 Introduction Theory Heuristics Biases Conclusion
Decision Theories Decision Theories
Prescriptive Decision Theory (Rational Model)
I Assumes an ideal decision maker who is fully informed and fully rational
I It is concerned with the questions how people should make decisions
I Decision analyisis
I The theory informs the design of decision support systems
[Eisenf¨uhret al., 2010]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 5 Introduction Theory Heuristics Biases Conclusion
Decision Theories Prescriptive Decision Theory
A Rational Decision Making Process 1. Define the problem (e.g., buying a new car) 2. Identify criteria (e.g., minimize cost, maximize comfort,...) 3. Weight the criteria (define a scoring system, e.g., dollars) 4. Generate alternatives (define courses of action) 5. Rate each alternative on each criterion 6. Compute the optimal decision
[Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 6 Figure 2: Herbert Simon Figure 3: Daniel Kahneman
[Source: http://www.nobelprize.org] Introduction Theory Heuristics Biases Conclusion
Decision Theories Decision Theories
Descriptive Decision Theory (Behavioral Model)
I Assumes an decision maker who is not fully informed and acts in a bounded rational way
I It is concerned with the questions how people do make
I Decision making
I The theory informs the design of choices
[Eisenf¨uhret al., 2010]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 8 [Source: http://www.nytimes.com/2011/11/27/books/review/ thinking-fast-and-slow-by-daniel-kahneman-book-review.html] Introduction Theory Heuristics Biases Conclusion
Dual-Process Theory Attributes of Dual-Process Theory
System 1 System 2 Fast Slow Automatic Controlled Experience-based decision making Abstract Independent of cognitive ability Correlated with cognitive ability Emotional Logical Biased responses Normative responses Intuitive Analytical Heuristic Systematic
Table 1: Attributes Frequently Associated With Dual-Process
[Evans et al., 2013]
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Dual-Process Theory Which Table is Longer?
Figure 4: Visual illusion, two tables
[Shepard, 1990] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 11 Introduction Theory Heuristics Biases Conclusion
Heuristics
Definition Heuristics are commonly defined as simple rules of thumb used when making judgments or decisions and can influence decision making in various ways. Heuristics can lead to judgement errors or biases. Common Heuristics
I Representativeness
I Availability
I Anchoring and Adjustment
[Bazerman and Moore, 2009, Hutchinson and Gigerenzer, 2013, Tversky and Kahneman, 1974]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 12 Introduction Theory Heuristics Biases Conclusion
Common Heuristics
People tend to look for traits an I Representativeness individial may have that represents previously formed I Availability stereotypes, when making a I Anchoring and judgment about an individual (or Adjustment object or event). [Tversky and Kahneman, 1974]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 13 Introduction Theory Heuristics Biases Conclusion
Common Heuristics
Judgments and decisions are I Representativeness likely to be influenced by the availability of thoughts, or ”the I Availability ease with which instances [i.e., I Anchoring and thoughts of products] can be Adjustment brought to mind” (p. 1127). [Tversky and Kahneman, 1974]
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Common Heuristics
I Representativeness People tend to use an initial piece of information as anchor, I Availability and adjust later judgments or I Anchoring and decisions around that anchor. [Tversky and Kahneman, 1974] Adjustment
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 15 Introduction Theory Heuristics Biases Conclusion
Biases Related To Availability Heuristic
Problem: Causes of Death Please rank order the following causes of death in the United States between 1990 and 2000, placing a 1 next to the most common cause, 2 next to the second most common, etc. Then, guess the frequency.
I Tobacco
I Poor diet and physical inactivity
I Motor vehicle accidents
I Firearms (guns)
I Illicit drug use
[Bazerman and Moore, 2009]
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Biases Related To Availability Heuristic Bias: Ease of Recall (based on vividness and recency)
Explanation People tend to underestimate the magnitude of differences between causes of death, due to vividness:
I Tobacco: 435,000
I Poor diet and physical inactivity: 400,000
I Motor vehicle accidents: 43,000
I Firearms (guns): 29,000
I Illicit drug use: 17,000
[Bazerman and Moore, 2009, Mokdad et al., 2004]
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Biases Related To Availability Heuristic Problem: Words with ”a”
Estimate
I Estimate the percentage of words in the English language that begin with the letter ”a.”
I Estimate the percentage of words in the English language that have the letter ”a” as their third letter.
[Bazerman and Moore, 2009]
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Biases Related To Availability Heuristic Bias: Retrievability Bias
Explanation People likely estimate that the number of words beginning with ”a” exceed the number of words with ”a” as the third letter.
This is because people are better at retrieving words beginning with the letter ”a” than words with ”a” as the third letter.
(Solution: 6% and 9%)
[Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 19 Introduction Theory Heuristics Biases Conclusion
Biases Related To Availability Heuristic The Availability Heuristic
Ease of Recall Bias Retrievability Bias
I Vividness influences I Ease of retrieval from recall memory
I Estimating death rates I Words with ”a”
I Airline security decisions I ”n” as sixth letter versus I Familiarity of names ending in ”ing”
I Performance evaluations I Geographic concentration of similar businesses
I Hiring decisions
[Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 20 Introduction Theory Heuristics Biases Conclusion
Biases Related To Representativeness Heuristic Problem: Down Syndrome
Lisa is 33 and is pregnant for the first time. She is worried about birth defects such as Down syndrome. Her doctor tells her that she need not worry too much because there is only a 1 in 1,000 chance that a woman of her age will have a baby with Down syndrome.
Nevertheless, Lisa remains anxious about this possibility and decides to obtain a test, known as the Triple Screen, which can detect Down syndrome. The test is moderately accurate: When a baby has Down syndrome, the test delivers a positive result 86% of the time. There is, however, a small ”false positive” rate: 5% of babies produce a positive result despite not having Down syndrome.
Lisa takes the Triple Screen and obtains a positive result for Down syndrome. Given this test result, what are the chances that her baby has
Down syndrome? [Bazerman and Moore, 2009] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 21 Introduction Theory Heuristics Biases Conclusion
Biases Related To Representativeness Heuristic Bias: Insensitivity to Base Rate
Solution to Down Syndrom Problem
I Per 1,000 women Lisas age, 999 do not have a baby with Down syndrome.
I 5% of women with a baby that does not have Down syndrome will receive a false positive test.
I 999 * .05 = 49.95 women per 1,000 receive a false positive.
I 86% of babies with Down syndrome test positive the first time.
I .86*1 = .86 per 1,000 women with a Down syndrome baby receive an accurate positive test
I .86/(.86 + 49.95) = 1.7% of women who receive a positive test have a baby with Down syndrome
Dr Markus Weinmann University of Liechtenstein Enhanced[Bazerman Decision and Making Moore, 2009] 22 Introduction Theory Heuristics Biases Conclusion
Biases Related To Representativeness Heuristic Problem: Boys Are Back in Town
A certain town is served by two hospitals. In the larger hospital, about 45 babies are born each day. In the smaller hospital, about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage of boys born varies from day to day. Sometimes it may be higher than 50 percent, sometimes lower. For a period of one year, each hospital recorded the days in which more than 60 percent of the babies born were boys. Which hospital do you think recorded more such days? [Bazerman and Moore, 2009]
I The larger hospital
I The smaller hospital
I About the same (within 5 percent of each other)
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Biases Related To Representativeness Heuristic Bias: Insensitivity to Sample Size
Solution to Boys Are Back in Town Problem People tend to fail to account for sample sizes. Because the smaller hospital has fewer babies born each day than the larger hospital, it is going to have more daily fluctuation in the percentage of boys and girls born each day even though in the long run, they should each produce a 50/50 split.
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Biases Related To Representativeness Heuristic Problem: Girls In A Row
You and your spouse have had three children together, all of them girls. Now that you are expecting your fourth child, you wonder whether the odds favor having a boy this time. What is the best estimate of your probability of having another girl?
I 6.25% (1 in 16) - odds of getting 4 girls in a row
I 50% (1 in 2) - equal chance of getting either
I Something in between (6.25 – 50%)
[Bazerman and Moore, 2009]
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Biases Related To Representativeness Heuristic Bias: Misconception of Chance / Gambler’s fallacy
Solution to Girls In A Row Problem Many people tend to assume that because they have already had 3 girls, they are somehow less likely to have another girl than they were when having the first baby. It is true that overall, there is only a 1 in 16 chance of giving birth to 4 girls in a row, but each birth is independent. Because we are looking only at the probability of having a fourth girl and not on the probability of having four girls overall, the correct answer is 50%. However, many people miss this problem because they fail to account for the independence of each birth.
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Biases Related To Representativeness Heuristic Problem: Describe Linda Linda is 31 years old, single, outspoken, and very smart. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and she participated in antinuclear demonstrations. [Tversky and Kahneman, 1983]
I a) Linda is a teacher in elementary school. I b) Linda works in a bookstore and takes yoga classes. I c) Linda is active in the feminist movement. I d) Linda is a psychiatric social worker. I e) Linda is a member of the League of Women Voters. I f) Linda is a bank teller. I g) Linda is an insurance salesperson. I h) Linda is a bank teller who is active in the feminist movement.
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 27 Introduction Theory Heuristics Biases Conclusion
Biases Related To Representativeness Heuristic Bias: Conjunction Fallacy
Solution to Describe Linda Problem When ranking the likelihood of descriptions for Linda, people tend to rank Linda is a bank teller who is active in the feminist movement as being more likely than Linda is active in the feminist movement and/or that Linda is a bank teller.
This is because the combination of being a bank teller and active in the feminist movement is easier for people to picture in their mind than either of these descriptions are alone.
However, it is necessarily the case that the probability of only one of the descriptions is more likely than both of them in combination. [Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 28 Introduction Theory Heuristics Biases Conclusion
Biases Related To Representativeness Heuristic The Representativeness Heuristic
Consequences
I Insensitivity to base rates
I Insensitivity to sample size
I Misconceptions of chance
I Regression to the mean
I The conjunction fallacy
[Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 29 Introduction Theory Heuristics Biases Conclusion
Biases Related To Anchoring and Adjustment Heuristic Problem: Taj Mahal
Guess the Date
I Write the last three digits of your phone number and add 1 to the front of the string, as if it were a year.
I Was the Taj Mahal completed before or after the date formed by your phone number?
I What year was the Taj Mahal completed?
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Biases Related To Anchoring and Adjustment Heuristic Bias: Status Quo Bias or Default Bias
Solution to Taj Mahal Problem People are typically influenced by arbitrary starting points and this can lead our estimates to be biased in one direction or another. People tend to be accurate at guessing whether the arbitrary date derived from their phone number is before or after the completion of the Taj Mahal, but likely did not adjust far enough in one direction or another. That is, if people guessed that the Taj Mahal was founded before your arbitrary date, they likely did not adjust downward enough and you probably guessed a date after 1648. If people guessed that the Taj Mahal was founded after their arbitrary date, they likely did not adjust upward enough and you probably guessed a date after 1648. [Bazerman and Moore, 2009]
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Biases Related To Anchoring and Adjustment Heuristic Problem: Finding Sequence
Find the Rule Write down other sequences of the the three numbers, your instructor will tell you whether or not your sequences follow the rule:
2 – 4 – 6
[Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 32 Introduction Theory Heuristics Biases Conclusion
Biases Related To Anchoring and Adjustment Heuristic Bias: Confirmation Trap / Bias
Explanation People tend to seek for information that confirms their prior beliefs. [Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 33 Introduction Theory Heuristics Biases Conclusion
Biases Related To Anchoring and Adjustment Heuristic The Anchoring and Adjustment Heuristic
Status Quo Bias / Defaults Confirmation Bias
I Arbitrary starting points I Seek for information to confirm beliefs I First impressions I To overcome cognitive I Stereotypes dissonance [Bazerman and Moore, 2009]
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 34 Introduction Theory Heuristics Biases Conclusion
Summary
Availability Heuristic Representativeness Heuristic
I Ease of recall bias I Insensitivity to base rates
I Retrievability bias I Insensitivity to sample size
I Misconceptions of chance
I Regression to the mean
I The conjunction fallacy
[Bazerman and Moore, 2009]
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References
Bazerman, M. H.and Moore, D. A. 2009. Judgement in Managerial Decision Making, Hoboken, NJ, US: John Wiley Sons, Inc.
Eisenf¨uhr,F., Weber, M., and Langer, T. 2010. Rational Decision Making, Heidelberg, Dordrecht, London, New York: Springer.
Evans, J. S. B. T., and Stanovich, K. E. 2013. ”Dual-Process Theories of Higher Cognition: Advancing the Debate,” Perspectives on Psychological Science (8:3), pp. 223 - 241.
Hutchinson, J. M. C., and Gigerenzer, G. 2005. ”Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet,” Behavioural Processes (69:2), pp. 97 - 124.
Johnson, E. J., and Goldstein, D. 2003. ”Do defaults save lives?,” Science (302:5649), pp. 1338 - 1339.
Johnson, E. J., Shu, S. B., Dellaert, B. G. C., Fox, C., Goldstein, D. G., Hubl, G., Larrick, R. P., Payne, J. W., Peters, E., Schkade, D., Wansink, B., and Weber, E. U. 2012. ”Beyond nudges: Tools of a choice architecture,” Marketing Letters (23:2), pp. 487 - 504.
Kahneman, D. 2011. Thinking, Fast and Slow, London: Penguin Group.
Mokdad, A. H., Marks, J. S., Stroup, D. F., and Gerberding, J. L. 2004. ”Actual causes of death in the United States,” Journal of the American Medical Association (291), pp. 1239 - 1245.
Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 36 Introduction Theory Heuristics Biases Conclusion
References
Plunkert, D. 2011. ”Thinking, Fast and Slow – New York Times,” http://www.nytimes.com/2011/11/27/books/review/thinking-fast-and-slow-by-daniel-kahneman-book- review.html Shepard, R. N. 1990. Original visual illusions, ambiguities, and other anomalies, with a commentary on the play of mind in perception and art, New York, NY, US: W H Freeman / Times Books / Henry Holt Co.
Simon, H. A. 1955. ”A behavioral model of rational choice,” The Quarterly Journal of Economics (69:1), pp. 99 - 118.
Thaler, R. H., Sunstein, C. R., and Balz, J. P. 2010. ”Choice Architecture,” SSRN Electronic Journal.
Tversky, A., and Kahneman, D. 1974. ”Judgment under Uncertainty: Heuristics and Biases.,” Science (185:4157), pp. 1124 - 1131.
Tversky, A., and Kahneman, D. 1983. ”Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment,” Psychological Review 90(4), pp. 293 – 315.
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