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Enhanced Decision Making (Heuristics and Biases) 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] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 10 Introduction Theory Heuristics Biases Conclusion 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] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 14 Introduction Theory Heuristics Biases Conclusion 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] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 16 Introduction Theory Heuristics Biases Conclusion 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] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 17 Introduction Theory Heuristics Biases Conclusion 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] Dr Markus Weinmann University of Liechtenstein Enhanced Decision Making 18 Introduction Theory Heuristics Biases Conclusion 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
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