Decision Making 2.pdf

Decision-Making Lecture 2 THE HEURISTICS AND BIASES MODEL Acknowledgement of Country  Lecture outline Focus questions Lecture sections What are the main heuristics All and which biases do they produce? What are the main...

Decision-Making Lecture 2 THE HEURISTICS AND BIASES MODEL Acknowledgement of Country  Lecture outline Focus questions Lecture sections What are the main heuristics All and which biases do they produce? What are the main Extensions and criticisms limitations of the heuristics and biases approach? Why is research on the confirmation bias often Extensions and criticisms exempt from the limitations of the heuristics and biases approach? The University of Adelaide Slide 3  Readings for this lecture The University of Adelaide Slide 4 Introduction and the representativeness heuristic  Timeline t u re 1 L ec Prospect Theory (1979) e3 tu re 4 tu r Lec Lec re 2 Fast and Lec tu Heuristics Bayesian models frugal and biases of cognition heuristics (1974) (1994) (1991) The University of Adelaide Slide 6 Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti- nuclear demonstrations. Which of the following is most likely? 1. Linda is a bank teller 2. Linda is a feminist bank teller Plous, 1993 Heuristics Biases  Heuristics Biases Representativeness Availability Anchoring and adjustment The representativeness heuristic Rule: “A is representative of B to the extent that A resembles B”. Plous, 1993 The representativeness heuristic Which of the following is most likely? 1. Linda is a bank teller 2. Linda is a feminist bank teller Plous, 1993 The representativeness heuristic Which of the following is most likely? 1. Linda is a bank teller 2. Linda is a feminist bank teller Plous, 1993 Bank tellers Feminist bank tellers Feminists Plous, 1993 Biases due to representativeness Conjunction fallacy Plous, 1993 Biases due to representativeness Conjunction fallacy Gambler’s fallacy Plous, 1993 Biases due to representativeness Conjunction fallacy Gambler’s fallacy Base rate neglect Bar-Hillel, 1980 Biases due to representativeness Conjunction fallacy Gambler’s fallacy Base rate neglect “A disease has a prevalence rate of 1/1000, and a test to detect it has a false positive rate of 5%. What is the chance that a person found to have a positive result actually has the disease, assuming you know nothing about the person’s symptoms or signs?” Bar-Hillel, 1980; Tversky and Kahneman, 1982, cited in Gigerenzer, 1991 Confirmation bias Confirmation bias Positive test heuristic: “When testing a hypothesis, ask questions that will yield an affirmative response if the hypothesis under consideration is true”. Nickerson, 1998 Wason Selection Task Select all the cards that are necessary to turn over to determine whether the following rule is true or false: “Whenever there is a vowel on one side, there is a number divisible by 13 on the other side”. A S 26 7 Nickerson, 1998 Confirmation bias Adaptive? Oaksford & Chater, 1994 Perfors & Navarro, 2009 Confirmation bias Adaptive? Hendrickson et al., 2016 Confirmation bias Adaptive? Hendrickson et al., 2016 Confirmation bias Adaptive? Hendrickson et al., 2016  A complex real-world situation when the positive test heuristic is not accurate Center for the Study of Intelligence (2009) The University of Adelaide Slide 25 A complex real-world situation when the positive test heuristic is not accurate Center for the Study of Intelligence (2009), p. 14 Slide 26  A complex real-world situation when the positive test heuristic is not accurate The confirmation bias be problematic in this situation because police can’t afford to investigate hypotheses one-by-one. They want to avoid the situation where they go down the rabbit hole for one hypothesis that proves false. They want to be investigating multiple hypotheses simultaneously. Image source The University of Adelaide Slide 28 Anchoring-and-adjustment Image source Image source Epley & Gilovich, 2005, Study 1  Anchor type Self-generated (56 people) Experimenter-provided (51 people) 4 questions: 4 questions: In what year was George Washington Is the population of Chicago lower or elected President of the United States? higher than 200,000? What is it? ____ In what year did the second European explorer land in the West Indies? Is the height of the tallest redwood tree What is the freezing point of vodka? lower or higher than 65 feet? What is it? What is the boiling point of water on the ____ top of Mount Everest? Is the length of the Mississippi River lower or higher than 2,000 miles? What After all questions were answered, is it? ____ participants were asked to indicate whether they knew of a particular relevant value for Is the height of Mount Everest lower or each question (e.g., for the first question, it higher than 45,500 feet? What is it? ____ was expected that many participants would cite the year of declaration of independence in the US: 1776) The University of Adelaide Epley & Gilovich, 2005, Study 1 Epley & Gilovich, 2005, Study 2 Extensions and criticisms  Representativeness heuristic Positive test heuristic Availability heuristic Anchoring and adjustment heuristic Prospect Theory (1979) Fast and Heuristics Bayesian models frugal and biases of cognition heuristics (1974) (1994) (1991)  “System 1” “System 2” Prospect Theory (1979) Fast and Heuristics Bayesian models frugal and biases of cognition heuristics (1974) (1994) (1991)  Criticisms of the heuristics and biases approach One of the main criticisms of the approach is that, while claiming that heuristics produce correct judgements sometimes, the approach does not state when that happens – under what conditions? The confirmation bias is exempt from this criticism. Research on the confirmation bias shows that the positive test heuristic that drives the bias produces accurate judgements when the causes and effects people are making judgements about appear fairly rarely in the environment. Another criticism is that the heuristics-and-biases model is too simplistic to explain why, under some conditions, biases are reduced or disappear (as happens with anchoring and adjustment when people are provided with both accuracy incentives and the opportunity to personally generate the anchor). Criticisms of the heuristics and biases approach will be discussed in more detail in the next lecture when we explore a model – the ‘fast and frugal’ heuristics approach – that developed in opposition to the then dominant heuristics and biases approach. The University of Adelaide Slide 38  Criticisms of the dual systems perspective Unconscious Conscious Fast Slow Unintentional Intentional Uncontrollable Controllable Kahneman (2011) One criticism of the dual systems perspective is that brain imaging research overwhelmingly points to processing centres in different parts of the brain working in a way that is more integrated (i.e., connected) and iterative (i.e., back-and-forth). Image source: Spunt (2015)  Criticisms of the dual systems perspective The associated research program focuses on identifying those supposedly rare cases where people make errors, but little is said about what makes those cases different from others. As a case in point, here is the description of the dual systems approach associated with the heuristics-and-biases view from Chapter 13 of one of the course textbooks: In our day-to-day lives, System 1 is often running the show. Many of the things we do are automatically controlled by System 1. We perceive things in the environment, react to a loud noise, read emotions in someone’s face, or negotiate a curve while driving. All of these things are taken care of by System 1. As we saw when we considered perception and attention, having some things taken care of automatically and without conscious effort is a good thing, because it means we don’t have to be monitoring our every thought and move. Kahneman sees System 1 as providing information for System 2—most of which is accurate and is accepted—while System 2 is idling in the background monitoring the information. However, when the going gets tough, System 2 can take over. Although System 1 may be taking care of routine driving, System 2 takes over when close attention is needed, as when entering a construction zone or passing a large truck at 70 miles (112 km) an hour. System 2 is also mobilized when a question arises for which System 1 doesn’t have an answer. As Kahneman puts it, System 1 automatically calculates 2 x 2 (you couldn’t keep yourself from saying 4, right?), but can’t deal with 27 x 13. This is a problem for System 2. Goldstein (2018) p. 423 The University of Adelaide Slide 40 41  References Bar-Hillel, M. (1980). The base-rate fallacy in probability judgments. Acta Psychologica, 44, 211–233. Gigerenzer, G. (1991). How to make cognitive illusions disappear: Beyond “heuristics and biases”. European Review of Social Psychology, 2, 83–115. Hendrickson, A. T., Navarro, D. J. & Perfors, A. (2016). Sensitivity to hypothesis size during information search. Decisions, 3, 62–80. Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2, 175. Oaksford, M. & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101, 608–631. Perfors, A. F. & Navarro, D. J. (2009). Confirmation bias is rational when hypotheses are sparse. In N. Taatgen, H. van Rijn, J. Nerbonne & L. Schomaker (Eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society (pp. 2471–2476). Austin, TX: Cognitive Science Society. Plous, S. (1993). The Psychology of Judgment and Decision Making. McGraw Hill. Chapter 10: The representativeness heuristic. Spunt, B. (2015). Dual-Process Theories in Social Cognitive Neuroscience. In Brain Mapping: An Encyclopedic Reference (Vol. 3, pp. 211–215). The University of Adelaide Slide 42