Reasoning and decision making
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1 Reasoning and decision making Reasoning Conclusions beyond info provided Deductive reasoning Inductive reasoning Decision making Make choices Psychology research question? Do people think logically? How well can people evaluate problems? How do we represent information? What are the biases in reasoning?
2 Decision making Utility approach If have all information, will choose most desirable outcome Complicated what is valuable: Not all pieces can be calculated Potential for inaccurate mental simulations Poor at predicting emotional reactions
3 Reasoning and decision making Heuristics Bias Representativeness heuristic Availability heuristic Anchoring and adjustment Framing effect Confirmation bias
4 Reasoning problem A nearby town is served by 2 hospitals. About 45 babies are born each day in the larger hospital. About 15 babies are born each day in the smaller hospital. Approximately 50% of all babies are boys. However, the exact percentage of babies who are boys will vary from day to day. Some days it may be higher than 50%, some days lower. For a period of 1 year, both the larger and smaller hospital recorded the number of days on which more than 60% of babies born were boys. Which hospital do you think recorded more such days? - Larger hospital - Smaller hospital - About the same (within 5% of each other)
5 Representativeness heuristic Which outcome is more likely? THHTHT or HHHTTT THHTHT judged as representative of random Judgment of similarity to general category Small-sample fallacy Hospital problem: 56% say same Ignore law of large numbers Descriptions change reasoning Base-rate fallacy Ignore statistics, decision based on descriptive information
6 Linda 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 she also participated in anti-nuclear demonstrations. Rank the following options in terms of the probability of their describing Linda. Give a rank of 1 to the most likely option and 8 to the least likely. Linda is a teacher at an elementary school. Linda works in a bookstore and takes yoga. Linda is active in the feminist movement. Linda is a psychiatric social worker. Linda is a member of the league of women voters. Linda is a bank teller. Linda is an insurance salesperson. Linda is a bank teller and active in the feminist movement.
7 Conjunction fallacy Tversky & Kahneman (1983) Most thought teller and feminist more likely Mathematically less likely conjunction Seems more appealing even though statistically less likely Naïve Intermed Sophisticated Bank teller Bank teller and feminist
8 Availability heuristic Are there more words that have K in the 1 st position or 3 rd? What is more likely (e.g. diseases) Availability heuristic How easily examples come to mind Generally correct, but can lead to errors Factors that influence: Recency, Familiarity, Knowledge McKelvie (1997): list of m/f names 12 famous m v. 14 f: 77% report more males in list
9 Decision making Imagine that the US is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two programs have been proposed. A: 200 people will be saved B: 1/3 probability that 600 will be saved, but 2/3 probability that no one will be saved Which program do you favor?
10 Decision making Imagine that the US is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. What if 2 different programs are proposed Opt. C: 400 people will die Opt D: 1/3 probability that nobody will die and 2/3 probability that 600 will die Which program do you favor?
11 Framing effect Subtle changes in wording can change interpretation/decision Tversky & Kahneman (1981) A vs. B: focus on lives saved 72% chose A: risk averse But, if asked choose between C: 400 people will die D: 1/3 probability that nobody will die and 2/3 probability that 600 will die 22% chose C: risk taking Identical deep structures (A/B vs. C/D) Depends on how question is framed
12 CogLab: Decision making F 10 data: Problem 1 Imagine the country is preparing for the outbreak of an unusual disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Set 1: Choice A: If program A is adopted, 200 people will be saved. Choice B: If program B is adopted, there is 1/3 probability that 600 people will be saved and 2/3 probability that no one will be saved. 83% choice A; 17% choice B Set 2: If program A is adopted, 400 people will die. If program B is adopted, there is 1/3 probability that nobody will die, and a 2/3 probability that 600 people will die. 33% choice A; 66% choice B
13 CogLab: Decision making F 10 data: Problem 2 Set 1: Consider the following 2-stage game. In the 1st stage there is a 75% chance to end the game without winning anything and a 25% chance to move into the 2 nd stage. If you reach the 2 nd stage you have a choice between the following options. Your choice must be made before the game begins. Choice A: A sure win of $30 Choice B: An 80% chance to win $45 83% Choice A; 17% Choice B Set 2: Which of the following do you prefer? Choice A: A 25% chance to win $30 Choice B: A 20% chance to win $45 66% Choice A; 33% Choice B
14 CogLab: Decision making F 10 data: Problem 3 Set 1: Imagine that you are about to purchase a jacket for $250 and a calculator for $30. The calculator salesman informs you that the calculator you wish to buy is on sale for $20 at the other branch of the store, located 20min away. Would you make the trip? Choice A: Yes; Choice B: No 17% Choice A; 83% Choice B Set 2: Imagine that you are about to purchase a jacket for $30 and a calculator for $250. The calculator you wish to buy is on sale for $240 at the other branch of the store, located 20min away. Would you make the trip? Choice A: Yes; Choice B: No 33% Choice A; 66% Choice B
15 CogLab: Decision making F 10 data: Problem 4 Imagine that you have decided to see a play and paid admission price of the $20 ticket. As you enter the theater, Set 1: you discover that you have lost it. Would you pay $20 for another ticket? Choice A: Yes; Choice B: No 33% Choice A; 66% Choice B Set 2: you discover that you have lost a $20 bill. Would you still pay $20 for a ticket to the play? Choice A: Yes; Choice B: No 100% Choice A; 0% Choice B
16 CogLab: Decision making F 10 data: Problem 5 Set 1: Would you accept a gamble that offers a 10% chance to win $95 and a 90% chance to lose $5? Choice A: Yes; Choice B: No 50% Choice A; 50% Choice B Set 2: Would you pay $5 to participate in a lottery that offers a 10% chance to win $100 and a 90% chance to win nothing? Choice A: Yes; Choice B: No 33% Choice A; 67% Choice B
17 Kahneman & Tversky (1984) Would you accept a gamble that offers a 10% chance to win $95 and a 90% chance to lose $5? Would you pay $5 to participate in a lottery that offers a 10% chance to win $100 and a 90% chance to win nothing? 41% gave different preferences Even though $5 is loss of gamble vs cost to play 32% said no to 1 st offer, but yes to 2 nd
18 Kahneman & Tversky (1984) Choose between A sure gain of $240 25% chance to gain $1000 and 75% chance to gain nothing Choose between A sure loss of $750 75% chance to loose $1000 and 25% chance to lose nothing
19 Kahneman & Tversky (1984) Choose between A sure gain of $240 25% chance to gain $1000 and 75% chance to gain nothing 84% (risk-averse) 16% Choose between A sure loss of $750 75% chance to loose $1000 and 25% chance to lose nothing 13% 87% (risk-seeking)
20 Framing: medical decisions McNeil et al (1982) Hospital physicians asked which form of treatment for patient with lung cancer (surgical or 6wk radiation) IV: prior information (framing) Of 100 people having surgery, 10 will die during treatment, 32 will have died by 1yr, and 66 will have died by 5yrs. Of 100 people having radiation therapy, none will die during treatment, 23 will have died by 1yr, and 78 will have died by 5yrs. Of 100 people having surgery, 90 will be alive immediately after treatment, 68 will be alive after 1yr, and 34 will be alive after 5yrs. Of 100 people having radiation therapy, all will be alive after treatment, 77 will be alive after 1yr, and 22 will be alive after 5yrs. Results: Framed in terms of dying: 44% choose radiation Framed in terms of living: 18% choose radiation
21 CogLab: Risky decisions Sp 12 Problems Get some additional money or lose money Choices Hyp Risky (probability) vs riskless choice When choices are gains: risk-avoiding When choices are losses: risk seeking Expected: % smaller for gain vs loss problems Results: % risky choice selected Gain: 48.5% (46% global) Loss: 12.1% (41% global)
22 Tversky & Shafir (1992) Imagine you have just taken a tough exam. It is the end of the semester, you feel tired and you find out that you Passed the exam Failed the exam and you will have to take it again in a couple of months Won t know the outcome of the exam for 2 more days You now have the opportunity to buy a 5-day vacation to Hawaii at a very low price. It expires tomorrow. Would you: Buy the vacation package? Not buy the vacation package? Pay a $5 nonrefundable fee in order to retain the right to buy the vacation at the same price the day after tomorrow?
23 Tversky & Shafir (1992) Pass/fail doesn t change % of decisions Each individual needs to have reason for decision! Justification process
24 Anchoring and adjustment Anchor: begin with first approximation Adjustment: changes based on added info Multiplication problem: 5s respond A: 8x7x6x5x4x3x2x1 B: 1x2x3x4x5x6x7x8 A grp median: 2,250 B grp median: 512 Correct answer: 40,320 Real world application First impressions Others?
25 Confirmation bias Tendency to only gather support; ignore disconfirming evidence Wason (1960) card task You will be given 3 #s which conform to a simple rule. Your aim is to discover this rule. Write down #s and reasons and I ll tell you if they conform to the rule or not. Results: Few participants who after they were correct tried to disconfirm their hypothesis. Lord et al. (1979) How convincing an article is depends on prior attitude Kuhn s Structure of a Scientific Revolution
26 Reasoning: Bias Framing Way alternatives are structured Consequences are the same Affects representation Representativeness heuristic Decision based on comparison to ideal Don t consider statistics Availability heuristic Tendency to use answer that easily comes to mind Anchoring and adjustment Influenced by starting point of problem Confirmation bias Tendency to seek/use info that supports belief Belief persistence
27 Neuroeconomics Economic decision making problems Examine influence of emotion (and mood) on decisions Expected emotions (predicted) Immediate emotions: integral vs incidental Emotion determines risk aversion (impact of loss greater than gain) Sanfey et al (2003) Ultimatum game (how to split $) IV: human vs computer partner Result: humans reject low offers b/c unfair Brain activity: Anterior insula activation when rejected offer Lerner et al (2004) View film (sad, disgust, neutral) Decision conditions: Sell: Set price to sell product Choice: price willing to choose product instead of accepting $ Result: sad/disgust grps set price lower
28 Neuroscience of thinking Major area involved: prefrontal cortex (PFC) Damage to PFC has effect on: Planning and perseveration Problem solving Understanding stories Reasoning Application: teenagers
29 Why are we imperfect? Why use heuristics? Less effort, less to remember Economical Faster to answer Usually correct Effective Reduce errors Approximation Examples/Problems purposefully created to create errors Help us understand cognitive process
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