Representativeness and Availability Kahneman & Tversky Umut Öztürk

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Transcript of Representativeness and Availability Kahneman & Tversky Umut Öztürk

  • Slide 1
  • Representativeness and Availability Kahneman & Tversky Umut ztrk
  • Slide 2
  • Representativeness Definition - Assessing the likelihood of an event`s occurrence by the similarity of that occurrence to stereotypes of similar occurrences. - The more X is similar to Y, the more likely we think X belongs to Y.
  • Slide 3
  • Insensitivity to Sample Size The size of a sample greatly affects the likelihood of obtaining certain results in it. People, however, often ignore sample size and only use the superficial similarity measures. For example, people ignore the fact that larger samples are less likely to deviate from the mean rather than smaller samples.
  • Slide 4
  • Misconception of Chance People expect that random sequences are representative even in small samples. - E.g. they consider a coin-toss run of HTHTHT to be more likely than HHHTTT or HHHHTH Gambler`s fallacy: A deviation from a stable equilibrium generates a force that restores the equilibrium. (misconception of the fairness of the laws of chance) The laws of chance: Deviations are not canceled as sampling proceeds, they are only diluted.
  • Slide 5
  • Misconception of Chance E.g. After a run of reds in a roulette, black will make the overall run more representative (self correcting process?) Even experienced research psychologists believe in a law of small numbers (small samples are representative of the population they are drawn from)
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  • Example on Gambler`s Fallacy The mean IQ of the 8th graders in a city= 100 (known) Random sample of 50 students The first student`s IQ=150 Expected mean IQ for the sample? Correct Answer= 101 Answer for large number of people=100 Why? Belief in self-correction
  • Slide 7
  • Insensitivity to Prior Probabilities The base(population) rate of outcomes should be a major factor in estimating their frequency. However, people often ignore it. Bayes Theorem: When we make a decision, we should take the prior probabilites into account unless we are absolutely certain about the decision. Is Representativeness Heuristic in accordance with Bayes Theorem?
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  • What is Tom`s Major High intelligence Need for order Neatness Dull and mechanical writing Little sympathy for other people Not enjoying interacting with others Self centered Deep moral sense
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  • GraduateMean Specializationjudged basesimilarity area rate (in %)rank Business153,9 Administration IT72,1 Engineering92,9 Humanities207,2 Law95,9 Library Science34,2 Medicine85,9 Physical Sciences124,5 Social Sciences178,2
  • Slide 10
  • What is Tom`s Major Kahneman & Tversky`s questions - What percentage of people in different majors? - How similar is Tom to each major? More than 95% of the respondents jugded that Tom is more likely to study IT than humanities, although they were surely aware of the fact that there are many more graduate students in the latter field. (ignoring the base rates)
  • Slide 11
  • Conjuctive Fallacy A & B can not be more probable than just A or B. Example: Sarah is 40 - single, outspoken and bright. She majored in philosophy and was interested in social equality as a student. Is Sarah a) a sales representative or b) a sales representative who is active in feminist movement?
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  • How to AVOID representativeness bias? Don`t be misled by detailed scenarios. Pay attention to the base rates. Don`t forget the Gambler`s fallacy. (chance is not self-correcting) Seperate representativeness from probability.
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  • Availability Heuristic Availability involves... Assessing the frequency, probability, or likely causes of an event based on the degree to which occurrences of the event are readily available in memory. - People inadvertently assume that readily available instances, examples or images represent unbiased estimates of statistical probabilities.
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  • Availability Biases: Ease of Retrievability Samples whose instances are more easily retrievable from memory will seem larger. For example, judging if a list of names had more men or women depends on the relative frequency of famous names.
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  • Example: List of Names Read the list once. Michael Jordan Sandra Grey Barbara Walters Maria Schulz George Bush Kim Melcher Indira Gandi Jack Smith Madonna Gill Williams
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  • Example List of Names Are there more men or women on the list? Judging if the list of names has more men or women depends on the relative frequency of famous names.
  • Slide 17
  • Experience Antecedent of Availability Bias A successful executive who attended Yale is likely to remember fellow alums he encounters in his business circle and his social life. Because of his special, circumscribed range of experiences he is likely to overestimate the relative proportion of successful Yale graduates. Thus, range of experiences can cause the availability bias.
  • Slide 18
  • Salience Antecedent of Availability Bias Unemployed executives are likely to overestimate unemployment among executives, whereas employed executives are likely to underestimate unemployment among executives. For each executive, employment estimates are biased by the vivid salience of their personal situation. Vivid salience can cause the availability bias.
  • Slide 19
  • Ease of Recall Events more easily recalled from memory, based upon recency, are regarded to be more numerous. Ex: Managers` appraisals of employees Ex: Watching an accident Ex: Loud repeated advertising
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  • Effectiveness of a Search Set We often form mental search sets to estimate how frequent some occurrences are. However, the effectiveness of the search might not relate directly to the real frequency.
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  • Effectiveness of a Search Set Consider the letters K,L,R,N,V. Are they more likely to appear in - the first position? - the third position? Result: Among the 152 subjects, 105 judged the first position to be more likely for a majority of the letters, even though in reality the third position is more frequent.
  • Slide 22
  • Ease of Imaginability The difficulty of imagining instances is used as an estimate of their frequency. - E.g. number of combinations of 2 out of 9 people, versus 7 out of 9 people. - Number of combinations of 2 people is seen more at first glance, it is more disctinctive and easier to visualize, even though the number of both combinations is the same. * Thus, imaginability might cause overestimation of likelihood of vivid scenarios, and underestimation of the likelihood of difficult to imagine ones.
  • Slide 23
  • Example Estimate the result of the following operation within 5 seconds! One group (87 people) is given 8x7x6x5x4x3x2x1 The other group (114 people) is given 1x2x3x4x5x6x7x8 The median estimates: 2250 for the first group and 512 for the second one. The correct value= 40320
  • Slide 24
  • Example Why? Because the results of the first steps of multiplication are larger in the descending sequence than in the ascending one, the former expression is judged larger than the latter.