Heuristics & Biases

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Heuristics & Biases. MAR 3053 February 28, 2012. The use and misuse of affect, availability, representative-ness, and anchors. Part 1: Heuristics & intuitive judgment. Two systems of reasoning. System 1. System 2. “ Reflective ” Controlled Effortful Slow & often serial May be abstract - PowerPoint PPT Presentation

Transcript of Heuristics & Biases

  • Heuristics & BiasesMAR 3053

    February 28, 2012

  • Part 1: Heuristics & intuitive judgmentThe use and misuse of affect, availability, representative-ness, and anchors

  • Two systems of reasoningSystem 1IntuitiveAutomaticEffortlessRapid & parallelConcreteAssociativeSystem 2ReflectiveControlledEffortfulSlow & often serialMay be abstractRule-based

  • Which bet would you choose?1 in 109 in 100

  • Who chooses the large box?Percentage of participants choosing the box with greater # of total balls(odds with small box = 10%; odds with large box = value shown on x-axis)







    Series 1


    Series 1Series 2Series 3






    To resize chart data range, drag lower right corner of range.

  • What is a heuristic?Mental shortcut used in judgment and decision makingEssential for living in an uncertain worldBut they can lead to faulty beliefs and suboptimal decisionsBy looking at errors and biases, we can learn how people are reasoning under uncertainty

  • Two types of heuristicsSpecial purpose heuristics use restricted to specific domainsHeight as a guide for ability as basketball player# of publications as guide for quality as an academicGeneral use heuristicsAffectAvailabilityRepresentativeness (similarity)

  • The affect heuristic## migrating birds die each year by drowning in uncovered oil ponds, which the birds mistake for bodies of water. Covering the ponds with nets could prevent these deaths. How much money would you be willing to pay to provide the needed nets?2,000 birds -- $8020,000 birds -- $78200,000 birds -- $88

  • The identifiable victim effectA death of a single Russian solder is a tragedy. A million deaths is a statistic. Joseph Stalin

  • AffectJudgments of life happiness:People asked 2 questions:1) How satisfied are you with your life these days?2) How many dates have you had in the last month?Correlation = -.12Another group asked in opposite order 2), then 1)Correlation = .66Strack et al., 1993

  • The availability heuristicMaking judgments about the frequency or likelihood of an event based on the ease with which evidence or examples come to mindExample: Category size


  • AvailabilityEgocentric allocations of responsibility: OverclaimingPeople claim more responsibility for collective endeavors than is logically possibleSelf-allocations sum to more than 100%Why? Because ones own contributions are more available than those of others

  • AvailabilityExperimental evidenceMarried couples asked to allocate responsibility for:Positive events: Making breakfast, planning activities, shopping for family, making important decisionsNegative events: Causing arguments, causing messes, irritating spouseResults: Overclaiming occurred for 16 of 20 activitiesEquivalent overclaiming for positive and negative eventsRoss & Sicoly, 1979; Kruger & Gilovich, 1999

  • AvailabilityWhat is availability? Two possibilities:1. Number amount of information generated2. Ease the ease with which information can be generatedIconic study teased them apart:Participants were asked to evaluate their own assertivenessBy generating either 6 (easy) or 12 (hard) examples of assertiveness or unassertiveness

  • Availability: number versus easeMoral: Ease influences judgments sometimes in spite of numberSchwarz et al., 1991




    Six examples

    Twelve examples

    Higher #s = more assertive


    Six examplesTwelve examplesSeries 3

    Assertive examples6.35.12

    Unassertive examples5.26.22

    Category 33.51.83

    Category 44.52.85

    To resize chart data range, drag lower right corner of range.

  • RepresentativenessDetermining class inclusion or likelihood by similarity:A member ought to resemble the overall categoryAn effect ought to resemble or be similar to the causeAn outcome ought to resemble the process that produced itLike goes with likeOften easier to assess similarity than probabilityDoes he look like an engineer?Does it look like it could cause a clogged artery?Does it look like a random sequence?

  • RepresentativenessLeads to several classic judgment errorsConjunction fallacyMisperceiving randomnessRegression fallacy

  • The Linda problemLinda 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 criminal justice, and also participated in anti-nuclear demonstrations.Rank likelihood that Linda is:A teacher in elementary schoolActive in the feminist movementA member of the League of Women VotersA bank tellerAn insurance salespersonA bank teller and active in the feminist movement

  • The Linda problemClass data (rankingslower numbers mean more likely):

    Active in the feminist movement: A bank teller: Active in feminist movement a bank teller:

  • Representativeness: Conjunction fallacyJudging the conjunction of two events to be more probable than one of the constituent elementsFeministsBank tellersP(A & B) > P(A) or P(B)/

  • Conjunction fallacyHow much would you be willing to pay for a new insurance policy that would cover hospitalization for:

    1. Any disease or accidentMean = $89.102. Any reasonMean = $41.53 Johnson et al., 1993

  • Conjunction fallacyHow much would you be willing to pay for flight insurance (1 flight to London) that covers death due to:

    1. Any act of terrorismMean = $14.122. Any reasonMean = $12.03Johnson et al., 1993

  • Representativeness: RandomnessEffects should resemble the process that produced themIf something is random, it should look random


  • The hot handIf Im on, I find that confidence just buildsyou feel nobody can stop you. Its important to hit that first one, especially if its a swish. Then you hit another, andyou feel like you can do anything.--Lloyd Free (a.k.a. World B. Free)

  • The hot handThe belief that success breeds success, and failure breeds failure100 basketball fans91% thought player has a better chance of making a shot after having just made his last two or three shots than he does after having just missed his last two or three shotsGiven a player who makes 50% of his shots, subjects thought that shooting percentage would be61% after having just made a shot42% after having just missed a shot84% thought that its important to pass the ball to someone who has just made several shots in a rowGilovich, Vallone, & Tversky, 1985

  • The hot handCalculate probability of making a shot after missing previous 1, 2, or 3 shots and after making previous 1, 2, or 3 shotsGilovich, Vallone, & Tversky, 1985

  • What the hot hand results meanThe independence between successive shots, of course, does not mean that basketball is a game of chance rather than skill, nor should it render the game less exciting to play, watch, or analyze. It merely indicates that the probability of a hit is largely independent of the outcome of previous shots, although it surely depends on other parameters such as skill, distance to the basket, and defensive pressureThe availability of plausible explanations may contribute to the erroneous belief that the probability of a hit is greater following a hit than following a miss. Gilovich et al., 1985, pp.312-313

  • Regression to the mean





















    Y-Value 1


    X-ValuesY-Value 1




















  • The SI jinx

  • The SI jinxIn sports (the SI jinx, the sophomore slump, rehiring the interim manager, etc.)In education (the illusory superiority of punishment over reward)In medicine (why its so easy to believe that a worthless remedy really works)In politics (be careful about taking office during an economic boom or a drop in crime)

  • Part 2: BiasesOverconfidence and its causes

  • Overconfidence in social predictionsWould the target personPrefer to subscribe to Playboy or the New York Review of Books?Describe his/her lecture notes as neat or messy?Say s/he would pocket or turn in $5 found on the ground?Object when the experimenter referred to him/her by the wrong name?Comb his/her hair before posing for a photograph in the lab?How confident are you in your answer (50-100%)?Mean confidence: 75.7%Mean accuracy: 60.8%When 100% confident, accuracy = 78.5%!Dunning et al., 1990

  • Overconfidence in self predictionsWill youVisit San Francisco more than 3 times this year?Participate in the dorm play?Drop a course?Question your decision to attend Stanford?Become best friends with your roommate?Visit a friend more than 100 miles away?Get a new boy/girlfriend?Overall confidence: 82.3%Overall accuracy: 68.2%When participants were 100% confident, they were correct only 77.4% of the time!Vallone et al., 1990

  • Causes of overconfidenceHindsight biasMotivated and non-motivated confirmatory thinkingConfirmation biasWishful thinkingNave realism

  • Nave realismYou drive up to San Francisco with friends to celebrate the end of the quarter. The plans include dinner and then some entertainment afterward.How much money will you personally spend on the dinner?You receive a telephone call from a survey firm. You initially agree