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Heuris,cs & Biases
MAR 3053
February 28, 2012
PART 1: HEURISTICS & INTUITIVE JUDGMENT
The use and misuse of affect, availability, representa,ve-‐ness, and anchors
Two systems of reasoning
System 1 • “Intui,ve” • Automa,c • Effortless • Rapid & parallel • Concrete • Associa,ve
System 2 • “Reflec,ve” • Controlled • EfforQul • Slow & oSen serial • May be abstract • Rule-‐based
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Which bet would you choose?
1 in 10 9 in 100
Who chooses the large box?
Percentage of par,cipants choosing the box with greater # of total balls (odds with small box = 10%; odds with large box = value shown on x-‐axis)
What is a heuris,c?
• “Mental shortcut” used in judgment and decision making – Essen,al for living in an uncertain world – But they can lead to faulty beliefs and subop,mal decisions
– By looking at errors and biases, we can learn how people are reasoning under uncertainty
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Two types of heuris,cs
• Special purpose heuris,cs – use restricted to specific domains – Height as a guide for ability as basketball player – # of publica,ons as guide for quality as an academic
• General use heuris,cs – Affect – Availability – Representa,veness (similarity)
The affect heuris,c
• ## migra,ng 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 -‐-‐ $80 • 20,000 birds -‐-‐ $78 • 200,000 birds -‐-‐ $88
The iden,fiable vic,m effect
• “A death of a single Russian solder is a tragedy. A million deaths is a sta,s,c.” – Joseph Stalin
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Affect
• Judgments of life happiness: • People asked 2 ques,ons: – 1) How sa,sfied are you with your life these days? – 2) How many dates have you had in the last month?
• Correla,on = -‐.12 • Another group asked in opposite order – 2), then 1) • Correla,on = .66
Strack et al., 1993
The availability heuris,c
Kansas? Nebraska?
• Making judgments about the frequency or likelihood of an event based on the ease with which evidence or examples come to mind – Example: Category size
Availability
• Egocentric alloca,ons of responsibility: “Overclaiming”
• People claim more responsibility for collec,ve endeavors than is logically possible
• Self-‐alloca,ons sum to more than 100% • Why? Because one’s own contribu,ons are more available than those of others
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Availability • Experimental evidence • Married couples asked to allocate responsibility for: – Posi,ve events: Making breakfast, planning ac,vi,es, shopping for family, making important decisions
– Nega,ve events: Causing arguments, causing messes, irrita,ng spouse
• Results: – Overclaiming occurred for 16 of 20 ac,vi,es – Equivalent overclaiming for posi,ve and nega,ve events
Ross & Sicoly, 1979; Kruger & Gilovich, 1999
Availability
• What is availability? Two possibili,es: – 1. Number – amount of informa,on generated – 2. Ease – the ease with which informa,on can be generated
• Iconic study teased them apart: – Par,cipants were asked to evaluate their own asser,veness…
– By genera,ng either 6 (easy) or 12 (hard) examples of asser,veness or unasser,veness
Availability: number versus ease
Moral: Ease influences judgments some,mes in spite of number Schwarz et al., 1991
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Representa,veness • Determining class inclusion or likelihood by similarity: – A member ought to resemble the overall category – An effect ought to resemble or be similar to the cause – An outcome ought to resemble the process that produced it
• Like goes with like • OSen easier to assess similarity than probability – Does he look like an engineer? – Does it look like it could cause a clogged artery? – Does it look like a random sequence?
Representa,veness
• Leads to several classic judgment errors – Conjunc,on fallacy – Misperceiving randomness – Regression fallacy
The Linda problem • Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimina,on and criminal jus,ce, and also par,cipated in an,-‐nuclear demonstra,ons.
• Rank likelihood that Linda is: – A teacher in elementary school – Ac,ve in the feminist movement – A member of the League of Women Voters – A bank teller – An insurance salesperson – A bank teller and ac,ve in the feminist movement
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The Linda problem
• Class data (rankings—lower numbers mean more likely):
• Ac,ve in the feminist movement: • A bank teller: • Ac,ve in feminist movement a bank teller:
Representa,veness: Conjunc,on fallacy
• Judging the conjunc,on of two events to be more probable than one of the cons,tuent elements
Feminists
Bank tellers
P(A & B) > P(A) or P(B) /
Conjunc,on fallacy
• How much would you be willing to pay for a new insurance policy that would cover hospitaliza,on for:
• 1. Any disease or accident – Mean = $89.10
• 2. Any reason – Mean = $41.53
Johnson et al., 1993
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Conjunc,on fallacy
• How much would you be willing to pay for flight insurance (1 flight to London) that covers death due to:
• 1. Any act of terrorism – Mean = $14.12
• 2. Any reason – Mean = $12.03
Johnson et al., 1993
Representa,veness: Randomness
• Effects should resemble the process that produced them
• If something is random, it should look random
• What does random look like? – HTHHHTTTTHTHHTTTHHHTH – HTHTHTTTHHTHTHTTHHHTH
The hot hand
• “If I’m on, I find that confidence just builds…you feel nobody can stop you. It’s important to hit that first one, especially if it’s a swish. Then you hit another, and…you feel like you can do anything.” – -‐-‐Lloyd Free (a.k.a. World B. Free)
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The hot hand • The belief that success breeds success, and failure breeds failure
• 100 basketball fans… – 91% thought player has a bever chance of making a shot aSer having just made his last two or three shots than he does aSer having just missed his last two or three shots
– Given a player who makes 50% of his shots, subjects thought that shoo,ng percentage would be… • 61% aSer having just made a shot • 42% aSer having just missed a shot
– 84% thought that it’s important to pass the ball to someone who has just made several shots in a row
Gilovich, Vallone, & Tversky, 1985
The hot hand
• Calculate probability of making a shot aSer missing previous 1, 2, or 3 shots and aSer making previous 1, 2, or 3 shots
Gilovich, Vallone, & Tversky, 1985
What the hot hand results mean • “The 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 exci,ng 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 pressure…The availability of plausible explana,ons 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
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Regression to the mean
The SI jinx
The SI jinx
• In sports (the SI jinx, the sophomore slump, rehiring the interim manager, etc.)
• In educa,on (the illusory superiority of punishment over reward)
• In medicine (why it’s so easy to believe that a worthless “remedy” really works)
• In poli,cs (be careful about taking office during an economic boom or a drop in crime)
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PART 2: BIASES Overconfidence and its causes
Overconfidence in social predic,ons
• Would the target person… – Prefer 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 predic,ons • Will you… – Visit San Francisco more than 3 ,mes this year? – Par,cipate in the dorm play? – Drop a course? – Ques,on your decision to avend 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 par,cipants were 100% confident, they were correct only 77.4% of the ,me!
Vallone et al., 1990
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Causes of overconfidence
• Hindsight bias • Mo,vated and non-‐mo,vated confirmatory thinking – Confirma,on bias – Wishful thinking
• Naïve realism
Naïve realism • You drive up to San Francisco with friends to celebrate the end of the quarter. The plans include dinner and then some entertainment aSerward. – How much money will you personally spend on the dinner?
• You receive a telephone call from a survey firm. You ini,ally agree to answer some ques,ons. There is a long series of ques,ons – How many minutes will you spend answering ques,ons before you end the call?
Griffin, Dunning, & Ross, 1990
Naïve realism
• Three condi,ons: – Control condi,on: Confidence intervals simply given a second ,me
– “Assumers” condi,on: Asked to assume that their image of the situa,on was, in fact, correct in all details
– Mul,ple construal condi,on: Asked to describe several alterna,ve ways the situa,on they would be in could turn out
Griffin, Dunning, & Ross, 1990
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Naïve realism
Griffin, Dunning, & Ross, 1990
Summary
• Engage “System 2” – Learn the common errors that people make in our uncertain world • They rely too much on affect, availability and representa,veness • They’re overconfident in their decisions
– Take a skep,cal mindset even when you like an ini,al judgment • Don’t be an “assumer”
– Invoke an audience to which you need to jus,fy your thinking
– Next ,me: What is construal?
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