Transcript Probability judgment Ec101 Caltech

Probability judgment
Ec101 Caltech
• How are probability distributions judged?
• Normative:
– Optimal inference using laws of statistics
– Bayesian updating
P(H1|data)/P(H2|data)=[P(H1)/P(H2)]xP(data|H1)/P(data|H2)]
 posterior odds
 prior odds
 likelihood ratio
•Behavioral: TK “heuristics-and-biases” program
– c. ‘74: Heuristic processes substitute for explicit calculation
– Representativeness, availability, anchoring
– Heuristics can be established by the "biases" from optimality
– c. ‘03: System 1 is heuristics, system 2 is rational override
•Note: Controversial! (see Shafir-LeBoeuf AnnRevPsych ’02)
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Examples
• P(event)
– Will Atty General Alberto Gonzales resign?
– Will Tomomi return to Japan after Fulbright?
– Will the TimeWarner-AOL merger succeed?
• Numerical quantities
– Box office gross of “Spiderman 3”
– Inflation rate next year
– Where do I rank compared to others?
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Major topics
1. System 1-2
2. Attribute substitution
3. Prototype heuristics
4. Partition-dependence
5. Optimism and overconfidence
6. Mis-Bayesian approaches
7. Probabilities expressed in markets
8. Research frontiers
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1. A two-system view
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Systems 1 and 2 in action
• “Mindless” behavior (Langer helping
studies)
• A bat and ball together cost $11
• The bat costs $10 more than the ball
• How much does the bat cost?
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Systems 1 and 2 in action
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A bat and ball together cost $11
The bat costs $10 more than the ball
How much does the bat cost?
System 1 guess $10
System 2 checks constraint satisfaction
x+y=11
x-y=10
System 1 “solves” x+y=11, x=10, y=1
System 2 notices that 10-1 ≠ 10
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Provides a tool to study individual
differences
• Cognitive reflection test (CRT, Frederick JEP 05)
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CRT scores
High CRT are more patient and risk-neutral
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2. “Attribute substitution” by system 1
•
“...when an individual assesses a
specified target attribute of a judgment
object by substituting another property of
that object—the heuristic attribute—which
comes more readily to mind.” (Kahneman
03 p 1460)
– Like the politician’s rules: Answer the question
you wish were asked, not what was actually
asked.
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Attribute substitution: Examples
• “Risk as feelings” (Loewenstein et al):
• Question: “Is it likely to kill you?”
• Substitute: “Does it scare you?”
– Role of lack of control and catastrophe
• Mad cow disease (labelling, Heath Psych Sci),
terrorism?, flying vs driving (post 9-11, Gigerenzer 04
Psych Sci)
• Personal interviews (notoriously unreliable):
– Question: “Will this person do a good job?”
– Substitute: “Do you like them, are they glib, etc?”
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Example: Competence judgment by
outsiders highly correlated with actual
Senate election votes (Todorov et al Sci 05)
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3. Prototype heuristics
• Tom W:
Neglect college major
base-rate when
prototype matching is
accessible
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Conjunction fallacy
• Stereotypes can
violate conjunction
laws
• P(feminist bank
teller)>P(bank teller)
• Easily corrected by
“Of 100 people like
Linda..”
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Which attribute is substituted depends
on “accessibility” (cf. availability)
• What is area? (top)
• What is total line
length? (bottom)
• Implies that displays
matter
• Role for supply-side
“marketing”
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Availability (c. 1974)
• Is r more likely to occur as 1st or 3rd letter?
• “illusory correlation” (Chapman ’67 J AbnPsych)
– E.g. gay men draw muscular or effeminate people in D-A-P test
– Resistant to feedback because of encoding bias and availability
• Confirmation bias: Overweight +/+ cell in 2x2 matrix
– Secret to astrology
• “Something challenging is going on in your life…”
– Wayne Lukas horse trainer…winningness in Breeder’s Cup! (and
losingest)
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Mystique of horse trainer
D. Wayne Lukas
- Has been the dominant
trainer in the Breeders' Cup
and is the only trainer to have
at least one starter every year.
Is the career leader in purse
money won, starters and
victories. Ten of his record 18
wins have come with 2-yearolds: five colts and five fillies.
Has also won the Classic
once, the Distaff four times,
the Mile once, the Sprint
twice …(ntra.com)
– http://www.ntra.com/images/2006
_MEDIA_Historical_trainers.pdf
–
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Mystique of horse trainer
D. Wayne Lukas
- Has been the dominant
trainer in the Breeders' Cup
and is the only trainer to have
at least one starter every year.
Is the career leader in purse
money won, starters and
victories. Ten of his record 18
wins have come with 2-yearolds: five colts and five fillies.
Has also won the Classic
once, the Distaff four times,
the Mile once, the Sprint
twice …(ntra.com)
Wayne
Lukas
All
others
Shug
McGaughey
Won 18
98
9
(%)
(12.4%)
(12.7%) (18.8%)
Lost
127
769
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– http://www.ntra.com/images/2006
_MEDIA_Historical_trainers.pdf
–
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Illusions of transparency
• Hindsight bias:
– Ex post recollections of ex ante guesses tilted
in the direction of actual events
• Curse of knowledge
– Hard to imagine others know less (Piaget on
teaching, computer manuals…)
• Other illusions of transparency
– Speaking English louder in a foreign country
– Gilovich “Barry Manilow” study
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4. Partition-dependence
• Events and numerical ranges are not always naturally
“partitioned”
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–
–
–
E.g.: Car failure “fault tree”
Risks of death
Income ranges (e.g. economic surveys)
{Obama, Clinton, Republican} or {Democrat, Republican}
• Presented partition is “accessible”
– Can influenced judged probability when system 2 does not
override
– Corollary: Difficult to create the whole tree
– “When you do a crime there are 50 things that can go wrong.
And you’re not smart enough to think of all 50”– Mickey Rourke
character, Body Heat)
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1/n heuristic in portfolio allocation
(Benartzi-Thaler AER)
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Probabilities are sensitive to the
partition of sets of events
(Fox, Clemen 05 Mgt Sci, S’ss are Duke MBAs judging starting salaries)
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Prediction markets for economic
statistics (Wolfers and Zitzewitz JEcPersp 06)
An Example: Price of Digital Options
Auction on Retail Trade Release for April 2005; Held May 12, 2005
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.094 .092
.087
.08
.087 .085
.074
.065
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.052
.04
.035
.063 .063
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.015
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.4
0
.02
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Debiasing partition-dependent
forecasts improves their accuracy
(Sonneman, Fox, Camerer, Langer, unpub’d)
• Suppose observed F(x) is a mixture of an unbiased B(x)
and diffuse prior (1/n)
F(x)=αB(x)+(1-α)(1/n)
compute B*(x|α)=(1/α)[F(x) – (1-α)(1/n)]
(e.g. B(.6<retail<.7|α=.6)=(1/.6)[.094 – (.4)(1/18)]=.120)
• Are inferred B(x) more accurate than observed F(x)?
– Yes: α=.6 mean abs error .673
(α=1 .682)
– Correction w/ α=.99 improves forecasts 58.2% (n=153, p<.01)
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Anchoring
• An “anchor” is accessible
• System 2 must work hard to override it
• E.g. % African nations in the UN (TK 74)
– (visibly random) anchor 10  median 25
anchor 45  median 65
• “Tom Sawyer” pricing
2
d
o
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1
0
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1
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2
1
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“Tom Sawyer” pricing of poetry reading
(+/- $5 anchor)
(Ariely et al)
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5. Optimism and overconfidence
• Some evidence of two “motivated
cognition” biases:
– Optimism (good things will happen)
– Overconfidence
• Confidence intervals too narrow (e.g. Amazon
river)
• P(relative rank) biased upward (“Lake Woebegone
effect”)
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Relative overconfidence and
competitor neglect
• Entry game paradigm (Camerer-Lovallo ‘99 AER):
– 12 entrants, capacity C (2,4,6,8,10)
– Top-ranked C earn $50 total. Bottom -$10
– Ranks are random, or based on skill (trivia)
– Also guess number of other entrants
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Earn more $ in random compared to skill
(aren’t thinking about competitor skill)
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Competitor neglect
• Joe Roth (Walt Disney studio head) on why big
movies compete on holidays:
• Substitute “Is my movie good?” attribute for “Is
my movie better?”
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• “Hubris” of CEO’s
correlated with
merger premiums (Roll,
’86 JBus; Hayward-Hambrick
97 ASQ)
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Narrow confidence intervals
• Surprising fact:
– 90% confidence intervals too narrow (50% miss)
– Feb. 7, Defense Secretary Donald Rumsfeld, to U.S. troops in
Aviano, Italy: "It is unknowable how long that conflict will last. It
could last six days, six weeks. I doubt six months.“
– Paul Wolfowitz, the deputy secretary of defense at the time, was
telling Congress that the upper range [60-95 B$] was too high and
that Iraq's oil wealth would offset some of the cost. "To assume
we're going to pay for it all is just wrong," Wolfowitz told a
congressional committee.
– Nonfatal examples: Lost golf ball or contact lens, project costs/time
(planning fallacy)
• But ask: How many will be wrong? People correctly say 5
of 10 (Sniezek et al)!
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Inside/outside view
(Kahneman-Lovallo 93 Mgt Sci, HBR 03)
• How could each decision be wrong but
aggregate statistics be right?
• Inside view:
– Rich, emotive, narrative, rosy
• Outside view:
– Abstract, acausal, statistical
• Examples: Marriage, merger
• No formal model of this yet
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6. ‘Mis-’Bayesian approaches
• Start with Bayesian structure
• Relax one or more assumptions
• Choose structure so resulting behavior
matches stylized facts
• Find new predictions
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‘Mis-’Bayesian approaches (cont’d)
• Confirmation bias (Rabin-Schrag, QJE 97)
• Law of small numbers (Rabin, QJE 01?)
• Overconfidence (Van de Steen, AER; Santos-Pinto &
Sobel, AER)
• Preference for optimistic beliefs (BrunnermeierParker AER)
• …many more
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Confirmation bias
• States A and B. Signals a, b.
• Signal a more indicative of A
(P(a|A)>P(a|B)
• P(A)>P(B)  a perceived correctly, b
misperceived with some probability (“see it
when you believe it”)
•  mistaken belief can persist a long time
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Relative overconfidence
• People differ in production functions based
on skill bundles (Santos-Pinto, Sobel AER)
• Invest in skills to maximize ability
• Compare themselves to others using their
own production function
– i.e,. implicitly overrate the importance of what
they are best at (cf. Dunning et al 93 JPSP)
• Prediction: Narrowly-defined skill will erase
optimism; it does
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7. Probability estimates
implicit in markets
• Do markets eliminate biases?
Pro: Specialization
– Market is a dollar-weighted average opinion
– Uninformed traders follow informed ones
– Bankruptcy of mistaken traders
• Con: Investors may not be selected for probability judgment
– Short-selling constraints
– Confidence (and trade size) may be uncorrelated with information
– Herd behavior (and relative-performance incentives) may reduce
capacity to correct mistakes (Zweibel, LTCM tale of woe)
• Example study:
– Camerer (1987)
• Experience reduces pricing biases but *increases* allocation biases
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Representativeness
bias is perceptible but
diminishes with
experience (bottom,
Camerer 87 AER)
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8. Research frontiers
• Aggregation in markets
• Novice system 2 deliberate calculations become
replaced by expert system 1 “intuitions” (cf.
“Blink”)
– How? And are they accurate?
• Reconciling mis-Bayesian and system 1-2
approaches
• Applications to field data
• Where are systems 1-2 in the brain?
• System 1-2 approach predicts fragility of some
results (e.g. Linda the bank teller)… how can
system 2 be turned on?
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