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Simple Heuristics That Make Us Smart
Gerd Gigerenzer
Max Planck Institute for Human Development Berlin
Which US city has more inhabitants,
San Diego or San Antonio?
Americans:62% correct
Germans:?
correct
Germans:100%correct
Goldstein & Gigerenzer, 2002, Psychological Review
If one of two objects is recognized and the other is not,
then infer that the recognized object has the higher value.
The heuristic is successful when ignorance is systematic rather than random, that is, when lack of recognition
correlates with the criterion.
Ecological Rationality
Recognition Heuristic
Wimbledon 2003
Frings & Serwe (2004)
ATPEntry
Ranking
50%
60%
70%
66%68%
69%
ATPChampions
Race
Seedings RecognitionLaypeople
RecognitionAmateurs
CorrectPredictions
Wimbledon 2003
Frings & Serwe (2004)
ATPEntry
Ranking
50%
60%
70%
66%68%
69%
66%
72%
ATPChampions
Race
Seedings RecognitionLaypeople
RecognitionAmateurs
CorrectPredictions
The Less-is-More Effect
The expected proportion of correct inferences c is
is the number of recognized objects
is the total number of objects
is the recognition validity, and
is the knowledge validityA less-is-more effect occurs when
>
where
n
N
Number of Objects Recognized (n)
0 50
100
50
80
75
70
65
60
55
Perc
enta
ge o
f C
orr
ect
Infe
ren
ces
(%)
=.5
=.6
=.7
=.8
Ignorance-based Decision Making: Recognition heuristic
kin recognition in animals food choice & failures of aversion learning in rats (Galef et al. 1990) overnight fame (Jacoby et al., 1989) less-is-more effect (Goldstein & Gigerenzer, 2002) advertisement without product information (Toscani, 1997) consumer choice based on brand name picking a portfolio of stocks (Borges et al.,1999; Boyd, 2001;
Ortmann et al., in press) Institutions competing for the public’s recognition memory
Gaze heuristic
Gaze heuristic
Gaze heuristic
Gaze heuristic
Gaze heuristic:One-reason Decision Making
• Predation and pursuit: bats, birds, dragonflies, hoverflies, teleost fish, houseflies
• Avoiding collisions:sailors, aircraft pilots
• Sports:baseball outfielders, cricket, dogs catching Frisbees
NOTE: Gaze heuristic ignores all causal relevant variables
Shaffer et al., 2004, Psychological Science; McLeod et al., 2003, Nature
Three Visions of Bounded Rationality
People don’t, deviations indicate reasoning fallacies
Cognitive limitations
People act like econometricians
As-if optimization under constraints
There is a world of rationality beyond optimization
Fast and frugal heuristics: Ecological rationality
Gigerenzer & Selten (Eds.) (2001). Bounded Rationality: The Adaptive Toolbox. MIT Press.
When Is Optimization Not An Option?
Well-defined problems:
• NP-hard problems (e.g., chess, traveling salesman, Tetris, minesweeper)
• Criterion lacks sufficient precision (e.g., Mill’s greatest happiness of all; acoustics of concert hall)
• Multiple goals or criteria (e.g.,shortest, fastest, and most scenic route)
• Problem is unfamiliar and time is scarce (Selten, 2001)• In domains like mate choice and friendship, “calculated” optimization
can be morally unacceptable.
All ill-defined problems
Four Mistaken Beliefs
People use heuristics because of their cognitive limits.
Real-world problems can always be solved by optimization.
Heuristics are always second-best solutions.
More information is always better.
The Science of Heuristics
Descriptive Goal: The Adaptive Toolbox
What are the heuristics, their building blocks, and the abilities they exploit?
Normative Goal: Ecological rationality
What class of problems can a given heuristic solve?
Engineering Goal: Design
Design heuristics that solve given problems. Design environments that fit given heuristics.
Gigerenzer et al. (1999). Simple heuristics that make us smart. Oxford University Press.
The Adaptive Toolbox: Heuristics, Building Blocks, Evolved Abilities
Gaze heuristic: “fixate ball, start running, keep angle constant”
Building block: “fixate ball”
Evolved ability: object tracking
Tit-For-Tat: “cooperate first, keep memory of size one, then imitate.”
Building block: “cooperate first”
Evolved ability: reciprocal altruism
Sequential Search Heuristics
Take The Best
Search rule: Look up the cue with highest validity vi
Stopping rule: If cue values discriminate (+/-), stop search. Otherwise go back to search rule.
Decision rule: Predict that the alternative with the positive cue value has the higher criterion value.
Tallying
Search rule: Look up a cue randomly.
Stopping rule: After m (1<m≤M) cues stop search.
Decision rule: Predict that the alternative with the higher number of positive cue values has the higher criterion value.
Don’t add Don’t weight
The Adaptive Toolbox: Heuristics
Class
• Ignorance-based decisions
• One-reason decisions
• Tallying
• Elimination
• Satisficing
• Motion pattern
• Cooperation
Heuristic
• Recognition heuristic, fluency heuristic
• Take The Best, Take The Last, QuickEst, fast & frugal tree
• Tally-3
• Elimination-by-aspect, categorization-by-elimination
• Sequential mate search, fixed or adjustable aspiration levels
• Gaze heuristic, motion-to-intention heuristics
• Tit-for-tat, behavior-copying
The Adaptive Toolbox: Building Blocks
Heuristic
• Take The Best• Take The Last• Minimalist• Fast and frugal tree• Tally-3• QuickEst• Satisficing
Building Blocks
Search rules:• Ordered search• Recency search• Random search
Stopping rules• One-reason stopping• Tally-n stopping (n>1)• Elimination• Aspiration level
Decision rules• Tally-n • One-reason decision making
The Adaptive Toolbox: Building Blocks
Heuristic
• Take The Best• Take The Last• Minimalist• Fast and frugal tree• Tally-3• QuickEst• Satisficing
Building Blocks
Search rules:• Ordered search• Recency search• Random search
Stopping rules• One-reason stopping• Tally-n stopping (n>1)• Elimination• Aspiration level
Decision rules• Tally-n • One-reason decision making
The Adaptive Toolbox: Building Blocks
Heuristic
• Take The Best• Take The Last• Minimalist• Fast and frugal tree• Tally-3• QuickEst• Satisficing
Building Blocks
Search rules:• Ordered search• Recency search• Random search
Stopping rules• One-reason stopping• Tally-n stopping (n>1)• Elimination• Aspiration level
Decision rules• Tally-n • One-reason decision making
The Adaptive Toolbox: Building Blocks
Heuristic
• Take The Best• Take The Last• Minimalist• Fast and frugal tree• Tally-3• QuickEst• Satisficing
Building Blocks
Search rules:• Ordered search• Recency search• Random search
Stopping rules• One-reason stopping• Tally-n stopping (n>1)• Elimination• Aspiration level
Decision rules• Tally-n • One-reason decision making
The Adaptive Toolbox: Building Blocks
Heuristic
• Take The Best• Take The Last• Minimalist• Fast and frugal tree• Tally-3• QuickEst• Satisficing
Building Blocks
Search rules:• Ordered search• Recency search• Random search
Stopping rules• One-reason stopping• Tally-n stopping (n>1)• Elimination• Aspiration level
Decision rules• Tally-n • One-reason decision making
The Adaptive Toolbox: Building Blocks
Heuristic
• Take The Best• Take The Last• Minimalist• Fast and frugal tree• Tally-3• QuickEst• Satisficing
Building Blocks
Search rules:• Ordered search• Recency search• Random search
Stopping rules• One-reason stopping• Tally-n stopping (n>1)• Elimination• Aspiration level
Decision rules• Tally-n • One-reason decision making
The Adaptive Toolbox: Building Blocks
Heuristic
• Take The Best• Take The Last• Minimalist• Fast and frugal tree• Tally-3• QuickEst• Satisficing
Building Blocks
Search rules:• Ordered search• Recency search• Random search
Stopping rules• One-reason stopping• Tally-n stopping (n>1)• Elimination• Aspiration level
Decision rules• Tally-n • One-reason decision making
Ecological Rationality
Sequential Search Heuristics
Take The Best
Search rule: Look up the cue with highest validity vi
Stopping rule: If cue values discriminate (+/-), stop search. Otherwise go back to search rule.
Decision rule: Predict that the alternative with the positive cue value has the higher criterion value.
Tallying
Search rule: Look up a cue randomly.
Stopping rule: After m (1<m≤M) cues stop search.
Decision rule: Predict that the alternative with the higher number of positive cue values has the higher criterion value.
Don’t add Don’t weight
Ecological Rationality
Take The Best Tallying
Weig
ht
Cue Cue
1 2 3 4 5 1 2 3 4 5
Martignon & Hoffrage (1999), In: Gigerenzer et al., Simple heuristics that make us smart. Oxford University Press
compensatorynon-compensatory
0
10
20
30
40
50
60
70
80
90
100
0-24 25-48 49-72 73-96 97-120 121-144 145-168
Ch
oic
es p
red
icte
d b
y T
ake T
he B
est
(%)
Non-compensatory
Feedback
CompensatoryFeedback
Reinforcement learning: Which heuristics to use?
Feedback Trials
Rieskamp & Otto (2004)
Ecological Rationality Heuristic
• Recognition heuristic
• Take The Best;
Fast & frugal tree
• Tallying
• QuickEst
• Imitation
Environment
• alpha > .5
• Noncompensatory information:
Scarce information: M<log2N
• Compensatory information, abundant information
• J-shaped distribution of objects on the criterion
• Stable environments, reliable information
Boyd & Richerson (2001); Goldstein et al. (2001); Hogarth & Karalaia (in press); Martignon & Hoffrage (1999, 2002).;
wj > ∑ wkk>j
Robustness
How accurate are fast and frugal heuristics?
Homelessness rates (50 cities in the United States)
Attractiveness judgments of famous men and women
Average motor fuel consumption per person (all states in the United States)
Rent per acre paid (58 counties in Minnesota)
House prices (Erie, Pennsylvania)
Professors' salaries (a midwestern college)
Car accident rate per million vehicle miles (Minnesota highways)
High school drop-out rates (all high schools in Chicago)
Czerlinski, Gigerenzer, & Goldstein (1999), In: Gigerenzer et al., Simple heuristics that make us smart. OUP
55
60
65
70
75
Take The Best
Tallying
Multiple Regression
Minimalist
Fitting Prediction
Robustness
Accuracy(% correct)
Czerlinski, Gigerenzer,& Goldstein (1999)
Czerlinski et al. (1999): Multiple regression (MR) improves performance (76%) but so does Take The Best (76%)
Hogarth & Karelaia (2004): Take The Best is more accurate than MR if:
- variability in cue validities is high- average intercorrelation between cues ≥ .5- ratio of cues to observations is high
Akaike’s Theorem: Assume two models belong to a nested family where one has fewer adjustable parameters than the other. If both have, on average, the same number of correct inferences on the training set, then the simpler model (i.e., the one with fewer adjustable parameters) will have greater (or at least the same) predictive accuracy on the test set.
Robustness: What if predictors are quantitative?
Design
Chest Pain = Chief ComplaintEKG (ST, T wave ∆'s)
History ST&T Ø ST T ST ST&T ST&TNo MI& No NTG 19% 35% 42% 54% 62% 78%MI or NTG 27% 46% 53% 64% 73% 85%MI and NTG 37% 58% 65% 75% 80% 90%
Chest Pain, NOT Chief ComplaintEKG (ST, T wave ∆'s)
History ST&T Ø ST T ST ST&T ST&T No MI& No NTG 10% 21% 26% 36% 45% 64%MI or NTG 16% 29% 36% 48% 56% 74%MI and NTG 22% 40% 47% 59% 67% 82%
No Chest PainEKG (ST, T wave ∆'s)
History ST&T Ø ST T ST ST&T ST&T No MI& No NTG 4% 9% 12% 17% 23% 39%MI or NTG 6% 14% 17% 25% 32% 51%MI and NTG 10% 20% 25% 35% 43% 62%
The heart disease predictive instrument (HDPI)
See reverse for definitions and instructions
CoronaryCareUnit
regularnursing
bed
chief complaint of chest pain?
CoronaryCareUnit
regularnursing
bed
yes
ST segment changes?
any one other factor? (NTG, MI,ST,ST,T)
yes
yesno
no
no
Fast and frugal classification: Heart disease
Green & Mehr (1997)
.0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1.0
.1
.2
.3
.4
.5
.6
.7
.8
.9
1S
en
sit
ivit
yP
rop
ort
ion
corr
ectl
y a
ssig
ned
False positive rateProportion of patients incorrectly assigned
Physicians
Heart DiseasePredictive Instrument
Fast and Frugal Tree
Emergency Room Decisions: Admit to the Coronary Care Unit?
Sequential Search Heuristics:One-reason decision making
Where? “Rules of thumb” in non-verbal animals Bail decisions in London courts (Dhami, 2003) Patient allocation to coronary care unit (Green & Mehr, 1997) Prescription of antibiotics to young children (Fischer et al. 2002) Parent’s choice of doctor when child is seriously ill (Scott, 2002) Physician’s prescription of lipid-lowering drugs (Dhami & Harris, 2001) Voting and evaluating political parties (Gigerenzer, 1982)
Why? Fisher’s “runaway” theory of sexual selection Zahavi’s handicap principle Environmental structures Robustness Speed, frugality, and transparency
The Science of Heuristics
The Adaptive Toolbox
Ecological rationality
Design
Gigerenzer et al. (1999). Simple Heuristics That Make Us Smart. Oxford University Press.
Gigerenzer & Selten (Eds.) (2001). Bounded Rationality: The Adaptive Toolbox. MIT Press.
Notes
• Start with “if you open a textbook”, then “the term heuristic of of Greek origin”• Collective wisdom arising from individual ignorance:
- Researchers from Oxford University and from the Georgia Institute of Technology developed computer programs mimicking honey bee heuristics to solve the problem of allocate computers to different applications when internet traffic is highly unpredictable. Economist, April17, 2004, p. 78-9.
• Selten, in his Nobel Laureate speech, used the term “repair program”• A widely shared conclusion among decision theorists: neglecting attributes means
neglecting information, thereby violating a central principle of good decision making.
• Duration: 45-50 minutes