Gigerenzer

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

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0-24 25-48 49-72 73-96 97-120 121-144 145-168

Ch

oic

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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

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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