Cognitive Economics
Miles Kimball
University of Michigan
Presentation at Osaka University
Definition of Cognitive
Economics:
The Economics of What
is in People’s Minds
Named by Analogy to
“Cognitive Psychology”
• Cognitive Psychology = the area of
psychology that examines internal mental
processes such as problem solving,
memory and language.
• Cognitive Psychology was a departure
from Behaviorism--the idea that only
outward behavior was a legitimate subject
of study.
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How is “Cognitive Economics”
Different from “Behavioral” or
“Psychological” Economics?
1. Cognitive economics is narrower.
2. Much of cognitive economics is inspired
by the internal dynamic of economics
rather than by psychology.
3. Cognitive economics is a field of study,
not a school of thought.
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Areas of Economics by
Distinctive Data Type
• Standard Economics (including “Mindless”
Psychological Economics a la Gul and
Pesendorfer): actual market choices only.
• Experimental Economics: choices in artificial
situations but with real stakes.
• Neuroeconomics: FMRI, saccades, skin
conductance, …
• Bioeconomics: genes, hormones
• Cognitive Economics: mental contents (based
on tests and self-reports) and hypothetical
choices.
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Four Themes of Cognitive
Economics
1.
2.
3.
4.
New Types of Data
Heterogeneity
Finite and Scarce Cognition
Welfare Economics Revisited
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1. Innovative Survey Data
•
•
•
•
•
fluid intelligence data
crystallized intelligence data
happiness data
survey measures of expectations
survey measures of preferences
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2. Individual Heterogeneity
•
•
•
•
heterogeneous expectations
heterogeneous preferences
heterogeneous emotional reactions
heterogeneous views on how the world
works (folk theories)
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3. Finite and Scarce Cognition
• Finite cognition=the reality that people
are not infinitely intelligent.
• Scarce cognition=some decisions
required by our modern environment—at
work and in private lives—can require
more intelligence for full-scale
optimization than an individual has
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4. Welfare Economics Revisited
•
•
•
Scarce cognition means that people sometimes make
mistakes. Thus, one can no longer use naïve revealed
preference for welfare economics.
Kimball and Willis, in “Utility and Happiness,” argue that
happiness data is not a magical touchstone for
diagnosing mistakes.
Then, what does count as evidence of mistakes?
–
–
–
–
Internal inconsistencies, such as lack of transitivity? But which
choice then deserves respect?
Regret?
Modification of choices after experience?
Differences in choices between those with high cognitive ability
and those with low cognitive ability?
•
e.g., Dan Benjamin and Jesse Shapiro show that low IQ students
had more low-stakes risk aversion and short-horizon impatience
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Some Research Questions
in Cognitive Economics
• Seek to make innovations in economic theory and
measurement to address:
– What are people’s limitations in knowledge, memory,
reasoning, calculation?
– What is the role of emotion, social context, conscious
vs. unconscious judgments and decisions?
– What is the role of health as determinant, outcome
and context for economic activity, decisions and well
being?
– What is connection between economic welfare and
measures of well being?
– Etc.
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New Types of Data:
Measurement of Cognition
in the HRS
• HRS has included cognitive measures from the
outset, but mostly focused on memory in order
to trace cognitive decline.
• Re-engineering HRS cognitive measures
– Led by Jack McArdle, a cognitive psychologist and
HRS co-PI, we have begun a project to “re-engineer”
our cognitive measures in order to improve our
understanding of the determinants of decision-making
about retirement, savings and health and their
implications for the well-being of older Americans
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New Types of Data:
Measurement of Cognition
in the HRS (cont.)
• Separate HRS-Cognition Study
– Begins with a separate sample of 1200 persons age 50+ who will
receive about three hours of cognitive testing of their fluid and
crystallized intelligence plus parts of the HRS questionnaire on
demographics, health and cognition
– Followed a month later by administration of an internet or mail
survey of questions designed by economists on financial literacy,
ability to compound-discount, hypothetical decisions about
portfolio choice, long term care
– Finally, telephone follow-up with HRS cognition items and
subjective probability questions
– Analysis of data will guide re-engineering of cognitive items for
HRS-2010
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New Types of Data: Survey
Measures of Expectations
What is the mapping between
probability beliefs in people’s minds
and the decisions they make?
(Robert Willis, Charles Manski, Mike Hurd, Jeff Dominitz, Adeline Delavande)
Direct Measurement of Subjective
Probability Beliefs in HRS
Probability questions use a format pioneered by
Tom Juster and Chuck Manski
(Manski, 2004)
HRS Survival Probability Question:
“Using a number from 0 to 100, what do you think
are the chances that you will live to be at least [target
age X]?”
X = 80 for persons 50 to 70 and increases to 85, 90,
95, 100 for each five year increase in age
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Two Key Findings From Previous
Research on HRS Probability Questions
1. On average, probabilities make sense
– Survival probabilities conform to life tables and are
predictive of actual mortality
(Hurd and McGarry 1995, 2002; Sloan, et. al., 2001 )
– Bequest probabilities behave sensibly
(Smith 1999), Perry (2006)
– Retirement incentives can be analyzed using expectational data
(Chan and Stevens, 2003)
– People can predict nursing home entry
(Finkelstein and McGarry, 2006)
– Early Social Security Claiming Depends on Survival Probability
(Delevande, Perry and Willis, 2006) , (Coile, et. al., 2002)
2. Individual probabilities are very noisy with heaping on focal
values of "0", "50-50" and "100“
(Hurd, McFadden and Gan, 1998)
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10 Year Mortality Rate vs. Subjective
Survival Probability to Age 75
3.00
2.76
2.49
2.50
Odds Ratio (50%=1.0)
Odds Ratio
of Death
by t+10
2.00
1.47
1.50
1.43
1.22
1.00
1.00
0.86
0.85
0.79
0.68
0.70
80
90
0.50
0.00
0
10
20
30
40
50
60
70
100
Probability of survival to age 75
Subjective Survival Probability at Time t
Source: Mortality Computations from HRS-2002 by David Weir
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10 Year Mortality Rate vs. Subjective
Survival Probability to Age 75
3.00
2.76
Strongest relationship between
subjective and objective risks for people
with low subjective survival beliefs
2.49
2.50
Odds Ratio (50%=1.0)
Odds Ratio
of Death
by t+10
2.00
1.47
1.50
1.43
1.22
1.00
1.00
0.86
0.85
0.79
0.68
0.70
80
90
0.50
0.00
0
10
20
30
40
50
60
70
100
Probability of survival to age 75
Subjective Survival Probability at Time t
Source: Mortality Computations from HRS-2002 by David Weir
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. Histograms of Responses to Probability Questions in the HRS
.3
.2
.2
F r a c tio n
Social Security less
generous
Double digit inflation
F r a c tio n
A. General Events
.1
.1
0
0
0
100
0
.3
.2
.2
0
0
0
0
50
W ill L iv e T o B e 7 5
100
.3
.4
.2
100
.1
.2
Leave inheritance
Work at age 62
50
In c o m e W ill K e e p U p W ith In fla tio n
F r a c tio n
.6
F r a c tio n
C. Events with
Personal Control
100
.1
.1
Survival to 75
Income increase
faster than inflation
50
D o u b le -D ig it In fla tio n
F r a c tio n
.3
F r a c tio n
B. Events with
Personal Information
50
S o c ia l S e c u rity T o B e L e s s G e n e ro u s
0
0
0
50
W ill L e a ve In h e rita n c e > = 1 0 ,0 0 0
100
0
50
W ill W o rk A t A g e 6 2
100
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Are Benefits of Greater Individual Choice
Influenced by Quality of Probabilistic
Thinking?
• Trend of increasing scope for individual choice in public
and private policy, especially as it affects those planning
for retirement or already retired
– Private sector shift from defined benefit to defined contribution
pension plans
– Proposals for “individual accounts” in Social Security
– Choice of when/whether to annuitize
– Choice of medical insurance plans and providers by employers
and by Medicare, new Medicare Prescription Drug program
• Economists generally view increased choice as a good
thing, but …
– General public wonders whether people will make wise use of
choice
– Decisions faced by older individuals balancing risks and benefits
of alternative financial and health care choices are genuinely
difficult
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Quality of Probabilistic Thinking and
Uncertainty Aversion
• Lillard and Willis (2001) began to look at the
pattern of responses to probability questions as
indicators of the degree to which they indicate
people’s capacity to think clearly about
subjective probability beliefs
• We explored the idea that focal answers of “0”,
“50” and “100” were perhaps indicators of less
coherent or well-formed beliefs than non-focal
(or “exact”) answers.
20/65
Index of Focal Responses
We treated the probability questions like a
psychological battery and constructed an empirical
propensity to give focal answers of “0”, “50” or “100”
number focal answers
Index of Focal Answers =
total number of probability questions
We found that people who had a lower propensity to
give focal answers tended to have higher wealth,
had riskier portfolios, and achieved higher rates of
return, controlling for conventional economic and
demographic variables
21/65
Uncertainty Aversion
• We hypothesized that people who give
more focal answers are more uncertain
about the true value of probabilities
• If the uncertainty is about a repeated risk,
such as the return to a stock portfolio held
over time, we show that people who have
more imprecise probability beliefs (i.e. are
more uncertain about the “true” probability)
will behave more risk aversely
22/65
Some Further Results on
Subjective Probabilities
• There is “optimism factor” common across all probability questions
which is correlated with stock-holding and associated with being
“healthy, wealthy and wise”
• Kezdi and Willis (2003)
• HRS has added direct questions on stock returns
– stockholding is related to probability beliefs
• Kezdi and Willis (2003) and Dominitz and Manski (2006)
– most people do not believe that stocks have positive returns, despite the
equity premium that economists know about
• Persons who provide more precise probability answers also exhibit
less risk aversion on subjective risk aversion questions in the HRS,
and they save a higher fraction of their full wealth.
• Sahm (2007), Pounder (2007)
• In 2006, HRS added questions to those who answer “50” to see
whether they mean “equally probable” or “just uncertain”. 75%
indicate they are uncertain.
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New Types of Data: Survey
Measures of Preferences Based on
Hypothetical Choices
Examples:
• Labor Supply Elasticities,
• Altruism,
• Social Rivalry,
• Risk Aversion,
• Elasticity of Intertemporal Substitution
24/65
Does Risk Tolerance Change?
Claudia Sahm
University of MichiganBoard of
Governors
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Measuring Time Preference and the
Elasticity of Intertemporal Substitution
Miles S. Kimball, Claudia R. Sahm
and Matthew D. Shapiro
September 6, 2006
Internet Project Meeting
Behavioral Model
 log c  s(r   )
• c is consumption,
• r is the real interest rate,
• s is the elasticity of intertemporal
substitution, and
• ρ is the subjective discount rate
50/65
Research Design
Vary Treatment : r
Observe Response : c
Estimate Parameters : s, ρ
51/65
Implementation
• Vary Interest Rate
– Vary cost of current consumption
– Vary length of time periods
• Measure Consumption Choice
– Choose among small set of paths
– Actively form a desired path
• Infer Preferences
– Summary statistics of responses
– Statistical model with response error
52/65
Previous Survey Measures
• HRS 1992 Module K, N = 198
– Analyzed by Barsky, Kimball, Juster, and
Shapiro (QJE 1997)
• HRS 1999 Mailout, N = 1,210
– Similar content to part of Internet Survey
Questions explicitly vary the cost of current
consumption and offer a discrete choice over a
small set of consumption paths
53/65
MS Internet Survey
Wave 2 (Fall 2004)
Use graphics on Internet to test other measures:
• Version 1, N = 350
– Vary cost of consumption
– Choose from set of pairs
• Version 2, N = 155
– Vary cost of consumption
– Move bars to create pair
• Version 3, N = 183
– Vary length of period
– Move bars to create pair
54/65
Series Introduction
- Version 1 -
• Series includes four questions with varying interest rates55/65
Introduction – 0% Interest Rate
• Sequence r = {0%, 4.6%, 9.2%, 13.8%} is random
• Introduction repeated for each interest rate
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Patterns – 0% Interest Rate
• Asked to choose two patterns
• Above screen (1 of 6) is identical to HRS Mail Out
57/65
Expansion Screen
• Follow-up if first choice
on boundary (A or E)
• New feature on Internet
58/65
Randomize Pair C
• Choice C positive, zero,
negative growth rate
• 3 values to the parameter
• New feature on Internet
• Top screen on mail out
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Randomize Left-to-Right
• Growth rates increase
or decrease left-to-right
• New feature on Internet
• Top screen on mail out
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Randomize Shifts with Interest Rate
• Example with r = 9.2%
• Choice of ($2750, $3900)
moves from E to C to A
• 3 values to the parameter
• New feature on Internet
• Middle screen on mail out
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Summary of Innovations in
Internet Question Series
• 18 different screen groups
• 6 different sequences of interest rates
• 11 discrete choices per question
Purpose of Innovations
• Encourage active choices
• Increase informative responses
• Isolate framing effects
62/65
Response Statistics
Internet
Mail
Any Responders
Complete Sequence
%
100.0
82.2
N
366
301
%
100.0
88.2
N
930
820
Complete Responders
Second Choice Pairs
All Extreme Pairs
100.0
77.1
2.0
301
232
6
100.0
90.6
17.2
820
743
141
• Internet lower completion rate
• Internet fewer second choices
• Internet fewer non-informative responses
63/65
Consumption Growth at 0%
Interest Rate
45
40
35
Percent
30
25
20
15
10
5
13.9
11.6
9.2
6.9
4.6
2.3
0.0
-2.3
-4.6
-6.9
-9.2
-11.6
-13.9
0
Consumption Growth at 0% Interest Rate
Internet
Mail
• Constant consumption is modal choice
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Change in Consumption Growth as
Interest Rate to 13.8% from 0%
60
50
Internet
% Increase Growth
48.5
% Decrease Growth
25.9
Percent
40
Mail
26.5
17.2
30
20
10
23.1
20.8
18.5
16.2
13.9
11.6
9.2
6.9
4.6
2.3
0.0
-2.3
-4.6
-6.9
-9.2
-11.6
-13.9
-16.2
-18.5
-20.8
-23.1
0
Growth at r = 13.8% Minus Growth at r = 0%
Internet
Mail
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• Interest rates change consumption more on Internet
Change in Consumption Growth as
Interest Rate Increases - Internet
45
40
35
% Increase Growth
% Decrease Growth
Percent
30
4.9%
34.2
22.9
13.8%
48.5
25.9
25
20
15
10
5
23.1
20.8
18.5
16.2
13.9
11.6
9.2
6.9
4.6
2.3
0.0
-2.3
-4.6
-6.9
-9.2
-11.6
-13.9
-16.2
-18.5
-20.8
-23.1
0
Consumption Growth - Growth at 0% Interest Rate
r = 4.6%
r = 13.8%
• Decrease in growth is a sign of survey response 66/65
error
Estimates of Parameters
Average in Sample
Growth at r = 0% : -sρ
Internet
0.1%
Mail
1.3%
IES by Interest Rate : s
0% to 4.6%
4.6% to 9.2%
9.2% to 13.8%
0.13
0.14
0.05
0.004
0.01
0.04
• Responses reveal low time preference and IES
• Median and modal values in both surveys equal 0
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Effect of Changes in Choice Set
50
Growth Rates in Choice Set
Choice
Decrease Stable Increase
% Increase
47.9
35.6
61.5
% Decrease
42.7
16.8
19.2
45
40
Percent
35
30
25
20
15
10
5
0
-23.1 to -6.9
-4.6 to -2.3
0
2.3 to 4.6
6.9 to 23.1
Growth at r=13.8% Minus Growth at r =0%
Decrease
Stable
Increase
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Estimates by Screen Group
Average in Sample
Growth at r = 0% : -sρ
IES by Interest Rate : s
0% to 4.6%
4.6% to 9.2%
9.2% to 13.8%
Respondents
Interest Rate Increases,
Growth Rates in Choice Set
Decrease Stable Increase
-0.14%
0.4%
0.15%
-0.10
0.30
-0.31
96
0.16
0.00
0.12
101
0.32
0.14
0.33
104
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More Graphical Questions
- Version 2 -
• Move bars to select a consumption path
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More Graphical Questions
- Version 3 -
• Vary length of current and future periods
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Extensions / Renewal
• Measure complementary parameters
– Diminishing marginal utility
– Labor supply elasticities
– Retirement elasticity
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Four Themes of Cognitive
Economics
1.
2.
3.
4.
New Types of Data
Heterogeneity
Finite and Scarce Cognition
Welfare Economics Revisited
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“Bounded Rationality” vs.
“Scarce Cognition”
•
•
Same meaning, but “bounded rationality”
seems a misnomer, since it is rational to
recognize one’s own cognitive limitations.
Two obstacles have prevented “Bounded
Rationality” from becoming part of the
mainstream
– theoretical difficulties stemming from the importance
of constrained optimization as a theoretical tool in
economics
– paucity of data
•
“Scarce Cognition” is meant to label a data-rich
research agenda, using new theoretical tools.
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The Reality of Finite Cognition
• Computers beat us at chess
• People don’t get perfect scores on
tests, even after they have studied
the material
• For hundreds of years, we had no
proof of Fermat’s last theorem
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The Reality of Scarce Cognition
Many people …
• spend time and money learning math
• pay others with higher wage rates to do
their taxes
• pay others to read law books for them
• pay for financial advice
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Modeling Scarce Cognition is Hard:
The Infinite Regress Problem (Conlisk)
• It is natural for economists to assume a
cost of computation, just like any other
cost—so why not more such models?
• Answer: figuring out how hard to think
about a problem is always a strictly harder
problem than the original problem
– Need the solution to the original problem to
calculate the benefit
– Need to know how to solve the problem to
know how many computational steps it needs
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Dodging the Infinite Regress
Problem by Breaking Taboos
1. Ignoring computational costs at the outer level.
(Maybe OK if the original problem is a
repeated choice.)
2. Using limited information transmission capacity
as a metaphor for limited intelligence. (“A thick
skull.”)
3. Subhuman intelligence:
--agent-based modeling
--rules of thumb (adaptive expectations, consume
income, statistical models)
4. Modeling folk theories ignorant of the
maintained hypotheses
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Modeling Unawareness Requires a
Subjective State Space Distinct
from the True State Space
(Dekel, Lipman and Rustichini)
• economic actor: subjective state space
• analyst: state space maintained as true
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Two Levels of Theory
• Folk theory: economic actor’s theory modeled in
the subjective state space
• Metatheory: the analyst’s theory which includes
a description of the relevant folk theories.
–
–
–
–
–
Preferences
Technology
Available Strategies
Active Information Structure
Folk Theories or Accounting Frameworks of Agents
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Desirable Properties for a Model of
a Folk Theory
• Accuracy in describing how people
actually view the world
• Providing a clear prediction for how people
will behave in various circumstances
• Representing clearly how people are
confused and what they do understand.
NOT REQUIRED: deep logical consistency
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An Example of Folk Physics
• Many people believe that if they swing a
stone around on a string and let it go, then
the stone will curve sideways in the
direction they were swinging it around.
• Other than going up and down in the
vertical direction, it actually goes straight
once released.
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An Example of Folk Finance
max
si  0
si 1

 (
i
  / 2)( si   s / 2)
2
i
2
i
i
i
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Household Finance and
Welfare Economics: The Possibility
of Strong Normative Statements
•
Fungibility: money is money
– At the end of the day, only the total value of the
portfolio matters, not the separate value of its
constituent parts.
– Fungibility is a legal term: OK to pay back a different
piece of currency as long as the value is the same.
(Not like a diamond ring)
– Very basic principle in economics: fungibility of
money is assumed in standard treatments of
revealed preference
– Noneconomists do not always understand fungibility:
“mental accounts”
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