Introduction, Definition, and Methodology
David Laibson
June 30, 2014
Note: Powerpoint deck includes many “hidden slides,” which were not used in actual presentation.
Outline
• Very quick introductions: Emily, Leana, Matthew, David
• Very quick introductions: you
– Name
– School
– Fields of interest
– Who you started rooting for in the world cup
• Definition of Behavioral Economics
• Methodology
• Seven properties
• Thumbnail history (for more details look at slides)
If you ask questions that are too aggressive, we’ll
use the following system to let you know.
=
Semantics
• Behavioral economics
– name irritates people
– are there any economists who aren’t studying
behavior?
• Other names you’ll hear:
– Psychology and economics
– Psychological economics
• Subfields:
– Behavioral Finance
– Behavioral Game Theory
– Behavioral Public Finance
– Behavioral IO
– etc…
Definition: Behavioral Economics
• Behavioral economics is just like the rest of
economics, but also includes psychological factors.
• Adds psychology to economics, particularly cognitive
psychology, social psychology, and neuroscience.
• Buy texts in these fields to learn the psychology
a. Schacter, Gilbert, and Wegner, Psychology
b. Ross and Nisbett, The Person and the Situation
c. Glimcher et al eds, Neuroeconomics
• Consider taking a couple of intro psych courses (tastes
good and good for you)
An obnoxious definition
• The Guardian: The study of “how people actually
make decisions rather than how the classic economic
models say they make them.”
• We don’t apply ideological litmus tests (like rationality
or dynamic consistency). Nothing is ruled out or
ruled-in ex-ante.
Definition
• Pay special attention to these psychological factors:
– Imperfect rationality
– Imperfect self-control
– Imperfect selfishnss (social preferences)
– But this list is only a start (e.g. psychological
conceptions of personality)
• Emphasize the importance of microfoundations
– Preferences
– Beliefs
– Cognition
• Take experimental evidence seriously
– but don’t rely exclusively on it
• Vote for Obama
Naïve quasi-hyperbolic agent
(ex-)Regulator-in-chief
Cass Sunstein
Administrator of the
White House Office of Information and Regulatory Affairs
But we also vote for David Cameron
(the conservative Prime Minister of the UK)
The Behavioural Insights Team
• “Set up in July 2010 with a remit to find innovative
ways of encouraging, enabling and supporting people
to make better choices for themselves.”
It turns out that behavioral economics has supporters
on both sides of the political aisle – e.g., the (US)
Pension Protection Act was bipartisan. This legislation
championed the use of defaults and auto-escalation.
Distinct from...
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Experimental economics
Psychology
Behavioralism (we are not Behavioralists)
Evolutionary psychology
Evolutionary economics (BE takes preferences and
cognition as primitives)
Sociology and economics
Radical economics
‘Economics sucks’ economics
Lazy economics
Sloppy economics
Ad hoc economics
Is behavioral economics a field?
No:
• Few “pure” jobs
• Difficult job market
• No journal
• Why ghettoize?
• Applied theory is not a
field, so why should
applied psychology be a
field?
Yes:
• Some courses
• You can take behavioral orals
• Some seminars
• Many conferences
• Some “methodological” fields
do exist: econometrics,
theory, experimental
economics
Future field status uncertain.
Our expectation/wish
• All economists will eventually incorporate
behavioral stuff where appropriate.
• Psychology is to “normal economics” as
game theory is to “normal economics.”
• Everyone uses it as a matter of course.
Methodology
• Experimental science
• What makes a good model?
• [Beware of multiple-testing bias (and p-hacking)]
Lab empirics (experiments)
• If experiments are run well, they will have high
internal validity
– I understand the specific causal mechanism that is
driving my result
– I can turn the result on and off by manipulating the
experimental treatment
– My result is robust and replicable (not “fragile”)
• But even a well-run experiment may have low
external validity
– The mechanism that I am studying is important for
particular real-world behaviors
• Experiments complement (do not substitute for) field
research
Problems with internal and external validity
in lab experiments.
Internal validity
• experimental artifacts
• demand effects (are the
subjects trying to respond
to the perceived
expectations of the
experimenter?)
External validity
• unrepresentative subjects
• under-experienced
subjects
• missing decision aids
• under-incentivized tasks
• non-naturalistic problems
• Thousands of other ways
that lab decisions differ
from field decisions
“The Rules”
“The Rules”
Psychology
Experimental
Economics
Behavioral
Economics
Deception
OK, if justified
Prohibited;
Require full information
Almost always Prohibited;
Almost always require full
information
Incentivecompatibility
using money
Rare;
Required
Money isn’t the only
motivator
Generally used
Context
Often rich
Attempt to strip away
context (vanilla context)
 Sometimes studied
 Recognize that context is
unavoidable
Exogeneous
treatment
Almost always
Sometimes
Usually
Documentation
Summary of design
Experimental instruments;
complete dataset
Experimental instruments;
complete dataset
Stationary
replication
Almost never
Common (plus emphasis on  Important if you care about
last period)
learning.
 First period also of great
interest
Adapted from George Loewenstein
Experimental Debriefing
(especially for pilots)
Aggressively use debriefing surveys.
• “Was the experiment confusing?”
• “What strategies did you use?”
• “How did you come up with your answer?”
• “What was the experiment about?”
• “What were the other subjects thinking?”
• What would your payoff have been if you had gone UP
instead of DOWN?”
Field experiments and
lab experiments are
complementary
• Neither is the gold standard
• They feed off (and stimulate) each other in useful ways
• Avoid making the mistake of thinking that just because
you’ve run a well-designed lab experiment you know how
the phenomenon will generalize
• Avoid making the mistake of thinking that just because
you’ve run a well-designed field experiment you know
how the phenomenon will generalize
Seven Properties
Gabaix and Laibson (2008)
These properties typically need to be traded off against
each other. No social science model achieves all of
these goals.
1. Parsimony
2. Tractability
3. Conceptual insightfulness
4. Generalizability (portability)
5. Falsifiability
6. Empirical accuracy
7. Predictive precision: the model makes sharp
predictions.
Figure 1: The value of parsimony.
6
Sample for estimation
of a 5th order polynomial
2
0
-2
The data (squares) is generated by sin(x/10) + ε, where ε is distributed
uniformly between -½ and ½. The sold line fits the first 50 data points to
a fifth-order polynomial – a non-parsimonious model. The polynomial
has good fit in sample.
Figure 1: The value of parsimony.
6
Sample for estimation
of a 5th order polynomial
2
0
-2
The data (squares) is generated by sin(x/10) + ε, where ε is distributed
uniformly between -½ and ½. The sold line fits the first 50 data points to
a fifth-order polynomial – a non-parsimonious model. The polynomial
has good fit in sample and poor fit out of sample (dashed line).
Figure 2:
Falsifiability, Empirical Consistency, and Predictive Precision
Model = “(X,Y) = (1,5)” =
Model = “X+Y > 1” =
Data =
Data =
Y
Y
5
1
1
1
X
Panel A: Model is falsifiable, empirically
consistent, and does not have predictive
precision.
1
X
Panel B: Model is falsifiable, empirically
inconsistent, and has predictive precision.
If physicists wrote theorems like
economists:
Theorem (existence and uniqueness): Given any initial
conditions for a set of mass-points in a vacuum, there
exists a unique continuation path that obeys the laws of
gravity.
This is falsifiable (is it interesting or useful?).
Useful classical physics:
Theory: At the surface of the earth gravity causes a constant
acceleration of g = 9.8 m/s².
Predictive precision: An object projected from the surface of
the earth will follow a parabolic path, attaining a height of
h = v2/(2g) before falling back to the surface (where v is the
vertical velocity of the object at t = 0).
Predictive Precision in Economics
Black-Scholes Option Pricing Formula
Auction Theory
Solow model with the Kaldor facts
Quantity theory of money
These theories are not exactly right, but they do make
precise quantitative predictions that are almost right.
The Role of Assumptions
• Models use assumptions – including axioms – to make
predictions.
• Scientific models do not have inviolate axioms.
• Scientific axioms – even seemingly sacrosanct axioms –
are usually modified with time.
– Earth is flat
– Planets and stars rotate around earth
• Ptolemaeus vs. Copernicus
– Space is three dimensional and Euclidean
• Newton vs. Einstein
Economic Assumptions
• Classical economic assumptions are also useful
approximations.
– Perfect rationality
– Dynamic consistency
– Revealed preference
• These assumptions should be continuously judged on their
ability to enhance the seven modeling properties enumerated
a few slides back.
Outline
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Quick introductions
Definition of Behavioral Economics
Methodology
Seven properties
Thumbnail history
Thumbnail history...
• Bounded rationality of Simon succeeded more as rhetoric
than as something for economists to do
• Satisficing wasn’t a precise theory that could be an
alternative to mainstream economics
• Anomalies of the 1950’s and 1960’s did not stop the rational
expectations revolution of the 1970’s
• “the rational model is a good approximation”
• 1970’s: heyday of “as-if” economics
1970’s
• 1974: Heuristics and Biases (K&T)
– representativeness (similarity heuristic)
– availability
– anchoring
• 1979: Prospect Theory
– probability weighting function
– risk-seeking in the loss domain
– risk-avoidance in the gain domain
– loss aversion
– framing
1980’s
• Endowment effect (Thaler)
– “Mugs,” markets, and the passage to economics.
• Experiments
• Anomalies Column (Thaler)
• Behavioral finance
• Not much formal modeling
1990’s
• Formalization
– Fairness, reciprocity, and social preferences
– Intertemporal choice
– Learning
– Behavioral Game Theory
– JDM biases-Quasi Bayesian approaches
• Self serving bias, Confirmatory bias,
Overconfidence
• Field evidence
• Acceptance of behavioral economics in the profession
2000+
• Clark Medal: Matthew Rabin
• Nobel Prizes:
– George Akerlof (2001)
– Daniel Kahneman (2002)
– Robert Shiller (2013)
• Interventions, policy, “nudges”
• Behavioral IO, development, public finance
• Behavioral economics starts to feel like normal science
(maybe it’s time to sell?)
What will probably be the key growth
areas in the coming decades?
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Theory
Field experiments/natural experiments
Structural estimation of behavioral models
Policy
Biosocial science
Outline
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Introductions
Definition of Behavioral Economics
Methodology
Seven properties
Thumbnail history (for more details look at slides)