Behavioral Economics
Natalia Shestakova
Ural State University
Spring 2010
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
1 / 25
Behavioral Economics: Lecture 1
OUTLINE
Behavioral economics
Keystones of traditional economics
Rationality
Expected Utility Theory
Discounted Utility Theory
Nash Equilibrium
Adding psychological insights
Class experiment (simple)
Course outline
Q&A
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
2 / 25
Behavioral Economics: Lecture 1
Introduction
Behavioral Economics
2002 Nobel prize in economics:
Daniel Kahneman: "for having integrated insights from
psychological research into economic science, especially concerning
human judgment and decision-making under uncertainty"
Vernon L. Smith: "for having established laboratory experiments as
a tool in empirical economic analysis, especially in the study of
alternative market mechanisms"
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
3 / 25
Behavioral Economics: Lecture 1
Introduction
Behavioral Economics
... sometimes called "Economics and Psychology"
Economics ...?
mathematically elegant models of interaction between economic agents
based on simpli…ed assumptions regarding individual behavior
Psychology
experiments to understand how people think and behave
Behavioral Economics
incorporates psychological regularities into economic models while
staying formal and predictive
runs experiments to test predictions of existing models
uses experimental (and …eld) evidence to motivate alternative models
of decision making
applies new models of DM to other …elds: Finance, IO, Labor
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
4 / 25
Behavioral Economics: Lecture 1
Introduction
Behavioral Economics
Why to care?
Strategies of real …rms
Ran Spiegler, 2006. "The Market for Quacks," RES
patient recovers with same probability no matter whether she receives
treatment from healer
if all patients are rational, market remains inactive
if some patients reason anecdotally, market becomes active
anecdotal reasoning: patients react to random casual stories as if they
are fully informative of actual quality of healers’ treatment
Policy recommendations
school cafeteria example from "Nudge"
kids are more likely to choose food displayed at eye level
you do not want to remove unhealthy food from menu
why not to display healthy food at eye level?
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
5 / 25
Behavioral Economics: Lecture 1
Introduction
Behavioral Economics
What to read?
Predictably Irrational, Dan Ariely, 2008
http://www.predictablyirrational.com/ ... for videos
Nudge, Richard H. Thaler and Cass R. Sunstein, 2008
http://nudges.org/ ... for more nudges
Behavior Economics: Past, Present, Future, Colin F. Camerer and
George Loewenstein, 2002
Chapter 1 in Advances in Behavioral Economics
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
6 / 25
Behavioral Economics: Lecture 1
Keystones of traditional economics
Rationality (MWG Ch.1)
Rational preferences: what is that?
Consider colors: green (G ), orange (O ), blue (B )
for each pair, de…ne preferred color: fG , O g, fO, B g, fG , B g
Completeness
for each pair, preference relation is de…ned
either G
O, or O G , or none
Transitivity
if G
O and O B, then should be G
same for indi¤erence
Natalia Shestakova (Ural State University)
Lecture Notes
B
Spring 2010
7 / 25
Behavioral Economics: Lecture 1
Keystones of traditional economics
Rationality (MWG Ch.1)
Rational preferences: where do we use them?
Utility function
only rational preferences can be represented by utility function
any model that has consumers but goes without utility function?
Consistent choices
WARP: if consumer chose X when Y was available, then she will
choose Y only when X becomes unavailable.
choice structure generated by rational preferences satis…es WARP
not every choice structure that satis…es WARP is necessarily generated
by rational preferences
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
8 / 25
Behavioral Economics: Lecture 1
Keystones of traditional economics
Expected Utility Theorem (MWG Ch.6)
Simple lotteries
set of possible outcomes, N elements
L = (p1 , ..., pN ) ... simple lottery assigns prob pn to each outcome
Continuity axiom
small changes in prob’s do not change ordering between two lotteries
Independence axiom
L
L0 if and only if αL + (1
α) L00
αL0 + (1
Expected utility form
assign numbers (u1 , ..., uN ) to outcomes, s.t.
U (L) = u1 p1 + ... + uN pN
α) L00 , α 2 (0, 1)
Expected Utility Theorem
If DM’s preferences over lotteries satisfy continuity and independence
axioms, then her preferences are representable by utility function with
expected utility form: L L0 if and only if U (L) U (L0 )
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
9 / 25
Behavioral Economics: Lecture 1
Keystones of traditional economics
Discounted Utility
Utility from consumption over time
ct ... consumption at time t
u ( ) ... instantaneous utility function
δ ... discount factor
Exponential discounting
U f ct g T
t =t 0
T
=
∑
δt
t0
u ( ct )
t =t 0
Time consistent choice
suppose "X today" is chosen over "Y tomorrow"
then "X in one month from today" should be chosen over
"Y in one month and one day from today"
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
10 / 25
Behavioral Economics: Lecture 1
Keystones of traditional economics
Nash Equilibrium (MWG Ch.7)
Classic Prisoner’s dilemma
two suspects are arrested but evidence is insu¢ cient for conviction
policeman asks each prisoner to testify for prosecution against another
if both remain silent, they are sentenced to only six months
if one betrays and another remains silent, betrayer goes free, silent one
receives 10-year sentence
if both betray, each receives 5-year sentence
they should decide simultaneously whether to stay silent or to betray
Nash equilibrium
each player chooses strategy
no player can bene…t by changing his strategy while another player is
not changing his
What is Nash equilibrium in Prisoner’s dilemma?
(betray, betray)
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
11 / 25
Behavioral Economics: Lecture 1
Adding psychology into economics
Violation of transitivity
Choose preferred color for slides:
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Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
12 / 25
Behavioral Economics: Lecture 1
Adding psychology into economics
Violation of transitivity
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Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
13 / 25
Behavioral Economics: Lecture 1
Adding psychology into economics
Violation of transitivity
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Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
14 / 25
Behavioral Economics: Lecture 1
Adding psychology into economics
Violation of transitivity
Choose preferred color for slides:
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Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
15 / 25
Behavioral Economics: Lecture 1
Adding psychology into economics
Violation of transitivity
Choose preferred color for slides:
334400
338800
334400
338800 ...?
334400 ...?
338800 ...?
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
16 / 25
Behavioral Economics: Lecture 1
Adding psychology into economics
Violation of transitivity
Most people are indi¤erent in …rst four choices
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Transitivity requires that
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But usually it is not
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or 338800 334400
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
17 / 25
Behavioral Economics: Lecture 1
Class experiment
Rules
keep silence
imagine you are choosing magazine subscription
read carefully description of all options
choose one option
submit your answers
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
18 / 25
Behavioral Economics: Lecture 1
Class experiment
Group #1
three alternatives
2nd alternative is de…nitely worse than 3rd alternative
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
19 / 25
Behavioral Economics: Lecture 1
Class experiment
Group #2
two alternatives
dominated alternative is removed
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
20 / 25
Behavioral Economics: Lecture 1
Class experiment
Discussion
example taken from "Predictably Irrational"
subjects from MIT’s Sloan School of Management/ our class
Group #1
Internet only subscription for $59 ... 16 students/ 4 students
Print only subscription for $125 ... zero student/ zero students
Print-and-Internet subscription for $125 ... 84 students/ 4 students
Group #2
Internet only subscription for $59 ... 68 students/ 9 students
Print-and-Internet subscription for $125 ... 32 students/ 3 students
context e¤ect
preferences between options depend on what other options are in
choice set
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
21 / 25
Behavioral Economics: Lecture 1
Course Outline
Main Requirements
Prepare experiment and participate in other experiments
5 groups of 4 students
10 points* for preparing experiment, 5 points for participating
5 bonus points for participating in each paid experiment
max 30 points, plus 10 bonus points possible
Apply behavioral theories for solving formal problems
home assignment
groups of 2 students
max 20 points
Find practical application of Behavioral Economics
essay/ research proposal
groups of 2 students
max 30 points
Final test
individual
max 20 points
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
22 / 25
Behavioral Economics: Lecture 1
Course Outline
Topics for experiments
20/04: framing, anchoring & preference reversal
27/04: do people choose according to EUT?
04/05: do people discount exponentially?
11/05: other regarding preferences
18/05: cognitive limitations
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
23 / 25
Behavioral Economics: Lecture 1
Course Outline
Experimental practices
from Hertwig & Ortmann 2001
Script enactment
state action choices explicitly
Repeated trials
allow gaining experience with situation
Financial incentives
set goal to perform as well as possible
Proscription against deception
exclude second-guessing about purpose of experiment
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
24 / 25
Behavioral Economics: Lecture 1
Course Outline
Q&A
Ask now ...
... or contact by email: [email protected]
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
25 / 25
Behavioral Economics
Natalia Shestakova
Ural State University
Spring 2010
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
1 / 18
Behavioral Economics: Lecture 2
Lecture plan
Review: standard assumptions about preferences
Class experiment
problem solving
discussion of possible e¤ects
presentation of results, comparison with results usually obtained
Summary: most common anomalies in preferences
de…nitions
how it may lead to choice inconsistency
potential explanations
Modeling anomalies in preferences
reference dependence
mental accounting
Applications
power of default option in saving for retirement
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
2 / 18
Behavioral Economics: Lecture 2
Review
Standard assumptions about preferences
completeness
for each pair of alternatives, X and Y , preferences uniquely de…ned
either X
Y , or Y
X , or none
transitivity
if X
Y and Y
Z , then should be X
same for indi¤erence
Z
invariance w.r.t.
current endowment / consumption level
irrelevant alternatives
elicitation procedure
=> consistency of choices
WARP: if consumer chose X when Y was available, then she will
choose Y only when X becomes unavailable.
choice structure generated by rational preferences satis…es WARP
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
3 / 18
Behavioral Economics: Lecture 2
Class experiment
Procedure
problem solving
discussion of possible e¤ects
presentation of results, comparison with results usually obtained
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
4 / 18
Behavioral Economics: Lecture 2
Summary
Framing e¤ect
framing e¤ect:
way how choice problem is stated a¤ects choice
may cause inconsistency:
DM chooses X over Y when Y is presented in terms of losses
but may choose Y over X when Y is presented in terms of gains
one explanation:
people are passive DMs: they rely on easily available heuristics
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
5 / 18
Behavioral Economics: Lecture 2
Summary
Anchoring e¤ect
anchoring e¤ect:
irrelevant factors a¤ect which values are assigned to alternatives
however, relative values are not a¤ected... coherent arbitrariness
may cause inconsistency:
DM assigns higher value to X than to Y when evaluates them together
but may assign higher value to Y than to X when evaluates separately
one explanation:
arbitrary number serves as original value
while …nal value is product of adjusting original value
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
6 / 18
Behavioral Economics: Lecture 2
Summary
Endowment e¤ect
endowment e¤ect:
ownership makes good more attractive
preferences for X and Y depend on which of them DM owns
may cause inconsistency:
DM chooses X over Y when she owns X
but may choose Y over X when she owns nothing
one explanation:
"yeah, whatever" heuristic
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
7 / 18
Behavioral Economics: Lecture 2
Summary
Preference reversal
preference reversal:
revealed preferences depend on elicitation procedure
likely to cause inconsistency:
DM chooses X over Y when asked directly to choose
but may request more money for giving up Y than for giving up X
competing explanations:
intransitivity: Y
CY
CX
X
Y
overpricing of Y , CY
Y , underpricing of X , X
Natalia Shestakova (Ural State University)
Lecture Notes
CX
Spring 2010
8 / 18
Behavioral Economics: Lecture 2
Summary
Context e¤ect
context e¤ect:
presence of other alternatives in choice set a¤ects choice
may cause inconsistency:
DM chooses X over Y when there is X in choice set
but may choose Y over X when X is removed
one explanation:
di¢ cult to compare X and Y
but easy to notice that X is better than X
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
9 / 18
Behavioral Economics: Lecture 2
Summary
Anomalies: common explanations
preferences are constructed
reference dependence & loss aversion
misleading but simple heuristics
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
10 / 18
Behavioral Economics: Lecture 2
Modeling anomalies in preferences
Reference dependence
(based on Kahneman & Tversky, 1991)
choice set... X = fx, y , z, ...g
reference structure... indexed preference relations x
r ...
r
y
complete, transitive, continuous
reference independence in standard theory
x
r
y if and only if x
s
y for all x, y , r , s 2 X
related questions
what determines reference state
how reference state a¤ects preferences
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
11 / 18
Behavioral Economics: Lecture 2
Modeling anomalies in preferences
Loss aversion: De…nition
(based on Kahneman & Tversky, 1991)
intuition... people dislike losses more than they like equivalent gains,
shift in reference point turns gains into losses
compare alternatives across two dimensions
x... work in Prague, y ... work in Ektb,
r ... study in Prague, s... study in Ektb
1st (location): x1 = r1 > s1 = y1
2nd (income): y2 > r2 = s2 > x2
preference relation satis…es loss aversion:
x
s
y implies that x
Natalia Shestakova (Ural State University)
r
y
Lecture Notes
Spring 2010
12 / 18
Behavioral Economics: Lecture 2
Modeling anomalies in preferences
Loss aversion: Illustration
DM at s-state compares:
v1 ( x1
s 1 ) + v2 ( x2
gain
v2 ( y2
s2 )
loss
s2 )
gain
DM at r-state compares:
v2 ( x2
r2 )
loss
v1 (y1 r1 ) + v2 (y2 r2 )
gain
loss
=> gain from x becomes loss from y
loss averse DM
dislikes losses more than likes gains
assume she is indi¤erent between x and y at s-state
then she should prefer x to y at r -state
what if she is indi¤erent between x and y at r -state?
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
13 / 18
Behavioral Economics: Lecture 2
Modeling anomalies in preferences
Mental accounting
(based on Kahneman & Tversky, 1984)
choice problem
store A: X costs 200RUB, Y costs 2000RUB
store B1: 20 min away, X costs 100RUB, Y costs 2000RUB
store B2: 20 min away, X costs 200RUB, Y costs 1900RUB
minimal account
disregard features that alternatives share
compare only di¤erences between alternatives
topical account
relate consequences of possible outcomes to reference level
comprehensive account
incorporate other factors, incl. current wealth, possible earnings, etc.
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
14 / 18
Behavioral Economics: Lecture 2
Applying knowledge of anomalies in preferences
Saving for retirement problem
standard economic theory suggests:
calculate how much you will earn over lifetime
…gure out how much you will need when you retire
save up enough for retirement without sacri…cing too much now
de…ned-bene…t retirement plans:
pensions are proportion to salary and years of service
advantage: easy to participate
disadvantage: not friendly to those who change jobs frequently
de…ned-contribution retirement plans:
participants have personal accounts to make speci…ed contributions
advantage: completely portable
disadvantage: too many decisions to make
main negative consequence of choice complexity:
too low participation rate
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
15 / 18
Behavioral Economics: Lecture 2
Applying knowledge of anomalies in preferences
Power of default option
(based on Madrian & Shea, 2001)
401k retirement plans in U.S.
worker can choose portion of her wage to be contributed to her 401k
account before income taxes are paid
initial form:
"Check this box if you would like to participate in a 401k. Indicate how
much you’d like to contribute."
participation rate 38%
updated form:
"Check this box if you would not like to have 3% of your pay check
put into a 401k."
participation rate 86%
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
16 / 18
Behavioral Economics: Lecture 2
Next lecture
Food for thought
How studied e¤ects may change predictions of your favorite theories
How studied e¤ects may be used to explain seeming paradoxes
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
17 / 18
Behavioral Economics: Lecture 2
Next lecture
Topics for experiments
20/04: framing, anchoring & preference reversal
27/04: do people choose according to EUT?
04/05: do people discount exponentially?
11/05: other regarding preferences
18/05: cognitive limitations
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
18 / 18
Behavioral Economics
Natalia Shestakova
Ural State University
Spring 2010
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
1 / 22
Behavioral Economics: Lecture 3
Lecture plan
Choice under risk and uncertainty:
expected utility theorem
risk attitude
EUT at work
Class experiment:
common consequence e¤ect
common ratio e¤ect
re‡ection e¤ect
fourfold pattern of risk attitude
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
2 / 22
Behavioral Economics: Lecture 3
Choice under risk and uncertainty
Preferences over lotteries
Simple lotteries:
set of possible outcomes, N elements
L = (p1 , ..., pN ) ... simple lottery assigns prob pn to each outcome
Rationality:
completeness
transitivity
Continuity:
there are no "jumps" in ordering of preferences
=> preferences are not lexicographic
EU form:
it is possible to assign numbers (u1 , ..., uN ) to outcomes, s.t.
U (L) = u1 p1 + ... + uN pN
Independence axiom:
L L0 if and only if αL + (1 α) L00 αL0 + (1 α) L00 , α 2 (0, 1)
possibility to get L00 should not a¤ect preferences between L and L0
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
3 / 22
Behavioral Economics: Lecture 3
Choice under risk and uncertainty
Preferences over lotteries: Example
Possible outcomes:
X1 ... rainy, X2 ... cloudy, X3 ... sunny
assign numbers to weather conditions:
X1 !?, X2 !?, X3 !?
Lotteries = resorts:
L = (p1 , p2 , p3 ), p1 ... prob rain, p2 ... prob clouds, p3 ... prob sun
L... Barcelona in June, L = (?, ?, ?)
EU form: U (L) =?
Equilateral triangle with altitude=1:
pn ... length of perpendicular from L to side opposite to vertex n
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
4 / 22
Behavioral Economics: Lecture 3
Choice under risk and uncertainty
Independence axiom: Closer look
Independence axiom implies that indi¤erence curves are:
straight: L
parallel: L
1 0
1
L0 if and only if L
2 L + 2 L (b)
1
2
1 0
2 00
00
0
L if and only if 3 L + 3 L
3 L + 3 L (c)
Illustration:
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
5 / 22
Behavioral Economics: Lecture 3
Choice under risk and uncertainty
Expected Utility Theorem
Assumptions on preferences over lotteries:
rational
continuous
satisfy independence axiom
Expected Utility Theorem:
preferences are representable by utility function with EU form
notation: L L0 if and only if U (L) U (L0 )
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
6 / 22
Behavioral Economics: Lecture 3
Choice under risk and uncertainty
Risk attitude
Expected outcome vs. expected utility
E (X ) = p1 X1 + ... + pN XN
EU (L) = p1 u (X1 ) + ... + pN u (XN )
Risk-neutral DM: EU (L) = U [E (X )]
indi¤erent between lottery and its expected outcome
Risk-averse DM: EU (L) < U [E (X )]
likes lottery less than its expected outcome
Risk-seeking DM: EU (L) > U [E (X )]
likes lottery more than its expected outcome
How risk attitude a¤ects shape of indi¤erence curves
more risk-averse DM has steeper I.C.’s
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
7 / 22
Behavioral Economics: Lecture 3
Class experiment
Do people choose according to EUT?
Motivation:
can EUT be supported empirically?
Procedure:
problem solving
discussion of possible e¤ects
presentation of results, comparison with results usually obtained
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
8 / 22
Behavioral Economics: Lecture 3
Class experiment
Common consequence e¤ect
Allais paradox #1:
outcomes: X1 ... $5, 000, X2 ... $1, 000, X3 ... $0
S 0 = (0, 1, 0) vs. R 0 = (0.1, 0.89, 0.01)
S 00 = (0, 0.11, 0.89) vs. R 00 = (0.1, 0, 0.9)
Structure of choice problem:
nonnegative monetary outcomes: X1 > X2 , X3 = 0, C
S = (0, p, 0, 1 p )
R = (αp, 0, (1 α) p, 1 p )
C ... common consequence, should have no e¤ect
Experimental evidence:
tendency to choose S when C = X2 (S 0 vs. R 0 )
tendency to choose R when C = X3 (S 00 vs. R 00 )
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
9 / 22
Behavioral Economics: Lecture 3
Class experiment
Common ratio e¤ect
Allais paradox #2:
outcomes: X1 ... $4, 000, X2 ... $3, 000, X3 ... $0
S 0 = (0, 1, 0) vs. R 0 = (0.8, 0, 0.2)
S 00 = (0, 0.25, 0.75) vs. R 00 = (0.2, 0, 0.8)
Structure of choice problem:
nonnegative monetary outcomes: X1 > X2 , X3 = 0
S = (0, p, 1 p )
R = (λp, 0, 1 λp )
λ... constant ratio of winning probabilities
p should have no e¤ect
Experimental evidence:
tendency to choose S when p is high
tendency to choose R when p is low
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
10 / 22
Behavioral Economics: Lecture 3
Class experiment
Re‡ection e¤ect
Kahneman & Tversky (1979)
related to framing e¤ect
Structure of choice problem:
monetary outcomes: jX1 j > jX2 j, X3 = 0
S = (0, p, 1 p )
R = (λp, 0, 1 λp )
Experimental evidence
tendency to choose S when X1 > X2 > 0
tendency to choose P when X1 < X2 < 0
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
11 / 22
Behavioral Economics: Lecture 3
Class experiment
Fourfold pattern of risk attitude
Domain of gains:
risk averse when probability of winning is high
risk seeking when probability of winning is low
Domain of losses:
risk averse when probability of losing is low
risk seeking when probability of losing is high
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
12 / 22
Behavioral Economics: Lecture 4
Lecture plan
Previous lecture:
independence axiom
risk attitude
Summary of class experiment:
common consequence e¤ect
common ratio e¤ect
re‡ection e¤ect
fourfold pattern of risk attitude
Alternative theories of choice under risk and uncertainty
generalizations of EUT
prospect theory
priority heuristic
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
13 / 22
Behavioral Economics: Lecture 4
Alternative theories of choice under risk and uncertainty
Generalizying expected utility model
"Fanning-out" hypothesis (Machina, 1982):
agents become more risk-averse as lotteries become better
utilities assigned to outcomes are lottery-speci…c
weak independence: L L0 i¤ for each α 2 (0, 1) there can found
β 2 (0, 1), s.t. αL + (1 α) L00 βL0 + (1 β) L00 for any L00
Theories with decision weights:
EU (L) = π (p1 ) u (X1 ) + ... + π (pN ) u (XN )
standard theory: π (pi ) = pi
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
14 / 22
Behavioral Economics: Lecture 4
Alternative theories of choice under risk and uncertainty
Reminder: reference dependence
Choice set... X = fx, y , ..., r , s, ...g
choosing between x and y , while having either r , or s
Reference structure... indexed preference relations x
r,
s ...
r
y
complete, transitive, continuous
Reference independence in standard theory
x
r
y if and only if x
Natalia Shestakova (Ural State University)
s
y for all x, y , r , s 2 X
Lecture Notes
Spring 2010
15 / 22
Behavioral Economics: Lecture 4
Alternative theories of choice under risk and uncertainty
Reminder: loss aversion
Intuition... people dislike losses more than they like equivalent gains,
shift in reference point turns gains into losses
Compare alternatives across two dimensions
x... unemployed in Prague, y ... work in Ektb,
r ... study in Prague, s... study in Ektb
1st (location): x1 = r1 > s1 = y1
2nd (income): y2 > r2 = s2 > x2
Preference relation satis…es loss aversion:
x
s
y implies that x
Natalia Shestakova (Ural State University)
r
y
Lecture Notes
Spring 2010
16 / 22
Behavioral Economics: Lecture 4
Alternative theories of choice under risk and uncertainty
Prospect theory (Kahneman & Tversky, 1979)
1st phase of choice process:
"edit" lotteries using decision heuristics
ex.#1: eliminate lotteries that do not satisfy chosen criterion
ex.#2: classify outcomes in terms of gains and losses
2nd phase of choice process:
evaluate "edited" lotteries using decision-weighted form
value of each outcome depends on its sign and size
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
17 / 22
Behavioral Economics: Lecture 4
Alternative theories of choice under risk and uncertainty
Prospect theory: valuation of outcomes
Shape of value function:
Properties:
kinked at reference point
concave for gains/ convex for losses , diminishing sensitivity
steeper in domain of losses , loss-aversion
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
18 / 22
Behavioral Economics: Lecture 4
Alternative theories of choice under risk and uncertainty
Priority heuristic
Search for:
minimum payo¤
probability of minimum payo¤
maximum payo¤
Stop search if:
di¤erence between minimum payo¤s is > 10% of maximum payo¤
di¤erence between probabilities of minimum payo¤s > 10%
maximum payo¤s are di¤erent
Decide for lotteries with:
larger minimum payo¤
smaller probability of minimum payo¤
larger maximum payo¤
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
19 / 22
Behavioral Economics: Lecture 4
Why do we need theory of decision making?
EUT at work: Corruption
Problem:
took credit for opening new business but it may take too long
Possible outcomes:
X1 ... never open, X2 ... open in 1 year, X3 ... open in 1 month
π 1 , π 2 , π 3 ... computed pro…ts/ losses in each case
Choice over two lotteries:
L = (p1 , p2 , p3 )... do everything legally
L0 = (p10 , p20 , p30 )... give bribery of size B, pay …ne of size F if caught
Decision rule: give bribery if and only if
p10 u (π 1 B F ) + p20 u (π 2 B ) + p30 u (π 3
p1 u (π 1 ) + p2 u (π 2 ) + p3 u (π 3 )
B)
Policy recommendations:
e¤ectiveness of measures against corruption
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
20 / 22
Behavioral Economics: Lecture 4
Why do we need theory of decision making?
Food for thought
How do we usually make theory-based policy recommendations?
assume speci…c functional forms
estimate parameters of functional forms using available data
do comparative statics
What if EUT is replaced with more general theory?
more functional forms to impose
more parameters to estimate
recommendations are more "conditional"
What to do? Where to go?
open question, solutions welcomed
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
21 / 22
Behavioral Economics: Lecture 4
Next lecture
Topics for experiments
20/04: framing, anchoring & preference reversal
27/04: do people choose according to EUT?
03/05: do people discount exponentially?
11/05: other regarding preferences
18/05: cognitive limitations
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
22 / 22
Behavioral Economics
Natalia Shestakova
Ural State University
Spring 2010
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
1 / 25
Behavioral Economics: Lecture 5
Lecture plan
Discounted utility model:
historical origins
model
implicit assumptions
Discounted utility anomalies (class experiment):
common di¤erence e¤ect
absolute magnitude e¤ect
gain-loss asymmetry
delay-speedup asymmetry
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
2 / 25
Behavioral Economics: Lecture 5
Discounted utility model
Historical origins
E¤ective desire for accumulation, Rae 1834:
promoted by: bequest and self-restraint
limited by: uncertainty and grati…cation from immediate consumption
these are determinants of intertemporal choice
Systematic underestimation of future wants, Bohm-Bawerk 1889:
intertemporal choice as decision about allocating resources to oneself
over di¤erent points in time
Time preference, Fisher 1930:
MRS of consumption today with consumption tomorrow
should controlled for diminishing MU of consumption
combination of various (psychological) intertemporal motives
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
3 / 25
Behavioral Economics: Lecture 5
Discounted utility model
Simple formulation
Discounted utility model, Samuelson 1937:
all psychological motives compressed into discount rate ρ
ct , ..., cT ... consumption pro…les
preferences transitive, complete, continuous
u (ct ) ... instantaneous utility function
U t (ct , ..., cT ) ... intertemporal utility function
U t (ct , ..., cT )
T t
=
∑
D (k ) u (ct +k ) , where
k =0
D (k )
1
1+ρ
=
k
= δk ... discount function
not psychologically plausible
not normatively plausible
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
4 / 25
Behavioral Economics: Lecture 5
Discounted utility model
More formulas
Intertemporal utility in continuous time:
U t (ct , ..., ct 0 , ..., cT ) =
Z T t
ρk
e
k =0
u (ct +k ) dt
How to impute discount rate:
given X at t, how big should be Y at t 0 to make you indi¤erent?
assumption: X at t and Y at t 0 are small relative to ct and ct 0
then U t (ct + X , ..., ct 0 , ..., cT ) = U t (ct , ..., ct 0 + Y , ..., cT )
0
implies X = Ye ρ(t t )
ρ=
Natalia Shestakova (Ural State University)
1
t0
Lecture Notes
t
ln
X
Y
Spring 2010
5 / 25
Behavioral Economics: Lecture 5
Discounted utility model
Consumption independence
Utility in period t + k is independent of consumption in period s
X , Y , Z ... consumption possibilities
X
Y in period r when Z is consumed in period r 0 i¤
X
Y in period r when Z is not consumed in period r 0
Example (Samuelson 1952):
X ... wine, Y ... milk, Z ... beer
r ... today, r 0 ... yesterday
assume X
Y in period r when Z is not consumed in period r 0
is it true that X
Y in period r when Z is consumed in period r 0 ?
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
6 / 25
Behavioral Economics: Lecture 5
Discounted utility model
Constant discounting and time consistency
Discount function
k 1
general form: D (k ) =
∏
n =0
1
1 + ρn
k
1
form imposed in DU model: D (k ) = 1 +
= δk
ρ
constraint: constant per-period discount rate, ρn = ρ 8n
Time-consistent intertemporal preferences:
later preferences "con…rm" earlier preferences
if (X at r ) t (Y at r + d ) for some r , then
(X at r ) t (Y at r + d ) for all r
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
7 / 25
Behavioral Economics: Lecture 5
Discounted utility model
Other implicit assumptions
Utility independence
distribution of utility across time makes no di¤erence
e.g. if higher utility at r in one consumption pro…le is compensated by
higher utilities at r 1 and r + 1 in another consumption pro…le, two
pro…les are treated as identical
Stationary instantaneous utility
u (ct ) = u (ct +1 ) if ct = ct +1
that is, tastes do not change over time
Diminishing marginal utility
motivates to spread consumption over time
Positive discount rate
motivates to concentrate consumption in present
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
8 / 25
Behavioral Economics: Lecture 5
Class experiment
Discounted utility anomalies
Motivation:
can DU model be supported empirically?
are deviations, if any, systematic?
Procedure:
problem solving
discussion of possible e¤ects
presentation of results, comparison with results usually obtained
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
9 / 25
Behavioral Economics: Lecture 5
Class experiment
Common di¤erence e¤ect
Predictions of DU model:
extra consumption: X at t or Y at t 0
X
ρ = t 0 1 t ln Y
only di¤erence between t 0 and t matters, not their values
Experimental task:
C1: (A) 1 apple today or (B) 2 apples tomorrow
C2: (A’) 1 apple in 365 days or (B’) 2 apples in 366 days
Experimental evidence:
[some] people choose A in C1 and B’in C2
this suggests dynamic inconsistency: people claim that B’is better
than A’, but, once 365 days pass, they choose A’over B’
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
10 / 25
Behavioral Economics: Lecture 5
Class experiment
Absolute magnitude e¤ect
Predictions of DU model:
extra consumption: X at t or Y at t 0
X
ρ = t 0 1 t ln Y
only ratio between X and Y matters, not their absolute values
Experimental task:
Q1: amount to be received in 1 month (Y ) that would make you
indi¤erent to 100RUB now (X )
Q2: amount to be received in 1 month (Y 0 ) that would make you
indi¤erent to 100, 000RUB now (X 0 )
Experimental evidence:
0
X
X
proportion in Q1 Y
is usually lower than in Q2 Y
0
this implies that ρ is lower for higher absolute values of X
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
11 / 25
Behavioral Economics: Lecture 5
Class experiment
Gain-loss asymmetry
Predictions of DU model:
gains/ equivalent losses: X at t or Y at t 0
X
ρ = t 0 1 t ln Y
only ratio between X and Y matters, not their signs
Experimental task:
Q1: friend cannot return you X today, how much would you require
him to return in one month (Y )?
Q2: you cannot return X today, how much would you o¤er to return in
one month (Y 0 )?
Experimental evidence:
answer in Q1 (Y ) is usually higher than in Q2 (Y 0 )
this implies that ρ is lower for losses than for gains
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
12 / 25
Behavioral Economics: Lecture 5
Class experiment
Delay-speedup asymmetry
Predictions of DU model:
extra consumption: X at t or delay/ speedup Y /Y 0 to t + 1/t
X
ρ = t 0 1 t ln Y
ratio
Y
X
should be same as
1
X
Y0
Experimental task:
Q1: have chance to receive Y at t 1 instead of X at t
Q2: have chance to receive Y 0 at t + 1 instead of X at t
Experimental evidence:
proportion in Q1 Y
is usually lower than in Q2 YX 0
X
this implies that ρ is lower for higher absolute values of X
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
13 / 25
Behavioral Economics: Lecture 6
Lecture plan
Discounted utility anomalies
summary of class experiments
Alternative models of intertemporal choice
hyperbolic discounting models
role of self-awareness
reference-point models
mental accounting
Why do we need models of intertemporal choice?
saving and consumption over time
addiction
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
14 / 25
Behavioral Economics: Lecture 6
Class experiments: summary
Discounted utility anomalies
Median responses from Thaler 1981:
X today
gain $15
gain $250
loss $15
equivalent Y
in 3 months
$30
$300
$16
discount
rate
277
73
26
equivalent Y
in 1 year
$60
$350
$20
discount
rate
139
34
29
Discount rate is lower for:
more distant time horizons
bigger gains
losses
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
15 / 25
Behavioral Economics: Lecture 6
Class experiments: summary
Discounted utility anomalies
Discount factor δ =
1
1 +ρ
as function of time:
increasing, implying decreasing discount rate ρ
constant if 1st period removed
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
16 / 25
Behavioral Economics: Lecture 6
Alternative models of intertemporal choice
Hyperbolic discounting
Discount function introduced in Phelps & Pollak 1968:
D (k ) =
1 if k = 0
βδk if k > 0
declining discount rate between today and future periods
constant discount rate between two periods in future
1 apple today or 2 apples tomorrow:
A vs. βδ2A
1 apple in 365 days or 2 apples in 366 days:
δ365 A vs. δ366 2A => A vs. δ2A
Time inconsistency when:
βδ <
1
2
but δ >
Natalia Shestakova (Ural State University)
1
2
Lecture Notes
Spring 2010
17 / 25
Behavioral Economics: Lecture 6
Alternative models of intertemporal choice
Role of seld-awareness
Naive DM
believes that future preferences will be identical to current
frequently has "planning fallacy"
Sophisticated DM
correctly predicts how preferences will change over time
demand for commitment: intention to exclude tempting future
alternatives
Partially naive DM
knows that will experience self-control problems but underestimates
their magnitude
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
18 / 25
Behavioral Economics: Lecture 6
Alternative models of intertemporal choice
Reference dependent utility
Instantaneous utility function
u (cτ , rτ ) = v (cτ rτ )
rτ ... reference point, determined by past cons, expectations, etc
v ( ) concave over gains, convex over losses
v ( ) allows loss-aversion
Implications
explains most anomalies experimentally observed
explains failure of Permanent Income Hypothesis: anticipated changes
in wages a¤ect consumption growth rate while they should not
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
19 / 25
Behavioral Economics: Lecture 6
Alternative models of intertemporal choice
Mental accounting
Basic idea
money spent on di¤erent purposes are not same as di¤erent
expenditures are assigned to di¤erent "mental accounts"
like keeping money in labeled jars
consumption of particular item is linked to payment for it
Implications
di¤erent ways of …nancing purchase can lead to di¤erent decisions
preference for prepayment
preference for getting paid after doing work
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
20 / 25
Behavioral Economics: Lecture 6
Why do we need models of intertemporal choice
Addiction within DU model
Rational addiction, Becker and Murphy 1988
well-being depends on consumption of nonaddictive goods, addictive
goods and addictive state
addictive state: " with use of substance, # with abstinence
tolerance: well-being # when addictive state "
addiction: MU of addictive good " when addictive state "
Justifying government intervention
educational policies to inform people about e¤ects
Pigouvian tax per unit = marginal external damage imposed on others
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
21 / 25
Behavioral Economics: Lecture 6
Why do we need models of intertemporal choice
Problematic empirical observations
Unsuccessful attempts to quit
70% of current smokers express desire to quit completely, 41% stop
smoking for at least one day, only 4.7% abstained for more than three
months
Starting again caused by cues
change in environment helps
stress and "priming" may bring addiction back
Self-control through precommitment
voluntary "lock-up" into rehabilitation
medication that generate unpleasant side-e¤ect if substance used
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
22 / 25
Behavioral Economics: Lecture 6
Why do we need models of intertemporal choice
Addiction within hyperbolic discounting model
Hyperbolic discounting, Gruber and Koszegi 2001
true preferences correspond to standard exponential discounting
decision-making according to hyperbolic discounting
present-biased preferences
Nonstandard policy implications
Pigouvian tax should count for "internalities"... externalities imposed
on future selves
educational policies not su¢ cient as they do not address causes of
present bias
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
23 / 25
Behavioral Economics: Lecture 6
Why do we need models of intertemporal choice
Addiction as decision-process malfunction
Two individual modes, Bernheim and Rangel 2004
"cold" mode: properly functioning decision-making process
"hot" mode: decisions and preferences may diverge
probability of entering "hot" mode depends on: addictive state, chosen
lifestyle, random events
addiction: " use of substance ) " addictive state ) " probability of
hot mode
Nonstandard policy implications
important: policies should not harm those who choose to use
substances in cold state
consumption in hot mode is less sensitive to taxes ) higher taxes
needed ) distorted decisions in cold mode ) e.g. higher probability
of committing crime
elimination of problematic cues helps (advertising, peer e¤ects)
promotion of counter-cues ("smoking kills")
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
24 / 25
Behavioral Economics: Lecture 6
Next lecture
Topics for experiments
20/04: framing, anchoring & preference reversal
27/04: do people choose according to EUT?
03/05: do people discount exponentially?
11/05: other regarding preferences
18/05: cognitive limitations
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
25 / 25
Behavioral Economics
Natalia Shestakova
Ural State University
Spring 2010
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
1 / 27
Behavioral Economics: Lecture 7
Lecture plan
Introduction to Game Theory
historical origins
basic elements and concepts
Do people play as theory predicts? (class experiment)
ultimatum game
dictator game
trust game
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
2 / 27
Behavioral Economics: Lecture 7
Introduction to Game Theory
Historical origins
Von Neumann and Morgenstern 1944
mathematician and economist created Game Theory
mathematical tool to describe human behavior in strategic situations
when payo¤s depend also on actions of others
Von Neumann as member of US Atomic Energy Commission
1994 Nobel prize in Economics
for pioneering analysis of equilibria in theory of noncooperative games
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
3 / 27
Behavioral Economics: Lecture 7
Introduction to Game Theory
Simultaneous game: Prisoner’s dilemma
Game:
Prisoners cannot communicate
Both suspected of a crime
Prisoner B Confess
Deny
Prisoner A
Confess
Deny
{3 years, 3 years} {1 year, 10 years}
{10 years, 1 year} {2 years, 2 years}
Players: Prisoner A, Prisoner B
Actions: Confess or Deny for both players
Payo¤s: numbers represent rational preferences over possible
outcomes s.t. higher number implies higher desirability
Equilibrium: {action of Prisoner A, action of Prisoner B}
Applications for oligopoly:
enter price war or keep prices constant at high level
start advertising campaign or not
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
4 / 27
Behavioral Economics: Lecture 7
Introduction to Game Theory
Sequential game: Market entry
Game: low-cost airline decides whether to enter Aero‡ot’s market
if
if
if
if
low-cost airline does not enter, Aero‡ot keeps market power
low-cost airline enters, Aero‡ot should decide whether to lower prices
Aero‡ot does not lower prices, low-cost airline gets big market share
Aero‡ot lowers prices, low-cost airline does not survive
Players: low-cost airline, Aero‡ot
Actions: Enter or Not Enter, Fight or Accommodate
Payo¤s: positively correlate with possible pro…ts
Strategies: same as actions, conditional on low-cost airline’s actions
Equilibrium: {action of low-cost airline, strategy of Aero‡ot}
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
5 / 27
Behavioral Economics: Lecture 7
Introduction to Game Theory
How to …nd equilibrium
Nash equilibrium ... set of actions
given particular outcome, does any player have incentive to deviate
incentive to deviate ... possibility of higher payo¤ from di¤erent
action assuming that another player does not deviate
equilibrium if there are no such incentives to any player
can be found using elimination of dominated actions
sometime there are no dominated actions but NE exists
Subgame perfect Nash equilibrium ... set of strategies
given particular outcome, does any player have incentive to deviate
incentive to deviate ... possibility of higher payo¤ from di¤erent
strategy assuming that another player does not deviate
equilibrium if there are no such incentives to any player
found using backward induction
SPNE is subset of NE, "empty threats" are excluded
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
6 / 27
Behavioral Economics: Lecture 7
Introduction to Game Theory
Underlying assumptions
Rational players
complete and transitive preferences over payo¤s
Common knowledge
each player knows that other players are rational
he also knows that they know that he knows that they are rational
and so on...
Complete information
possible actions and payo¤s are known to all players
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
7 / 27
Behavioral Economics: Lecture 7
Class experiment
Do people play games as theory predicts?
Motivation:
what are conditions under which theory works (if any)?
if there are any deviations, are they systematic?
Procedure:
problem solving
presentation of results, comparison with results usually obtained
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
8 / 27
Behavioral Economics: Lecture 7
Class experiment
Ultimatum game
Roles:
Player A: propose share of endowment to Player B
Player B: accept or reject
Rules:
if Player B accepts, then endowment is divided as proposed
if Player B rejects, everybody gets nothing
you know your role but not with whom you are matched
Game theory predictions:
Player A proposes minimum possible
Player B accepts whatever is proposed
Common results:
average o¤er is 40%
o¤ers below 20% are rejected
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
9 / 27
Behavioral Economics: Lecture 7
Class experiment
Dictator game
Roles:
Player A: allocate endowment between yourself and Player B
Player B: passive
Rules:
whatever Player A proposes is accepted
completely anonymous
Game theory predictions:
Player A gives nothing to Player B
Results:
40% of Players A give nothing
40% of Players A split evenly
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
10 / 27
Behavioral Economics: Lecture 7
Class experiment
Trust game
Roles:
Player A: invest share of endowment to Player B
Player B: return share of "accumulated capital" to Player A
Rules:
endowment invested by Player A is multiplied by factor k
whatever Player B returns is accepted
Game theory predictions:
Player A invests nothing
Player B keeps everything
Results:
trust: positive amounts invested
trustworthiness: positive amounts returned
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
11 / 27
Behavioral Economics: Lecture 8
Lecture plan
Human behavior in simple games
discussion of class experiments
Alternative theories of interactive behavior
inequity aversion
fairness equilibrium
What is game theory good for
monopoly pricing as ultimatum game
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
12 / 27
Behavioral Economics: Lecture 8
Class experiments: discussion
Experimental practices
from Hertwig & Ortmann 2001
Script enactment
state action choices explicitly
clear connection between action and payo¤
"clear" means there is no confusion, though uncertainty is possible
Repeated trials/ practice rounds
allow gaining experience with situation
feedback makes connection between action and payo¤ more clear
Financial incentives
set goal to perform as well as possible
Proscription against deception
exclude second-guessing about purpose of experiment
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
13 / 27
Behavioral Economics: Lecture 8
Class experiments: discussion
Ultimatum game
Comparison across countries, Roth et al. 1991:
country
USA
Japan
Israel
Slovenia
1-10
11-20
21-30 31-40 41-50
o¤er frequencies
0.04
0.03
0.13
0.17
0.20
0.03
0.33
0.34
0.57
0.27
0.63
0.48
0.07
0.70
51-100
mean
0.46
0.43
0.35
0.47
conditional rejection frequencies
USA
Japan
Israel
Slovenia
1.00
0.00
0.25
Natalia Shestakova (Ural State University)
0.20
0.17
1.00
0.22
0.10
0.12
0.63
Lecture Notes
0.12
0.14
0.00
0.05
0.19
0.14
0.13
0.24
Spring 2010
14 / 27
Behavioral Economics: Lecture 8
Class experiments: discussion
Ultimatum game
Other interesting …ndings:
farmers in developing countries, children and chimpanzee make on
average lower o¤ers and accept lower amounts
they are more self-interested than adults in developed countries
Potential explanation:
people are born sel…sh but social norms make them more altruistic
punishment and its anticipation
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
15 / 27
Behavioral Economics: Lecture 8
Class experiments: discussion
Dictator game
Role of …nancial incentives and social distance
condition
0
1-10 11-20 21-30 31-40 41-50 51-100
frequency of allocation to other person
without pay
with pay $5
with pay $10
recipient’s ID
mutual ID
+communic
0.14
0.35
0.21
0.28
0.17
0.08
0.06
0.06
0.11
0.28
0.13
0.03
0.07
0.05
0.29
0.10
0.12
0.26
0.09
0.18
0.05
0.47
0.18
0.21
0.30
0.82
0.41
0.02
0.05
0.03
0.11
0.30
mean
0.38
0.23
0.24
0.26
0.50
0.48
stakes do not matter much
reputation matters
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
16 / 27
Behavioral Economics: Lecture 8
Class experiments: discussion
Trust game
Discriminating between trust and altruism:
treatment A: standard trust game
treatment B: player B cannot return anything
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
17 / 27
Behavioral Economics: Lecture 8
Class experiments: discussion
Trust game
Discriminating between trustworthiness and altruism:
treatment A: standard trust game
treatment C: player A is passive, player B decides which proportion to
return from amounts received by players B in treatment A
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
18 / 27
Behavioral Economics: Lecture 8
Alternative models of interactive behavior
Inequity aversion: basic idea
Fehr & Schmidt 1999
Intuition:
there is fraction of subjects who dislike inequitable outcomes
Utility function:
two players with payo¤s xi and xj
rational preferences represented as
Ui (x ) = xi
assume βi
αi and 0
Natalia Shestakova (Ural State University)
αi max xj xi , 0
|
{z
}
disadvantageous
inequality
β max xi xj , 0
|i
{z
}
advantageous
inequality
βi < 1
Lecture Notes
Spring 2010
19 / 27
Behavioral Economics: Lecture 8
Alternative models of interactive behavior
Inequity aversion: illustration
Preferences with inequity aversion
utility loss from being better o¤ is lower than from being worse o¤
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
20 / 27
Behavioral Economics: Lecture 8
Alternative models of interactive behavior
Inequity aversion: implications
Constraints on parameters
βi
αi : loss-aversion in social comparisons
βi
0: no subjects who like to be better o¤ than others
what if βi = 1... ?
what if αi
1... ?
Applied to Ultimatum game
no o¤ers above 0.5
o¤ers of 0.5 are always accepted
acceptance threshold is αj / 1 + 2αj where j is Responder
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
21 / 27
Behavioral Economics: Lecture 8
Alternative models of interactive behavior
Fairness equilibrium: basic idea
Rabin 1993
Main idea is to incorporate following stylized facts:
people reward those partners who are nice to them
and they punish those who are mean to them
emotions have stronger e¤ect as material costs become smaller
Done with including following elements into utility function:
your strategy... ai
your belief about other player’s strategy choice... bj
belief about other player’s belief about your strategy... ci
Equilibrium
ai = bi = ci and aj = bj = cj
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
22 / 27
Behavioral Economics: Lecture 8
Alternative models of interactive behavior
Fairness equilibrium: utility function
Kindness function fi (ai , bj ):
how kind i is by choosing ai when she believes that j will choose bj
π hj bj / π lj bj ... max/ min possible payo¤s for j with strategy bj
π rj bj ... avg possible payo¤ for j with strategy bj
π j bj , ai ... actual payo¤ for j with strategy bj when i plays ai
π j (b j ,a i ) π rj (b j )
h
l
fi ai , bj = f πj (bj ) πj (bj ) 2 [ 1, 12 ]
h
0 if π j (b j )=π lj (b j )
Kindness belief function:
i’s belief about how kind j is being to him
π i (ci ,b j ) π ri (ci )
h
l
gj bj , ci = f πi (hci ) πi (cil ) 2 [ 1, 21 ]
0 if π i (ci )=π i (ci )
notations as before
Utility function:
Ui (ai , bj , ci ) = π i (ai , bj ) + gj (bj , ci ) [1 + fi (ai , bj )]
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
23 / 27
Behavioral Economics: Lecture 8
Alternative models of interactive behavior
Fairness equilibrium: behavioral implications
When i believes that j is treating her badly:
this implies that gj bj , ci < 0
to compensate, i chooses ai s.t. fi ai , bj < 0
that is, i treats j badly
When i believes that j is treating her nicely
with same logic, i treats j nicely
When material payo¤s grow:
as gj bj , ci and fi ai , bj are bounded, their relative impact on utility
becomes lower
players care less about fairness
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
24 / 27
Behavioral Economics: Lecture 8
What is game theory good for
Monopoly pricing as ultimatum game
Game-theoretic approach to monopoly pricing
c... monopolist’s cost, v ... consumer’s valuation
monopolist picks market price p 2 [c, v ]
consumer either accepts or rejects
alternatively, consumer selects reservation price r 2 [c, v ]
SPNE... ?
Evidence from Kahneman et al. 1986
consumers see conventional monopoly prices as unfair
they refuse to buy even if price is lower their valuation
lesson: monopolist cannot set as high prices as theory predicts
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
25 / 27
Behavioral Economics: Lecture 8
What is game theory good for
Monopoly pricing as ultimatum game: fairness
Consumer kindness
fC (r , p ) = f 01ififr r <pp
r > p... no fairness equilibrium
r < p... no trade
Monopolist’s kindness when p = r = z
fM (z, z ) = (c
z ) /2 (v
c) < 0
What if consumer deviates from p = r = z
UC = f v
fM (z ,z )[1 + 1 ] if r <z
z +fM (z ,z )[1 +0 ] if r =z
Highest price consistent with fairness equilibrium
z = 2v 2
2cv + c / [1 + 2v
Natalia Shestakova (Ural State University)
Lecture Notes
2c ] < v
Spring 2010
26 / 27
Behavioral Economics: Lecture 8
Next lecture
Topics for experiments
20/04: framing, anchoring & preference reversal
27/04: do people choose according to EUT?
03/05: do people discount exponentially?
11/05: other regarding preferences
18/05: cognitive limitations
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
27 / 27
Behavioral Economics
Natalia Shestakova
Ural State University
Spring 2010
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
1 / 15
Behavioral Economics: Lecture 9
OUTLINE
How do we think?
predictable biases in judgment
two cognitive systems
Class experiment
"Beauty-contest" game
market entry game
From rationality to bounded rationality
always making best choice?
optimization under constraints
bounded rationality: satis…cing
bounded rationality: fast and frugal heuristics
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
2 / 15
Behavioral Economics: Lecture 9
How do we think?
Predictable biases in judgment
Two tables (from Shepard 1990):
Guess ratio of length to width of each table
Typical guesses: 5 to 1 for left, 1.5 to 1 for right
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
3 / 15
Behavioral Economics: Lecture 9
How do we think?
Predictable biases in judgment
Availability, accessibility, and salience
familiar risk is seen as more serious than less familiar risk
Representativeness
trying to …nd patterns in random sequences
Anchoring and adjustment
when guessing, you need to start from something but adjustment is
usually insu¢ cient
Status quo bias
tendency to stick with original choice
Framing
choice depends on whether problem is formulated as gains or losses
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
4 / 15
Behavioral Economics: Lecture 9
How do we think?
Two cognitive systems
Automatic system
uncontrolled
e¤ortless
associative
fast
unconscious
skilled
most biases disappear
Re‡ective system
controlled
e¤ortful
deductive
slow
self-aware
rule-following
when re‡ective system is on
does it happen with anomalies in risky and intertemporal choices?
what determines which system is on?
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
5 / 15
Behavioral Economics: Lecture 9
Class experiments
Cognition and coordination
Motivation
does using re‡ective system always lead to correct decisions?
what your belief about others’rationality should be?
Procedure
problem solving: several trials
discussion of results
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
6 / 15
Behavioral Economics: Lecture 9
Class experiments
"Beauty-contest" game
Rules
everyone submits integer between [0, 100]
average is computed and multiplied by k < 1
number closer to resulting number wins
Nash equilibrium
everyone’s guess is 0
requires iterated thinking
Typical results
peaks at certain levels
winning numbers between 10 and 20 (k = 2/3)
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
7 / 15
Behavioral Economics: Lecture 9
Class experiments
"Beauty-contest" game
Distribution of choices (Bosch-Domenech et al. 2002)
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
8 / 15
Behavioral Economics: Lecture 9
Class experiments
Market entry game
Rules
market capacity c is announced
everyone decides whether to enter
payo¤ k if stay out
payo¤ k + r (c m ) where m... number of entrants, r > k
Nash equilibria: aggregate level
pure strategy: m = c and m = c 1
how is it decided who enters and who stays out?!
Typical results
NE at aggregate level is achieved!
individual strategies are di¤erent
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
9 / 15
Behavioral Economics: Lecture 9
Class experiments
Market entry game
Individual strategies (Sundali et al. 1995):
s Index measures decision consistency
s Index = 30 ... pure strategies
s Index = 15.8 ... mixed strategies
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
10 / 15
Behavioral Economics: Lecture 9
From rationality to bounded rationality
Always making best choices?
What is rationality [once again]?
always leads to consistent choices
What prevents you from always choosing best?
…nancial resources are limited (standard budget constraint)
information is limited
uncertainty: what is ex ante optimal may not be ex post optimal
…nding best option is cognitively demanding
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
11 / 15
Behavioral Economics: Lecture 9
From rationality to bounded rationality
Optimization under constraints
Diamond paradox
many examples when under perfect competition prices are higher than
marginal costs
Explanation
consumer does not know price level at particular shop before visiting it
traveling to next shop is costly
there is no need in price undercutting for …rms
Crucial element: stopping rule
compare costs and bene…ts of further search to decide when to stop
computing costs and bene…ts requires information and cognition
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
12 / 15
Behavioral Economics: Lecture 9
From rationality to bounded rationality
Bounded rationality: satis…cing
Simon 1956
search continues until a priori set aspiration level is achieved
Problems
how aspiration level is set?
how particular alternative is compared with aspiration level?
which alternative is considered …rst?
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
13 / 15
Behavioral Economics: Lecture 9
From rationality to bounded rationality
Bounded rationality: fast and frugal heuristics
Early example: elimination by aspects
searching for apartment
aspects to compare: price, distance from center, renovation, living area
eliminate ‡ats with price > 15000 RUB
what if there is perfect option for 15500 RUB?
Recent example: priority heuristics
see lecture on choice under risk and uncertainty
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
14 / 15
Behavioral Economics: Lecture 9
Course summary
Behavioral Economics
Standard economic models are practical and elegant but
sometimes too abstract
Psychological insights and understanding of human behavior in
given situations help to make models more realistic
But they often lose their elegance
especially, when authors attempt to keep generality
Open question: how to solve trade-o¤ between elegance and
realism?
Natalia Shestakova (Ural State University)
Lecture Notes
Spring 2010
15 / 15