Transcript Decision Heuristics

The Behavioural Economics
Revolution?
Chris Starmer
TSU Short course in Experimental and Behavioural
Economics, 5-9 November 2012
Overview of Sessions
1.
2.
3.
4.
5.
The Behavioural Revolution in Econ
The Experimental Economist
Individual Decisions
Strategic Decisions
Markets
Rules of Engagement
• Do ask questions if you want
• Access to slides
• References
Behavioural Economics is Popular
• Gaining momentum
– Economic theory, Applied economics,
Policy circles, Media discussions, Private
enterprise
• So what is it?
– How does it differ from conventional
Econ?
• Is it the future of economics (or a fad)?
• Should we welcome it?
So What is BE?
• No single definition
– For e.g’s - Google: Colin Camerer, Richard
Thaler, George Loewenstein
• Common assertions – what it is:
– More ‘realistic’ psychological foundations for
economics
– Bounded Rationality
• Common claims – what it does:
– More ‘realistic’ theories
– Improved prediction of human behaviour
– Useful policy tools (cheap/effective)
Popular science summaries
Great summary
Implications for Policy
BE - Scientific Revolution?
• Changing character of economics
• How, as economists, we think
about:
–Evidence
–Theory
–Rationality
Evidence
“Experimental Turn” in Economics
– Explosion in Experimentation (since 1980s)
– Fundamental to development of BE
“It is rarely, if ever, possible to conduct
controlled experiments with the
economy. Thus economics must be a
non-laboratory science.”
Richard Lipsey (1979) An Introduction to
Positive Economics
Two classic experiments
• Individual preferences
– The endowment effect
• Social Preferences
– The pull of the crowd
• Both very simple
– Maybe you could
have done it if you’d
thought of it first!
Jack Knetsch
(American Economic Review, 1989)
What proportion of people prefer
mugs to chocolate bars?
Give up mug to get chocolate?
(89% prefer mugs, n=76)
Give up chocolate to get mug?
(10% prefer mugs, n=87)
“Endowment Effect”
Pull of the crowd
• Bryan, J.H. and Test,
M.A. (1967) “Models
and helping:
naturalistic studies in
aiding”, Journal of
Personality and Social
Psychology, 6, 400-7.
Impact of Experimentation
• Produced Many ‘ANOMALIES’
• Patterns of behaviour
• Influences on behaviour
– Surprising (relative to std Econ Theory)
• Examples
– Time and Risk Responses
– Social Preferences (altruism, reciprocity)
– Myopia, Status quo bias
– Partial information
– Context sensitivity of choice
– Experience matters
From anomaly to (behavioural) theory
Anomalies
New Theories
(many)
Experiments testing
new theories
Some successes
e.g. “Prospect Theory”
Kahneman/Tversky
Leading to New Breed(s) of Theory
• Empirically Grounded (vs axiomatic)
• More “realistic” psychological foundations
• Bounded Rationality vs Full Rationality
More ‘realistic’ assumptions
Realistic about what?
Two key dimensions –
Preferences and Reasoning
Preferences
• Individual
preferences
– Risk
– Time
e.g. ‘prospect theory’
Kahneman & Tevrsky
(Econometrica,1979)
• Social preferences
– Egoism
– Fairness
– Reciprocity
e.g. ‘Theory of fairness,
competition and
cooperation’ Fehr &
Schhmidt (QJE, 1999)
Reasoning
• Cognitive limitations
– Calculating ability
– Myopia
– Memory
• Abilities
– Speed
– Adaptability
Bounded Rationality:
Herbert Simon , ‘How to decide what to do’, Bell Journal, 1978.
Giggerenzer, Tod, ABC, Simple Heuristics That Make us Smart,
Oxford, UP 2000.
Bounded Rationality in a Nutshell
• Because of:
– Limits of computational capacity (e.g. Memory)
– Costs of deliberation (e.g. time)
• Agents develop/use:
– decision heuristics, rules of thumb
• Rules of thumb, help agents navigate
complex world
• Sometimes
– Rules of thumb lead to suboptimal decisions
– But also support fast effective decisions
Who’s the best driver: economicus or
heuristicus?
Simple Heuristics That Make Us Smart
Gigerenzer et al, OUP, 1999
Session 1 - Part II
Behavioural Economics in Action
(i) A classic Theory
(ii) Some Applications
(i) A classic model in
Behavioural Economics
Prospect Theory, Kahneman and
Tversky, Econometrica, 1979
Prospect theory - Background
• Theory of Decision Under Risk
• Competitor to Expected Utility Theory
• One of most highly cited papers in
economics
– (Google Scholar: 24k+ Cites, Nov 2012)
• Often cited as key development in
‘behavioural economics”
– Kahneman shared Nobel prize, 2002
• Aspects of prospect theory becoming
‘mainstream’? (e.g. loss aversion)
A thoroughly behavioural theory
• Built from experimental evidence
– Anomalies relative to standard theory
• Informed by psychological theory
– E.g. Psychophysics of perception
• Features assumptions about both
– (limitations of) Reasoning
– Non-standard preferences
• Claims improved predictive power
– Relative to expected utility theory
Structure of PT
• Theory of choice among risks or “prospects”
– Prospect is prob. Dist. over consequences
(p1, x1; p2, x2.........pn, xn)
• “Two Step” Theory
– Step 1: ‘editing’
– Step 2: ‘evaluation’
Editing Step
• Before evaluating prospects individuals
‘edit’ choice set
• Editing involves (simplification)
heuristics:
• e.g.1. rounding outcomes/probs
• e.g.2 ignoring v. small prob events
• e.g.3 elimination of (transparent) dominance
Example: Choose A or B
Option A
90% white
$0
Option B
90% white
$0
6% red
win $45
1% green
win $30
3% yellow
lose $15
7% red
win $45
1% green
lose $10
2% yellow
lose $15
Tversky and Kahneman, 1986: 58% chose A
Rearrange the information
90% white
6% red
1% green
1% blue
2% yell
A:
B:
win $45
win $45
win $30
win $45
lose $15
lose $10
lose $15
lose $15
$0
$0
Easy to see that B dominates A
Using second ‘framing’ everyone chose B (n=88)
Evaluation Step
Uses (Non-standard) preference function
applied to edited choice set
(Roughly) Max V(q) =  (pi)v(xi)
(pi) is a “probability weighting” function
v(xi) a utility function on outcomes
EUT is Special case where
(pi)=p and V(.) is vNM utility function
Evaluation is….
Model of maximisation, but…..
Incorporating (empirically grounded)
assumptions about human perception:
1) probability ‘distortion’
2) loss aversion
Common interpretation is that these are
‘biases’ relative to optimal decisions
Probability distortion
(p)
• PT assumes
Overweigthing ‘small’ p
Underweighting ‘large’ p
Support
Psychophysics
Field evidence
Gambling
Risk assessment
Provides fit to ‘anomaly
evidence’
p
The Value Function
• Built on three main ‘psychological’
assumptions:
– Carriers of value are changes relative to a
reference point
– Gains and losses evaluated separately
– “Loss aversion”
• losses loom larger than gains
Value of Δx
on gain scale
-Δx
+Δx
Value of Δx
on loss scale
Assessment of Prospect theory
• More ‘realistic’ decision model
– surely, people do simplify complex
decisions
– Considerable evidence of loss aversion,
probability distortion
• Some additional predictive content
– More complex model
• Spawned a large research
programme
– Developing and testing PT
– Using it to explain field phenomena
Applications
Behavioural Economics in the wild
Prospect theory in the Wild:
Predicting Investment
• The Equity Premium
Puzzle
– Excess return of stocks
over bonds (long run)
– Why do people invest
so much in safer
assets?
• Benartzi/Thaler,
Quart. J. Econ, 1995.
– Loss Aversion
– Myopia
BE and Public Policy
Policy makers have become interested in BE
Because maybe it helps explain why people do
‘suboptimal things’
– Not save enough, drink too much, drive too
fast, waste things (Energy, water, food)
• Might help identify (cheap but effective)
new tools for policy intervention
– The ‘Nudge’ agenda
“Our government will find intelligent ways to
encourage, support and enable people to make
better choices for themselves.”
Benartzi and Thaler, JPE, 2004
• Companies concerned re low level of
employee savings
– not increasing in line with income growth
• Prescriptive savings program (Smart)
– based on findings of behavioural economics
• “Status quo bias”
– pre-commit to savings increases out of future
income growth
• Opt out facility (instead of opt in)
• Increased savings rate from 4% to 12% over
2 year period
Encouraging repayment of court fines
Source: Behavioural Insights Team & HMCTS, 2012
CabinetOffice
Behavioural Insights Team
Encouraging repayment of court fines
CabinetOffice
Behavioural Insights Team
Repayment rates
40%
33%
30%
23%
25%
20%
10%
5%
0%
No text
Standard text Standard text Standard text
+ amount
+ name
Source: Behavioural Insights Team & HMCTS, 2012
That’s it
Tomorrow – the experimental
economist