1. Lecture 4 Basic concepts of behavioral decision making, prospect theory

2. Kahneman: Maps of Bounded Rationality
Summary in three slides

3. Bernoulli’s error
”The proposition that decision makers evaluate outcomes by the utility of final asset positions has been retained in economic analyses for almost 300 years.” –Kahneman Bernoulli’s theory was meant to be prescriptive and descriptive. But Kahneman argues it’s a bad description.

4. Concave
Convex

5. Key takeaways Reference point Concave-convex
Everything is evaluated in comparison to a reference point
Concave-convex
Risk aversion for gains
Risk seeking for losses
Steeper for losses than gains

6. Who: Daniel Kahneman
2002 Nobel Memorial Prize in Economics Developed Prospect Theory with Amos Tversky Extremely influential in heuristics & biases

8. More prospect theory

9. Prospect Theory Two phases:
(1) editing phase
(2) evaluation phase
The editing phase consists of a preliminary analysis of the offered prospects
seeks to generate a simpler representation of these prospects
application of several operations that transform the outcomes and probabilities associated with the offered prospects
In the second phase, the edited prospects are evaluated and the prospect of highest value is chosen.

10. Operations of editing phase
Coding:
Coding in terms of losses and gains from a reference point (usually the current asset position)
Combination of probabilities with identical outcomes
 Segregation:
if possible, segregate the riskless component from the risky one.
For example (300,.8; 200,.2) is naturally decomposed into a sure gain of 200 and the risky prospect (100,.8)
Cancellation:
discarding of components shared by the offered prospects (isolation effect)
 Simplification:
rounding, discarding of extremely unlikely outcomes
Detection of dominance:
rejection of dominated alternatives

11. Formation of reference points?
Applying for a job
Current salary
Salary in the position
Your friends’ salary …
Price of chocolate in the store?
Suitable wine in a restaurant?

12. Editing phase
In the coding phase, what reference point should be used?
Formation of reference point
Can the reference point change over time? Do we understand how?
Note: Many anomalies of preference result from the editing of prospects.
PT is a descriptive, not a prescriptive theory of choice!

13. Evaluation phase
Consider simple prospects of the form (x,p;y,q). A prospect is regular, if it is neither strictly positive nor strictly negative. Otherwise it is not regular. If (x,p;y,q) is a regular prospect, then V(x,p;y,q)= Π(p)v(x) + Π(q)v(y), where v(0)=0, (0)=0, and (1)=1 For example: V(400,.25; -100,.75) = Π(.25)v(400) + Π(.75)v(-100)

14. Evaluation phase
The evaluation of strictly positive and strictly negative prospects follows a different rule.
such prospects are segregated into two components: the riskless component and the risky component (the actual additional gain or loss which is at stake)
If (x,p;y,q) is not a regular prospect, i.e. is either strictly positive or negative, and x is the more extreme outcome
V(x,p;y,q) = v(y) + Π(p)[v(x)-v(y)]
For example V(400,.25; 100,.75) = v(100) + (.25)[v(400)-v(100)]

15. The pi function
Low probabilities are overweighted Large probabilities are underweighted

16. Comparison of EU and PT
Expected utility 𝒖 𝑨 = 𝒑 𝒊 𝒖( 𝒙 𝒊 ) 𝒑 𝒊 is probability of outcome 𝒊 𝒙 is the payoff of outcome 𝒊 𝒖 is the utility function
Prospect theory 𝒖 𝑨 = 𝝅(𝒑 𝒊 ) 𝒖 𝒑𝒕 ( 𝒙 𝒊 −𝒓) where 𝝅 transforms probabilities 𝒓 is the reference point 𝒖 𝒑𝒕 is the PT utility function

17. Camerer: Prospect Theory in the Wild: Evidence from the Field

18. ”[e]conomists often say they will incorporate more psychological ideas into economics if those ideas can parsimoniously accound for field data better than standard theories do.” –Colin Camerer
Presents 10 cases where:
Naturally occurring data (not lab experiments)
Prospect theory explains them better than standard theory

19. The cases Equity premium puzzle Disposition effect Labor supply
Asymmetric price elasticities
Insensitivity to bad news
6. Endowment effect 7. Favorite-longshot bias 8. End-of-the-day effect 9. State lotteries 10. Telephone wire insurance

20. Equity premium puzzle
Most of our time, stock returns are 8% higher than bonds
Why do people invest in bonds? -> risk aversion
But this would imply {51 209} ~{50 000; }
Benartzi & Thaler: equity premium puzzle explained if
loss-aversion coefficient is 2.25 and
the horizon is 1 year
I.e. investors are myopic

21. Disposition effect People are much more likely to sell winning stocks
But rationally, only future changes matter
This effect does not exist in December, when investors sell losers to avoid taxes
Explained with loss aversion: people don’t want to lock-in their losses

22. Labor supply
New York cab drivers work long on bad days, and work short hours on good days Optimal solution would be to do the exact reverse! Explained with 1-day targets and loss aversion

23. Asymmetric price elasticities
Loss aversion implies asymmetric elasticities
Price increases are worse than price decreases
Customers cut back more when increases happen, than what they increase when prices fall
Exactly this effect has been found

24. Insensitivity to bad news
If you know pay will be cut next year, what do you do? EU implies you should cut consumption now But in fact, people do not do that Explained with loss aversion relative to reference point, and gambling on the pay cut not being so bad next year

25. Endowment effect
Status quo bias: a change in offered healthcare plans in Harvard
Older faculty decided often to continue with their current healthcare plan, even though they could have changed
Defaults: PA/NJ had similar auto insurance, but different defaults
This led to different choices of consumers!
Endowment: Mug study
Selling price of given mugs was 2-3 x buying price

26. Favorite-longshot bias
On the racetrack, longshot horses are bet on too much Bettors dislike the low return on favorites And they overweigh the small probability of a longshot win

27. End-of-the-day effect
At the end of the betting day, many bettors really prefer longshots This is because they are trying to get in the black And this is only possible with a longshot bet Explanation: reference points

28. State lotteries
Lottery ticket purchases correlate strongly with main jackpot size, but not with probability of victory A rational player would take probability into account Explanation: overweighting low probabilities

29. Telephone wire insurance
Customers could either: Pay 60$ to repair wire damage Or Pay .45$ wire insurance / month Choosing B implies ridiculous risk aversion Explanation: Overweighting low probability

30. Conclusions
Prospect theory can account for many observed ”anomalies” of decision behavior:
via changing reference point (A house, B house)
via form of the value functions (concave for gains, convex for losses – steeper for losses)
via overweighting small probabilities