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Summary SV Gedragseconomie (Prof. Ranoua Bouchouicha)
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Behavioural economics 2021-2022Lecture 1: Introduction and standard economic modelling 3
Lecture 2&3 : Heuristics and expected utility paradoxes 4
01. Heuristics and bias 4
a. Definitions and introductory concepts 4
b. Heuristic 1: Representativeness 5
c. Heuristic 2: Availability 5
d. Heuristic 3: Anchoring effect 5
02. Expected Utility paradoxes 6
a. When does EUT work? 6
b. Violations of EUT 6
c. Allais paradox 6
d. Ellsberg paradox 6
e. Rabin’s Calibration Theorem 7
f. Reference dependence 7
g. Loss aversion 7
h. Endowment effect 7
i. Framing effect 8
03. Some applications 8
a. Application 1 : Target income and target wage 8
b. Application 2: Housing market 8
c. Application 3: Life Cycle Hypothesis 9
d. Application 4: Saving for the future 9
04. Summary 9
Lecture 4.1: Decision making under risk 10
01. Conventional utility function 10
02. Attitudes toward risk 10
03. Prospect theory (PT) 11
04. Applications 12
a. Investor behaviour - Equity premium puzzle EPP 12
b. Tax evasion 12
c. Judiciary system - court settlement 12
Lecture 4.2: Time preferences 13
01. Discounting 13
02. Time inconsistency 13
a. Hyperbolic discounting (Thaler, 1981) 13
b. Quasi-hyperbolic discounting 13
03. Application for time preferences 14
a. Consumer behaviour 14
b. Environmental economics 15
Lecture 5: Interaction and social preferences 15
01. Introduction to game theory 15
02. Importance of coordination and social motives 16
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,Lecture 6: Applications social preferences 18
01. Application 1: Public good game 18
02. Application 2: Giving to charity 19
03. Application 3: Price and wage rigidity (stijfheid) 19
04. Application 4: Contract theory 19
05. Application 5: Financial market 19
Lecture 7: Behavioural insights and nudge 19
01. Definitions 19
02. Applications 20
a. Organ donations 20
b. Saving accounts 21
c. Reducing spillage in Schiphol urinals 21
d. Environment 21
e. Tax 21
f. International development 21
Lecture 8: Research in behavioural economics 22
01. Measuring preferences 22
a. Survey measures 22
b. Incentivised measures 22
02. Preferences and financial and economic outcomes 23
a. Micro-evidence on risk tolerance 23
b. Risk tolerance, growth and entrepreneurship 23
c. Risk tolerance and fertility 23
d. Trust and corporate cash holding 23
03. Determinants of preferences 23
a. Climate shocks 23
b. Economic environment 23
c. Political and cultural environment 23
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,Lecture 1: Introduction and standard economic modelling
Historical names:
● Vilfredo Pareto (20th century)
- Broke economics from psychology ! anti-Behavioural Economics !
- Choice rather than desire
● Herbert Simon (21st century)
- Questioned rational models
- Bounded rationality = rationality is bounded because there are limits to our thinking
capacity, available information & time.
● Kahneman & Tversky
- Evidence that the assumptions of the standard economic models are limited
- Cognitive bias, framing effects, reference point
Nobel prizes:
● Richard H. Thaler “for his contributions to behavioural economics”
● Vernon L. Smith “for having established laboratory experiments as a tool in empirical economic
analysis, especially in the study of alternative market mechanisms”
● Daniel Kahneman “for having integrated insights from psychological research into economic science,
especially concerning human judgement and decision-making under uncertainty”
● Herbert A. Simon “for his pioneering research into the decision-making process within economic
organisations”
STANDARD ECONOMIC MODEL
Economic agents are rational
- People act with full information = full external knowledge
- People have known preferences = full internal knowledge
- People choose the best option available = rational choices
THEORIES
→ Normative: tell us how to behave to obtain a certain goal
→ Descriptive: how people really behave, may not me the same as the normative theory
Expected value theory (EVT) - standard economic theory
EV (x, p; y) = px + (1-p)y
! St. Petersburg paradox !
A fair coin is tossed. If it comes up heads, you are paid €2. Then the coin is tossed again. If it comes up heads
again, you are paid €4= 22; and so on.
When the coin comes up tails, you are paid the accumulated outcome up to
that point (e.g. €2+€4=€6 if tails comes up on the third toss) and the game ends.
EVT says that the expected value is infinite, but players will not be willing to put infinite
money in the game. ⇒ Proves that people do not make their decisions based on EVT.
Solution: EUT
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,Expected utility theory - Daniel Bernoulli
“The determination of the value of an item must not be based on the price but rather on the utility it
yields”.
EU (x%, p%) = Σ p% U(x%) ↔ EV (x%, p%) = Σ p% x% (expected value theory)
Bernoulli: U(x) = Ln(x)
→ Marginal utility of money reduces (after a certain wealth level any additional € will be worth less
in utility terms).
Certainty equivalent = the amount you would accept instead of playing the game
U(CE) = pU(x) + (1-p) U(y)
→ Compare to EVT, same formula but utility is incorporated
ADJUSTED STANDARD ECONOMIC MODEL TO UTILITY THEORY
- Economic agents are motivated by expected utility maximisation.
- An agent’s utility is governed by purely selfish concerns, in the narrow sense that it does not
take into consideration the utility of others.
- Agents are Bayesian probability operators.
- Agents have consistent time preferences according to the discounted utility model.
Lecture 2&3 : Heuristics and expected utility paradoxes
01. Heuristics and bias
a. Definitions and introductory concepts
Heuristic = a simple rule of behaviour by which a person solves a problem.
Bias = Systematic suboptimal judgments that can result as a
consequence of the heuristic process.
Search heuristics:
● Try everything
● Satisficing: try until aspiration level is met
● Elimination by aspects: Consider different
aspects of choice (e.g. taste, price…) and
eliminate the goods that fail to meet your
aspiration level for each aspect (e.g. Not tasty
enough, too expensive,...)
● Directed cognition: compare each good to the last one you tried, if not better
keep the last one.
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, b. Heuristic 1: Representativeness
Influences the decision “what is the likelihood that A belongs to category B”.
The decision is based on the known or presumed characteristics of B.
E.g.: “John is very shy and withdrawn, invariably helpful, but with little interest in people, or in
the world of reality. He is very tidy, he has a need for order and structure, and a passion for
detail”.
What is the likelihood that John is a farmer, salesman, librarian or physician?
→ Many people would say that the chance of John being a librarian is bigger.
→ These people decided based on representativeness, which is theoretically
incorrect.
Assume that we did not get a description of John’s character, then we could say that
the probability of John being a salesman is higher, because there are more salesmen
than farmers, librarians and physicians.
→ This decision is made based on the base rate theory, purely objective.
Bayes rule/base rate = describes the probability of an event, based on prior
knowledge of conditions that might be related to the event.
⇒ Voorwaardelijke kans in dutch
P(b|a) = [P(a|b) . P(b)] / P(a)
Subject to P(a) = P(a|b) . P(b) + P(a|1-b) . P(1-b)
P(b|a) = the chance that John is a librarian if he’s shy.
P(a|b) = the chance that John is shy if he’s a librarian = 1, because we know he is shy.
P(a) = the chance that John is shy = 1
P(b) = the chance that John is a librarian
[P(a|b) . P(b)] / P(a) = [1 . P(b)]/1 = P(b)
→ The chance that John is a librarian if he’s shy is equal to the chance that he is a librarian
⇒ From the moment people are giving a description of a character, even when it is a
neutral description, people tend to ignore the Bayes rule. This is what we call ‘failure
of Bayesian updating”.
c. Heuristic 2: Availability
The decision is based on how many instances of that event come readily to mind.
E.g.: People are more likely to take fire insurance after their house caught fire, even though
the chances of their house catching fire have not increased.
d. Heuristic 3: Anchoring effect
The decision is based on the first piece of information you receive. This first piece of
information is called the reference value, this value is sticky, not easily adapted over
the rest of the experiment.
E.g.: If you start with a higher reference value, your WTP will be higher over the whole
experiment.
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