____________________________________________________________________________________________________ Two Papers in Behavioral Economics Presentation by Olga Koslova, Kira Stearns, Yanzhi Xu, and Ye Zhang NEUROECONOMICS: USING NEUROSCIENCE TO MAKE ECONOMIC PREDICTIONS Colin F. Camerer 1. Neuroscientific facts and tools 1.1. Facts The important facts about the brain: The brain is weakly modular (i.e. not every brain area contributes to every behavior). The brain is plastic (i.e. responsive to environment as brain ‘software‘ is gradually ‘installed’) Because attention and consciousness are scarce, the brain has evolved to off-load decisions by automating activity through learning. For example, Americans going to England are accustomed to looking to the left (automaticity) when crossing the street, but in England cars are approaching from the left. To avoid this mistake (which can lead to accident) the brain needs attention and consciousness, hence, people whose conscious attention is absorbed elsewhere (e.g. talking on the phone), are more likely to be killed when crossing the street. The brain of the human is the primate brain with an extra neocortex (see Figure 1), and the primate brain is simpler mammalian brain with some neocortex. Because of the similarities of the brain structure, the experiments with animals are so informative about human behavior. 1.2. Tools To identify the areas of the brain that are active in performing a particular task the following technologies are used: Figure 1: Location of neocortex.
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Two Papers in Behavioral Economics Presentation by Olga Koslova, Kira Stearns, Yanzhi Xu, and Ye Zhang
NEUROECONOMICS: USING NEUROSCIENCE TO MAKE ECONOMIC
PREDICTIONS
Colin F. Camerer
1. Neuroscientific facts and tools
1.1. Facts
The important facts about the brain:
§ The brain is weakly modular (i.e. not every brain area contributes to every behavior).
§ The brain is plastic (i.e. responsive to environment as brain ‘software‘ is gradually
‘installed’)
§ Because attention and consciousness are scarce, the brain has evolved to off-load
decisions by automating activity through learning. For example, Americans going to
England are accustomed to looking to the left (automaticity) when crossing the street, but
in England cars are approaching from the left. To avoid this mistake (which can lead to
accident) the brain needs attention and consciousness, hence, people whose conscious
attention is absorbed elsewhere (e.g. talking on the phone), are more likely to be killed
when crossing the street.
§ The brain of the human is the primate
brain with an extra neocortex (see Figure
1), and the primate brain is simpler
mammalian brain with some neocortex.
Because of the similarities of the brain
structure, the experiments with animals
are so informative about human
behavior.
1.2. Tools
To identify the areas of the brain that are active in performing a particular task the following
technologies are used:
Figure 1: Location of neocortex.
____________________________________________________________________________________________________ § Functional magnetic resonance imaging (fMRI) uses magnetic resonance imaging to
measure the change in blood flow related to neural activity in the brain.
§ Positron Emission Tomograhpy (PET) is a scanning technology that injects radioactive
solution in the body to create 3D picture of the processes in the body (or brain in
particular).
To study whether the behavior of subjects studied changes when parts of the circuit are broken
or disrupted, scientists employ the following:
§ Studies of patients with brain lesions (abnormal tissues in the brain, caused by either
disease, congenital malformation, trauma, etc.)
§ Transcranial magnetic stimulations (TMS) performed on animals – ‘knocks out‘ or
activates certain brain areas to observe what targeted areas do.
Older tools:
§ Electroencephalogram (EEG) records electrical activity from outer brain areas by firing
the neurons within the brain; this can be used to interpolate activity in deep areas of the
brain.
§ Pshychophysiological recording of skin conductance, hear rate and pupil dilation. Its
benefit is that it is cheap and easy.
§ Eye tracking measure the motion of the eye relative to the head.
2. Evidence for Rational Choice Principles
Empirical evidence comes from the studies of animals:
§ Platt and Glimcher (1999) in their research ‘Neural correlates of decision variables in
parietal cortex’ find correlation between rate at which neurons in monkey lateral
intraparietal cortex (LIP) (area responsible for transforming visual signals into eye-
movement commands) fire and value of an upcoming juice reward. Hence, observing
larger gain (in a sense of higher value of juice reward) modulates higher activity of
neurons in the lateral intraparietal cortex.
§ Deaner et al. (2005) in their research ‘Monkeys pay per view: adaptive valuation of social
images by rhesus macaques’ find that monkeys can trade off juice rewards with exposure
to visual images. Hence, researchers conclude that monkeys can evaluate the information
The Ellsberg paradox, which violates the expected utility hypothesis (explained in further
below), suggests that when two events are equally likely but poorly understood, revealed
decision weights seem to combine judgment of likelihood and additional factor, which leads
to an aversion to betting under ambiguity. Hsu et al. (2005) in ‘Nonlinear Probability
Weighting in the Brain’ found additional activity in the dorsolateral prefrontal area ([9] and
[46] in Figure 2), orbitofrontal cortex ([10], [11], and [47] in Figure 2), and the amygdala (a
‘vigilance area‘ which is shown to be responsible for processing and memory of emotional
reactions located deep in the medial temporal lobes of the brain). Subjects with higher right
orbitofrontal cortex (OFC) activity in response to ambiguity also had higher ambiguity-
aversion parameters.
3.3. Nonlinear probability weighting
The nonlinear probability weighting in particular overweighting low probabilities and
underweighting probabilities close to one is studied in the neuroeconomics by the way how
caudate (a temporal lobe area including the striatum which is associated with rewards of any
type) responds to anticipated reward. Hsu et al. (2006) in ‘Nonlinear Probability Weighting in
the Brain’ by observing activity in the left and right caudate areas controlling for the payoff
amount find modest nonlinearity of activity across levels or probability p.
3.4. Limited Strategic Thinking
Camerer et al. (2004) in ‘A cognitive hierarchy model of games‘ propose ‘cognitive hierarchy‘
theory which suggests there are three steps of strategic thinking: step-0 players randomize,
step-1 players anticipate randomization and best-respond it, step-2 players best-respond to a
mixture of step-0 and step-1 players, and so on. The highest step players anticiate correctly the
distribution of the actions of other players, hence, their beliefs are in equilibrium. The
empirical evidence of Bhatt and Camerer (2005) in ‘Self-referential thinking and equilibrium
as states of mind in games: fMRI evidence‘ looks at fMRI of players when they are making
choices and when they express beliefs about what other players will do. Because players who
are in equilibrium are imagining how others are choosing, then there is overlap between
making own choice and expressing beliefs about choice of other players, which is supported
____________________________________________________________________________________________________ by the images of brain activity during choosing and belief expression. In contrast, for players
are out of equilibrium, there was higher activity when making a choice than when expressing
a belief (note that lower type players put higher weight in their own choice than to a choice of
other players).
4. Evidence for New Psychological Variables
§ The largest payoff from neuroeconomics may come from pointing out biological
variables which have a large influence on behavior and are underweighted or ignored
in standard theory
§ Preferences are both are both the output of a neural choice process and an input which
can be used in economic theory to study responses to change in price and wealth
Summary of Hsu et all (2005)
§ Difference between “risky” (betting on roulette) and “ambiguous”(the possibility of a
terrorist attack) events
§ In subjective expected utility theory, the probabilities of outcomes should influence
choices, NOT one’s confidence in those probabilities
o However, people are more willing to bet on risky events than ambiguous ones,
when holding the perceived probability of the outcomes constant
§ The Ellsberg Paradox:
o Imagine one deck of 20 cards composed of 10 red and 10 blue cards (the risky
deck). Another deck has 20 red or blue cards, but the composition of red and
blue cards is completely unknown (the ambiguous deck). A bet on a color pays
a fixed sum (e.g. $10) if a card with the chosen color is drawn, and zero
otherwise. In experiments with these choices, many would rather bet on a red
draw from the risky deck than on a red draw from the ambiguous deck, and
similarly for blue draw. If betting preferences are determined only by
probabilities and associated payoffs, this pattern is a paradox: in theory,
disliking the bet on a red draw from the ambiguous deck implies that its
subjective probability is lower [Pamb(red)<Prisk(red)]. The same aversion for
blue bets implies [Pamb(blue)< Prisk(blue)]. But these inequalities, and the fact
MYOPIC LOSS AVERSION AND THE EQUITY PREMIUM PUZZLE
Sholmo Benartzi and Richard H, Thaler
I Equity Premium Puzzle
This paper is a behavioral finance paper; its main purpose is to use the combination of
loss aversion and short period of evaluation, which is called myopic loss aversion, to explain
the equity premium puzzle in the finance market.
In this section, we will go through the concepts of equity premium puzzle, and
demonstrate the existence of it. Then we list the alternative explanations of equity premium
puzzle in previous studies. Finally we briefly introduce the behavioral finance explanation,
provided by Benartzi and Thaler.
§ Equity Premium Puzzle and Its Existence
The key difference of stocks and bonds is their different riskiness and return rates :
stocks have higher returns and higher variances while bonds are more stable but offer a lower
return. Siegel (1991,1992) shows that in 1926-1990, the real compound equity return was 6.4
percent, while the return of short-run government bond is 0.5 percent, implying that stocks
have outperformed bonds by a large margin. This phenomenon suggests that, even though
investment on stocks yields much higher return than bonds in a long run, the investors still
prefer bonds to stocks. MaCurdy and Shoven explain that “People must be confused about the
relative safety of different investments over long horizons”.
Mehra and Prescott (1985) demonstrate that in order to reconcile the much higher
returns of stocks compared to government bonds in the United States, individuals must have
an incredibly high risk aversion parameter, which should exceed 30 (we call it a explanatory
parameter) whereas the previous estimations and theoretical arguments suggest that the actual
parameter should be closer to 1. This huge gap between the explanatory risk aversion
parameter (30) and actual one (1) cannot be well explained by the risk-aversion theory alone.
[A vivid demonstration of this over 30 risk aversion parameter is as follows: when an
individual with such a risk aversion parameteris offered with a gamble, with a 50 percent
chance of winning $100,000, with a 50 percent chance of winning $50,000, the indifferent
____________________________________________________________________________________________________ certainty equivalent for him is $51,209! Few people can be this afraid of risk; note the
certainty equivalent should be $75,000 for a risk neutral individual.)
§ Previous Explanation of Equity Premium Puzzle
Explanation 1(Reitz, 1988):
Equity premium is a rational response to economic catastrophe.
Comments in this paper: Not a plausible explanation.
Reason: First, the great depression (1929) has been included in the data, but the high
premium still exists. Second, the catastrophe should affect stocks and not bonds, however, in
reality, a bout of hyperinflation affects bonds more than stocks.
Explanations 2:
Relax the link between the coefficient of relative risk aversion and the elasticity of
the intertemporal substitution to explain equity premium puzzle.
Model 2.1 Weil (1989) nonexpected utility preferences theory.
Comments in this paper: Just transform the equity premium puzzle into a “risk free
rate puzzle”, and fail to truly solve the puzzle.
Model 2.2 Epstein and Zin (1990) use Yaari’s Dual theory of choice, which is also a
nonexpected utility preferences theory.
Comments in this paper: It can only explain ⅓ of observed equity premium.
Model 2.3 Mankiw and Zeldes (1991) investigate whether the homogeneity
assumtions necessary to aggregate across consumers could explain the puzzle. They found
only a minority of Americans hold stocks, whose consumption behaviors are different from
nonstockholders.
Comments in this paper: This can only partly explain the puzzle.
Habit-formation model, which means the utility of consumption is assumed to
depend on past levels of consumptions, especially averse to reduce their consumptions.
Comments in this paper: This model better explain the intertemporal dynamics of
returns, it fails to explain the differences in average returns across assets.
II Myopic Loss Aversion: Loss Aversion + Frequent Evaluation
Myopic loss aversion is a combination of loss aversion and frequent evaluation. In this
section, we will briefly introduce loss aversion and frequent evaluation. Then talk about the
Samuelson paradox, and the underlying connection of Samuelson paradox and equity
premium puzzle.
§ Loss aversion
According to prospect theory (Kahneman & Tversky,1979), loss aversion means
individuals are more sensitive to loss than to gain, e.g. the disutility of giving up 1
dollar is almost twice the utility of acquiring 1 dollar.
In this paper, the authors use cumulative prospect theory (Tversky & Kahneman, 1991)
and its corresponding parameter to measure loss aversion.
Equation 2 is the value function. X measures the loss or gains, rather level of wealth. λ is
the coefficient of loss aversion, which is set as 2.25 in this paper. α and β measure the
diminishing of sensitivity.
Equation 3 is the describe the weighted value of a gamble G, which pays off ix with
probability of ip .In the function, iπ is subjective decision weight, which is a simple nonlinear
____________________________________________________________________________________________________ transform of ip in prospect theory(1979), but in this paper, they use the cumulative prospect
theory, iπ depends on the cumulative distribution of the gamble, rather than on individual
ip .Denote w as the nonlinear transform of the cumulative distribution of the gamble G. The
parameter approximation of probability ip is
In equation 4, γ is 0.61 in the domain of gain, γ is 0.69 in the domain of loss. Here we
offer a graphic description of equation 4, which is cited from cumulative prospect theory
paper (Tversky & Kahneman, 1991).See figure I.
§ Frequent Evaluation
The evaluation period is a concept in mental accounting theory (Kahneman & Tversky
1984; Thaler 1985). Mental accounting refers to implicit methods individuals use to code
and evaluate financial outcomes, because the existence of loss aversion, mental accounting
causes the none-neutral dynamic aggregation rules. For example, assume an individual wins
$100 from a gamble, then loses $50 because of speeding ticket. If the gain and loss are
evaluated separately, his/her total utility is 0, because the loss of $50 is twice as painful as
gain and cancels the utility gaining from gaining $100. If the gain and loss are aggregated to
a net gain $50, then this individual will have a positive total utility. In this example, the
evaluation period matters, if they evaluate the outcome too often, they will always separate