An Overview of Neuroeconomics
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Dante Monique Pirouz
Doctoral Student
Psychology and Capital Markets Workshop
December 13, 2006
Some Neuroecon Humor…
What is Neuroeconomics?
• Studies how the brain interacts with the
environment to produce economic behavior
• Integrates economics, psychology,
neuroscience, and cognitive science
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Financial decision making
Game theory strategy
Influence of emotion, biases, etc.
Social dynamics on economic behavior
Developmental similarities and differences
Addictive consumption
Influence of cues such as advertising, brands, etc.
Development of Neuroeconomics
• Experimental Economics
– Uses lab experiments to test economic
models
• Behavioral Economics
– Combines economics and psychology
• Behavioral Finance
– Combines finance and psychology
Development of Neuroeconomics
• Cognitive Neuroscience
– Seeks to understand the neural mechanisms
underlying higher brain function
• Language, learning, memory, attention, emotion,
decision making, perception
Why Combine Economics with
Neuroscience?
• Neoclassical economists ask “Given rational
people, how do models behave?”
– Rational choice theory, expected utility theory
• Psychologists ask “Why do people behave the
way they do?”
– Prospect theory
• Looking into the “black box”
– At the neuronal and biochemical level
– To understand what makes people happy, risk
seeking or averse, trusting or trustworthy and what
drives preference and choice
Neuroscience Methods
– Animal studies
– Human studies
– Lesion studies
– Single and multiunit recordings
– Measuring hormone levels, pupil dilation,
galvanic skin response
– Stimulation
• Transcranial magnetic stimulation (TMS)
– Imaging of brain activity
Main Brain Regions
Frontal Lobe
Parietal Lobe
Temporal Lobe
Occipital Lobe
Limbic System
Thalamus
Cingulate Gyrus
Hippocampal
Formation
Striatum
Corpus
Callosum
Hypothalamus
Amygdala
Pons
Cerebellum
Spinal Cord
Brain Imaging Techniques
Methodology
What is imaged?
How?
Electroencephalography
(EEG)
Changes in electrical
brain current
Electrodes placed on scalp
measure electrical brain waves
Computed (Axial)
Tomography Scan (CT or
CAT)
X-ray images of the brain Multiple images (tomograms) are
taken by rotating X-ray tubes.
Does not image function
Positron Emission
Tomography (PET)
Emissions from
radioactive chemicals in
the blood
Radioactive isotopes injected into
the blood are detected like X-rays
Magnetoencephalography
(MEG)
Changes in electrical
brain current
Similar to EEG but magnetic
brain waves are measured instead
of electrical waves
Functional Magnetic
Resonance Imaging (fMRI)
Blood flow;
oxyhemoglobin to
deoxyhemoglobin ratio
Relies on magnetic properties of
blood. Shows brain function
spatially and temporally
EEG
CT/CAT
PET
MEG
Functional Magnetic Resonance
Imaging (fMRI)
• Uses strong magnetic fields to
create images of biological
tissue
– Measures hemodynamic signals
related to neural activity
• Blood Oxygenation Level
Dependent (BOLD) contrast
• MR signal of blood is dependent
on level of oxygenation
• Changes in deoxyhemoglobin
• Blood flow in the brain implies
function
Source: UC Irvine Center for Functional Onco-Imaging
– Studies have shown regional brain
activity when exposed to cues
(Huettel et al. 2004)
Why is fMRI so exciting?
• Non-invasive
• Better temporal
resolution
• Good and improving
spatial resolution
• Can be used in
conjunction with
other methods (Savoy
2005)
Source: MGH/MIT/HMS Athinoula A. Martinos Center
for Biomedical Imaging Visiting Fellowship Program in
fMRI, 2005
Caveats of fMRI
• Interpreting the results
– Direct vs. indirect measure of brain activity
– Inferring behavior
• Experimental design
• Statistical methods
– Learning the procedure and statistical methods
• Cost
• Comfort/safety/cooperation of the subject
The Neural Basis of Financial
Risk Taking
• Kuhnen & Knutson, Neuron, 2005
– Is individual investor deviation from optimal
behavior due to emotion?
• Brain imaging evidence that anticipation of gain vs.
loss activate different regions
– Nucleus accumbens (NAcc) of ventral striatum =gains
– Anterior insula = loss
– Examined whether anticipatory neural activity
could predict optimal and suboptimal choices
in financial choices
• Event related fMRI with 1.5T scanner
• 19 subjects (experts and non-experts)
Stimuli
• Behavioral Investment Allocation Task (BIAS)
– 20 blocks 10 trials each
– Randomly assigned one stock to be bad and other good
Results
• NAcc and MPFC activation related to anticipation of
risk-seeking choices
• Insula activation related to anticipation of risk-averse
choices
Investment Behavior and the Negative
Side of Emotion
• Shiv, Loewenstein, Bechara, Damasio, &
Damasio, 2005, Psychological Science
– Do emotions cause poor investment decisions?
• Compared subjects with stable focal brain lesions
disabling emotional regions with control patients with
no impairment
• 19 normal subjects, 15 lesion patients with damage in
emotional regions, 7 lesion controls with damage in
non-emotion related regions
• Endowed with $20 play money (exchange for gift
certificate)
Investment Game
• Participants told they would making several
rounds of investment decisions
– Choose between 2 options: invest or don’t
invest
• If invest, give $1 to researcher; if not, keep $1
– Researcher will flip coin
• If heads, then lose $1
• If tails, then get $2.50
• Rational choice: Always invest!!
– EV of investing is higher than not investing
Results
• Lesion patients with
emotional neural
damage make more
advantageous
investment decisions
than normal subjects
– Target patients invested
consistently across
rounds;
controls/normal
subjects increasingly
declined to invest
The Neurobiology of Trust
• Zak, Kurzban & Matzner, Annals of New
York Academy of Science, 2004
– Do hormones, such as oxytocin, regulate
trust behavior?
• Oxytocin
– Neuropeptide involved in social recognition and
bonding
• Trust game
– Subjects arranged into DM1-DM2 dyads
– DM1 asked to split $10
– Decision will determine how much they earn
• 28 mL of blood drawn after each decision
• 2 conditions: Intention and random draw
Trust Game
A: Investor
B: Trustee
$0
$10
$15
$15
$0
$30
• At node A, the investor has the option of either path
• Moving left ends the game with the outcomes: $0 to player 1 and $10
to player 2
• Moving right allows trustee to move (after investment is increased)
• Trustee can choose either path at node B
• Once trustee moves the game ends and payoffs are distributed
(McCabe 2003a)
Results
• Oxytocin (OT) levels were higher (2x) with an intentional
trust signal from DM1s in DM2s than in random draw
condition
• Also, behavior changed with an intentional trust signal
– DM2s returned 53% of the money they received vs. 18% in the
random draw condition
Oxytocin Increases Trust in Humans
• Kosfeld, Heinrichs, Zak,
Fishbacher & Fehr, 2005,
Nature
– 194 subjects
• Conducted in Switzerland
• Subjects received OT via nasal
spray or placebo
• 4 rounds with randomly
assigned partners
– Trust game
• 2 conditions: Trust and risk
(random trustee decision)
Results
• OT increased trusting behavior in investor
• OT did not increase trustworthy behavior
in trustee
• Trustee behavior dominated by principle of
reciprocity: OT had no effect
• OT also modulates neural networks to
enhance trusting behavior
Neuronal Substrates for Choice Under
Ambiguity, Risk, Gains, and Losses
• Smith, Dickhaut, McCabe, Pardo, Management
Science, 2002
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Positron Emission Tomography (PET)
9 subjects
Initial endowment $190 cash
Presented with Ellsberg Paradigm
• Asked to indicate from which of 2 containers containing 90
red, blue and yellow marbles they wanted draw a marble ‘
• 4 task conditions
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Risk gains
Risk losses
Ambiguity gains
Ambiguity losses
Task
Results
Results
• Activation of ventromedial and dorsomedial
network only with gain – loss difference in the
risky gambles
– Not activated in ambiguous gambles (Row A)
– Tied to amygdala and hypothalamus
– Dorsomedial system more involved with loss
• Study shows that belief (ambiguity vs. risk)
interacts with payoff structure (gain vs. loss) to
affect brain activity during choice
Criticisms
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Theory?
Preference for existing models
Press coverage
Consumer concern
Commercial ventures
Resources & Labs
• Center for the Study of Neuroeconomics
– George Mason: Kevin McCabe & Vernon Smith
• Stanford Neuroeconomics Lab
– Stanford: Antonio Rangel
• Human Neuroimaging Lab
– Baylor: Read Montague
• Center for Neuroeconomics Studies
– Claremont: Paul Zak
• The Camerer Lab
– Caltech: Colin Camerer
• Society for Neuroeconomics
• Neural Systems of Social Behavior Conference
Other Key Researchers
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Antoine Bechara - USC
Baba Shiv – Stanford
George Loewenstein – Carnegie Mellon
Antonio Damasio – USC
John Dickhaut – University of Minnesota
Camelia Kuhnen – Northwestern
Paul Glimcher - NYU
Recommended Reading
• “Neuroeconomics: How Neuroscience Can Inform
Economics.”
– Colin F. Camerer, George Loewenstein, and Drazen Prelec,
2005, Journal of Economic Literature 43(1): 9.
• “Behavioral Economics: Past, Present, Future”
– Colin F. Camerer and George Loewenstein, 2002
– In Advances in Behavioral Economics
• Functional Magnetic Resonance Imaging
– Scott A. Huettel, Allen W. Song, and Gregory McCarthy, 2004
• The Secret Life of the Brain, PBS