Ming Hsu

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Neural Systems Responding to Degrees of
Uncertainty in Human Decision-Making

• Ming Hsu
• Meghana Bhatt
• Ralph Adolphs
• Daniel Tranel
• Colin Camerer

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What is Neuroeconomics

• Neuroeconomics seeks to ground economic theory in
  details about how the brain works.
• Adjudicate competing models
• Debates between rational-choice and behavioral
  models usually revolve around psychological
  constructs
• E.g. loss-aversion and a preference for immediate
  rewards.
• Before, these constructs have typically been
  unobservable.
• Provide new data and stylized facts to inspire
  and constrain models.

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Example Dual-self models

• A number of them in recent years
• Bernheim Rangel 2004
• Benahib and Bisin 2004
• Benabou and Pycia 2002
• Brocas and Carrillo 2005
• Fudenberg Levine 2005
• Miao 2005
• This is consistent with recent evidence from MRI
  studies, such as McClure et al. 2004, that
  suggests that short-term impulsive behavior is
  associated with different areas of the brain than
  long-term planned behavior. (Fudenberg Levine)
• The notion of a dual-self has been around since
  Plato.
• Neuroscientific data new.

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Tools of Neuroeconomics

• These (and other) tools enable us to study
  economic behavior at the neural level
• Functional magnetic resonance imaging (fMRI)
• Indirect observation of neuronal activity
• Temporal resolution 2-3 secs
• Spatial resolution 2-3 mm3
• Lesion patients
• Assess the necessity of brain region for certain
  behavior.
• Spatial resolution varies with size of lesion.
• Modularity this organizing principle of the
  brain is what allows us to use these tools.

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Decision Making Under Risk and Ambiguity

• Ambiguity and ambiguity aversion is a
  long-standing topic in decision theory.
• Knight, Keynes, Ellsberg, and co.
• There is a large theoretical and empirical
  literature to draw upon.
• Schmeidler 1989
• Gilboa Schmeidler 1988
• Camerer Weber 1992
• Invoked to explain a number of economic phenomena
• Home bias
• Equity premium
• Entrepeneurship
• The behavioral phenomenon is robust
• Camerer Weber reviews experimental evidence.

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Decision Making Under Risk and Ambiguity

• Ambiguity is uncertainty about probability,
  created by missing information that is relevant
  and could be known.
• Risk Probability of head on a fair coin toss
  (known p, p 0.5)
• Ambiguity Probability of head on a biased coin
  of unknown bias (unknown p, p ?)
• Ellsberg Paradox
• Urn A with n balls n/2 red, n/2 green.
• Urn B with n balls k red, n-k green (k unknown).
• Lottery choose color, then ball from urn. If
  match, win x. If mismatch, 0.
• Most people indifferent between choosing red or
  green in either urn A or urn B.
• Non-trivial proportion prefer urn A.

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Approaches to Decision-Making Under Ambiguity

• Deny existence of ambiguity/risk distinction
• Models of ambiguity aversion
• Non-additive probabilities (capacities and
  Choquet integrals)
• set-valued probabilities (min-max)
• 2nd order prior and nonlinear weighting
• State dependent utility models
• Overgeneralization of a rational aversion to
  asymmetric information

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What Neuroeconomics Can Say?

• Are risk and ambiguity distinguished at a neural
  level.
• If so, are the underlying neural circuitry
• Two systems
• Competing
• Independent
• One system
• Can this data be used to constrain the existing
  models.

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fMRI Experiment Design

• Ellsberg type gambles
• Canonical example of decision-making under
  ambiguity
• World knowledge questions
• Control for possible framing effects of numerical
  information
• Closer analog of real-world decisions
• Adverse selection
• Unnatural habitat hypothesis.
• Betting against agent who has better information.

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Ellsberg Type Questions
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Real World Questions
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Betting Against Informed Opponent
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Experimental Sequence
Ambiguous condition
Risk condition

• Self paced trials
• 48 trials total
• Stimuli present for 2 sec after choice
• Blank screen 4-10 sec
• Each session about 10-15 min

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Statistical Analysis of fMRI Data
Courtesy of http//www.fil.ion.ucl.ac.uk/spm
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Data Analysis

• Individual Analysis
• Ambiguity gt Risk ?iamb gt ?irisk
• Risk gt Ambiguity ?irisk gt ?iamb
• Group Analysis Random Effects
• ?amb gt ?risk
• ?risk gt ?amb

• Linear model
• 64x64x32 time series
• Dummies
• damb ambiguity trial
• drisk risk trial
• dpost post-decision interval
• ? Hemodynamic response convolution operator

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Results

• We find three main clusters of activation
• Amygdala Fear of the unknown
• Lateral orbitofrontal (OFC) integration of
• Dorsal striatum
• They appear to separate into two processes
• A fast-responding, vigilance signal process
  (amygdala OFC).
• A slower-responding, anticipated reward region
  (dorsal striatum).
• Constitute a generalized system for
  decision-making under uncertainty (including both
  risk and ambiguity).
• Behavioral experiments with lesion patients show
  that the OFC is necessary for distinguishing risk
  and ambiguity.

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Ambiguity gt Risk
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Risk gt Ambiguity
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Correlation of Behavior with Imaging
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Lesion Patient Experiment

• Lesion patients allow us to assess the necessity
  of a brain region for behavior.
• Two groups
• OFC lesion location of damage overlaps with OFC
  activation.
• Control lesion temporal lobe patients, lesions
  do not overlap with activation.
• Groups matched on IQ, verbal abilities, etiology.

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Lesion Patient Experiment
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Risk and Ambiguity Attitudes
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Conclusion

• Our results suggest
• Risk and ambiguity are product of a single system
• Produced by two possibly competing processes
• To distinguish between levels of uncertainty
• With ambiguity and risk being limiting cases
• The OFC is necessary for proper functioning of
  the system.

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Future Research

• Behavioral Typing (Ellsberg 1967)
• There are those who do not violate the axioms, or
  say they wont, even in these situations such
  subjects tend to apply the axioms rather their
  intuition.
• Some violate the axioms cheerfully, even with
  gusto.
• Others sadly but persistently, having looked into
  their hearts, found conflicts with the axioms and
  decided, in Samuelsons phrase, to satisfy their
  preferences and let the axioms satisfy
  themselves.
• Still others tend, intuitively, to violate the
  axiom but feel guilty about it and go back into
  further analysis.
• Further establish direction of causality
• Exogenously stimulate the amygdala.
• Look in special populations of striatal
  differences.

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