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Neural Computation Underlying Individual and Social Decision-Making

Neural Computation Underlying Individual and Social Decision-Making. Ming Hsu Haas School of Business University of California, Berkeley. Forbes, 01.September 2002. Neesweek, 09.August 2004. The Big Picture. Economics: formal, axiomatic, global. Human Behavior.

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Neural Computation Underlying Individual and Social Decision-Making

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  1. Neural Computation Underlying Individual and Social Decision-Making Ming Hsu Haas School of Business University of California, Berkeley

  2. Forbes, 01.September 2002 Neesweek, 09.August 2004

  3. The Big Picture Economics: formal, axiomatic, global Human Behavior Psychology: intuitive, empirical, local Neuroscience: biological, computational evolutionary

  4. The Big Picture Economics: formal, axiomatic, global. Neuroeconomics “A mechanistic, behavioral, and mathematical explanation of choice that transcends [each field separately].” - Glimcher and Rustichini. Science (2004) Human Behavior Psychology: intuitive, empirical, local. Neuroscience: biological, circuitry, evolutionary.

  5. The Big Picture Economics: formal, axiomatic, global. Neuroeconomics Studies how the brain encodes and computes values that guide behavior. Allows us to improve models, design markets/AI, create new diagnostic tools Human Behavior Psychology: intuitive, empirical, local. Neuroscience: biological, circuitry, evolutionary.

  6. Tools That We Used Functional Magnetic Resonance Imaging (fMRI) Special Populations

  7. fMRI Scanner

  8. Activated State fMRI: Changes in Magnetization Basal State

  9. Agenda • Individual Decision-Making • Ambiguity aversion • fMRI and brain lesion • Sociopaths • Social preferences • Special population • Take-aways

  10. Simple Decisions: Blackjack

  11. Simple Decisions: Blackjack

  12. More Complicated: Investing Stock? Bond? Domestic? Foreign? Diversify Think long-term

  13. Complicated: Love/Marriage Whether? Who? When? Where? 37% Rule (Mosteller, 1987) “Dozen” Rule (Todd, 1997)

  14. Simple Complex Most of life’s decisions Precise knowledge of probabilities Little knowledge of probabilities

  15. Uncertainty about uncertainty?

  16. Ellsberg Paradox 1961

  17. 5 Red 5 Blue Urn I: Risk Most people indifferent between betting on red versus blue

  18. 10 - x Red x Blue Urn II: Ambiguity ? ? ? ? ? ? ? ? ? ? Most people indifferent between betting on red versus blue

  19. Choose Between Urns Urn I (Risk) Urn II (Ambiguous) ? ? ? ? ? ? ? ? ? ? Many people prefer betting on Urn I over Urn II.

  20. P(RedII)=P(BlueII) P(RedII) < 0.5 P(BlueII) < 0.5 Where Is The Paradox? Urn I (Risk) Urn II (Ambiguous) P(RedI) = P(BlueI) P(RedI) = 0.5 P(BlueI) = 0.5 ? ? ? ? ? ? ? ? ? ? P(RedI) + P(BlueI) = 1 P(RedII) + P(BlueII) = 1

  21. Not ambiguity averse Simple Complex Verizon Jennifer or Deutsche Telekom or Angelina

  22. Verizon or Deutsche Telecom? French & Poterba, American Economic Review (1991).

  23. fMRI Experiment Hsu, Bhatt, Adolphs, Tranel, and Camerer. Science. (2005)

  24. fMRI Experiment Hsu, Bhatt, Adolphs, Tranel, and Camerer. Science. (2005)

  25. Expected Reward Region y - Brain response A(.) - Ambiguity trials R(.) - Risk trials E(.) - Expected value of choices W(.) - Nuisance parameters

  26. % Signal Change Lower Activity under Ambiguity

  27. Orbitofrontal Cortex Region Reacting to Uncertainty y - Brain response A(.) - Ambiguity trials R(.) - Risk trials E(.) - Expected value of choices W(.) - Nuisance parameters N.B. This region does not correlate with expected reward.

  28. Stochastic Choice Model Link Between Brain and Behavior Brain Imaging Data Behavioral Choice Data

  29. A Signal for Uncertainty? ? Late Early

  30. Orbitofrontal Control Lesion Subjects

  31. Lesion Experiment 100 Cards 50 Red 50 Black 100 Cards x Red 100-x Black Choose between gamble worth 100 points OR Sure payoffs of 15, 25, 30, 40 and 60 points.

  32. Orbitofrontal Lesion Control Lesion Estimated Risk and Ambiguity Attitudes Orbitofrontal lesion patients more rational!

  33. Imputed value OFC lesion estimate  = 0.82 Stochastic Choice Model Linking Neural, Behavioral, and Lesion Data Brain Imaging Data Behavioral Choice Data

  34. Agenda • Individual Decision-Making • Ambiguity aversion • fMRI and brain lesion • Sociopaths • Social preferences • Special population How neuroscience can help economics How economics can help neuroscience

  35. Norman Bates Psycho, 1960

  36. Criminality • Estimated psychopathy rates among prisoners (various times after 1990) • North American: 20.5% (2003 PCL-R manual) • Canada: 15 – 25% (federal prison) • Iran: 23% • UK: 26% • Younger beginnings (14 y.o. vs. 28 y.o. ) • “Instrumental” homicides

  37. MeasuringPsychopathy • Psychopathy Checklist-Revised, Screening version (PCL-R SV) • 24 point scale: 12 traits scored 0, 1, 2 • Two factors • Interpersonal-affective factor (6 traits) • Impulsivity-social deviance (6 traits) • Impulsivity-social deviance (Factor 2) is less important for us • Except for safety concerns, of course!

  38. Interpersonal-affective factor • Callous and unemotional • Superficial charm • Grandiosity • Lack of empathy and shallow affect • Deception and manipulativeness • Lack of remorse • Not accepting responsibility

  39. Characterizing Psychopathy using Economic Games • What we’re doing • Characterize behavior in these individuals • Provide a quantitative measure of (social) behavior • Where we want to go • Use this measure to search for neural and genetic correlates of psychopathy • And other psychiatric and neurological diseases

  40. Responder Game Your payoff Your payoff Other’s payoff Other’s payoff

  41. B: Costless punishment Selfish Generous

  42. B: Costly Reward Selfish Generous

  43. Responder Game: Intentions Matter

  44. Responder Game: Intentions Matter

  45. Power matters? SPs (only): Refuse to let Player B choose

  46. Responder Game: Intentions MatterPower matters I would not give control over to another person, even for more money.

  47. Responder Game: Intentions MatterPower matters? I would not give control over to another person, even for more money. Seems like A1 is the more “dominant.”

  48. Take-aways • Neuroeconomics is possible • Studying neural mechanisms of economic decision-making • Nascent field, only about 10 years old • Much progress during that time • Many open questions, opportunities • Moral decision-making • Strategic thinking • Financial bubbles • http://neuroecon.berkeley.edu

  49. Acknowledgements Eric Set Edelyn Verona Colin Camerer Ralph Adolphs Daniel Tranel Steve Quartz Peter Bossaerts Meghana Bhatt CédricAnen Shreesh Mysore

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