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

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

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

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

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

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

7. fMRI Scanner 7

8. fMRI: Changes in Magnetization Basal State Activated State

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

10. Simple Decisions: Blackjack

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

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

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

15. Uncertainty about uncertainty?

16. Ellsberg Paradox 1961

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

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

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

20. Where Is The Paradox? P(Red II )=P(Blue II ) P(Red II ) < 0.5 P(Blue II ) < 0.5 ? ? ??? ? ??? ? P(Red I ) = P(Blue I ) P(Red I ) = 0.5 P(Blue I ) = 0.5 P(Red I ) + P(Blue I ) = 1 P(Red II ) + P(Blue II ) = 1 Urn II (Ambiguous) Urn I (Risk)

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

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. Lower Activity under Ambiguity % Signal Change

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

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

29. Early Late ? ? A Signal for Uncertainty?

30. Lesion Subjects Orbitofrontal Control

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

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

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. Measuring Psychopathy 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 Other’s payoff Your payoff Other’s payoff

41. B: Costless punishment Generous Selfish

42. B: Costly Reward Generous Selfish

43. Responder Game: Intentions Matter

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

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

47. Power matters? Responder Game: Intentions Matter Power 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.eduhttp://neuroecon.berkeley.edu

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