The Neuroeconomics of Personal and Impersonal Exchange McCabe

7/23/2019 The Neuroeconomics of Personal and Impersonal Exchange McCabe

1/56

The Neuroeconomics of Personal and

Impersonal Exchange

KevinA.McCabe

ProfessorofEconomicsandLaw

GeorgeMasonUniversity

[email protected]

www.neuroeconomics.net (Center)

CSN

THECENTERFORTHESTUDYOF

NEUROECONOMICSATGEORGEMASONUNIVERSITY
mailto:[email protected]://www.neuroeconomics.net/http://www.neuroeconomics.net/mailto:[email protected]


2/56

5

Support clique

15

Sympathy group

35

Hunter-gatherer overnight camps

150

Hunter-gatherer clans

500

Megaband

1500

Tribe

Evolution and Social ExchangeCSN


3/56

Passingham (2002)

Social brain hypothesis (Dunbar, 1998)

Social Cognition andPrefrontal Cortex (PFC)SN


4/56

Earl K.Mil ler,THE PREFRONTAL CORTEX AND COGNITIVE CONTROL,NATURE REVIEWS NEUROSCIENCE (1)2000, pp. 59 85.

Prefrontal Cortex (PFC)CSN

Inputs Actions


5/56

CSN

What we ask?

How have our brains adapted to solvethe problems of personal and impersonalexchange?

How does the way our brains workdetermine how our economic systems

work?


6/56

The Transition to Markets Based

on Douglass Norths Research

SN

Two Kinds of Exchange

Personal Impersonal

Localized Trade Long Distance TradeWho you know What you know

Reciprocity Rule of LawExclusive Groups Inclusive Groups


7/56

Ultimatum Game: Bargaining

Investment Game: Trust

Studying Personal Exchange

CSN


8/56

CSN

DictatorGameControls

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5

Amount Sent

%Freque

ncy

0

0.2

0.4

0.6

0.8

1

0 1 2 3 4 5

Amount Sent

%Freque

ncy

Hoffman, E., McCabe, K., Shachat, K., Smith, V. (1994).

Preferences, property rights and anonymity in bargaining

games. Games and Economic Behavior, 7(3) 346- 380.

Ask subject how much they want to send another subjectof the Ten to One Hundred dollars they have been given.

Single Blind Double Blind


9/56

(1) Two Players 1 and 2, are given

10( 100) to split.

(2) Player one makes a proposal to

player

(3) Player two then accepts or rejects.

(4) If rejected they both get zero.

The Ultimatum Game

CSN


10/56

0

2

4

6

8

10

12

14

1 2 3 4 5

Divide-Random $10

Ultimatum Game Results

N = 24

Frequency

Offer

CSN


11/56

Hoffman, E., McCabe, K., Shachat, K., Smith, V., 1994.

Preferences, property rights, and anonymity in bargaining

games. Games and Economic Behavior 7, 346380.

Compare standard divide 10 Ultimatum Game withrandom

assignment to 10 Ultimatum Game where player ones earn

the right to be first mover in a contest and where subjects are

placed in an exchange context.

Property Rights

CSN


12/56

0

2

4

6

8

10

12

14

1 2 3 4 5

0

2

4

6

8

10

12

14

1 2 3 4 5

Property Right Results

N = 24

Offer Offer

Frequency

Divide-Random $10 Property Right $10

CSN


13/56

The Investment Game

Berg, Joyce, John Dickhaut, Kevin

McCabe (1995), Trust, Reciprocity,

and Social History, Games andEconomic Behavior, 10, pp. 122-142.

CSN


14/56

The Investment Game

Room A

Room B

CSN

$10

3K

K

X$10

- K

+ 3K

X

- X

+ X


15/56

CSN

Design

Play the Investment Game Once

With A Random Counterpart

and a Random Role Both Start With $10 (show up fee)

Investor invests his or her show up fee

Decisions made Double Blind


16/56

0

5

10

15

20

25

30

Surplus

Amount Sent

Amount Returne

$5.16

$15.48$4.66

Results: N = 32

CSN


17/56

CSN

Social History Treatment

Inexperienced Players

See What First Group Did (as avg.return by amount sent)

Everything Else the Same


18/56

0

5

10

15

20

25

30

Surplus

Amount Sent

Amount Returne

$5.36

$16.08

$6.46

Social History Results: N = 28

CSN


19/56

Kevin McCabe, Daniel Houser, Lee Ryan, Vernon Smith,

and Theodore Trouard, A Functional Imaging Study of

Cooperation in Two-Person Reciprocal Exchange,

Proceedings of the National Academy of Sciences, (98)2001,

pp. 11832-11835.

Imaging Two Person

Exchange

SN


20/56

COMPUTER


21/56

UMAN


22/56

UMAN >

COMPUTER


23/56

The neural basis of economic decision making in the

Ultimatum Game.

Alan G. Sanfey*, James K. Rilling*, Jessica A. Aronson,

Leigh E. Nystrom, Jonathan D. Cohen

Science, June 2003

* authors note: these two authors contributed equally to

this manuscript; authorship was determined arbitrarily

Imaging the Ultimatum

Game

SN


24/56

CSN

Main Results

Individuals exhibit an negative emotionalresponse to unfair offers and must controlthis response to accept unfair offers.


25/56

UnfairFair Human Offers


26/56


27/56

Subjects44 volunteers (22 males, 22 females; age: 28.3 7.1 years;education:17.3 2.1 years)

Frank Krueger, Kevin McCabe, Jorge Moll, Nikolaus

Kriegeskorte, Roland Zahn, Maren Strenziok, ArminHeinecke, Jordan Grafman, Neural correlates oftrust, Proceedings of the National Academy ofSciences, Early Edition December 4, 2007.

Two Systems for Trust

CSN


28/56

Conditional Trust Strategy

Unconditional Trust Strategy

Individuals use two trust strategies withdifferent benefits and costs:

CSN

Main Results


29/56

CSN

Simultaneous scanning


30/56

(10,15) (5,5)(0,25)

Game

6s

Blank Screen

Processing &Decision-Making

Jitter

2-6s

Blank Screen

Blank Screen

Processing &Decision-Making

Blank Screen

6s

(10,15) (5,5)(0,25)

Procedure

Result

4s

Processing

Processing

(10,15) (5,5)(0,25)

(10,15) (5,5)(0,25)

Introduction

2s

time (s)

DM1

DM1 DM2

DM1 DM2

ScannerII

ScannerI

VTG

CSN


31/56

20

40

60

80

100

Stage I Stage II Stage I Stage II

Non-Defectors Defectors

LowMediumHigh

Trust(%)perPayoff-Type

*P


32/56

Paracingulate Cortex

3 5

t(43)

(BA9/32; 5,39,22)

Trust>Control

-0.2

0.0

0.2

0.4

0.6

0.8

Stage I Stage II

Non-Defectors Defectors

*P


33/56

b

3 5

Septal Area

Unconditional Trust

Goal Knowledge

dmPFC

b

3 5

Krue er et al. Proc Natl Acad Sci2007.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Building Maintenance

Activation

Results: Unconditional Trust StrategyCSN


34/56

b

3 5

Ventral Tegmental Area

Conditional Trust

Goal Knowledge

dmPFC

b

3 5

Krue er et al. Proc Natl Acad Sci2007.

0.0

0.10.2

0.3

0.4

0.5

0.6

0.7

Building Maintenance

A

ctivation

Results: Conditional Trust StrategiesCSN


35/56

3

Mediator

Conclusions: Trust StrategiesCSN

Conditional Trust

VTA

Unconditional Trust

Septal Area


36/56

Studying Impersonal Exchange

CSN

Institutions

Markets


37/56

NA

MPFC

The Dopamine System

CSN


38/56

CSN

Error Prediction in the Brain

Dopamine neurons (n = 188) from

the ventral midbrain areas were

recorded when monkeys were

shown a stimulus indicating theprobability of a juice reward and

then either received or didnt

receive the reward.

Schultz W, Dayan P, Montague PR. 1997. A

neural substrate of prediction and reward.Science 275:159399


39/56

Conditioned

Stimulus (CS)

Reward (R)

No Reward

Reward (R)

CSN

Data on Firing Rates

Temporal differencelearning


40/56

Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001a).

Anticipation of increasing monetary reward selectively recruits

nucleus accumbens. Journal of Neuroscience, 21(RC159), 1-5.

Knutson, B., Fong, G. W., Adams, C. M., Varner, J. L.,

& Hommer, D. (2001b). Dissociation of reward anticipation

and outcome with event-related FMRI. NeuroReport,

12, 3683-3687.

Reward and Money

CSN


41/56

Task

CSN


42/56

Rt. Nucleus Accumbens Rt. Medial Prefrontal Cortex

CSN

Resulting Bold Response


43/56

Sensitivity to Scale

CSN


44/56

CSN

Stock Market Bubbles: When

Good Computations Go Bad


45/56

CSN

Typical Bubble


46/56

Bubbles in the Scanner

CSN

Terry Lohrenz; Kevin McCabe; Colin Camerer; P. Read

Montague. Neural signature of fictive learning signals

in a sequential investment task, Proceedings of the

National Academy of Sciences, May 29th, 2007,

104(22), pp. 9493-9498. Early Edition (Open Access)


47/56

Task

CSN

Reveal Bar On Submit Reveal

.75 sec. random .75 sec.

Amount in market

Change inportfolioPortfolio Value

Price History


48/56

Fictive Learning Signal

CSN

The fictive learning signal looksat what could have beenachieved if you had made yourearlier choice with newerinformation.

If prices go up how much gainwould you have made if youwere 100% in the market.


49/56

Fictive Error Predicts Change

in Investment

SN


50/56

Separation of Fictive and

Temporal Difference Learning

SN


51/56

CSN

Three Systems for Exchange

Emotional EmpathyNetwork

Dorsal Striatum

Ventral Striatum

Goal Directed Learning

Theory of Mind

Anterior

Paracingulate


52/56

Personal and Impersonal

Exchange

SN

Social Actor-Critic(who you know)

Selfish Actor-Critic(what you know)

InstitutionsOrganizations

OptimalControl

OptimalUse


53/56

Ventral Striatum

Dorsal Striatum

DLPFC

An

terior

Pa

racingulate

Choosing An Actor-Critic

(Representation)

SN

Caudate

Social Actor-Critic Selfish Actor-Critic

Stimulus

ResponseResponse


54/56

VentralStriatum

DorsalStriatum

DLPFC

Anterio

r

Choosing Between Actor-Critics

(Conflict Resolution)

SN

Caudate

Social Actor-Critic Selfish Actor-CriticStimulus

Response


55/56

When Does Impersonal

Exchange Fail?

SN

The answer in part is that our adapted

mind defaults to personal exchange.

When impersonal exchange institutions

are perceived to be too uncertain or not

self serving we fall back on our adaptedpropensity for personal exchange. This

creates a cognitive conflict between

optimal use and optimal control.


56/56

Thank You

CSN

The Neuroeconomics of Personal and Impersonal Exchange McCabe

Documents

Transcript of The Neuroeconomics of Personal and Impersonal Exchange McCabe