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Neuroeconomics & Experimental Economics: What They Can Tell us about Human Risk & Decision Making. Kevin A. McCabe Professor of Economics, Law, and Neuroscience Director, Center for the Study of Neuroeconomics George Mason University [email protected] www.neuroeconomics.net ( Center).

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slide1
Neuroeconomics & Experimental Economics: What They Can Tell us about Human Risk & Decision Making
  • Kevin A. McCabe
  • Professor of Economics, Law, and Neuroscience
  • Director, Center for the Study of Neuroeconomics
  • George Mason University
  • [email protected]
      • www.neuroeconomics.net (Center)
slide2
THE CENTER FOR THE STUDY

OF NEUROECONOMICS AT

GEORGE MASON UNIVERSITY

Allegra 3T Scanner

LAN

Brain Imaging

Virtual Worlds

slide3
First Wave

1960 Siegel And Fouraker, Bargaining and Group Decision Making

1962 Smith, “An Exp. Study of Competitive Market Behavior”

1959 Sauermann and Selten, “Ein Oligopolexperiment.”

  • Santa Monica Seminar: The Design of Experiments in Decision Processes
  • sponsored by the Ford Foundation
slide4
Inducing Individual Demand

1962 Smith, “An Exp. Study of Competitive Market Behavior”

Buyer 1

Unit Value

1st $9

2nd $6

3rd $3

If you buy your first unit for $6 you

earn $9 - $6 = $3.

B1

B1

At a price of $4.50, how many units is Buyer 1 willing to purchase?

B1

slide5
Inducing Individual Supply

1962 Smith, “An Exp. Study of Competitive Market Behavior”

Seller 1

Unit Cost

1st$1

2nd $4

3rd $7

B1

If you sell your first unit for $6 you

earn $6 - $1 = $5.

S1

At a price of $4.50, how many units is seller 1 willing to sell?

S1

S1

slide6
Smith, 1962

Aggregate Supply and Demand

slide7
What is Missing?

1962 Smith, “An Exp. Study of Competitive Market Behavior”

Every Institution Consists of Four Elements

(1)A message space.

  • A set of message sending rules indicating
  • who gets to send what messages when.

(3) A set of message processing rules

indicating how messages update information

(4) A set of production rules which

translates messages into outcomes.

slide8
The Double Auction

1962 Smith, “An Exp. Study of Competitive Market Behavior”

(1)Message space: Bid, Ask, Buy, Sell.

  • Message sending rules: Buyers Bid & Buy;
  • Sellers Ask & Sell; Improvement Rule;
  • Starting and Stopping rules.

(3) Message processing rules:Update book;

Determine standing bid and ask.

  • (4) Production rules: Buy or Sell: results in
    • Buyer sending Seller cash and Seller
    • sending Buyer a unit.
slide9
Double Auction in Practice

1962 Smith, “An Exp. Study of Competitive Market Behavior”

ID BID ASK ID

B1 7

B2 7.5

B1 7.75

B1 Accepts

8.5 S3

8.25 S2

8 S3

slide10
Smith, 1962

First Data Point

slide11
Smith, 1962

Testing Robustness: Comparative Statics

slide12
“I am still recovering from the shock of the experi-mental results.”

Written by Smith in 1981,

“Experimental Economics at Purdue.”

Smith, 1962

slide13
Second Wave

1960 Siegel And Fouraker, Bargaining and Group Decision Making

1962 Smith, “An Exp. Study of Competitive Market Behavior”

1973 Plott @ Cal. Tech & 1975 Smith @ Arizona

1979 Kahneman and Tversky, “Prospect Theory”

1982 Smith, “Microeconomic Systems as an

Experimental Science”

1976 Smith, “Induced Value Theory”

1959 Sauermann and Selten, “Ein Oligopolexperiment.”

  • Santa Monica Seminar: The Design of Experiments in Decision Processes
  • sponsored by the Ford Foundation
slide14
18

14

10

6

2

0

8

16

24

32

12

10

8

6

4

ID BID ASK ID

2

B1 7

B1 7.5

B3 7.75

B1 Accepts

8.5 S5

8.25 S1

8 S5

B1

0

0

4

8

12

16

Microeconomic System

1982 Smith, “Microeconomic Systems as an

Experimental Science”

Environment

Outcomes (p,q)

Institution: DA

Governs Computes

Messages

slide16
Experimental Design

9 Traders

Assigned to One of Three Classes

Example:

Class One: Cash = $2.25

Assets = 3

Class Two: Cash = $5.85

Assets = 2

Class Three: Cash = $9.45

Assets = 1

Dividend Distribution {0, 8, 28, 60}, p =1/4, Ed = 24 cents

Fifteen Period Double Auction

Everyone is a Trader (buy and sell)

Random Dividend Draw After Each Period

slide18
Parallels to Natural World

What happened here

slide19
Third Wave

1960 Siegel And Fouraker, Bargaining and Group Decision Making

1962 Smith, “An Exp. Study of Competitive Market Behavior”

1973 Plott @ Cal. Tech & 1975 Smith @ Arizona

1979 Kahneman and Tversky, “Prospect Theory”

2002 Nobel Prize in Exp. Econ.

1998 Experimental Economics J.

1986 Economic Science Assoc. Founded

1982 Smith, “Microeconomic Systems as an

Experimental Science”

1976 Smith, “Induced Value Theory”

1959 Sauermann and Selten, “Ein Oligopolexperiment.”

  • Santa Monica Seminar: The Design of Experiments in Decision Processes
  • sponsored by the Ford Foundation
slide20
The Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 2002

"for having integrated insights from psycho-logical research into economic science, …”

Daniel Kahneman

"Daniel Kahneman - Diploma". Nobelprize.org. 21 Jul 2010 http://nobelprize.org/nobel_prizes/economics/laureates/2002/kahneman-diploma.html

"for having established laboratory experiments as a tool in empirical economic analysis, …”

"Vernon L. Smith - Diploma". Nobelprize.org. 21 Jul 2010 http://nobelprize.org/nobel_prizes/economics/laureates/2002/smith-diploma.html

slide21
A Microeconomic Systems

Perspective

Experimenter would like to

improve performance.

Performance

Environment (E)

Outcomes (Q)

Institution (I)

Governs Computes

Behavior

Messages

bi: E X I  M

i = 1 , …, n

(M)

Experimenter would like to predict behavior.

Daniel Kahneman

slide22
Smith’s Interest: Control For

Preferences

Q2

x2* = h2(p1, p2, I)

u3

u2

u1

Q1

x1* = h1(p1, p2, I)

1976 Smith, “Induced Value Theory”, AER

slide23
K & T’s Interest: Study Preferences

Daniel Kahneman

Floris Heukelom, “Kahneman and Tversky and the Origin of Behavioral Economics”, TI 2007-003/1 Tinbergen Institute Discussion Paper

slide24
Effect of Increasing Variance

On Expected Utility: Measures Risk Aversion

Utility

m1

p

g

U(m)

1-p

m2

M1

EU(g)

p

EU(f)

f

1-p

M2

Money

M1

m1

EV

m2

M2

slide25
Measuring Risk Preferences

Risk Aversion and Incentive Effects

Charles A. Holt and Susan K. Laury

The American Economic ReviewVol. 92, No. 5 (Dec., 2002), pp. 1644-1655

slide26
Decision Tasks

EVA EVB

118.5

480 202

470 305.5

460 399

450 492.5

440 586

430 679.5

420 773

410 866.5

400 960

slide30
Decision Task for Judges’ Experiment

EVA EVB

14.70 3.80

14.40 6.60

14.10 9.40

13.80 12.20

13.50 15.00

13.20 17.80

12.90 20.60

12.60 23.40

12.30 26.20

12.00 29.00

slide31
Judges’ Experiment (in progress)

Fraction Choosing Safe Option A

slide32
But are preferences over risk independent of context?

Joyce Berg, John Dickhaut, and Kevin McCabe, “Risk preference instability across institutions: A dilemma,” PNAS, 2005, 102(11), pp. 4209-4214.

slide33
A Technology Time Line

Computerized

DA on Plato

IBM/360

WWW

LANs

micro

1960

1980

MS-Dos

VB

BASIC

JAVA

Python

1960-

1980-

Markets

and Games.

Economic

Systems

Design

slide34
Resources

z-Tree

slide35
Resources

http://econwillow.sourceforge.net/

slide36
An Approx. Technology Time Line

Computerized

DA on Plato

IBM/360

WWW

LANs

Micro

computer

fMRI

1960

1980

2000

MS-Dos

VB

BASIC

JAVA

Python

1960-

1980-

2000-

Markets,

Behavioral,

and Games.

ESD

Neuroeconomics,

Internet field experiments

slide37
Neuroeconomics

Environment

Outcomes (q)

Institution

Governs Computes

Sensors

Homeostatic

Condition

Messages

Neuronal

Assemblies

Governs Computes

Effectors

Neural

Activity

slide39
Bubbles in the Scanner

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)

slide40
Task

Price History

Amount in market

Change in

portfolio

Portfolio Value

slide41
Fictive Learning Signal

The fictive learning signal looks at what could have been achieved if you had made your earlier choice with updated information.

If prices go up how much gain would you have made if you were 100% in the market.

slide43
Separation of Fictive and

Temporal Difference Learning

slide44
An Approx. Technology Time Line

Computerized

DA on Plato

Z-Tree

Web 2.0

Virtual Worlds

IBM/360

WWW

LANs

fMRI

micro

1960

1980

2000

2010

MS-Dos

VB

BASIC

Human Genome Sequenced

JAVA

Python

1960-

1980-

2000-

2010-

Markets,

Behavioral,

and Games.

ESD

Virtual Worlds,

Web 2.0 Experiments,

and Genetics Experiments

Neuro-economics, Internet Field experiments

slide45
TerraEcon in Second Life

Second Life is

3D, Visualize

immersive, Feels real

user-created, Build once use

often

persistent, Start where

you left off

and, online. On your computer

slide46
Importance of Collaboration

Kitt Peak for Astronomers

slide47
Virtual Worlds Colloboration for

Experimental Economists

slide48
Differences

Between Virtual Worlds

SecondLife

Online: 62,100

Cost: $8,040

Support: High

Control: Low

Difficulty: Medium

OpenSim

16 Snapshot

$2,018 Annual

Low User Based

High

High

slide49
Some Things We Can Push

With Virtual Worlds Experiments

Bridge Lab and Field.

Role of Context and Language.

Emergence of Norms and Rules.

Formation of Social Networks and Organizations.

Larger Scale and Longer Time Horizons.

Persistence.

.

slide50
Heterogeneous Dispositions to Cooperate and the Effects of Communication in a Second Life Collective Action Dilemma

Kevin McCabe, Peter Twieg, JaapWeel

slide51
|A Typical Economics Problem

Elinor Ostrom, Governing the Commons, 1990,

Cambridge University Press.

Managing the Commons

A Resource under Common Control

Benefits are Non-Excludable

Can result in individual Free Ridingbehavior

When individuals don’t do their Fair Share

Of the Work

Examples include risk management strategies

slide52
|Design for the Commons*

Mechanisms for supporting cooperation

Sorting: Into Similar Preference Groups

Communication: Of Intentions

Monitoring: Of Actions

Progressive Punishment: Of Non-cooperators

Rewarding: Cooperators

*Learned from Field and Laboratory Research

slide53
|Arial View of Hurricane Island

N - WDS

Yellow

Brown

Check In

Check in

Teal

Orange

E - WDS

E - WDS

N - WDS

Purple

Blue

Green

Red

slide54
|Implementation

Weather Defense Station

Avatar

scale

Inside

A House

slide55
|Schematic of Island

N1

Yellow

Brown

Teal

Orange

E1

E3

N3

Purple

Red

Blue

Green

slide56
|Game Theory Predictions

But then avatars in the

upper Two Thirds each have

an incentive to defend at N1.

N1

Yellow

Brown

In equilibrium the

Avatars in the upper Two

Thirds should choose to

defend everyone.

Teal

Orange

N3

Purple

Avatars in the Lower Third

each have an incentive to

defend at N3.

Red

Blue

Green

slide57
|Game Theory Prediction for

Eastern Storms

In equilibrium the

Avatars in the right Two

Thirds should choose to

defend everyone.

Yellow

Brown

Teal

Orange

E1

E3

Purple

Red

Blue

Green

slide58
|Experimental Design

(Increase Complexity)

Direction of Storms

A

North

B

North & East

One

Person

WDS Occupancy Requirement

Two

Person

slide59
|Labor Allocation to WDS

Number of Times Manned N1

Y=Yellow, B = Brown, T = Teal, O = Orange

P = Purple, R = Red, BL = Blue, G = Green

Number of Times Manned E1

* Of these 21 times it was manned by P or R.

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