Sobei H. OdaKyoto Sangyo University

The Sixth International Workshop on Agent-based Approaches The Sixth International Workshop on Agent-based Approaches in Economic and Social Complex Systemsin Economic and Social Complex SystemsNational Chengchi University, TaipeiNational Chengchi University, Taipei

14 November 2009 14 November 2009

Moving Viewpoint:Moving Viewpoint:what makes human subjects what makes human subjects differentdifferentfrom computer agents?from computer agents?

Computer Agents

Human Subjects

Program Action Dynamics

Strategy Action Dynamics

known

unknownobservable

100% is special, while difference between 99% and 98% is a matter of degree.

Allais’ Paradox

× 4 × 4reversed

TWD400 (20 per cent) > TWD300 (25 per cent)

TWD400 (80 per cent) < TWD300 (100 per cent)

(in 100 days) < (in 101 days)100

Hyperbolic Discounting

- 100 days - 100 daysreversed

9998

99

97

98

96

97

80

81

70

71

60

61

50

51

40

41

30

31

20

21

10

11

9 10

8 97 86 75 64 53 42 31 day

20 day

1 day

1155

1166

1177

・・ ・・ ・・ ・・ ・・ 2211

2222

Nov. Nov. 20092009

Feb. Feb. 20102010

22 Feb. 22 Feb. 20102010

23 Feb. 23 Feb. 20102010

- 99 - 99- 98 - 98- 97 - 97- 96 - 96- 80 - 80- 70- 60- 50- 40- 30- 20- 10- 9- 8- 7- 6- 5- 4- 3- 2- 10 day - 70- 60- 50- 40- 30- 20- 10- 9- 8- 7- 6- 5- 4- 3- 2- 10 day

1144

TWD100000 (today) > TWD100100 (tomorrow)

TWD100000 < TWD100100

Similarity and Difference between Allais’ Paradox and Hyperbolic Discounting

TWD4000 (20 %) > TWD3000 (25 %)

butTWD4000 (80 %) < TWD3000 (100 %)

can be rational

TWD100000 (in 100 d.) < TWD100100 (in 101 d.)

but

TWD100000 (today) > TWD100100 (tomorrow)

cannot be rational

(maintained)

Kyoto Kyoto Experimental Experimental

Economics Economics LaboratoryLaboratory

(KEEL)(KEEL)

fMRIfMRIat Brain Activityat Brain ActivityImaging CentreImaging Centre

Combination of alternativesCombination of alternatives

todaytoday 1 week1 weeklaterlater

2 2 weeksweekslaterlater

100%100%

80%80%

40%40%

+

Atoday

4000 yen100 %

B2 weeks5000 yen

100 %

+

Atoday

4000 yen80 %

Btoday

8000 yen40 %

+

time12 seconds

choice presentation

decision making1-5 seconds

choice presentation

decision making1-5 seconds

Results (Results (GreenGreen--BlueBlue))

BA39 is BA39 is involved in involved in calculation (?) calculation (?)

today1

weeklater

2 weeks later

100%

80%

40%

BA39BA39

OFCOFC

Results (Blue-Green)

parahippocampalgyrus

precuneus

Self-projection

PFCPCC

neural activity in these regions tracks the revealed subjective value of delayed rewards.Kable & Glimcher(Nature Neuroscience2007) striatum

today1

weeklater

2 weeks later

100%

80%

40%

OFC

parahippocampal gyrus

Self Projection reflects the workings of the same Self Projection reflects the workings of the same core brain network.core brain network.

Remember past to imagine future.Why have we memory? Because with memory we can make better decisions and have greater chance to survive.

Remembering

Theory ofMind

Prospection

Navigation

Self-Projection (Bucker and Carroll, TRENDS in TRENDS in Cognitive Science Cognitive Science 2006)

Present Self Future SelfPast Self

Another Person

Narrator

Self Projection as Moving viewpoint

remembering

navigation

prospection

theory of mind

Self-Self-projectionprojection

PFCPFC

parahippocampalparahippocampalgyrusgyrus

precuneusprecuneus

The regions contain The regions contain precuneusprecuneus and and parahippocampal gyrusparahippocampal gyrus, which are , which are considered to be activated when considered to be activated when people are involved in complicated people are involved in complicated decision-making.decision-making.

Together with other observations, it Together with other observations, it seems to support seems to support self-projectionself-projection, , suggesting also why people reveal suggesting also why people reveal such intertemporal preference that such intertemporal preference that does not allow a simple explanation.does not allow a simple explanation.

today1 week

later2 weeks

later

100%

80%

40%

annual discounting factorsNo convergence is observed. Why?

テキスト

Frederick, Loewenstein and O'Donghue (JEL2002)

Intertemporal choiceRisky choice

Self-projectionCalculation

(?)

Brain

Instable Stable

fMRIfMRI

lab lab fieldfield

Observation

Questions;(Environman

t)

Answers;(Behaviors)

more complicated

stimuluscondition

behaviour

insider

conscious thinking

unconscious processoutsider

fMRI

At least one of you have a white hat on your head. Can you tell whether your hat is

white or not?Yasugi and Oda (2002, 2003)

I (B)I (B) know:know:

Girl B’s inferenceGirl B’s inference

inferenceinference

She (A) knows that my hat She (A) knows that my hat or her hat or both hats are or her hat or both hats are white; she does not know white; she does not know whether her hat is white or whether her hat is white or not; she knows whether my not; she knows whether my hat is white or not.hat is white or not.

I (B)I (B) know:know:

My hat is My hat is white.white.

I (A)I (A) know:know:

Girl A’s inferenceGirl A’s inference

inferenceinference

She (B) has a white hat She (B) has a white hat on her head; at least one on her head; at least one of us wears a white hat.of us wears a white hat.

I (A)I (A) does not does not know:know:

where My hat is where My hat is white or not.white or not.

I (A)I (A) know:know:

She (B) has a white hat She (B) has a white hat on her head; at least one on her head; at least one of us wears a white hat.of us wears a white hat.

(Yasugi and Oda 2002, 2003)(Yasugi and Oda 2002, 2003)I (A)I (A) do not know whether my hat is white or not. do not know whether my hat is white or not.

I (A)I (A) know:know:

My hat My hat is whiteis white

I (A)I (A) know: know:My hat My hat is not is not whitewhite

Preference:what they

preferInformation:what they

knowOptions:what they can

do

Action:what they

do

inference

Human behaviour

emotion, instinct, experience, etc.:what they feel consciously or unconsciously

Jumping out of the system (Hofstader 1979)

Jumping out controllable Jumping out controllable Jumping out uncontrollableJumping out uncontrollable

Bertrand Duopoly with product differentiation

•Participants:Class:14Participants:Class:1400 LAB:58LAB:58•All pairs are All pairs are reshuffled reshuffled randomlyrandomly•All players’ decisions All players’ decisions are posted are posted simultaneouslysimultaneously

Player A’s Reaction Player A’s Reaction CurveCurve

Player B’s Player B’s Reaction Reaction

CurveCurve

Nash Nash EquilibriumEquilibrium

Perticipants/Perticipants/experiments:120-142experiments:120-142

Participants/experiments: Max 28Participants/experiments: Max 28

0

20

40

60

80

100

1 2 3 4 5 6 7

Period

Perc

enta

ge o

f C

hoic

e

Classroom Lab

Percentage of students who chosePrice = 3: Nash Equilibrium strategy

%%

Percentage of students who chosePrice = 7: Pareto optimal strategyPrice = 7: Pareto optimal strategy

Percentage of students who chosePrice = 7: Pareto optimal strategyPrice = 7: Pareto optimal strategy

%%

0

20

40

60

80

100

1 2 3 4 5 6 7

Period

Perc

enta

ge o

f C

hoic

e

Classroom: P=3 Lab:P=3Classroom: P=7 Lab:P=7

Percentage of students who chose Price = 2: What strategy?

%%

0

20

40

60

80

100

1 2 3 4 5 6 7

Period

Perc

enta

ge o

f C

hoic

e

Classroom:P=2 Lab:P=2

Classroom:P=3 Lab:P=3

Classroom:P=7 Lab:P=7

　 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 0 6 10 47 73 104 183 282 403 544 707 890 1095 1320

2 6 0 16 52 78 110 188 288 408 550 712 896 1100 1326

3 10 16 0 37 63 94 173 272 393 534 697 880 1085 1310

4 47 52 37 0 26 58 136 236 356 498 660 844 1048 1274

5 73 78 63 26 0 31 110 209 330 471 634 817 1022 1247

6 104 110 94 58 31 0 79 178 299 440 603 786 991 1216

7 183 188 173 136 110 79 0 100 220 362 524 708 912 1138

8 282 288 272 236 209 178 100 0 121 262 425 608 813 1038

9 403 408 393 356 330 299 220 121 0 142 304 488 692 918

10 544 550 534 498 471 440 362 262 142 0 163 346 551 776

11 707 712 697 660 634 603 524 425 304 163 0 184 388 614

12 890 896 880 844 817 786 708 608 488 346 184 0 205 430

131095

1100

1085

1048

1022

991 912 813 692 551 388 205 0 226

141320

1326

1310

1274

1247

1216

1138

1038

918 776 614 430 226 0

• Choosing 2 is the unique Dominant strategy for each player if they maximises not their profit but the difference between their profits and their opponents’.

Player A’s profit >Player B’s profitPlayer A’s profit >Player B’s profit

Player A’s profit =Player B’s profitPlayer A’s profit =Player B’s profit

Player A’s profit <Player B’s profitPlayer A’s profit <Player B’s profit

Q=2 Q=3

P=2 363,363 388,372

P=3 372,388 402,402

Without Monetary rewards students Without Monetary rewards students played not P=3 to maximize their played not P=3 to maximize their profits but P=2 to beat their profits but P=2 to beat their opponents, which are also confirmed opponents, which are also confirmed in the debriefing questionnaires. in the debriefing questionnaires.

Cournot-Stackelberg Duopoly

11stst mover’s SPNE mover’s SPNE strategystrategy

Maximum 1Maximum 1stst mover’s profit on mover’s profit on the 2the 2ndnd mover’s mover’s reaction Curve: reaction Curve:

SPNESPNE

•All pairs are All pairs are fixedfixed..•First 1First 1stst movers’ movers’ decisions are shown and decisions are shown and then 2then 2ndnd movers make movers make decisionsdecisions

11stst mover’s Reaction mover’s Reaction CuarveCuarve

22ndnd mover’s mover’s Reaction Curve: Reaction Curve:

22ndnd mover’s SPNE mover’s SPNE strategystrategy

(8,8)(8,8)11stst mover profit mover profit

=2624 2=2624 2ndnd mover mover profit =2624profit =2624

Percentage of pairs who realised Nash Equilibrium: (13,5)

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9 10 11Period

Per

cent

age

of E

quilibr

ium

Ach

ieve

men

t

Classroom Lab

• Without Money are students more “rational”?

(13,5)(13,5)11stst mover Profit mover Profit

3172317222ndnd mover Profit mover Profit

12201220

Nash EquilibriaNash Equilibria

11stst player’s strategy player’s strategy

22ndnd player’s player’s strategystrategy

(3, 10)(3, 10)11stst mover Profit mover Profit

1362136222ndnd mover Profit mover Profit

45404540Nash Equilibrium Nash Equilibrium

most advantageous most advantageous for the 2for the 2ndnd mover mover

11stst mover’s mover’s profitprofit

increasesincreases

22ndnd mover’s mover’s profit profit

increasesincreases

1

2

3

4

5

6

7

8

9

10

11

2nd mover’s choice2nd movers are more Submissive in Classroom.

Experiment 3 Profit tableExperiment 3 Profit table

22ndnd mover’s mover’s Reaction Reaction

CurveCurve

ClassroomClassroom LaboratoryLaboratory

Pareto Pareto optimaloptimal

1

2

3

4

5

6

7

8

9

10

Practically Ultimatum gamePractically Ultimatum gameCooperation seeking (?)Cooperation seeking (?)No-conditional acceptNo-conditional accept

Conditional acceptConditional acceptRejectReject

0

10

20

30

40

50

0

10

20

30

40

50

Dynamics of 1st mover’s choiceClassroom Period 1Classroom Period 1

Classroom Period 10Classroom Period 10

Lab Period 1Lab Period 1

Lab Period 11Lab Period 11

0

10

20

30

40

50

0

10

20

30

40

50

3 8 133 8 13

3 6 8 133 6 8 133 8 133 8 13

3 6 8 133 6 8 13

In the classroom the second movers were In the classroom the second movers were ready for accepting the SPNE, which is the ready for accepting the SPNE, which is the Nash Equilibrium least favorable to them. Nash Equilibrium least favorable to them. In the debriefing Questionnaires they were In the debriefing Questionnaires they were only too happy to give a rational explanation only too happy to give a rational explanation why they had not earned more. why they had not earned more. They seemed to have changed their They seemed to have changed their objective: from maximising their profit to objective: from maximising their profit to explaining why they couldn’t, which change explaining why they couldn’t, which change was not observed in the laboratory. was not observed in the laboratory. Monetary rewards prevented subjects from Monetary rewards prevented subjects from setting their own goal by themselves and setting their own goal by themselves and made them play seriously; though it may not made them play seriously; though it may not be the case in every economic experiment.be the case in every economic experiment.

Laboratory Classroom

With Monetary Rewards With Monetary Rewards

Perticipants/experiments: Max 28 Participants/experiments: 120-142

3 experiments/1day 1 experiment/1dayNo debriefing for each

experiment Debriefing for each experiment

Other Differences Other Differences

…… ……

Differences between the classroom Differences between the classroom and the laboratoryand the laboratory

•They move their viewpoint to make They move their viewpoint to make decisions so that they can make better decisions so that they can make better decisions; which process is realised by decisions; which process is realised by dynamic brain activities.dynamic brain activities.

•They do not limit their inference within the They do not limit their inference within the system; they do meta-thinking to make system; they do meta-thinking to make decisions.decisions.

•They change (find or create) new objectives They change (find or create) new objectives if they think the original ones are not if they think the original ones are not interesting or too difficult to be realised.interesting or too difficult to be realised.

Human subjects can (cannot but) Human subjects can (cannot but) jump out of the system.jump out of the system.

[1] Allais, Maurice (1953): ''Le comportement de l’homme rationel devant le risque, [1] Allais, Maurice (1953): ''Le comportement de l’homme rationel devant le risque, critique des postulates et axiomes de l’ecole americaine'', in conometrica, vol. 21, pp. critique des postulates et axiomes de l’ecole americaine'', in conometrica, vol. 21, pp. 503-546.503-546.

[2] Barron, Greg & Ido Erev (2003): ''Small feedback-based decisions and their limited [2] Barron, Greg & Ido Erev (2003): ''Small feedback-based decisions and their limited correspondence to description-based decisions'', in Journal of Behavioral Decision correspondence to description-based decisions'', in Journal of Behavioral Decision Making, vol. 16, pp. 215-233. Making, vol. 16, pp. 215-233. 

[3] Buckner, Randy L. & Daniel C. Carroll (2007): ''Self-projection and the Brain'', in [3] Buckner, Randy L. & Daniel C. Carroll (2007): ''Self-projection and the Brain'', in Trends in Cognitive Sciences, vol. 11, pp.49-57.Trends in Cognitive Sciences, vol. 11, pp.49-57.

[4] Camerer, Colin F. & George Loewenstein & Drazen Prelec (2005): [4] Camerer, Colin F. & George Loewenstein & Drazen Prelec (2005): ''Neuroeconomics: How neuroscience can inform economics'', in Journal of Economic ''Neuroeconomics: How neuroscience can inform economics'', in Journal of Economic Literature, vol. 43 (no. 1), pp. 9-64. Literature, vol. 43 (no. 1), pp. 9-64. 

[5] Frederick, Shane & George Loewenstein & Ted O'Donoghue (2002): ''Time [5] Frederick, Shane & George Loewenstein & Ted O'Donoghue (2002): ''Time Discounting and Time Preference: A Critical Review'', in Journal of Economic Literature, Discounting and Time Preference: A Critical Review'', in Journal of Economic Literature, vol. 40 (June), pp. 351-401. vol. 40 (June), pp. 351-401. 

[6] Glimcher, Paul W. (2003): ''Decisions, Uncertainty, and the Brain : The Science of [6] Glimcher, Paul W. (2003): ''Decisions, Uncertainty, and the Brain : The Science of Neuroeconomics'', MIT Press, Cambridge, Massachusetts, USA; Neuroeconomics'', MIT Press, Cambridge, Massachusetts, USA;  宮 下 英 三宮 下 英 三 【【 訳訳 】】(2008): (2008): 『『神経経済学入門神経経済学入門 ------不確実な状況で脳はどう意思決定するのか不確実な状況で脳はどう意思決定するのか』』 , , 生産性出版生産性出版 . . 

references

[7] Gul, Faruk & Wolfgang Pesendorfer (2005): “The Case for Mindless Economics”, http://www.princeton.edu/˜pesendor/mindless.pdf.

[8] Hertwig, R. & Greg Barron & E. Weber & Ido Erev (2004):“Decisions from experience and the weighting of rare events”, in Psychological Science vol.15, pp. 534-539.

[9] Hofstadter, Douglas R. (1979, Escher, Bach: an Eternal Golden Braid, Basic Books, New York, USA; 野崎昭弘・はやしはじめ・柳瀬尚紀【訳】 (1985): 『ゲーデル , エッシャー , バッハ—あるいは不思議の環』 , 白揚社 .

[10] Hsu, Ming & Meghana Bhatt & Ralph Adolphs & Daniel Tranel & Colin F. Camerer (2005): “Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making”, in Science, vol. 310, pp.1680-1683.

[11] Kable, Joseph W. & Paul W. Glimcher (2007): “The Neural Correlates of Subjective Value During Intertemporal Choice”, in Nature NeuroScience, vol. 10(12), pp.1625-1633.

[12] Kelman, Mark & Yuval Rottenstreich & Amos Tversky (1996): “Context-Dependence in Legal Decision Making”, in Journal of Legal Studies, vol. 25, issue 2, pp. 287-318.

[13] Laibson, David (1997): “Golden Eggs and Hyperbolic Discounting”, in Quarterly Journal of Economics, vol. 112, pp. 443-477.

[14] Logothetis, Nikos K. (2008): “What we can do and what we cannot do with fMRI”, in Nature, vol. 453, pp. 869-878.

references

[15] McClure, Samuel M & David I. Laibson & George Loewenstein & Jonathan D. Cohen (2004): “Separate Neural Systems Value Immediate and Delayed Monetary Rewards”, in Science, vol. 306, pp.503-507.

[16] Moreno, Jonathan D. (2005): Mind Wars: Brain Research and National Defense, Dana Press, New York, USA; 久保田競【監訳】 (2008): 『マインド・ウォーズ　操作される脳』 , アスキー・メディアワークス角川グループパブリッシング .

[17] 奥田次郎 (2008): 「未来への予見に携わる脳神経ネットワーク」 , 『玉川大学脳科学研究所紀要』 , 第 1 号 , pp. 13-23.

[18] Pasinetti, Luigi, L. (1981): Structural Change and Economic Growth: a theoretical essay on the dynamics of the wealth of nations, Cambridge University Press, Cambridge, UK; 大塚勇一郎・渡会勝義【訳】 (1983): 『構造変化と経済成長—諸国民の富の動学に関する理論的エッセイ』 , 日本評論社 .

[19] Rubinstein, Ariel (2006): “ Discussion of “Behavioral Economics”: “Behavioral Economics” (Colin Camerer) and “Incentives and Self-Control” (Ted O’Donoghue and Matthew Rubin)”, in Advances in Economics and Econometrics: Theory and Applications, Ninth World Congress. Volume 2 (Econometric Society Monographs, no. 42) edited by Richard Blundell & Whitney K. Newey & Torsten Persson, Cambridge University Press, Cambridge and New York: pp. 246-54.

[20] Samuelson, Paul, A. (1947): Foundation of Economic Analysis, Harvard University Press, Cambridge, Mass.; 佐藤隆三【訳】 (1967): 『経済分析の基礎』 , 勁草書房 .

references

[21] Shafir, Sharoni & Taly Reich & Erez Tsur & IdoErev & Arnon Lotem (2008): “Perceptual accuracy and conflicting effects of certainty on risk-taking behaviour”, in Nature, vol. 453, pp. 917-921.

[22] Smith, Vernon L. (1982): “Microeconomic Systems as an Experimental Science”, in American Economic Review, vol. 66, pp. 274-279.

[23] Weber, Bethany J. & Scott A. Huettel (2008): ‘The neural substrates of probabilistic and intertemporal decision making”, in Brain Research, doi: 10.1016/j.brainres.2008.07.105.

[24] Yasugi, Mariko & Sobei H. Oda (2002): “A Note on the Wise Girls Puzzle”, in Economic Theory, vol. 19 (no. 1), pp. 145-156.

[25] Yasugi, Mariko & Sobei H. Oda (2003): “Notes on Bounded Rationality”, in Scientiae Mathematicae Japonicae, vol. 57 (no. 1), pp. 83-92.

references

International ConferenceInternational Conference

How and why economists and How and why economists and philosophers do experiments:philosophers do experiments:

dialogue between dialogue between Experimental economics Experimental economics

and experimental philosophyand experimental philosophy

Kyoto Sangyo University, Kyoto, Japan27-28 Kyoto Sangyo University, Kyoto, Japan27-28 March 2010March 2010