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Moving Viewpoint: what makes human subjects different from computer agents?. Sobei H. Oda Kyoto Sangyo University. The Sixth International Workshop on Agent-based Approaches in Economic and Social Complex Systems National Chengchi University, Taipei 14 November 2009. Computer Agents. - PowerPoint PPT Presentation
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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.
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[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 . .
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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