Multi-Agent Auction, Bidding and Contracting Support Systems
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Transcript of Multi-Agent Auction, Bidding and Contracting Support Systems
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Multi-Agent Auction, Bidding and Contracting Support Systems
D.-J. Wu, Yanjun Sun
FMEC
May 11-12, 2000
Philadelphia
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ABC in Human History
• Marrying daughter in ancient China using bidding, Song Dynasty
• Auction women as wives in Babylonia, Fifth Century, B.C.
• Farming contracting in ancient China, 452 B.C.
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Auction Literature
• Tradition auction (single item)• English, Dutch• Sealed bid, open cry• First price winner and second price winner• Goods are storable
• Combinatorial auction (multi item)• iBundle (AuctionBot)
• Generalized Vickery auction
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ABC in Information Economy (Adapted from Vakrat and Seidmann, 1999)
Business Model Auction Mechanism
Amazon-auctions.amazon.com (C2C)
Auction Platform Multiple-unit Uniform-price. Also dutch auctions
eBay-www.ebay.com (C2C)
Auction platform Multiple-unit discriminating price auctions. Also dutch auctions
OnSale-www.onsale.com (B2C)
Most goods are factory-direct
Multiple-unit, discriminating price auctions.
SurplusAuction-www.surplusauction.com (B2C)
Takes title on the goods it auctions
Multiple-unit, discriminating price auctions.
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Persona-Logic
Firefly Bargain-
Finder
Jango Kasbah Auction-Bot
Tete-a-Tete
Product Brokering
Merchant Brokering
Negotiation
Developer PersonaLogic
MIT Anderson Consulting
Jango MIT Michigan MIT
Roles and Examples of Agent Systems as Mediators in Electronic Commerce (Adapted from Guttman, Moukas, and Maes, 1999)
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eBAC Auction
Literature eBAC
Bid Discrete Continuous
Storability YES NO
Seller Single Many
Market One Two
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Research Questions:
• Can artificial agents discover the equilibrium if it exists?
• Can artificial agents learn reasonably good policies when facing automated markets?
• What kind of mechanisms will induce coordination, cooperation and information sharing among agents?
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Seller 1 Seller 2 Seller 3
Blackboard
Adaptive Learning
Seller 1Seller 2Seller 3
Figure XXX: Myopic Bidding System
Myopic Bidding System
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Agents Learning in Myopic System
• Floating point representation.
• Identical initial population.
• Rule strength is current profit.
• Memory size = 1.
• Learning via genetic algorithms.
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Model for Myopic Bidding
• Bidding Price
• Bidding Capacity
Maximize E x t x tX t
i i ii ( )
( ( )| ( )) 1
Maximize E L t L tL t
i i ii ( )
( ( )| ( )) 1
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Technology and Capacity Parameters for the Three-Supplier Examples
i 1 2 3
Ex. 1 ci = Si + G(bi) 10 10 18
Ki 40 40 30
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Price Bidding Model
Max E
x s G b
D x
Q Min D K
x s G b Q
xi
i i i
i i
i i i
i i i i i
i
( )
( )
[ , ]
[ ( )]
100
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S1
18 55
S2 S2
18 55 18 55
S3
19 (328, 328, 1) (320, 0, 30) (0, 320, 30) (338, 338, 30)
55 (328, 328, 0) (320, 129, 80) (129, 320, 80) (737, 737, 454)
Nash Equilibria for Three-Supplier Normal Form Game
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Capacity Bidding Model
Max E
p K L
p s G b Q p s G b L
Li
ii
ii
i i i i i i i
i
1
3
1
3
[ ( )] [ ( )]
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Results: c = (10, 10, 18), K = (40, 40, 30)
Bidding Price Bidding Capacity
Path Independent
Pure YES. No co-op.
(18, 18, 19)
NO.
Mixed YES. No co-op.
(18, 18, 19)
YES. Co-op.
(27, 27, 19)
Path Dependent (Observed Average Profit)
YES. No co-op.
(18, 18, 19)
YES. Co-op.
(27, 28, 18)
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Dynamic Pure Strategy Price Bidding, Path Dependent (Ex. 1)
0
5
10
15
20
25
30
35
40
Series1
Series2
Series3
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Dynamic Pure Strategy Capacity Bidding, Path dependent (Ex. 1)
0
5
10
15
20
25
30
35
Series1
Series2
Series3
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D xi i ( )77 D xi i ( )115 D xi i ( )100
ci
Ki
ci
Ki
ciKi
ciKi
ci
Ki
ci
Ki
*
= 13, 13, 13= 30, 30, 30
PD (16, 16, 16) (30, 30, 30) (21, 21, 21)
PI (16, 16, 16) (30, 30, 30) (21, 21, 21)
= 11, 21, 21= 44, 23, 23
PD (21, 22, 22) (40, 31, 31) (No, No, No)
PI (21, 22, 22) (40, 31, 31) (No, No, No)
= 16, 16, 25= 34, 34, 22
PD (No, No, 26) (31, 31, 31) (No, No, No)
PI (No, No, 26) (31, 31, 31) (No, No, No)
= 7, 12, 17= 45, 26, 19
PD (No, No, 18) (39, 30, 30) (No, No, No)
PI (No, No, 18) (38, 30, 30) (No, No, No)
*=10,10,18=40,40,30
PD (No, No, 19) (No, No, No) (18, 18, 19)
PI (No, No, 19) (No, No, No) (18, 18, 19)
* =10,12,14=40,30,20
PD (14, 14, 15) (38, 29, 29) (No, No, No)
PI (14, 14, 15) (38, 29, 29) (No, No, No)
* Borrowed from WKZ.
Orthogonal Experiment
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Summary of Myopic Price Bidding
• No cooperation exists under any climate.
• Bidding tends to have equilibrium under amenable climate.
• No difference between path dependent and independent.
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Non-Myopic Bidding
• No learning (Fixed strategy tournament)
• One agent learning
• All agents learning
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Strategy Profit
1 (R, R, R) (17089, 14500, 5982)
… … …
14 (N, N, N) (22091, 22091, 13623)
… … …
26 (T, T, N) (22091, 22091, 13623)
27 (T, T, T) (22091, 22091, 13623)
Tournament 1: Fixed Three-Strategy (Random, Nice, and Tit-for-Tat)
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1 (R, R, R) (14718, 13427,7705)
… … …
22 (N, N, N) (22091, 22091, 13623)
… … …
42 (T, T, N) (22091, 22091, 13623)
43 (T, T, T) (22091, 22091, 13623)
… … …
64 (A, A, A) (10656, 10656, 934)
Tournament 2: Fixed Four-Strategy (Random, Nice, Tit-for-Tat, and Nasty)
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Maximize Ex t
ii ( )
E x t x ti i t i it
, ( ( )| ( )),
1
30
One Agent Learning
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… … …
65 (G, R, R) (26356, 9842, 4143)
… … …
70 (G, N, N) (52800, 3857, 2379)
… … …
75 (G, T, T) (23115, 21483, 13248)
… … …
80 (G, A, A) (17473, 5929, 9123)
Tournament 3: One Agent Learning
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Strategy List 1 Strategy List 2 Strategy List 3
Seller 1 Seller 2 Seller 3
Strategy Discovery
Tournament
Figure XXX: Non-Myopic Bidding System
Tournament 4: All Agents Learning
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Agent Learning in Non-Myopic System
• Representation: Each rule specifies multi-period bidding strategy.
• Randomly generated initial population.
• Rule learning via genetic algorithms.
• Rule strength is expected tournament profit.
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Maximize Ex g t
ii ( , )
E
x g t x g t x g t
H Hi
i g t i j kt
T
k
H
j
H
, , ( ( , ), ( , ), ( , ))
111
Maximize Ex g t
jj ( , )
Ex g t x g t x g t
H Hj
j g t i j kt
T
k
H
i
H
, , ( ( , ), ( , ), ( , ))
111
MaximizeExgt
kk(,)
E
x g t x g t x g t
H Hk
k g t i j kt
T
j
H
i
H
, , ( ( , ), ( , ), ( , ))
111
Model for Non-Myopic Bidding
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The Emergence of Trust
• Learning to cooperate
• Conditions for cooperation
• Impact of climate
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0
10000
20000
30000
40000
50000
60000
0 50 100
Proportion of Nasty
Pro
fit
of
Seller
1
Random
Nice
T4T
Nasty
GA
Agent Learning in Dynamic Environment: Experiment 1
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1 2 3 4 5 6 7 8 9 10
0% 54 54 54 54 54 54 54 54 54 54
25% 54 54 54 54 54 54 54 54 54 54
50% 54 34 48 55 53 55 53 34 55 53
75% 54 34 55 39 34 55 39 34 55 39
100% 54 34 55 39 55 39 55 39 55 39
Agent Learning in Dynamic Environment, Experiment 2
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0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
0 25 50 75 100
Proportion of Nasty
Pro
fit
of
Seller
1
Profit Changing in Dynamic Environment, Experiment 2
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Ongoing Research
• The ring of King Solomon• Agent communication
• Computational principles of trust• Agent coalition and Bargaining
• Role of Larmarcian learning
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Summary• Artificial agents are viable in automated
marketplace.• Discover optimal bidding and contracting strategies in
the equilibrium if exist.• Find better strategies in a complex dynamic
environment where equilibrium do not exist.
• The emergence of trust.• Depends on the auction mechanism: Capacity bidding
induces cooperation.• Non-myopic bidding leads to cooperation while myopic
bidding does not.• Climate has impact on agents cooperation.