Fall 2008
Shi-Chung Chang
訊號控制及對策 (Information, Control and Games)
Syllabus
PREREQUISITE: Signal and Systems, Control Systems, Probability Theory, Operations Research, or consent of
instructor.
INSTRUCTOR:
Shi-Chung Chang, Rm. 245, EE-II Bldg. (電二), 2362-5187, scchang@cc.ee.ntu.edu.tw.
Office hours: Mon/Wed 12:00-13:00.
CLASSES: Thursday 14:20 – 17:20, 電二 225.
TEXT:
Prajit K. Dutta, Strategies and Games, Theory and Practice, MIT Press, 1999.
Goal: Game theory for the design of decision and control policies of multi-agent network systems
This course is designed for senior and graduate level engineering students to study static and dynamic
optimization problems among multiple decision-makers (DMs) in a networked system, where each DM has his/her
own objective function, accesses to public and private information, and possesses individual decision-making
authorities. The course objectives are as follows:
(i) to develop students’ common sense for gaming problems and the role of information;
(ii) to equip students with interdisciplinary models, theories, and solution methods from mathematical optimization,
system and control, and economic game; and
(iii) to guide students in applications to networked systems of their interest via case studies.
To achieve the course goal, there are three course segments. The first segment is on basic game theory, and
covers games in strategic form, games in extensive form, and repeated games. Both the Nash equilibrium concept
and hierarchical games will be discussed. The second segment involves the intricate interactions of information
and decision-making in dynamic games, and covers team decision theory and games with incomplete information.
The third segment is on the application of the theory to market design, and covers auction theory and applications
to engineering systems. The uniqueness of the course lies in the treating of the subjects from both control theoretic
and economic view points; the use of online/experimental games and market games developed through research
projects; and the practical contexts of electricity markets, communication and computing services and supply
chain management.
Tentative Outline:
I.
1.
2.
3.
4.
5.
6.
7.
8.
9.
Introduction to Game Theory
Motivation, Course Outline, and a First Look. [Chapters 1, 2, and 27*] 09/18
Strategic Form Games, Dominance, and Nash Equilibrium. [Chapters 3, (4), 5, and 28] 09/25
A Brief Introduction of Mathematical Optimization - Static and Dynamic. [Chapters 25 (and 26)] 10/2
Zero-Sum Games. Extensive Form Games and Backward Induction. [Chapters 10, 11, (12), and 13] 10/9
Introduction to Experimental Game Theory [4]. 10/16 (by Prof.Yi-Nung Yang, 1pm-3:50 pm)
Newsvendor Experiment [5,6] 10/23 (by Prof.Yi-Nung Yang, 1 pm-3:50 pm)
Repeated Games and Dynamic Games. [Chapters 14, 15, 16 (and 18)] 10/23 (Makeup class)
Hierarchical Games. [7,8] 10/30
Midterm Exam. 11/6
II. Decision-Making with Incomplete Information
7. Team Decision Theory. [9] 11/13
8. Moral Hazard, Incentives, and Games with Incomplete Information. [Chapters 19, 20, and 21] 11/20
1
III.
9.
10.
11.
12.
13.
14.
15.
*:
Market Design
Mechanism Design and Term Project Proposal. [Chapter 22] 11/27
Auctions. [Chapter 23] 12/4
Electricity Auctions. [10,11] 12/11
Strategic Behavior in Electricity Auctions. Signaling Games. [Chapter 24] 12/18
Pricing of Communication and Computing Services [12,13] 12/25
Co-evolution of Supply Chain Management [14,15] 01/08
Term Project Presentations. 01/15
Refer to chapters in the textbook by Dutta.
Grading:
Classroom Participation
Homework
Mid Term
Term Project
Presentation
Report
Total
10%
20%
40%
10%
30%
110%
REFERENCES:
1. T. Basar and G. J. Olsder, Dynamic Noncooperative Game Theory, Society for Industrial & Applied Math; 2nd
Edition, 1998.
2. D. P. Bertsekas, Nonlinear Programming, Second Edition, Athena Scientific, Belmont, MA, 1999.
3. M. Bichler, The Future of e-Markets: Multidimensional Market Mechanisms, Cambridge U. Press, 2001.
4. Vincent P. Crawford, “Introduction to Experimental Game Theory,” Journal of Economic Theory, vol. 104,
issue 1, 2002, pp. 1-15.
5. M. E. Schweitzer and G. P. Cachon, “Decision bias in the newsvendor problem with a known demand
distribution: Experimental evidence,” Mngt Sci (2000) 46(3): 404–420.
6. U Benzion , Y Cohen, R Peled and T Shavit, “Decision-making and the newsvendor problem: an experimental
study,” Journal of the Operational Research Society (2008) 59, 1281–1287.
7. T. S. Chang, P. B. Luh, “Derivation of Necessary and Sufficient Conditions for Single-Stage StackelbergGames
via the Inducible Region Concept,”IEEE Transactions on Automatic Control, Vol. 29, No. 1, Jan. 1984, pp. 6366.
8. P. B. Luh, S. C. Chang, and T. S. Chang, “Solutions and Properties of Multi-Stage
StackelbergGames,”Automatica, Vol. 20, No. 2, March 1984, pp. 251-256.
9. Y. C. Ho, “Team Decision Theory and Information Structures,”Proceedings of IEEE, Vol. 68, No. 6, June 1980,
pp. 644-654.
10. X. Guan, P. B. Luh, H. Yan, and J. A. Amalfi, “An Optimization-Based Method for Unit
Commitment,”International Journal of Electrical Power & Energy Systems, Vol. 14, No. 1, February 1992, pp.
9-17.
11. P. B. Luh, W. E. Blankson, Y. Chen, J. H. Yan, G. A. Stern, S. C. Chang, and F. Zhao, “Payment Cost
Minimization Auction for the Deregulated Electricity Markets Using Surrogate Optimization,”IEEE Trans. on
Power Systems, Vol. 21, No. 2, May 2006, pp. 568-578.
12. T.C. Lin, Y. S. Sun, S.-C. Chang, S.-I Chu, Y.-T. Chou, M.-W. Li, “Management of Abusive and
Unfair Internet Access by Quota-based Priority Control,” Computer Networks, Vol. 44, Issue 4,
March 2004, pp. 441-462.
13. S.-I. Chu, S.-C. Chang, “Time-of-day Internet Access Management by Combining Empirical Data-based
Pricing with Quota-based Priority Control,” IET Communications, vol. 1, issue 4, August 2007, pp. 587-596.
14. S.-C. Chang, M.-M. Hsieh, C.-W. Chen, “Reverse Auction-Based Job Assignment among Foundry Fabs,”
MASM Special Issue of International Journal of Production Research, Jan., 2007. pp. 653-657.
15. M G Jacobides, Winter S G, “Co-evolution of Capability and Transaction Costs: Explaining the Institutional
Structure of Production,” Strategic Management Journal 2005 May Vol 26:5 p 395-413
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