DSS
Decision Sciences & Systems
Technical University of Munich
 

Florian Brandl, Christian Geist, and Prof. Dr. Felix Brandt 

Seminar SS 2015

Economics and Computation

 

Content

In recent years, there has been an increasing interest in topics at the intersection of economics and computer science, as witnessed by the continued rapid rise of research areas such as algorithmic game theory and computational social choice. This development is due to the emergence of computational networks such as the Internet as well as the need to get a grip on algorithmic questions in economics.
The emphasis in this seminar lies on the independent study of classic economics papers, but also, and in particular, more recent papers from computer science. Among the topics to be covered are matching theory, mechanism design, and voting theory. 

Registration (*please read carefully*)

Seats are split up between students from computer science and students from other programs. All interested students have to attend the overview meeting and send an application by email briefly describing their background (including relevant courses) and motivation (up to 250 words) as well as 2-5 papers they are interested in. Deadline for applications: January 29, 2015 (11:59pm). Students from computer science additionally have to use the matching system for indication of interest. Notifications will be sent out on January 30 (mathematics) and Februrary 13 (computer science) and include assignment of papers and supervisors. Registration in TUMonline will be taken care of by the lecturers by end of February; no further action is required.

Time and venue

Overview (Vorbesprechung):

  • Wednesday, January 21, 11.30 - 12.30, room 01.10.011
 
First meeting:
 
  • Tuesday, April 21, 14.00 - 16.00, room 01.10.011

Talks:

  • Tuesday, May 12, 14.00 - 17.00, room 01.10.011
    • Talk 1
      Speaker: Katharina
      Session chair: Taha
      O. Cailloux and U. Endriss. Eliciting a suitable voting rule via examples. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 2014.
      V. Hashemi and U. Endriss. Measuring diversity of preferences in a group. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 2014.
  • Talk 2
    Speaker: Christian
    Session chair: Alona
    P. Tang and F. Lin. Computer-aided proofs of Arrow’s and other impossibility theorems. Artificial Intelligence, 173(11):1041–1053, 2009.
 
  • Tuesday, June 2, 14.00 - 17.00, room 01.10.011
    • Talk 3
      Speaker: Vadim
      Session chair: Markus
      J. Bartholdi, III, C. A. Tovey, and M. A. Trick. The computational difficulty of manipulating an election. Social Choice and Welfare, 6(3):227–241, 1989.
    • Talk 4
      Speaker: Taha
      Session chair: Katharina
      M. D. Intriligator. A probabilistic model of social choice. Review of Economic Studies, 40(4):553–560, 1973.
      A. Gibbard. Manipulation of schemes that mix voting with chance. Econometrica, 45(3):665–681, 1977.
 
  • Tuesday, June 16, 14.00 - 17.00, room 01.10.011
    • Talk 5
      Speaker: Eugen
      Session chair: Stefan
      D. C. Fisher and J. Ryan. Optimal strategies for a generalized “scissors, paper, and stone” game. American Mathematical Monthly, 99(10):935–942, 1992.
    • Talk 6
      Speaker: Alona
      Session chair: Christian
      A. Bogomolnaia and M. O. Jackson. The stability of hedonic coalition structures. Games and Economic Behavior, 38(2):201–230, 2002.
      J. Hajdukovà. Coalition formation games: A survey. International Game Theory Review, 8(4):613–641, 2006.
 
  • Tuesday, June 23, 14.00 - 17.00, room 01.10.011
    • Talk 7
      Speaker: Stefan
      Session chair: Vadim
      U. Endriss, N. Maudet, F. Sadri, and F. Toni. Negotiating socially optimal allocations of resources. Journal of Artificial Intelligence Research, 25:315–348, 2006.
    • Talk 8
      Speaker: Markus
      Session chair: Eugen
      J. A. Kroll, I. C. Davey, and E. W. Felten. The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. In Proceedings of the 12th Workshop on the Economics of Information Security (WEIS 2013), 2013.
       

Preliminary selection of articles

A. Abdulkadiroglu and T. Sönmez. House allocation with existing tenants. Journal of Economic Theory, 88(2):233–260, 1999.

A. Abdulkadiroglu and T. Sönmez. School choice: A mechanism design approach. American Economic Review, 93(3):729—747, 2003.

M. Balinski and T. Sönmez. A tale of two mechanisms: Student placement. Journal of Economic Theory, 84(1):73–94, 1999.

S. Barberà. Majority and positional voting in a probabilistic framework. Review of Economic Studies, 46(2):379–389, 1979.

J. Bartholdi, III, C. A. Tovey, and M. A. Trick. The computational difficulty of manipulating an election. Social Choice and Welfare, 6(3):227–241, 1989.

A. Bogomolnaia and M. O. Jackson. The stability of hedonic coalition structures. Games and Economic Behavior, 38(2):201–230, 2002.

A. Bogomolnaia and H. Moulin. A new solution to the random assignment problem. Journal of Economic Theory, 100(2):295–328, 2001.

S. J. Brams and A. D. Taylor. An envy-free cake division protocol. The American Mathematical Monthly, 102(1):9–18, 1995.

O. Cailloux and U. Endriss. Eliciting a suitable voting rule via examples. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 2014.

V. Conitzer, T. Sandholm, and J. Lang. When are elections with few candidates hard to manipulate? Journal of the ACM, 54(3), 2007.

X. Deng and C. H. Papadimitriou. On the complexity of cooperative solution concepts. Mathematics of Operations Research, 12(2):257–266, 1994.

J. Duggan and T. Schwartz. Strategic manipulability without resoluteness or shared beliefs: Gibbard- Satterthwaite generalized. Social Choice and Welfare, 17(1):85–93, 2000.

U. Endriss, N. Maudet, F. Sadri, and F. Toni. Negotiating socially optimal allocations of resources. Journal of Artificial Intelligence Research, 25:315–348, 2006.

P. C. Fishburn. Probabilistic social choice based on simple voting comparisons. Review of Economic Studies, 51(4):683–692, 1984.

P. C. Fishburn. SSB utility theory: An economic perspective. Mathematical Social Sciences, 8(1):63–94, 1984.

D. C. Fisher and J. Ryan. Optimal strategies for a generalized “scissors, paper, and stone” game. American Mathematical Monthly, 99(10):935–942, 1992.

A. Gibbard. Manipulation of schemes that mix voting with chance. Econometrica, 45(3):665–681, 1977.

J. Hajdukovà. Coalition formation games: A survey. International Game Theory Review, 8(4):613–641, 2006.

V. Hashemi and U. Endriss. Measuring diversity of preferences in a group. In Proceedings of the 21st European Conference on Artificial Intelligence (ECAI), 2014.

M. D. Intriligator. A probabilistic model of social choice. Review of Economic Studies, 40(4):553–560, 1973.

R. W. Irving. An efficient algorithm for the “stable roommates” problem. Journal of Algorithms, 6(4):577–595, 1985.

M. O. Jackson and A. Wolinsky. A strategic model of social and economic networks. Journal of Economic Theory, 71(1):44–74, 1996.

J. A. Kroll, I. C. Davey, and E. W. Felten. The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. In Proceedings of the 12th Workshop on the Economics of Information Security (WEIS 2013), 2013.

E. Maskin. Nash equilibrium and welfare optimality. Review of Economic Studies, 66(26):23–38, 1999.

D. Monderer and L. S. Shapley. Potential games. Games and Economic Behavior, 14(1):124–143, 1996.

N. Nisan and A. Ronen. Algorithmic mechanism design. Games and Economic Behavior, 35(1):166–196, 2001.

A. D. Procaccia and J. Wang. Fair enough: Guaranteeing approximate maximin shares. In Proceedings of the 15th ACM Conference on Economics and Computation (ACM-EC), pages 675–692, 2014.

T. Roughgarden and É. Tardos. How bad is selfish routing? Journal of the ACM, 49(2):236–259, 2002.

T. Sandholm. Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence, 135(1–2):1–54, 2002.

P. Tang and F. Lin. Computer-aided proofs of Arrow’s and other impossibility theorems. Artificial Intelligence, 173(11):1041–1053, 2009.

H. P. Young. Optimal voting rules. Journal of Economic Perspectives, 9(1):51–64, 1995.

Other resources

  • Feedback guidelines (1) (2) [in addition to what is presented during the first meeting]

Practicalities 

  • The seminar will be held in English (i.e., all presentations will have to be in English, too)

Module-Codes

  • IN2107 (Master-Seminar in the Master program Informatik)

  • IN0014 (Seminar in the Bachelor programs Informatik, Wirtschaftsinformatik)

  • For all other programs: Please check first whether this seminar fits in your curriculum. For example, mathematics students should find it listed as a mathematics seminar, too.

Contact

Florian Brandl
Christian Geist

Decision Sciences & Systems (DSS), Department of Informatics (I18), Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany
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