Economics & Computation

SupervisionProf. Dr. Felix BrandtChris DongPatrick Lederer, René Romen

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 should attend the overview meeting and apply by mail, describing their background (including relevant courses) and motivation (up to 250 words) as well as 2-5 papers they are interested in. (Own suggestions of new papers are welcome!) Deadline for applications:  February 16, 2022 (11:59pm). Students from computer science and mathematics additionally have to use the matching system for indication of interest. Notifications will be sent out at the end of Februrary and include assignment of papers and supervisors. Registration in TUMonline will be taken care of by the lecturers; no further action is required.
→ Apply

Technical details

We currently plan to run the seminar onsite in Garching. Hence, participation in the seminar requires you to be in Munich in the next semester. We may switch to an online format if the Corona pandemy requires us to do so. 

Time and venue

Overview (Vorbesprechung):

We offer two possibilities for the overview meeting, one in person and one online via zoom. 

First meeting (Kick-Off):

  • ~ April, 14.00 - 15.00

Talks:

  • ~ June, 9.00 - 17.00

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.

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

S. Bouveret and M. Lemaître. Characterizing conflicts in fair division of indivisible goods using a scale of criteria. Autonomous Agents and Multi-Agent Systems, 30:259–290, 2016.

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

A. Cseh. Popular matchings. In U. Endriss, editor, Trends in Computational Social Choice, chapter 6. AI Access, 2017.

A. Damamme, A. Beynier, Y. Chevaleyre, and N. Maudet. The power of swap deals in distributed resource allocation. In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 625–633. IFAAMAS, 2015.

D. Kurokawa, A. D. Procaccia, and J. Wang. Fair enough: Guaranteeing approximate maximin shares. Journal of the ACM, 65(2), 2018.

A. E. Roth, T. Sönmez, and M. U. Ünver. Pairwise kidney exchange. Journal of Economic Theory, 125:151-188, 2005.

H. Aziz, F. Brandl, F. Brandt, P. Harrenstein, M. Olsen, and D. Peters. Fractional hedonic games. ACM Transactions on Economics and Computation, 7(2), 2019.

H. Aziz, F. Brandt, and P. Harrenstein. Pareto optimality in coalition formation. Games and Economic Behavior, 82:562–581, 2013.

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.

H. Aziz, M. Brill, V. Conitzer, E. Elkind, R. Freeman, and T. Walsh. Justified Representation in Approval-Based Committee Voting. Social Choice and Welfare, 48(2):461-485, 2017.

F. Brandt, C. Geist, and D. Peters. Optimal bounds for the no-show paradox via SAT solving. Mathematical Social Sciences, 90:18–27, 2017.

F. Brandt, C. Saile, and C. Stricker. Strategyproof social choice when preferences and outcomes may contain ties. 2019. Working paper.

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

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

P. Faliszewski, P. Skowron, A. Slinko, and N. Talmon. Multiwinner voting: A new challenge for social choice theory. In U. Endriss, editor, Trends in Computational Social Choice, chapter 2. 2017.

C. Geist and D. Peters. Computer-aided methods for social choice theory. In U. Endriss, editor, Trends in Computational Social Choice, chapter 13, pages 249–267. AI Access, 2017.

R. Meir. Iterative voting. In U. Endriss, editor, Trends in Computational Social Choice, chapter 4. 2017.

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.

N. Aswal, S. Chatterji, and A. Sen. Dictatorial domains. Economic Theory, 22(1):45-62, 2003.

H. Moulin. On strategy-proofness and single peakedness. Public Choice, 35(4):437-455, 1980.

A. Gibbard. Manipulation of schemes that mix voting with chance. Econometrica, 45(3):665–681, 1977.
Possibly combined with: S. Barberà. Majority and positional voting in a probabilistic framework. Review of Economic Studies, 46(2):379–389, 1979.

F. Brandl, F. Brandt, M. Eberl, and C. Geist. Proving the incompatibility of efficiency and strategyproofness via SMT solving. Journal of the ACM, 65(2), 2018.

F. Brandt. Rolling the dice: Recent results in probabilistic social choice. In U. Endriss, editor, Trends in Computational Social Choice, chapter 1, pages 3–26. AI Access, 2017.

F. Brandl, F. Brandt, and H. G. Seedig. Consistent probabilistic social choice. Econometrica, 84(5):1839–1880, 2016.

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.

Other resources

  • Slides of the overview session (1)

  • 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.