Algorithmic game theory
Algorithmic game theory is an area in the intersection of game theory and computer science, with the objective of understanding and design of algorithms in strategic environments.
Typically, in Algorithmic Game Theory problems, the input to a given algorithm is distributed among many players who have a personal interest in the output. In those situations, the agents might not report the input truthfully because of their own personal interests. We can see Algorithmic Game Theory from two perspectives:
 Analysis: look at the current implemented algorithms and analyze them using Game Theory tools: calculate and prove properties on their Nash equilibria, price of anarchy, bestresponse dynamics ...
 Design: design games that have both good gametheoretical and algorithmic properties. This area is called algorithmic mechanism design.
On top of the usual requirements in classical algorithm design, say polynomialtime running time, good approximation ratio, ... the designer must also care about incentive constraints.
History
NisanRonen: a new framework for studying algorithms
In 1999, the seminal paper of Nisan and Ronen [1] drew the attention of the Theoretical Computer Science community to designing algorithms for selfish (strategic) users. As they claim in the abstract:
We consider algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own selfinterest. As such participants, termed agents, are capable of manipulating the algorithm, the algorithm designer should ensure in advance that the agents’ interests are best served by behaving correctly.
Following notions from the field of mechanism design, we suggest a framework for studying such algorithms. In this model the algorithmic solution is adorned with payments to the participants and is termed a mechanism. The payments should be carefully chosen as to motivate all participants to act as the algorithm designer wishes. We apply the standard tools of mechanism design to algorithmic problems and in particular to the shortest path problem.
This paper coined the term algorithmic mechanism design and was recognized by the 2012 Gödel Prize committee as one of "three papers laying foundation of growth in Algorithmic Game Theory".[2]
Price of Anarchy
The other two papers cited in the 2012 Gödel Prize for fundamental contributions to Algorithmic Game Theory introduced and developed the concept of "Price of Anarchy". In their 1999 paper "Worstcase Equilibria",[3] Koutsoupias and Papadimitriou proposed a new measure of the degradation of system efficiency due to the selfish behavior of its agents: the ratio of between system efficiency at an optimal configuration, and its efficiency at the worst Nash equilibrium. (The term "Price of Anarchy" only appeared a couple of years later.[4])
The Internet as a catalyst
The Internet created a new economy—both as a foundation for exchange and commerce, and in its own right. The computational nature of the Internet allowed for the use of computational tools in this new emerging economy. On the other hand, the Internet itself is the outcome of actions of many. This was new to the classic, ‘topdown’ approach to computation that held till then. Thus, game theory is a natural way to view the Internet and interactions within it, both human and mechanical.
Game theory studies equilibria (such as the Nash equilibrium). An equilibrium is generally defined as a state in which no player has an incentive to change their strategy. Equilibria are found in several fields related to the Internet, for instance financial interactions and communication loadbalancing. Game theory provides tools to analyze equilibria, and a common approach is then to ‘find the game’—that is, to formalize specific Internet interactions as a game, and to derive the associated equilibria.
Rephrasing problems in terms of games allows the analysis of Internetbased interactions and the construction of mechanisms to meet specified demands. If equilibria can be shown to exist, a further question must be answered: can an equilibrium be found, and in reasonable time? This leads to the analysis of algorithms for finding equilibria. Of special importance is the complexity class PPAD, which includes many problems in algorithmic game theory.
Areas of research
Algorithmic mechanism design
Mechanism design is the subarea of economics that deals with optimization under incentive constraints. Algorithmic mechanism design considers the optimization of economic systems under computational efficiency requirements. Typical objectives studied include revenue maximization and social welfare maximization.
Inefficiency of equilibria
The concepts of price of anarchy and price of stability were introduced to capture the loss in performance of a system due to the selfish behavior of its participants. The price of anarchy captures the worstcase performance of the system at equilibrium relative to the optimal performance possible.[5] The price of stability, on the other hand, captures the relative performance of the best equilibrium of the system.[6] These concepts are counterparts to the notion of approximation ratio in algorithm design.
Complexity of finding equilibria
The existence of an equilibrium in a game is typically established using nonconstructive fixed point theorems. There are no efficient algorithms known for computing Nash equilibria. The problem is complete for the complexity class PPAD even in 2player games.[7] In contrast, correlated equilibria can be computed efficiently using linear programming,[8] as well as learned via noregret strategies.[9]
Computational social choice
Computational social choice studies computational aspects of social choice, the aggregation of individual agents' preferences. Examples include algorithms and computational complexity of voting rules and coalition formation.[10]
Other topics include:
 Algorithms for computing Market equilibria
 Fair division
 Multiagent systems
And the area counts with diverse practical applications:[11][12]
 Sponsored search auctions
 Spectrum auctions
 Cryptocurrencies
 Prediction markets
 Reputation systems
 Sharing economy
 Matching markets such as kidney exchange and school choice
 Crowdsourcing and peer grading
 Economics of the cloud
Conferences
 ACM Conference on Economics and Computation (EC) [13]
 Conference on Web and Internet Economics (WINE) [14]
 International Symposium on Algorithmic Game Theory (SAGT) [15]
Algorithmic Game Theory papers also often appear in general Theoretical Computer Science conferences such as STOC[16] and FOCS,[17] or Artificial Intelligence conferences such as AAAI[18] and IJCAI.[19]
Journals and newsletters
Algorithmic Game Theory papers are often also published in Game Theory journals such as GEB,[22] Economics journals such as Econometrica, and Computer Science journals such as SICOMP.[23]
See also
References
 Nisan, Noam; Ronen, Amir (1999), "Algorithmic mechanism design", Proceedings of the 31st ACM Symposium on Theory of Computing (STOC '99), pp. 129–140, doi:10.1145/301250.301287, ISBN 9781581130676
 "ACM SIGACT Presents Gödel Prize for Research that Illuminated Effects of Selfish Internet Use" (Press release). New York. Association for Computing Machinery. 20120516. Archived from the original on 20120526. Retrieved 20180108.
 Koutsoupias, Elias; Papadimitriou, Christos (May 2009). "Worstcase Equilibria". Computer Science Review. 3 (2): 65–69. doi:10.1016/j.cosrev.2009.04.003.
 Papadimitriou, Christos (2001), "Algorithms, games, and the Internet", Proceedings of the 33rd ACM Symposium on Theory of Computing (STOC '01), pp. 749–753, CiteSeerX 10.1.1.70.8836, doi:10.1145/380752.380883, ISBN 9781581133493
 Tim Roughgarden (2005). Selfish routing and the price of anarchy. MIT Press. ISBN 0262182432.CS1 maint: uses authors parameter (link)

 Anshelevich, Elliot; Dasgupta, Anirban; Kleinberg, Jon; Tardos, Éva; Wexler, Tom; Roughgarden, Tim (2008). "The Price of Stability for Network Design with Fair Cost Allocation". SIAM J. Comput. 38 (4): 1602–1623. doi:10.1137/070680096.

 Chen, Xi; Deng, Xiaotie (2006). Settling the complexity of twoplayer Nash equilibrium. Proc. 47th Symp. Foundations of Computer Science. pp. 261–271. doi:10.1109/FOCS.2006.69. ECCC TR05140..
 Papadimitriou, Christos H.; Roughgarden, Tim (2008). "Computing correlated equilibria in multiplayer games". J. ACM. 55 (3): 14:1–14:29. CiteSeerX 10.1.1.335.2634. doi:10.1145/1379759.1379762.
 Foster, Dean P.; Vohra, Rakesh V. (1996). "Calibrated Learning and Correlated Equilibrium". Games and Economic Behavior.
 Felix Brandt; Vincent Conitzer; Ulle Endriss; Jérôme Lang; Ariel D. Procaccia, eds. (2016), Handbook of Computational Social Choice (PDF)
 Tim Roughgarden (2016). Twenty lectures on algorithmic game theory. Cambridge University Press. ISBN 9781316624791.CS1 maint: uses authors parameter (link)
 "EC'19  20th ACM Conference on Economics and Computation".
 EC 2019
 WINE 2018
 SAGT 2019
 STOC 2019 call for papers
 FOCS 2019 call for papers
 AAAI 2019 accepted papers
 IJCAI 2019 accepted papers
 TEAC
 SIGEcom Exchanges
 Chawla, Shuchi; Fleischer, Lisa; Hartline, Jason; Tim Roughgarden (2015), "Introduction to the Special Issue – Algorithmic Game Theory – STOC/FOCS/SODA 2011", Games and Economic Behavior, 92: 228–231, doi:10.1016/j.geb.2015.02.011
 SICOMP
 John von Neumann, Oskar Morgenstern (1944) Theory of Games and Economic Behavior. Princeton Univ. Press. 2007 edition: ISBN 9780691130613
 Vazirani, Vijay V.; Nisan, Noam; Roughgarden, Tim; Tardos, Éva (2007), Algorithmic Game Theory (PDF), Cambridge, UK: Cambridge University Press, ISBN 9780521872829.
External links
 gambit.sourceforge.net  a library of game theory software and tools for the construction and analysis of finite extensive and strategic games.
 gamut.stanford.edu  a suite of game generators designated for testing gametheoretic algorithms.