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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, best-response dynamics ...
  • Design: design games that have both good game-theoretical and algorithmic properties. This area is called algorithmic mechanism design.

On top of the usual requirements in classical algorithm design, say polynomial-time running time, good approximation ratio, ... the designer must also care about incentive constraints.

History

Nisan-Ronen: 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 self-interest. 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

Main article: 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 "Worst-case 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, ‘top-down’ 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 load-balancing[citation needed]. 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 Internet-based 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

The main areas of research in algorithmic game theory include:

And the area counts with diverse practical applications:

Conferences

  • ACM Conference on Economics and Computation (EC) [5]
  • Conference on Web and Internet Economics (WINE) [6]
  • International Symposium on Algorithmic Game Theory (SAGT) [7]

Algorithmic Game Theory papers also often appear in general Theoretical Computer Science conferences such as STOC[8] and FOCS[9], or Artificial Intelligence conferences such as AAAI[10] and IJCAI[11].

See also

References

  1. ^ 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 978-1581130676
  2. ^ "ACM SIGACT Presents Gödel Prize for Research that Illuminated Effects of Selfish Internet Use" (Press release). New York. Association for Computing Machinery. 2012-05-16. Archived from the original on 2012-05-26. Retrieved 2018-01-08. {{cite press release}}: |archive-date= / |archive-url= timestamp mismatch; 2013-07-18 suggested (help)
  3. ^ Koutsoupias, Elias; Papadimitriou, Christos (May 2009). "Worst-case Equilibria". Computer Science Review. 3 (2): 65–69. doi:10.1016/j.cosrev.2009.04.003.
  4. ^ 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 978-1581133493
  5. ^ EC 2019
  6. ^ WINE 2018
  7. ^ SAGT 2019
  8. ^ STOC 2019 call for papers
  9. ^ FOCS 2019 call for papers
  10. ^ AAAI 2019 accepted papers
  11. ^ IJCAI 2019 accepted papers
  • 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 game-theoretic algorithms.
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