Abstract
Query complexity is a very widespread and recurring theme in the analysis of algorithms and computational complexity. Algorithms are assumed to have access to their input data via certain stylised queries, which impose a constraint on the way an algorithm can behave. In the context of computing equilibria of games, this is a relatively recent line of work, which we review here. The talk mostly focuses on the paper Fearnley et al. (Learning equilibria of games via payoff queries. In: Proceedings of the 14th ACM-EC, pp 397–414, 2013).
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Notes
- 1.
In a more literal sense than the common usage of this phrase.
- 2.
For example, separation of randomized and deterministic computation [1, 11] in the context of computing boolean functions. Ambainis et al., [1], note that “the advantage of query complexity is that we can often prove tight lower bounds and have provable separations between different computational models. This is in contrast to the Turing machine world where lower bounds and separations between complexity classes often have to rely on unproven assumptions”.
References
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Goldberg, P.W. (2017). Learning Game-Theoretic Equilibria Via Query Protocols. In: Díaz, J., Kirousis, L., Ortiz-Gracia, L., Serna, M. (eds) Extended Abstracts Summer 2015. Trends in Mathematics(), vol 6. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-51753-7_11
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