On the Absence of Predictive Complexity for Some Games
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This paper shows that if the curvature of the boundary of the set of superpredictions for a game vanishes in a nontrivial way, then there is no predictive complexity for the game. This is the first result concerning the absence of complexity for games with convex sets of superpredictions. The proof is further employed to show that for some games there are no certain variants of weak predictive complexity. In the case of the absolute-loss game we reach a tight demarcation between the existing and non-existing variants of weak predictive complexity.
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- On the Absence of Predictive Complexity for Some Games
- Book Title
- Algorithmic Learning Theory
- Book Subtitle
- 13th International Conference, ALT 2002 Lübeck, Germany, November 24–26, 2002 Proceedings
- pp 164-172
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- Online ISBN
- Series Title
- Lecture Notes in Computer Science
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- Springer Berlin Heidelberg
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- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 1. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano
- 2. Department of Computer Science, Tokyo Institute of Technology
- 3. Institut für Theoretische Informatik, Universität zu Lübeck
- Author Affiliations
- 6. Department of Computer Science, Royal Holloway, University of London, TW20 0EX, Egham, Surrey, UK
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