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  Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory

Geiger, P., Besserve, M., Winkelmann, J., Proissl, C., & Schölkopf, B. (2019). Coordinating Users of Shared Facilities via Data-driven Predictive Assistants and Game Theory. In 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019) (pp. 286-295). Red Hook, NY, USA: Curran.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-7253-E Version Permalink: http://hdl.handle.net/21.11116/0000-0005-1F05-4
Genre: Conference Paper

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 Creators:
Geiger, P, Author
Besserve, M1, 2, Author              
Winkelmann, J, Author
Proissl, C, Author
Schölkopf, B3, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which assistant algorithms reach optimal predictions? First we lay conceptual ground for this setting where user preferences are a priori unknown and predictions influence outcomes. Addressing (1), we establish conditions under which self-fulfilling prophecies, i.e., "perfect" (probabilistic) predictions of what will happen, solve the coordination problem in the game-theoretic sense of selecting a Bayesian Nash equilibrium (BNE). Next we prove that such prophecies exist even in large-scale settings where only aggregated statistics about users are available. This entails a new (nonatomic) BNE existence result. Addressing (2), we propose two assistant algorithms that sequentially learn from users' reactions, together with optimality/convergence guarantees. We validate one of them in a large real-world experiment.

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 Dates: 2019-072019
 Publication Status: Published in print
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Title: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019)
Place of Event: Tel Aviv, Israel
Start-/End Date: 2019-07-22 - 2019-07-25

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Title: 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019)
Source Genre: Proceedings
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Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: 49 Start / End Page: 286 - 295 Identifier: ISBN: 978-1-5108-9156-2