A Dynamic Mechanism Design for Controllable and Ergodic Markov Games-Reference-Cited by-同舟云学术

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A Dynamic Mechanism Design for Controllable and Ergodic Markov Games

Published:2022-02-21 Issue:3 Volume:61 Page:1151-1171
ISSN:0927-7099
Container-title:Computational Economics
language:en
Short-container-title:Comput Econ

Author:

Clempner Julio B.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10614-022-10240-y.pdf

Reference26 articles.

1. Asiain, E., Clempner, J. B., & Poznyak, A. S. (2019). Controller exploitation-exploration: A reinforcement learning architecture. Soft Computing, 23(11), 3591–3604.

2. Athey, S., & Segal, I. (2013). An efficient dynamic mechanism. Econometrica, 81(6), 2463–2485.

3. Baumann, T., Graepel, T., & Shawe-Taylor, J. (2019). Adaptive mechanism design: Learning to promote cooperation. http://arxiv.org/abs/1806.04067, v2.

4. Bergemann, D., & Said, M. (2011). Dynamic auctions. In Wiley encyclopedia of operations research and management science (pp. 1511–1522). Wiley.

5. Bergemann, D., & Välimäki, J. (2010). The dynamic pivot mechanism. Econometrica, 78(2), 771–789.

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1. Joint Observer and Mechanism Design;Optimization and Games for Controllable Markov Chains;2023-12-14

2. Mechanism Design;Optimization and Games for Controllable Markov Chains;2023-12-14

3. A Bayesian reinforcement learning approach in markov games for computing near-optimal policies;Annals of Mathematics and Artificial Intelligence;2023-06-10

4. Revealing perceived individuals’ self-interest;Journal of the Operational Research Society;2023-04-05

5. Learning attack-defense response in continuous-time discrete-states Stackelberg Security Markov games;Journal of Experimental & Theoretical Artificial Intelligence;2022-10-22

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