Abstract
We consider the dynamic optimization of large-population system with partial information. The associated mean-field game is formulated, and its consistency condition is equivalent to the wellposedness of some Riccati equation system. The limiting state-average is represented by a mean-field stochastic differential equation driven by the common Brownian motion. The decentralized strategies with partial information are obtained, and the approximate Nash equilibrium is verified.
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This work is supported by the financial support from RGC Earmarked Grants 500613, 502412.
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Communicated by Mauro Pontani.
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Huang, J., Wang, S. Dynamic Optimization of Large-Population Systems with Partial Information. J Optim Theory Appl 168, 231–245 (2016). https://doi.org/10.1007/s10957-015-0740-x
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DOI: https://doi.org/10.1007/s10957-015-0740-x
Keywords
- Dynamic optimization
- Forward–backward stochastic differential equation
- Large-population system
- Mean-field game
- Partial information