Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/98666
| Title: | Dynamic optimization of large-population systems with partial information | Authors: | Huang, J Wang, S |
Issue Date: | Jan-2016 | Source: | Journal of optimization theory and applications, Jan. 2016, v. 168, no. 1, p. 231-245 | 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. | Keywords: | Dynamic optimization Forward–backward stochastic differential equation Large-population system Mean-field game Partial information |
Publisher: | Springer New York LLC | Journal: | Journal of optimization theory and applications | ISSN: | 0022-3239 | EISSN: | 1573-2878 | DOI: | 10.1007/s10957-015-0740-x | Rights: | © Springer Science+Business Media New York 2015 This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10957-015-0740-x. |
| Appears in Collections: | Journal/Magazine Article |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Huang_Dynamic_Optimization_Large-Population.pdf | Pre-Published version | 684.15 kB | Adobe PDF | View/Open |
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