«THE BULLETIN OF IRKUTSK STATE UNIVERSITY». SERIES «MATHEMATICS»
«IZVESTIYA IRKUTSKOGO GOSUDARSTVENNOGO UNIVERSITETA». SERIYA «MATEMATIKA»
ISSN 1997-7670 (Print)
ISSN 2541-8785 (Online)

List of issues > Series «Mathematics». 2022. Vol 42

On Control of Probability Flows with Incomplete Information

Author(s)
Dmitry V. Khlopin1

1N. N. Krasovskii Institute of Mathematics and Mechanics UB RAS, Yekaterinburg, Russian Federation

Abstract
The mean-field type control problems with incomplete information are considered. There are several points of view that can be adopted to study the dynamics in probability space. Eulerian framework describes probability flows by the continuity equation. Kantorovich formulation describes each probability flows in terms of a single distribution on the set of admissible trajectories. The superposition principle connects these frameworks for uncontrolled dynamics. In this article, a probability flow in the both frameworks must be generated by a control that based on incomplete information about state and/or the probability at every time instance. This article presents some links between these frameworks in the case of incomplete information. In particular, besides the convexity condition, the assumptions are founded that guarantees the equivalence between the Kantorovich and Eulerian framework. This expands [6, Theorem 1] to mean-field type control problem with incomplete information.
About the Authors
Dmitry V. Khlopin, Cand. Sci. (Phys.–Math.), N. N. Krasovskii Institute of Mathematics and Mechanics UB RAS, Yekaterinburg, 620108, Russian Federation, khlopin@imm.uran.ru
For citation
Khlopin D. V. On Control of Probability Flows with Incomplete Information. The Bulletin of Irkutsk State University. Series Mathematics, 2022, vol. 42, pp. 27–42. https://doi.org/10.26516/1997-7670.2022.42.27
Keywords
probability flow, continuity equation, incomplete information, mean-field optimal control
UDC
517.977.5
MSC
49N30, 49K15, 34A60
DOI
https://doi.org/10.26516/1997-7670.2022.42.27
References
  1. Ambrosio L., Gigli N., Savare G. Gradient flows: in metric spaces and in the space of probability measures. Basel, Birkhauser Verlag, 2005, 334 p.
  2. Averboukh Y., Khlopin D. Pontryagin maximum principle for the deterministic mean field type optimal control problem via the Lagrangian approach. arXiv preprint arXiv:2207.01892. 2022.
  3. Averboukh Yu., Marigonda A., Quincampoix M. Extremal Shift Rule and Viability Property for Mean Field-Type Control Systems. J. Optim. Theory Appl., 2021, vol.189, pp. 244–270. https://doi.org/10.1007/s10957-021-01832-z
  4. Bensoussan A., Frehse J., Yam P. Mean field games and mean field type control theory. NY, Springer, 2013. https://doi.org/10.1007/978-1-4614-8508-7
  5. Beer G. Wijsman convergence: a survey. Set-Valued Anal., 1994, vol. 2, no. 1–2, pp. 77–94. https://doi.org/10.1007/BF01027094
  6. Cavagnari G., Lisini S., Orrieri C., Savar´e G. Lagrangian, Eulerian and Kantorovich formulations of multi-agent optimal control problems: Equivalence and Gamma convergence. J. Diff. Eq., 2022, vol. 322, pp. 268–364. https://doi.org/10.1016/j.jde.2022.03.019
  7. Cesaroni A., Cirant M. One-dimensional multi-agent optimal control with aggregation and distance constraints: qualitative properties and mean-field limit. Nonlinearity, 2021, vol.34, no.3, 1408. https://doi.org/10.1088/1361-6544/abc795
  8. Pogodaev N. Program strategies for a dynamic game in the space of measures. Optim. Lett., 2019, vol. 13, pp. 1913–1925. https://doi.org/10.1007/s11590-018-1318-y

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