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Dynamic Control of Major Players. / Kolokoltsov, Vassili N.; Malafeyev, Oleg A.

Springer Series in Operations Research and Financial Engineering. Springer Nature, 2019. p. 71-87 (Springer Series in Operations Research and Financial Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Harvard

Kolokoltsov, VN & Malafeyev, OA 2019, Dynamic Control of Major Players. in Springer Series in Operations Research and Financial Engineering. Springer Series in Operations Research and Financial Engineering, Springer Nature, pp. 71-87. https://doi.org/10.1007/978-3-030-12371-0_3

APA

Kolokoltsov, V. N., & Malafeyev, O. A. (2019). Dynamic Control of Major Players. In Springer Series in Operations Research and Financial Engineering (pp. 71-87). (Springer Series in Operations Research and Financial Engineering). Springer Nature. https://doi.org/10.1007/978-3-030-12371-0_3

Vancouver

Kolokoltsov VN, Malafeyev OA. Dynamic Control of Major Players. In Springer Series in Operations Research and Financial Engineering. Springer Nature. 2019. p. 71-87. (Springer Series in Operations Research and Financial Engineering). https://doi.org/10.1007/978-3-030-12371-0_3

Author

Kolokoltsov, Vassili N. ; Malafeyev, Oleg A. / Dynamic Control of Major Players. Springer Series in Operations Research and Financial Engineering. Springer Nature, 2019. pp. 71-87 (Springer Series in Operations Research and Financial Engineering).

BibTeX

@inbook{572dcdfb3b104c56b2226ae9c1e93a32,
title = "Dynamic Control of Major Players",
abstract = "Here we begin to exploit another setting for major player behavior. We shall assume that the major player has some planning horizon with both running and (in case of a finite horizon) terminal costs. For instance, running costs can reflect real spending, while terminal costs can reflect some global objective, such as reducing the overall crime level by a specified amount. This setting will lead us to a class of problems that can be called Markov decision (or control) processes (for the principal) on the evolutionary background (of permanently varying profiles of small players). We shall obtain the corresponding LLN limit for both discrete and continuous time. For discrete time, the LLN limit turns into a deterministic multistep control problem in the case of one major player, and to a deterministic multistep game between major players in the case of several such players.",
author = "Kolokoltsov, {Vassili N.} and Malafeyev, {Oleg A.}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2019",
doi = "10.1007/978-3-030-12371-0_3",
language = "English",
series = "Springer Series in Operations Research and Financial Engineering",
publisher = "Springer Nature",
pages = "71--87",
booktitle = "Springer Series in Operations Research and Financial Engineering",
address = "Germany",

}

RIS

TY - CHAP

T1 - Dynamic Control of Major Players

AU - Kolokoltsov, Vassili N.

AU - Malafeyev, Oleg A.

N1 - Publisher Copyright: © 2019, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2019

Y1 - 2019

N2 - Here we begin to exploit another setting for major player behavior. We shall assume that the major player has some planning horizon with both running and (in case of a finite horizon) terminal costs. For instance, running costs can reflect real spending, while terminal costs can reflect some global objective, such as reducing the overall crime level by a specified amount. This setting will lead us to a class of problems that can be called Markov decision (or control) processes (for the principal) on the evolutionary background (of permanently varying profiles of small players). We shall obtain the corresponding LLN limit for both discrete and continuous time. For discrete time, the LLN limit turns into a deterministic multistep control problem in the case of one major player, and to a deterministic multistep game between major players in the case of several such players.

AB - Here we begin to exploit another setting for major player behavior. We shall assume that the major player has some planning horizon with both running and (in case of a finite horizon) terminal costs. For instance, running costs can reflect real spending, while terminal costs can reflect some global objective, such as reducing the overall crime level by a specified amount. This setting will lead us to a class of problems that can be called Markov decision (or control) processes (for the principal) on the evolutionary background (of permanently varying profiles of small players). We shall obtain the corresponding LLN limit for both discrete and continuous time. For discrete time, the LLN limit turns into a deterministic multistep control problem in the case of one major player, and to a deterministic multistep game between major players in the case of several such players.

UR - http://www.scopus.com/inward/record.url?scp=85098065682&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-12371-0_3

DO - 10.1007/978-3-030-12371-0_3

M3 - Chapter

AN - SCOPUS:85098065682

T3 - Springer Series in Operations Research and Financial Engineering

SP - 71

EP - 87

BT - Springer Series in Operations Research and Financial Engineering

PB - Springer Nature

ER -

ID: 72679107