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Finite Difference Scheme for Stochastic Differential Games with Several Singular Control Variables and Its Environmental Application

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Finite Difference Methods. Theory and Applications (FDM 2018)

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Abstract

Stochastic differential games have recently been key mathematical tools for resolution of environmental and ecological optimization problems. A finite difference scheme is proposed for solving a variational inequality arising in a stochastic differential game having several singular control variables: optimization of algae population management under model ambiguity. The present scheme employs fitted-exponential and upwind discretization methods to generate stable numerical solutions. Accuracy of the scheme is verified against an exact solution to a simplified problem and an asymptotic solution to a more complicated problem. The scheme is finally applied to numerical computation of the optimal algae population management policy against a range of an incurred cost. The computational results suggest that qualitatively different optimal policies are obtained depending on the magnitude of the incurred cost.

Y. Yaegashi—Research Fellow of Japan Society for the Promotion of Science.

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Acknowledgments

This work was supported by The River Foundation under grant The River Fund No. 285311020, The Japan Society for the Promotion Science under grant KAKENHI No. 17K15345 and No. 17J09125, and Water Resources Environment Center under grant The WEC Applied Ecology Research Grant No. 2016-02.

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Correspondence to Hidekazu Yoshioka .

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Yoshioka, H., Yaegashi, Y. (2019). Finite Difference Scheme for Stochastic Differential Games with Several Singular Control Variables and Its Environmental Application. In: Dimov, I., Faragó, I., Vulkov, L. (eds) Finite Difference Methods. Theory and Applications. FDM 2018. Lecture Notes in Computer Science(), vol 11386. Springer, Cham. https://doi.org/10.1007/978-3-030-11539-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-11539-5_10

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