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Mathematics 2018, 6(8), 131; https://doi.org/10.3390/math6080131

A Markovian Mechanism of Proportional Resource Allocation in the Incentive Model as a Dynamic Stochastic Inverse Stackelberg Game

Vorovich Institute of Mathematics, Mechanics and Computer Sciences, Southern Federal University, ul. Milchakova 8A, Rostov-on-Don 344090, Russia
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Received: 4 July 2018 / Revised: 23 July 2018 / Accepted: 27 July 2018 / Published: 30 July 2018
(This article belongs to the Special Issue Mathematical Game Theory)
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Abstract

This paper considers resource allocation among producers (agents) in the case where the Principal knows nothing about their cost functions while the agents have Markovian awareness about his/her strategies. We use a dynamic setup of the stochastic inverse Stackelberg game as the model. We suggest an algorithm for solving this game based on Q-learning. The associated Bellman equations contain functions of one variable for the Principal and also for the agents. The new results are illustrated by numerical examples. View Full-Text
Keywords: dynamic inverse Stackelberg game; incentives; online learning; resource allocation dynamic inverse Stackelberg game; incentives; online learning; resource allocation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Belyavsky, G.; Danilova, N.; Ougolnitsky, G. A Markovian Mechanism of Proportional Resource Allocation in the Incentive Model as a Dynamic Stochastic Inverse Stackelberg Game. Mathematics 2018, 6, 131.

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