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

Grigory Belyavsky, Natalya Danilova and Guennady Ougolnitsky
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Grigory Belyavsky: Vorovich Institute of Mathematics, Mechanics and Computer Sciences, Southern Federal University, ul. Milchakova 8A, Rostov-on-Don 344090, Russia
Natalya Danilova: Vorovich Institute of Mathematics, Mechanics and Computer Sciences, Southern Federal University, ul. Milchakova 8A, Rostov-on-Don 344090, Russia
Guennady Ougolnitsky: Vorovich Institute of Mathematics, Mechanics and Computer Sciences, Southern Federal University, ul. Milchakova 8A, Rostov-on-Don 344090, Russia

Mathematics, 2018, vol. 6, issue 8, 1-10

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.

Keywords: dynamic inverse Stackelberg game; incentives; online learning; resource allocation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2018
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