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

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.

MAS-Based Interaction Simulation within Asymmetric Information on Emergency Management of Urban Rainstorm Disaster

“…Repetitive interactions are hard to implement with mathematical methods, which in contrast is a peculiarity of agent-based simulation. Agent-based simulation is widely used in many fields to deal with problems covered in complex systems [48], like resource allocation [49], crowd evacuation [50], decision-making systems [51], and cooperative game [52]. e application of agent-based simulation in disaster management can improve search and rescue efficiency [53].…”

A Game-Theoretic Loss Allocation Approach in Power Distribution Systems with High Penetration of Distributed Generations

“…The suggested approach offers a criterion to which the result of different loss allocation techniques can be compared. In the concept of resources allocation, various frameworks are proposed to find an optimal allocation among agents [20,21]. Embedded cost allocation in cooperative games based on Shapley Value method provides stable decisions [22], where the resultant allocations are deemed to be the most equitable solution possible [23].…”