Harnessing Individual Motivation for Collective Efficiency: A Mechanism-Driven Distributed Optimization Method
For industrial multi-agent decision-making problems where centralized control is infeasible, this work provides a theoretically grounded approach to reconcile individual self-interest with collective optimality.
This paper proposes a mechanism-driven distributed optimization method that uses incentives to align self-interested agents' decisions with global efficiency in multi-agent systems with coupled objectives and constraints. The method combines a distributed optimization algorithm with two incentive mechanisms (shadow pricing and VCG), forming a closed loop that guarantees convergence and motivates participation.
In industrial scenarios involving multi-agent collective decision-making, centralized decision-making may not be admissible due to restrictive access to individual local information, while the conflicts between participants' self-interest and global performance may also impede collaborative distributed decision-making. This paper proposes a mechanism-driven distributed decision-making method, wherein incentives are employed and designed to motivate participants to collaborate in a distributed fashion even though each participant's decision is driven primarily by self-interest. Focusing on optimization problems with coupled objective functions and coupled constraints, we design a distributed optimization algorithm tailored for this class of problems and provide guarantees for its convergence. Furthermore, we design two incentive mechanisms, the shadow pricing mechanism and the Vickrey-Clarke-Groves mechanism, and demonstrate that participants are willing to engage in distributed collaboration under these mechanisms. The mechanism drives the execution of the distributed algorithm, and the optimal result of distributed computation guides the determination of incentives in the mechanism, both of which are interrelated to form a closed loop. Finally, numerical experiments illustrate the effectiveness of the proposed algorithm and mechanisms.