Mechanism design is essentially reverse engineering of games and involves inducing a game among strategic agents in a way that the induced game satisfies a set of desired properties in an equilibrium of the game. Desirable properties for a mechanism include incentive compatibility, individual rationality, welfare maximisation, revenue maximisation (or cost minimisation), fairness of allocation, etc. It is known from mechanism design theory that only certain strict subsets of these properties can be simultaneously satisfied exactly by any given mechanism. Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied. In such cases, a prominent recent approach is to use a deep learning based approach to learn a mechanism that approximately satisfies the required properties by minimizing a suitably defined loss function. In this paper, we present, from relevant literature, technical details of using a deep learning approach for mechanism design and provide an overview of key results in this topic. We demonstrate the power of this approach for three illustrative case studies: (a) efficient energy management in a vehicular network (b) resource allocation in a mobile network (c) designing a volume discount procurement auction for agricultural inputs. Section 6 concludes the paper.

翻译：机制设计本质上是博弈的逆向工程，旨在通过策略性智能体之间的博弈诱导，使所诱导的博弈在均衡状态下满足一系列期望性质。机制的理想性质包括激励相容、个体理性、社会福利最大化、收益最大化（或成本最小化）、分配公平性等。机制设计理论表明，任何给定机制仅能精确同时满足这些性质的某些严格子集。现实应用所需的机制往往需要同时实现理论上不可能并存的子集。针对此类问题，近期的主流方法是采用基于深度学习的方法，通过最小化恰当定义的损失函数来学习近似满足所需性质的机制。本文从相关文献中提炼技术细节，系统阐述将深度学习方法应用于机制设计的实现路径，并综述该课题的关键研究成果。我们通过三个典型案例论证该方法的有效性：（a）车辆网络中的高效能源管理（b）移动网络中的资源分配（c）农业投入品批量折扣采购拍卖机制设计。第6节对全文进行总结。