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反向工程，其核心在于引导策略性智能体参与特定博弈，使得诱导出的博弈在其均衡状态下满足一系列期望性质。一个理想机制应具备的性质包括激励相容性、个体理性、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，对于任意给定机制，这些性质中仅有某些严格子集能够被同时精确满足。在实际应用中，所需机制往往要求满足理论上无法同时达成的性质子集。针对此类情形，近期一种主流方法是采用基于深度学习的技术，通过最小化适当定义的损失函数，学习能够近似满足所需性质的机制。本文基于相关文献，详细阐述了运用深度学习方法进行机制设计的技术细节，并对该领域的核心成果进行了系统性综述。我们通过三个案例研究展示了该方法的有效性：(a) 车载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设计。第六部分对全文进行总结。