打破传统:应用于经济和复杂环境的算法机制设计综述。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qian Chen, Xuan Wang, Zoe Lin Jiang, Yulin Wu, Huale Li, Lei Cui, Xiaozhen Sun
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引用次数: 0

摘要

机制设计理论不仅可以应用于经济领域,还可以应用于政治、军事等多个领域,对处于制度创新和转型期的国家具有重要的现实意义和战略意义。正如诺贝尔奖获得者保罗所说,实体经济的复杂性使“无组织市场”难以确保供需平衡和资源的有效分配。当传统经济理论无法解释和计算复杂的现实场景时,我们需要一个基于传统理论的高性能计算解决方案来评估机制,同时获得更好的社会福利。机构设计理论无疑是最好的选择。与其他现有的基于最优解理论探索或场景单视角分析的工作不同,本文关注的是更真实、更复杂的市场。它探索发现应用程序的常见困难和可行的解决方案。首先,我们回顾了传统机构设计和算法机构设计的历史。随后,我们提出了设计实际数据驱动的市场机制的主要挑战,包括机制设计理论中的固有挑战、新市场带来的挑战以及两者面临的共同挑战。此外,我们还对理论支持和计算机辅助方法进行了详细的梳理和讨论。本文指导了希望首次探索现实市场中资源分配问题的跨学科研究人员,并为努力解决复杂社会问题的研究人员提供了一个不同的视角。最后,我们讨论并提出新的想法,展望未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments.

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments.

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments.

Breaking the traditional: a survey of algorithmic mechanism design applied to economic and complex environments.

The mechanism design theory can be applied not only in the economy but also in many fields, such as politics and military affairs, which has important practical and strategic significance for countries in the period of system innovation and transformation. As Nobel Laureate Paul said, the complexity of the real economy makes it difficult for "Unorganized Markets" to ensure supply-demand balance and the efficient allocation of resources. When traditional economic theory cannot explain and calculate the complex scenes of reality, we require a high-performance computing solution based on traditional theory to evaluate the mechanisms, meanwhile, get better social welfare. The mechanism design theory is undoubtedly the best option. Different from other existing works, which are based on the theoretical exploration of optimal solutions or single perspective analysis of scenarios, this paper focuses on the more real and complex markets. It explores to discover the common difficulties and feasible solutions for the applications. Firstly, we review the history of traditional mechanism design and algorithm mechanism design. Subsequently, we present the main challenges in designing the actual data-driven market mechanisms, including the inherent challenges in the mechanism design theory, the challenges brought by new markets and the common challenges faced by both. In addition, we also comb and discuss theoretical support and computer-aided methods in detail. This paper guides cross-disciplinary researchers who wish to explore the resource allocation problem in real markets for the first time and offers a different perspective for researchers struggling to solve complex social problems. Finally, we discuss and propose new ideas and look to the future.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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