论文标题
复杂性，混合游戏和动态算法的稳定性以及共享经济的学习
Complexity, Stability Properties of Mixed Games and Dynamic Algorithms, and Learning in the Sharing Economy
论文作者
论文摘要
共享经济（包括Airbnb，Apple，Alibaba，Uber，Wework，Wework，eBay，Didi Chuxing，Amazon）在世界各地盛开，引发了行业的结构性变化，并主要通过不服从许多国家的各种法规和法律，主要影响了国际资本流动。他们还非法降低并改变许多行业的竞争性质，通常会损害社会福利。本文为SEO开发了新的动态定价模型，并获得了混合游戏和动态算法的某些稳定性，从而消除了反托拉斯责任，还减少了死权损失，贪婪，遗憾和GPS操纵。新的动态定价模型违反了Myerson Satterthwaite的不可能定理。
The Sharing Economy (which includes Airbnb, Apple, Alibaba, Uber, WeWork, Ebay, Didi Chuxing, Amazon) blossomed across the world, triggered structural changes in industries and significantly affected international capital flows primarily by disobeying a wide variety of statutes and laws in many countries. They also illegally reduced and changing the nature of competition in many industries often to the detriment of social welfare. This article develops new dynamic pricing models for the SEOs and derives some stability properties of mixed games and dynamic algorithms which eliminate antitrust liability and also reduce deadweight losses, greed, Regret and GPS manipulation. The new dynamic pricing models contravene the Myerson Satterthwaite Impossibility Theorem.
