Complexity, Stability Properties of Mixed Games and Dynamic Algorithms, and Learning in the Sharing Economy
This addresses regulatory and competitive challenges in the sharing economy, though it appears incremental in applying game theory to a specific domain.
The paper tackles the problem of antitrust violations and social welfare issues in sharing economy platforms by developing new dynamic pricing models that eliminate antitrust liability and reduce deadweight losses, greed, regret, and GPS manipulation, while contravening the Myerson-Satterthwaite Impossibility Theorem.
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.