Complexity, and New Dynamic-Pricing 'Learning' Models that Reduce Antitrust Liability, Deadweight Losses, Regret and GPS-Manipulation In the 'Sharing Economy'

Abstract

During 2005-2018, the “Sharing Economy” (which includes Airbnb; Apple; Alibaba, Uber; WeWork; Liquidspace, Lyft; Ebay; Didi Chuxing; Taxify; Amazon; HomeAway; etc.) blossomed across the world, triggered structural changes in industries and significantly affected international capital flows primarily by disobeying a wide variety of statutes laws in many countries and illegally reducing and changing the nature of competition in many industries often to the detriment of social welfare. Furthermore, Uber’s and Airbnb’s pricing systems are not efficient and can generate deadweight-losses and Regret. Other “sharing economy companies” (“SEOs”) also face similar pricing, antitrust, deadweight-loss and Regret issues (eg. WeWork; Lyft; Taxify; LiquidSpace; ShareDesk; Homeaway; etc.). This article: i) develops new dynamic pricing models for the Sharing Economy companies; ii) derives some stability properties of mixed games and dynamic algorithms that are inherent in new dynamic pricing models introduced herein which eliminate antitrust liability for SEOs and also reduce deadweight losses, greed, Regret and GPS manipulation - that is, the new dynamic pricing models exhibit “Multi-Stability”. The new dynamic pricing models have desirable properties such as individual rationality; exact and approximate welfare maximization; strong budget balance; dominant-strategy incentive compatibility of sellers and buyers; pareto optimality and feasibility – and thus, each model contravenes the Myerson-Satterthwaite Impossibility Theorem. The pricing models include multi-sided auctions wherein at any time t, several auctions can simultaneously occur, and the payoff functions of any buyer-seller pair in any auction depends on the bidding done by at least another buyer-seller pair either at the same time, or at a different time (this “long-memory” bias of buyers and sellers is new in the literature). More importantly the issues and models discussed herein have or can have significant implications for inequality, labor mobility and income dynamics (for employees of SEOs).