Title: Complexity, Stability Properties of Mixed Games and Dynamic Algorithms, and Learning in the Sharing Economy
ArXiv ID: 2001.08192
Date: 2020-01-24
Authors: 원문에 명시된 저자 리스트가 제공되지 않았습니다. —
📝 Abstract
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.
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occupants; sanitation; etc.); fire-protection (number of occupants; use of flammable materials; damage of fire doors; lack of knowledge of fire drills; etc.), housing, zoning, hotel, tax, securities, labor, taxi/transportation; insurance; antitrust; banking; electronic commerce; and consumer protection statutes. Government regulators often cannot afford to enforce those laws. There is abundant evidence that SEOs' past and current activities significantly harm cities and reduce social welfare in many economies.
Third, Uber’s “Surge Pricing” is a form of dynamic pricing technology (dynamic pricing has been around for more than twenty five years). As of 2017, Uber’s “Surge Pricing” was available only in high-demand periods and could result in prices that were ten times the normal prices. Mohlmann & Zalmanson (December 2017) and newspaper articles have noted that Uber’s drivers knowingly colluded to increase “Surge Prices” 1 ; and that Uber’s customers can game the “Surge Pricing” system to reduce prices (eg. by crossing to the other side of the street and or by waiting for five minutes). Uber’s drivers have been reported to have manipulated GPS readings (measurements of distance used for calculating Uber’s fees) by using a third-party GPS app named Lockito 2 .
Fourth, the SEOs’ difficulties in developing and implementing efficient pricing mechanisms translates into, and amplifies other problemssuch as antitrust violations; deadweight losses, Regret; etc.. Fifth, dynamic algorithms have not been addressed adequately in the literature, especially their stability properties. Dynamic Algorithms are a class of algorithms whose path or process changes or can change as statevariables change. On algorithms in general, see: Garcia, Berlanga, Molina & Davila (2004).
Sixth, mixed games have not been addressed adequately in the literature, especially their stability properties mixed games are situations wherein two more games-types exist or can exist simultaneously and either continuously or discontinuously.
Seventh, the issues and models discussed herein have or can have significant implications for inequality, labor mobility and income dynamics (for employees of SEOs). Many such employees around the world depend on SEOs for all or part of their monthly income, which is related to their aspirations, social mobility and geographical mobility (eg. qualified drivers of Uber or Lyft ). However, its become clear that in addition to reducing Social welfare (increasing Noise; pollution; code violations; etc.) many SEOs also increase Inequality by underpaying their workers (through inefficient pricing algorithms).
By addressing these issues, this article contributes to the mechanism design, policy, dynamic algorithms, theoretical computer science; Labor Economics, Social Welfare and Complex Systems literatures.
1 See: “Uber Drivers Gang Up To Cause Surge Pricing, Research Says”. The Telegraph (UK). Cara McGoogan. August 2, 2017. http://www.telegraph.co.uk/technology/2017/08/02/uber-drivers-gang-cause-surge-pricingresearch-says/
. (stating that “Researchers at the University of Warwick found that Uber drivers in London and New York have been tricking the app into thinking there is a shortage of cars in order to raise surge prices. According to the study, drivers manipulate Uber’s algorithm by logging out of the app at the same time, making it think that there is a shortage of cars. Uber raises its fare prices when there is a high demand for vehicles and a short supply of drivers available. Fares are known to increase during peak times such as rush hour, during public events and late at night. Surge pricing can boost the cost of rides to multiple times the normal rate. The study said drivers have been coordinating forced surge pricing, after interviews with drivers in London and New York, and research on online forums such as Uberpeople.net.….…Separate research at Northeastern University (USA) has previously found passengers can game surge pricing with simple tricks such as waiting five minutes or crossing the road.…….”). See: “Uber Drivers Work Together To Create Price Surge And Charge Customers More, Researchers Find -Some Drivers Are Deliberately Going Offline In Unison So That Prices Surge And They Can Charge Customers More When They Log Back Into The App”. Ben Chapman, August 2, 2017. https://www.independent.co.uk/news/business/news/uber-drivers-work-together-price-surge-go-offline-chargecustomers-more-game-app-supply-demand-a7872871.html
. 2 See: “Uber Drivers In Lagos Are Using A Fake GPS App To Inflate Rider Fares”. Yemisi Adegoke. November 13, 2017. https://qz.com/1127853/uber-drivers-in-lagos-nigeria-use-fake-lockito-app-to-boostfares/?utm_source=qzfb
. Cojocaru, et. al. (2013); Makhdoumi, Malekian & Ozdaglar (2017); and Nakhe (2017) developed dynamic pricing models for various markets. Mashayekhy, Nejad & Grosu (2014) studied two-sided matching. Jacobs (2012) discussed coalgebras. Nissam (2007) summarized mechanism d
