Regulating TNCs: Should Uber and Lyft Set Their Own Rules?

We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) a minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings pushes the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard economic theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floors curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles

On Design and Analysis of Cyber-Physical Systems with Strategic Agents

In contrast to traditional CPS where a designer can specify an action plan for each agent, in CPS with strategic agents, every agent acts selfishly and chooses his strategy privately so as to maximize his own objective. In this dissertation, we study problems arising in the design and analysis of CPSs with strategic agents. We consider two classes of design problems. In the first class, the designer utilizes her control over decisions and resources in the system to incentivize the agents via monetary incentive mechanisms to reveal their private information that is crucial for the efficient operation of the system. In particular, we consider market mechanism design for the integration of renewable energy and flexible loads into power grids. We consider a model that captures the dynamic and intermittent nature of these resources, and demonstrate the advantage of dynamic market mechanism over static market mechanisms that underly the existing architecture of the electricity markets. In the second class of design problems, the designer utilizes her informational advantage over the agents and employ informational incentive mechanisms to disclose information selectively to the agents so as to influence the agents' decisions. Specifically, we consider the design of public and private information disclosure mechanisms in a transportation system so as to improve the overall congestion. We also study the analysis of CPS with strategic agents as a stochastic dynamic game of asymmetric information. We present a set of conditions sufficient to characterize an information state for each agent that effectively compresses his private and common information over time. This information state provides a sufficient statistic for decision-making purposes in strategic and non-strategic settings. Accordingly, we provide a sequential decomposition of the dynamic game over time, and formulate a dynamic program that enables us to determine a set of equilibria of the game. The proposed approach generalizes and unifies the existing results for dynamic teams with non-classical information structure and dynamic games with asymmetric information.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140850/1/tavaf_1.pd