The next generation urban ecosystem empowered by the internet of things has at its core a sharing economy where physical resources and data are easily aggregated and exchanged. In particular, advances in technology have lead to the proliferation of smart devices that provide access to streaming data and platforms for novel sharing mechanisms. This has, in turn, resulted in an emerging marketplace in which data is a commodity. At the same time, many urban constituents are increasingly becoming aware of the value of their data and its usefulness for operations. In such an environment, new learning and optimization schemes which consider users as strategic data sources and resource seekers are needed.
In this talk, we will discuss the emerging data market, its incentive structure (players and their motivations), and tools for learning with strategic resources seekers. Focusing on the design of adaptive incentive mechanisms under adverse selection, we will construct an algorithm for online utility learning and incentive design and show convergence results for both the case where players are rational (play according to Nash) and myopic. We will see through a tutorial example how the algorithm performs.
Switching to more application driven work, I will discuss our recent work on the development of new game-theoretic, data-informed models of urban parking that leverage queuing theory to investigate the value of information in user choices. Finally, I will conclude with some open questions and future directions with a focus on new market structures in the sharing economy.
Lillian Ratliff was a postdoctoral researcher in Electrical Engineering and Computer Sciences at the University of California, Berkeley from 2015 to 2016, where she obtained her Ph.D. in 2015. Her research interests lie at the intersection of game theory, optimization and statistical learning. She applies tools from these domains to address inefficiencies and vulnerabilities in next-generation urban infrastructure systems. She is the recipient of a National Science Foundation Graduate Research Fellowship.