1 research outputs found
Competitive Statistical Estimation with Strategic Data Sources
In recent years, data has played an increasingly important role in the
economy as a good in its own right. In many settings, data aggregators cannot
directly verify the quality of the data they purchase, nor the effort exerted
by data sources when creating the data. Recent work has explored mechanisms to
ensure that the data sources share high quality data with a single data
aggregator, addressing the issue of moral hazard. Oftentimes, there is a
unique, socially efficient solution.
In this paper, we consider data markets where there is more than one data
aggregator. Since data can be cheaply reproduced and transmitted once created,
data sources may share the same data with more than one aggregator, leading to
free-riding between data aggregators. This coupling can lead to non-uniqueness
of equilibria and social inefficiency. We examine a particular class of
mechanisms that have received study recently in the literature, and we
characterize all the generalized Nash equilibria of the resulting data market.
We show that, in contrast to the single-aggregator case, there is either
infinitely many generalized Nash equilibria or none. We also provide necessary
and sufficient conditions for all equilibria to be socially inefficient. In our
analysis, we identify the components of these mechanisms which give rise to
these undesirable outcomes, showing the need for research into mechanisms for
competitive settings with multiple data purchasers and sellers.Comment: accepted in the IEEE Transactions on Automatic Contro