2023/10/23 by Thomas Falconer, Jalal Kazempour, Falconer, Thomas +3 · 1 voice · 2 citations
Business, Management and Accounting · Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Auction Theory and Applications #Bayesian inference #Bayesian linear regression #Bayesian probability #Computer science #Consumer Market Behavior and Pricing #Econometrics #Economics #Mathematics #Regression #Regression analysis #Sports Analytics and Performance #Statistics #cs.LG
paper · pdf · doi:10.48550/arxiv.2310.14992
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Although machine learning tasks are highly sensitive to the quality of input data, relevant datasets can often be challenging for firms to acquire, especially when held privately by a variety of owners. For instance, if these owners are competitors in a downstream market, they may be reluctant to share information. Focusing on supervised learning for regression tasks, we develop a regression market to provide a monetary incentive for data sharing. Our mechanism adopts a Bayesian framework, allowing us to consider a more general class of regression tasks. We present a thorough exploration of the market properties, and show that similar proposals in literature expose the market agents to sizeable financial risks, which can be mitigated in our setup.