Bayesian Regression Markets
This addresses data acquisition challenges for firms, especially when data is held by competitors, though it is incremental as it builds on existing market mechanisms.
The paper tackles the problem of incentivizing private data sharing for regression tasks by developing a Bayesian regression market, and shows that it mitigates financial risks present in prior proposals.
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.