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Bayesian Regression Markets (arxiv.org)
48 points by sebg on Oct 26, 2023 | hide | past | pdf | 2 comments on HN

In plain words: A marketplace pays companies to lend private data for prediction tasks, using a Bayesian framework (reasoning about uncertainty) to handle a wider range of regression problems. Unlike earlier proposals, it cuts the sizeable financial risk those designs leave with participants.

Abstract

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.

Thomas Falconer, Jalal Kazempour, Pierre Pinson
arXiv:2310.14992 · cs.LG · submitted Oct 23, 2023 · updated Jul 1, 2024
abstract · pdf · html · 35 pages, 11 figures, 3 tables. Published in Journal of Machine Learning Research (2024)

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Oooh this is by my thesis supervisors, Thomas and Jalal! They're great guys, and the concept of regression markets is very interesting. I am writing a blog on the topic, if anyone's more interested. Here is the first post: https://carlgronvald.github.io/carlgronvald//jekyll/update/2..., of currently three total. If you have any questions or thoughts on the area, I'd love to discuss!
I've recently been enjoying https://manifold.markets/ for some low stakes prediction market fun. I wonder if this could also be turned into a similar "non-serious" regression market.