Abstract:Although both data availability and the demand for accurate forecasts are increasing, collaboration between stakeholders is often constrained by data ownership and competitive interests. In contrast to recent proposals within cooperative game-theoretical frameworks, we place ourselves in a more general framework, based on prediction markets. There, independent agents trade forecasts of uncertain future events in exchange for rewards. We introduce and analyse a prediction market that (i) accounts for the historical performance of the agents, (ii) adapts to time-varying conditions, while (iii) permitting agents to enter and exit the market at will. The proposed design employs robust regression models to learn the optimal forecasts' combination whilst handling missing submissions. Moreover, we introduce a pay-off allocation mechanism that considers both in-sample and out-of-sample performance while satisfying several desirable economic properties. Case-studies using simulated and real-world data allow demonstrating the effectiveness and adaptability of the proposed market design.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2510.13385 [cs.LG] |
| (or arXiv:2510.13385v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2510.13385 arXiv-issued DOI via DataCite |
Submission history
From: Michael Vitali [view email]
[v1]
Wed, 15 Oct 2025 10:23:28 UTC (2,938 KB)
[v2]
Sun, 1 Feb 2026 19:00:35 UTC (2,931 KB)
[v3]
Wed, 13 May 2026 14:27:59 UTC (3,036 KB)