← 研踪 PaperTrailarXiv:2510.13385 · v4
Probabilistic Prediction Markets with Intermittent Contributions
首次提交 2025-10-15 18:23
版本更新 2026-11-03 00:28
雷达观测 2026-11-04 23:41
摘要
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 operating with a fixed pool of agents that (i) accounts for the historical performance, (ii) adapts to time-varying conditions, while (iii) accommodates intermittent forecast submissions. 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 demonstrate the effectiveness and adaptability of the proposed market design.
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v4 · 2026-11-03 00:28 · 最新观测版本
Probabilistic Prediction Markets with Intermittent Contributions
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 operating with a fixed pool of agents that (i) accounts for the historical performance, (ii) adapts to time-varying conditions, while (iii) accommodates intermittent forecast submissions. 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 demonstrate the effectiveness and adaptability of the proposed market design.
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