Prediction Markets with Intermittent Contributions

2025-10-15

AI Summary

This paper addresses the challenge of collaborative forecasting among multiple parties hindered by data ownership constraints and competitive incentives. To this end, we propose a prediction market mechanism supporting voluntary entry/exit and adaptation to time-varying environments. Methodologically, we design a performance-weighted aggregation framework that jointly incorporates historical accuracy and out-of-sample adaptability; employ robust regression to learn optimal forecast combinations; explicitly handle intermittent submissions and missing data; and construct a payoff allocation scheme satisfying key economic properties—including incentive compatibility and individual rationality. Our key contribution is the first integration of out-of-sample adaptivity into both pricing and weight updating within prediction markets, enabling non-continuous participation. Experiments on synthetic and real-world datasets demonstrate substantial improvements in predictive accuracy, alongside enhanced stability and environmental adaptability.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Ensemble Methods

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Developing prediction markets for intermittent agent participation
• Combining forecasts while handling missing data submissions
• Allocating payoffs based on historical and predictive performance

Innovation

• Prediction market with historical performance tracking
• Robust regression handling missing forecast submissions
• Pay-off allocation using in-sample and out-of-sample performance