Prediction Markets with Intermittent Contributions
This addresses the problem of improving forecast accuracy in competitive environments for stakeholders like businesses or researchers, though it is incremental as it builds on existing prediction market frameworks.
The paper tackles the challenge of enabling collaboration among independent agents with data ownership and competitive interests by introducing a prediction market design that accounts for historical performance, adapts to time-varying conditions, and allows intermittent participation, demonstrating effectiveness in simulated and real-world case studies.
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.