A Market for Trading Forecasts: A Wagering Mechanism

TL;DR

This paper introduces a wagering-based market platform for aggregating private forecasts, incentivizing data owners to contribute predictive information through a payoff mechanism that ensures desirable economic properties.

Contribution

It designs and analyzes a novel wagering mechanism for forecast aggregation, ensuring economic incentives and properties suitable for electronic prediction markets.

Findings

The payoff mechanism satisfies key economic properties.

The forecast aggregation operator effectively combines private predictions.

Numerical examples demonstrate the mechanism's practical structure.

Abstract

In many areas of industry and society, e.g., energy, healthcare, logistics, agents collect vast amounts of data that they deem proprietary. These data owners extract predictive information of varying quality and relevance from data depending on quantity, inherent information content, and their own technical expertise. Aggregating these data and heterogeneous predictive skills, which are distributed in terms of ownership, can result in a higher collective value for a prediction task. In this paper, we envision a platform for improving predictions via implicit pooling of private information in return for possible remuneration. Specifically, we design a wagering-based forecast elicitation market platform, where a buyer intending to improve their forecasts posts a prediction task, and sellers respond to it with their forecast reports and wagers. This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution. We propose a payoff mechanism and prove that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, we discuss the properties of the forecast aggregation operator and scoring rules to emphasize their effect on the sellers' payoff. Finally, we provide numerical examples to illustrate the structure and properties of the proposed market platform.