4.6 Article

A market for trading forecasts: A wagering mechanism

Journal

INTERNATIONAL JOURNAL OF FORECASTING
Volume 40, Issue 1, Pages 142-159

Publisher

ELSEVIER
DOI: 10.1016/j.ijforecast.2023.01.007

Keywords

Mechanism design; Wagering mechanism; Predictive distribution; Elicitation of probabilities; Value of forecast; Scoring rules

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This paper introduces a platform for improving predictions by implicitly pooling private information, which can enhance collective value in prediction tasks by aggregating data and predictive skills. Specifically, a wagering-based forecast elicitation market platform is designed, allowing buyers to post prediction tasks and sellers to respond with their forecasts and wagers, with payoffs allocated to sellers post-event.
In many areas of industry and society, including energy, healthcare, and logistics, agents collect vast amounts of data that are deemed 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, a platform for improving predictions via the implicit pooling of private information in return for possible remuneration is envisioned. Specifically, a wagering-based forecast elicitation market platform has been designed, in which 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. A payoff mechanism is proposed and it is proven that it satisfies several desirable economic properties, including those specific to electronic platforms. Furthermore, the properties of the forecast aggregation operator and scoring rules are discussed in order to emphasize their effect on the sellers' payoff. Finally, numerical examples are provided in order to illustrate the structure and properties of the proposed market platform. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of International Institute of Forecasters. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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