Prediction Markets in the Corporate Setting

Prediction Markets in The Corporate Setting — EA Forum This website requires javascript to properly function. Consider activating javascript to get access to all site functionality. Hide table of contents Prediction Markets in The Corporate Setting by NunoSempere , Misha_Yagudin , elifland Dec 31 2021 40 min read 15 87 Forecasting Prediction markets Corporate governance Frontpage Prediction Markets in The Corporate Setting Executive Summary Introduction What are prediction markets Value proposition Track record High-profile companies that have used prediction markets. Academic consensus What is left unsaid in the academic literature Requirements and challenges for a well-functioning prediction market Categorization scheme The market must have a low enough cost to create and maintain The questions provided by the market must provide more value to decision-makers than the cost to create and predict on them The market must be attractive enough to traders to elicit accurate predictions The market must not have too large negative side-effects, such as costs to the company's dynamics and morale Other Information Aggregation Mechanisms External platforms Specialized machine learning/data-analysis systems Internal forecasting competitions Delphi Method Automatic Prediction Markets, Pseudo Prediction Markets Low-tech options: Surveys and Interviews Conclusion Nuño Sempere Misha Yagudin Eli Lifland 15 comments What follows is a report that Misha Yagudin, Nuño Sempere, and Eli Lifland wrote back in October 2021 for Upstart , an AI lending platform that was interesting in exploring forecasting methods in general and prediction markets in particular. We believe that the report is of interest to EA as it relates to the institutional decision-making cause area and because it might inform EA organizations about which forecasting methods, if any, to use. In addition, the report covers a large number of connected facts about prediction markets and forecasting systems which might be of interest to people interested in the topic. Note that since this report was written, Google has started a new internal prediction market . Note also that this report mostly concerns company-internal prediction markets, rather than external prediction markets or forecasting platforms, such as Hypermind or Metaculus. However, one might think that the concerns we raise still apply to these. Executive Summary We reviewed the academic consensus on and corporate track record of prediction markets. We are much more sure about the fact that prediction markets fail to gain adoption than about any particular explanation of why this is. The academic consensus seems to overstate their benefits and promisingness. Lack of good tech, the difficulty of writing good and informative questions, and social disruptiveness are likely to be among the reasons contributing to their failure. We don't recommend adopting company-internal prediction markets for these reasons. We see room for exceptions

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