Prediction Markets in The Corporate Setting

# Prediction Markets in The Corporate Setting
By NunoSempere, Misha_Yagudin, elifland
Published: 2021-12-31
What follows is a report that Misha Yagudin, Nuño Sempere, and Eli Lifland wrote back in October 2021 for [Upstart](https://wikiless.org/wiki/Upstart_(company)?lang=en), 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](https://forum.effectivealtruism.org/tag/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](https://cloud.google.com/blog/topics/solutions-how-tos/design-patterns-in-googles-prediction-market-on-google-cloud). 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**
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*   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: using them in limited contexts or delegating external macroeconomic questions to them.
*   We survey some alternatives to prediction markets. Generally, we prefer these alternatives' pros and cons.

**Introduction**
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This section:

*   Defines prediction markets
*   Outlines their value proposition

### **What are prediction markets**

Prediction markets are markets in which contracts are traded that have some value if an event happens, and no value if an event doesn't happen. For example, a share of "Democrat" in a prediction market on the winner of the 2024 US presidential election will pay $1 if the winner of the 2024 election is a Democrat, and $0 if the winner is not.

Prices in prediction markets can be interpreted as probabilities. For example, the expected value of a "Democrat" contract in the previous market is \\($1 \cdot p + $0 \cdot (1-p)\\), where \\(p\\) is the chance that a Democrat will win. To the extent that the market is efficient, one expects the expected value of a contract to b

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