People bet a lot on sports despite sports odds having very confusing notation. many non-professionals trade options or crypto with numerical interfaces. a lot of popular videogames have resources denominated in numbers.
The way info from the non-numerate gets incorporated into financial markets today is that more sophisticated people & firms scrape social media or look at statistics (like generated by consumer activity). markets do not need to be fully accessible for markets to be accurate.
I’m very skeptical of the need to represent functions.
That said I’m always game to try out building new, simpler trading interfaces. At manifold we tried a mobile interface where you swiped left to bet no and right to bet yes. It was kinda fun and saw limited use but we ended up killing it because it was a lot to maintain
The way info from the non-numerate gets incorporated into financial markets today is that more sophisticated people & firms scrape social media or look at statistics (like generated by consumer activity). markets do not need to be fully accessible for markets to be accurate.
I agree with this in general, but it doesn’t seem true for the specific use-case motivating the post. The problem I am thinking about here is how to use a prediction market inside an organization. In this case we cannot rely on anyone who could get the information to put it into the market because the public does not participate—we either get the specific person who actually knows to participate, or the market lacks the information.
I expect this to run into all the usual problems of getting people at work to adopt a toolchain unrelated to their work. These projects normally fail; it looks like it needs to be basically zero effort to bet your information for it to work, which is heroically difficult.
Prediction markets with even very small amounts of traders are remarkably well-calibrated. Try playing around with the data in https://calibration.city/. Albeit these are traders out of a wider population.
I am skeptical of prediction markets as tools within organizations, even very large organizations (like Google’s Gleangen or Microsoft). It hasn’t been very useful, and I don’t think this is a just a UX or culture issue, I think the information just isn’t valuable enough. Better off running polls, doing user studies, or just letting project owners execute their big-brained vision. I more bullish of prediction markets that are part of a product/service, or part of an advertising campaign.
I’m inclined to agree with your skepticism. Lately I attribute the low value of the information to the fact that the organization is the one that generates it in the first place. In practical terms the performance of the project, campaign, etc. will still be driven by the internal incentives for doing the work, and it is not remotely incompatible for bad incentives to go unchanged leading to consistently failing projects that are correctly predicted to consistently fail. In process terms, it’s a bit like what’s happening with AI art when it consumes too much AI art in training.
Yes, and firms already experiment with different economic mechanisms to produce this self-generated information—this is just compensation and employee benefits, including stock options, commissions, bonuses. In this frame, it’s seems like a bad idea to let employees bet against, like projects shipping on time. A negative stake is the least aligned form of compensation possible. There are hacks on top of a pure prediction market you could do to prevent people from having a negative stake. But I think once you realize that the recursive aspect of the market you may as well just … design good compensation.
I’m also more enthusiastic about prediction markets on things mostly outside of employees’ control that are still relevant to business decisions—market trends, actions of competitors and regulators, consumer preferences maybe. Though there’s less reason for these to be internal.
People bet a lot on sports despite sports odds having very confusing notation. many non-professionals trade options or crypto with numerical interfaces. a lot of popular videogames have resources denominated in numbers.
The way info from the non-numerate gets incorporated into financial markets today is that more sophisticated people & firms scrape social media or look at statistics (like generated by consumer activity). markets do not need to be fully accessible for markets to be accurate.
I’m very skeptical of the need to represent functions.
That said I’m always game to try out building new, simpler trading interfaces. At manifold we tried a mobile interface where you swiped left to bet no and right to bet yes. It was kinda fun and saw limited use but we ended up killing it because it was a lot to maintain
I agree with this in general, but it doesn’t seem true for the specific use-case motivating the post. The problem I am thinking about here is how to use a prediction market inside an organization. In this case we cannot rely on anyone who could get the information to put it into the market because the public does not participate—we either get the specific person who actually knows to participate, or the market lacks the information.
I expect this to run into all the usual problems of getting people at work to adopt a toolchain unrelated to their work. These projects normally fail; it looks like it needs to be basically zero effort to bet your information for it to work, which is heroically difficult.
Prediction markets with even very small amounts of traders are remarkably well-calibrated. Try playing around with the data in https://calibration.city/. Albeit these are traders out of a wider population.
I am skeptical of prediction markets as tools within organizations, even very large organizations (like Google’s Gleangen or Microsoft). It hasn’t been very useful, and I don’t think this is a just a UX or culture issue, I think the information just isn’t valuable enough. Better off running polls, doing user studies, or just letting project owners execute their big-brained vision. I more bullish of prediction markets that are part of a product/service, or part of an advertising campaign.
I’m inclined to agree with your skepticism. Lately I attribute the low value of the information to the fact that the organization is the one that generates it in the first place. In practical terms the performance of the project, campaign, etc. will still be driven by the internal incentives for doing the work, and it is not remotely incompatible for bad incentives to go unchanged leading to consistently failing projects that are correctly predicted to consistently fail. In process terms, it’s a bit like what’s happening with AI art when it consumes too much AI art in training.
Yes, and firms already experiment with different economic mechanisms to produce this self-generated information—this is just compensation and employee benefits, including stock options, commissions, bonuses. In this frame, it’s seems like a bad idea to let employees bet against, like projects shipping on time. A negative stake is the least aligned form of compensation possible. There are hacks on top of a pure prediction market you could do to prevent people from having a negative stake. But I think once you realize that the recursive aspect of the market you may as well just … design good compensation.
I’m also more enthusiastic about prediction markets on things mostly outside of employees’ control that are still relevant to business decisions—market trends, actions of competitors and regulators, consumer preferences maybe. Though there’s less reason for these to be internal.