All: HN has had many threads with generic arguments about how prediction markets are/aren't useless, casinos, social ills, and so on. It would be good to avoid that in this case, because OP is full of specific information and arguments. It deserves a less generic discussion.
It's fine, of course, to be for/against/etc. and have whatever view you have. Just please engage with the specific article. It will make for a less repetitive and (therefore) more interesting thread.
Random aside: I distinctly remember getting on a phone call with people from the SEC (US Gov't) with the goal of understanding if I could legally start a prediction market. This was during 2020 or 2021. I recall them saying basically "no way" and that it wouldn't be legal, and would be rife with abuse.
It sounds like they should be called "indicator markets" rather than "prediction markets", as the data shows they largely just summarize the current knowledge, with little predictive ability.
>as the data shows they largely just summarize the current knowledge, with little predictive ability.
What counts as "little predictive ability"? Do weather forecasts count as "predictions", or are they "indicators" too? Sure, they might have a more consistent track record, but then again weather is less susceptible to human interference than whatever happens in geopolitics within the next year. Prognostications about future climate might be less reliable, do those have to be downgraded to "indicators" too? On the flip side, prediction markets have a very good track record when forecasting certain events, such as interest rate decisions. Does that mean whether it's a "prediction" or a "indicator" depends on what you're forecasting?
Sort of. Putting current knowledge into a number can be pretty interesting / useful though. Like many people, I read headlines and pay attention to what's happening in international politics, but from those it's hard to have any sense of how much reality there is to bluster in Iran/Panama/Venezuela/Greenland just from general discourse and media. For me, prediction markets have been very helpful in offering some sort of grounding beyond the general noise in areas where I have very little intuition or realistic sense of the possibilities.
Interesting read. Regarding the relationship between volume and accuracy, there need not be one in limit-order-book markets like Kalshi and Polymarkets. In theory, as long as quotes are accurate and adjust quickly to new information, there is no need (and no incentive) to trade since prices are efficient. This is the case in US equity markets: most price discovery occurs through quote updates, not through trades.
Studying prediction markets is one of my current research areas. In my latest paper (preprint at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103), we find that on Polymarkets, markets are, on average, quite accurate and unbiased. We did see a similar non-pattern between trade volume and accuracy, past a certain threshold.
I will definitely take a look. Anecdote but I’m familiar with one multi-year example of a small casual prediction market that seemed like a very good predictor and another one that I don’t have the data from that seemed effective as well over time. I’ve hypothesized why that might be the case but never came to firm conclusions.
Nice article. One small comment, it's very hard to conclude anything about accuracy over time because the comparisons may not be apples to apples. For example if there used to be lots of questions about if it will rain in Boston and now there are lots of questions about if it will rain in Phoenix, it will look like predictions are getting more accurate, but the questions are just getting easier.
Author here. Agree, and I wrote in that section "Absolute accuracy is hard to compare across markets on one platform, and across platforms, because different forecasting questions have different difficulties. I addressed this by tracking similar markets on a single platform over time."
Even doing this, it's not apples-to-apples. One thing is, in this article, I filter only to "interesting" markets, so that controls for the % that are "easy" as you describe.
Given the request about engaging with this specific article:
>Ive thought hard about how to sell prediction markets to consumers. In 2020, I created Google’s current internal prediction market. Since then, I’ve served as the CTO of Metaculus, a non-market-based crowd-forecasting website, and now run FutureSearch, a startup that provides AI forecasters and researchers.
I feel like openly saying you professionally try to make people believe in markets reduced the impact of any further claim.
>Still, there is a benefit to speed. On March 11, 2026, the Financial Times reported that, upon news of Iran War escalation, the Polymarket odds of inflation at or above 2.8% rose to above 90%. This illustrated an immediate domestic impact to US foreign policy, which could influence the public in a way that updates months later from professional economists might not.
I don't understand the idea that this or similar predictions are of any value? "People strongly believe a war will worsen inflation" is information you could get anywhere and not necessarily based on any high quality decision making.
I dove into the prediction markets rabbit hole a number of years back. And I’ve personally seen witnessed scenarios where the wisdom of crowds seems to really work. What I have not really—including in this piece—read is rigorous theory of what makes it effective or not. There are hints here and in the Wisdom of Crowds book but I’ve never read a really comprehensive theory.
Author here. Hal Varian pointed me to this 1992 paper, which I think is still considered the canonical empirical piece on what is actually going on in trading behavior that leads to accuracy (or not): https://www.jstor.org/stable/2117471
Insider trading is a part of it. If someone bets a few billion dollars that America will invade Iran, the probability shoots up to 98%, even though nobody else thinks it will happen. They can then run a press release about how their platform predicted the invasion before anyone else did.
These were Oscar predictions and similar. So no insider trading and, when I wrote about, the prevalence of major prediction sites on the Internet seemed to degrade the crowd wisdom because so many people just went with what a few sites were picking.
One thing that really jumps out to me is the lack of a performance gap between the 90-day and 30-day resolution times. If 2-months of new information doesn't lead to materially improved forecast, then to me this seems to strongly reinforce the takeaway that these markets aren't really forecasting, so much as "the oracle is largely saying what other oracles already say, just updated faster." Am I misunderstanding the data here?
edit: I'm also going back to my bayesian theory days and would be super interested to see a deep dive into whether these markets are rationally updating their beliefs in time. My recollection is super vague here, but I recall something like non-transitive belief loops can lead to dutch-books (so like Johnny Punter things that Trump will win an election against Biden, Biden would win against Ross Perot, and Ross Perot would win against Trump). I'd like to know more about whether these kinds of issues are showing up in these markets?
Author here. Great point, and I think this is due to what another commenter points out, that the questions are different.
The right test of this is to take the _same_ markets that run for 90+ days, and check accuracy 90 days out vs 30 days out. I've done this on other prediction market datasets, though not on Kalshi and Polymarket, and found that forecasts are in fact more accurate 30 days out.
I agree that if they weren't, that would be incredibly suspicious!
Yeah. People have put together a Prediction Market Database [1] (in a Google sheet), I think it's pretty well sourced and shows a good number of both real money and play money prediction markets from before 2002.
I recently tried to launch a site for friends and family that allowed people to make confidence predictions on various outcomes so they could track their calibration over time. It was like "I'm 84% certain Kansas City will beat Buffalo." I had a lot of fun with it since I'm a nerd about this stuff, and I actually demonstrably improved my calibration. But the only sources I could find for rapid repeatable bets were sports predictions. And I definitely did not want to include money or betting for all the annoying legal reasons. People had fun using it once for March Madness 2025 but traffic really dwindled after that. My conclusion was that the overall subject just wasn't inherently fun enough to do it without money involved, so I made the site dormant.
Getting better calibrated really is worthwhile, I just wish there was more of an appetite to do that without involving money.
All: HN has had many threads with generic arguments about how prediction markets are/aren't useless, casinos, social ills, and so on. It would be good to avoid that in this case, because OP is full of specific information and arguments. It deserves a less generic discussion.
It's fine, of course, to be for/against/etc. and have whatever view you have. Just please engage with the specific article. It will make for a less repetitive and (therefore) more interesting thread.
Random aside: I distinctly remember getting on a phone call with people from the SEC (US Gov't) with the goal of understanding if I could legally start a prediction market. This was during 2020 or 2021. I recall them saying basically "no way" and that it wouldn't be legal, and would be rife with abuse.
Fun times.
Too bad open bribes weren't as popular back then. A lil grease goes a long way these days (͡° ͜ʖ ͡°)
Should have just went for it
It's like Uber getting on a phone call with the city to ask if it's legal to run taxis that aren't taxis.
It sounds like they should be called "indicator markets" rather than "prediction markets", as the data shows they largely just summarize the current knowledge, with little predictive ability.
>as the data shows they largely just summarize the current knowledge, with little predictive ability.
What counts as "little predictive ability"? Do weather forecasts count as "predictions", or are they "indicators" too? Sure, they might have a more consistent track record, but then again weather is less susceptible to human interference than whatever happens in geopolitics within the next year. Prognostications about future climate might be less reliable, do those have to be downgraded to "indicators" too? On the flip side, prediction markets have a very good track record when forecasting certain events, such as interest rate decisions. Does that mean whether it's a "prediction" or a "indicator" depends on what you're forecasting?
I tend to go back to this article when I discuss these markets with people https://en.wikipedia.org/wiki/Wisdom_of_the_crowd ... in particular, these markets are designed to tease out the "surprisingly popular" answer https://en.wikipedia.org/wiki/Surprisingly_popular because they incentivize divergence from average response.
You'll note from "Challenges and solution approaches" that it comes with significant caveats and is easily undermined.
Sort of. Putting current knowledge into a number can be pretty interesting / useful though. Like many people, I read headlines and pay attention to what's happening in international politics, but from those it's hard to have any sense of how much reality there is to bluster in Iran/Panama/Venezuela/Greenland just from general discourse and media. For me, prediction markets have been very helpful in offering some sort of grounding beyond the general noise in areas where I have very little intuition or realistic sense of the possibilities.
It's true they are "just" summarizing current knowledge. But there are better and worse summaries of current knowledge!
Some summaries, like on some prediction markets, have objective accuracy that is much better than chance.
Interesting read. Regarding the relationship between volume and accuracy, there need not be one in limit-order-book markets like Kalshi and Polymarkets. In theory, as long as quotes are accurate and adjust quickly to new information, there is no need (and no incentive) to trade since prices are efficient. This is the case in US equity markets: most price discovery occurs through quote updates, not through trades.
Studying prediction markets is one of my current research areas. In my latest paper (preprint at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6443103), we find that on Polymarkets, markets are, on average, quite accurate and unbiased. We did see a similar non-pattern between trade volume and accuracy, past a certain threshold.
I will definitely take a look. Anecdote but I’m familiar with one multi-year example of a small casual prediction market that seemed like a very good predictor and another one that I don’t have the data from that seemed effective as well over time. I’ve hypothesized why that might be the case but never came to firm conclusions.
Nice article. One small comment, it's very hard to conclude anything about accuracy over time because the comparisons may not be apples to apples. For example if there used to be lots of questions about if it will rain in Boston and now there are lots of questions about if it will rain in Phoenix, it will look like predictions are getting more accurate, but the questions are just getting easier.
Author here. Agree, and I wrote in that section "Absolute accuracy is hard to compare across markets on one platform, and across platforms, because different forecasting questions have different difficulties. I addressed this by tracking similar markets on a single platform over time."
Even doing this, it's not apples-to-apples. One thing is, in this article, I filter only to "interesting" markets, so that controls for the % that are "easy" as you describe.
Given the request about engaging with this specific article:
>Ive thought hard about how to sell prediction markets to consumers. In 2020, I created Google’s current internal prediction market. Since then, I’ve served as the CTO of Metaculus, a non-market-based crowd-forecasting website, and now run FutureSearch, a startup that provides AI forecasters and researchers.
I feel like openly saying you professionally try to make people believe in markets reduced the impact of any further claim.
>Still, there is a benefit to speed. On March 11, 2026, the Financial Times reported that, upon news of Iran War escalation, the Polymarket odds of inflation at or above 2.8% rose to above 90%. This illustrated an immediate domestic impact to US foreign policy, which could influence the public in a way that updates months later from professional economists might not.
I don't understand the idea that this or similar predictions are of any value? "People strongly believe a war will worsen inflation" is information you could get anywhere and not necessarily based on any high quality decision making.
Its based on high quantity decision making, and quantity is a sort of quality if you squint and turn your head
"Quantity has a quality all its own" —known economic genius, Joseph Stalin
“More is different.” - Nobel laureate Phillip W. Anderson
https://www.tkm.kit.edu/downloads/TKM1_2011_more_is_differen...
I dove into the prediction markets rabbit hole a number of years back. And I’ve personally seen witnessed scenarios where the wisdom of crowds seems to really work. What I have not really—including in this piece—read is rigorous theory of what makes it effective or not. There are hints here and in the Wisdom of Crowds book but I’ve never read a really comprehensive theory.
Author here. Hal Varian pointed me to this 1992 paper, which I think is still considered the canonical empirical piece on what is actually going on in trading behavior that leads to accuracy (or not): https://www.jstor.org/stable/2117471
Insider trading is a part of it. If someone bets a few billion dollars that America will invade Iran, the probability shoots up to 98%, even though nobody else thinks it will happen. They can then run a press release about how their platform predicted the invasion before anyone else did.
These were Oscar predictions and similar. So no insider trading and, when I wrote about, the prevalence of major prediction sites on the Internet seemed to degrade the crowd wisdom because so many people just went with what a few sites were picking.
One thing that really jumps out to me is the lack of a performance gap between the 90-day and 30-day resolution times. If 2-months of new information doesn't lead to materially improved forecast, then to me this seems to strongly reinforce the takeaway that these markets aren't really forecasting, so much as "the oracle is largely saying what other oracles already say, just updated faster." Am I misunderstanding the data here?
edit: I'm also going back to my bayesian theory days and would be super interested to see a deep dive into whether these markets are rationally updating their beliefs in time. My recollection is super vague here, but I recall something like non-transitive belief loops can lead to dutch-books (so like Johnny Punter things that Trump will win an election against Biden, Biden would win against Ross Perot, and Ross Perot would win against Trump). I'd like to know more about whether these kinds of issues are showing up in these markets?
Author here. Great point, and I think this is due to what another commenter points out, that the questions are different.
The right test of this is to take the _same_ markets that run for 90+ days, and check accuracy 90 days out vs 30 days out. I've done this on other prediction market datasets, though not on Kalshi and Polymarket, and found that forecasts are in fact more accurate 30 days out.
I agree that if they weren't, that would be incredibly suspicious!
Most people don't know, that "prediction markets" are acutally based on an idea by DARPA in 2002, after 9/11/2001.
Prediction markets, by any reasonable definition, existed long before 2002.
Yeah. People have put together a Prediction Market Database [1] (in a Google sheet), I think it's pretty well sourced and shows a good number of both real money and play money prediction markets from before 2002.
DARPA did have a big role though, too.
[1] https://docs.google.com/spreadsheets/d/1vGjnJPxdnBKwag3Q9Uy_...
I recently tried to launch a site for friends and family that allowed people to make confidence predictions on various outcomes so they could track their calibration over time. It was like "I'm 84% certain Kansas City will beat Buffalo." I had a lot of fun with it since I'm a nerd about this stuff, and I actually demonstrably improved my calibration. But the only sources I could find for rapid repeatable bets were sports predictions. And I definitely did not want to include money or betting for all the annoying legal reasons. People had fun using it once for March Madness 2025 but traffic really dwindled after that. My conclusion was that the overall subject just wasn't inherently fun enough to do it without money involved, so I made the site dormant.
Getting better calibrated really is worthwhile, I just wish there was more of an appetite to do that without involving money.