Forecasting is Way Overrated, and We Should Stop Funding It

mabramov

Forecasting is Way Overrated, and We Should Stop Funding It — LessWrong

398 Forecasting is Way Overrated, and We Should Stop Funding It

by mabramov

25th Apr 2026

6 min read

398

Summary

EA and rationalists got enamoured with forecasting and prediction markets and made them part of the culture, but this hasn’t proven very useful, yet it continues to receive substantial EA funding. We should cut it off.

My Experience with Forecasting

For a while, I was the number one forecaster on Manifold. This lasted for about a year until I stopped just over 2 years ago. To this day, despite quitting, I’m still #8 on the platform. Additionally, I have done well on real-money prediction markets (Polymarket), earning mid-5 figures and winning a few AI bets. I say this to suggest that I would gain status from forecasting being seen as useful, but I think, to the contrary, that the EA community should stop funding it.

I’ve written a few comments throughout the years that I didn’t think forecasting was worth funding. You can see some of these here and here. Finally, I have gotten around to making this full post.

Solution Seeking a Problem

When talking about forecasting, people often ask questions like “How can we leverage forecasting into better decisions?” This is the wrong way to go about solving problems. You solve problems by starting with the problem, and then you see which tools are useful for solving it.

The way people talk about forecasting is very similar to how people talk about cryptocurrency/blockchain. People have a tool they want to use, whether that be cryptocurrency or forecasting, and then try to solve problems with it because they really believe in the solution, but I think this is misguided. You have to start with the problem you are trying to solve, not the solution you want to apply. A lot of work has been put into building up forecasting, making platforms, hosting tournaments, etc., on the assumption that it was instrumentally useful, but this is pretty dangerous to continue without concrete gains.

We’ve Funded Enough Forecasting that We Should See Tangible Gains

It’s not the case that forecasting/prediction markets are merely in their infancy. A lot of money has gone into forecasting. On the EA side of things, it’s near $100M. If I convince you later on in this post that forecasting hasn’t given any fruitful results, it should be noted that this isn’t for lack of trying/spending.

The Forecasting Research Institute received grants in the 10s of millions of dollars. Metaculus continues to receive millions of dollars per year to maintain a forecasting platform and conduct some forecasting tournaments. The Good Judgment Project and the Swift Centre have received millions of dollars for doing research and studies on forecasting and teaching others about forecasting. Sage has received millions of dollars to develop forecasting tools. Many others, like Manifold, have also been given millions by the EA community in grants/investments at high valuations, diverting money away from other EA causes. We have grants for organizations that develop tooling, even entire programming languages like Squiggle, for forecasting.

On the for-profit side of things, the money gets even bigger. Kalshi and Polymarket have each raised billions of dollars, and other forecasting platforms have also raised 10s of millions of dollars.

Prediction markets have also taken off. Kalshi and Polymarket are both showing ATH/growth in month-over-month volume. Both of them have monthly volumes in the 10s of billions of dollars. Total prediction market volume is something like $500B/year, but it just isn’t very useful. We get to know the odds on every basketball game player prop, and if BTC is going to go up or down in the next 5 minutes. While some people suggest that these trivial markets help sharpen skills or identify good forecasters, I don’t think there is any evidence of this, and it is more wishful thinking.

If forecasting were really working well and was very useful, you would see the bulk of the money spent not on forecasting platforms but directly on forecasting teams or subsidizing markets on important questions. We have seen very little of this, and instead, we have seen the money go to platforms, tooling, and the like. We already had a few forecasting platforms, the market was going to fund them itself, and yet we continue to create them.

There has also been an incredible amount of (wasted) time by the EA/rationality community that has been spent on forecasting. Lots of people have been employed full-time doing forecasting or adjacent work, but perhaps even larger is the amount of part-time hours that have gone into forecasting on Manifold, among other things. I would estimate that thousands of person-years have gone into this activity.

Hits-based Giving Means Stopping the Bets that Don’t Pay Off

You may be tempted to justify forecasting on the grounds of hits-based giving. That is to say, it made sense to try a few grants into forecasting because the payoff could have been massive. But if it was based on hits-based giving, then that implies we should be looking for big payoffs, and that we have to stop funding it if it doesn’t.

I want to propose my leading theory for why forecasting continues to receive 10s of millions per year in funding. That is, it has become a feature of EA/rationalist culture. Similar to how EAs seem to live in group houses or be polyamorous, forecasting on prediction markets has become a part of the culture that doesn’t have much to do with impact. This is separate from parts of EA culture that we do for impact/value alignment reasons, like being vegan, donating 10%+ of income, writing on forums, or going to conferences. I submit that forecasting is in the former category.

At this point, if forecasting were useful, you would expect to see tangible results. I can point to you hundreds of millions of chickens that lay eggs that are out of cages, and I can point to you observable families that are no longer living in poverty. I can show you pieces of legislation that have passed or almost passed on AI. I can show you AMF successes with about 200k lives saved and far lower levels of malaria, not to mention higher incomes and longer life expectancies, and people living longer lives that otherwise wouldn’t be because of our actions. I can go at the individual level, and I can, more importantly, go at the broad statistical level. I don’t think there is very much in the way of “this forecasting happened, and now we have made demonstrably better decisions regarding this terminal goal that we care about”. Despite no tangible results, people continue to have the dream that forecasting will inform better decision-making or lead to better policies. I just don’t see any proof of this happening.

Feels Useful When It Isn’t

Forecasting is a very insidious trap because it makes you think you are being productive when you aren’t. I like to play bughouse and a bunch of different board games. But when I play these games, I don’t claim to do so for impact reasons, on effective altruist grounds. If I spend time learning strategy for these board games, I don’t pretend that this is somehow making the world better off. Forecasting is a dangerous activity, particularly because it is a fun, game-like activity that is nearly perfectly designed to be very attractive to EA/rationalist types because you get to be right when others are wrong, bet on your beliefs, and partake in the cultural practice. It is almost engineered to be a time waster for these groups because it provides the illusion that you are improving the world’s epistemics when, in reality, it’s mainly just a game, and it’s fun. You get to feel that you are improving the world’s epistemics and that therefore there must be some flow-through effects and thus you can justify the time spent by correcting a market from 57% to 53% on some AI forecasting question or some question about if the market you are trading on will have an even/odd number of traders or if someone will get a girlfriend by the end of the year.

Conclusion

A lot of people still like the idea of doing forecasting. If it becomes an optional, benign activity of the EA community, then it can continue to exist, but it should not continue to be a major target for philanthropic dollars. We are always in triage, and forecasting just isn’t making the cut. I’m worried that we will continue to pour community resources into forecasting, and it will continue to be thought of in vague terms as improving or informing decisions, when I’m skeptical that this is the case.

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Forecasting is Way Overrated, and We Should Stop Funding It

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[-]Shankar Sivarajan25d15772

During the 2016 election, many prominent media outlets presented odds of ~99% for a Hillary Clinton victory, and then defended such claims after the election by saying things like "well, 1-in-a-100 events do still happen." In my experience, people have mostly stopped doing this and present more reasonable figures, and I attribute this largely to the rise of prediction markets, which many news outlets have started citing directly.

They have clearly "raised the sanity waterline," one of the famous goals of at least the rationalists, if not EA.

[-]DirectedEvolution25d*7315

In October, PredictIt and PredictWise had Clinton at 83 and 91 cents respectively.

The night before the 2016 election, WCNC published the next-day forecasts by the NY Times, 538 (when Nate Silver was still running it) and the Huffington Post, which gave odds of Clinton winning of 84%, 68%, and 98% respectively.

The Princeton Election Consortium‘s model developed by data scientist Sam Wang forecasted 99% for Clinton. Wang ate a cricket on live TV.

Reuters/Ipsos forecasted 90% Clinton.

Overall, seems like prediction markets were in the same ballbark of wrongness as the media forecasts. Admittedly, the forecast dates here are not identical - a more rigorous breakdown would be welcome.

But in this bunch, the best result was obtained by a professional data scientist, Nate Silver, not by a prediction market.

There’s been a huge amount of discourse around the failed 2016 forecasts, and they all attribute it to failure to take correlated polling errors into account (which Silver did model, explaining his less wrong prediction). There might have been underlying partisan bias or conformity warping modeling decisions, but those biases also exist in prediction markets. Money and reputation is on the... (read more)

[-]Lukas Finnveden25d199

Markets can be better or worse depending on eg liquidity. My guess would be that today’s markets are better. (The large difference between 83 and 91 cents failing to disappear from arbitrage is an indication that at least one of those markets weren’t so great, though I haven’t checked how current markets look on that metric.)

3DirectedEvolution25d

Either way, they were in the same ballpark as the other forecasts. Even if both were poor markets, that only strengthens the argument that they shouldn’t receive credit for “raising the sanity waterline” around the 2016 election.

[-]Steven Byrnes24d4022

Shankar's original claim was that the 2016 election was BEFORE functional prediction markets, and that the bit of "raising the sanity waterline" in question happened between then and today.

I really don't think PredictIt should count as a prediction market at all in this context, I recall that they had crazy rules that made it basically impossible for serious people to make serious money by correcting even blindingly obvious market errors. (Don't know anything about PredictWise.)

[-]Daniel Tilkin24d182

Yes, at the time the limit at Predictit was $850 per user per market. When the CFTC originally issued its no-action letter to Predictit, it was on the basis that it was for research purposes.

4DirectedEvolution24d

But this argument relies on an alternative history, like "functional modern high-volume prediction markets would have called the election better than Nate Silver, had they existed at that time." But that's an (implicit) assertion without evidence. The confident calls for Clinton weren't by bloviating pundits making obviously wrong calls, they were by professional data-driven forecasters with methodological disagreements and oversights. If people want to argue that prediction markets have "raised the sanity waterline" by preventing disastrously bad forecasts about high stakes events, they need to point to examples of where functional modern prediction markets have directly competed with i.e. boutique models published by media outlets. Define the comparison! Now, that said, I can put forth a different reason they've raised the sanity waterline in specific areas. Taking hotbutton issues, breaking them down into empirically verifiable factors, and getting a money-backed continuously updating estimate on those factors is genuinely helpful when I'm specifically interested in a relevant issue. Even if I think the estimate is wildly wrong, it's useful to see where the consensus lies. But I've read extensively on them, participated in them, and have a pretty deep knowledge of their mechanics, limitations, etc. But I also think there are some underappreciated dangers in prediction markets. Of course, we're now seeing how they are being used for insider trading, providing a mechanism for information leaks, and are genuinely being used as something approaching an assassination market. They're motivating harassment of journalists whose articles are being used to resolve the markets. That's directly counterproductive to raising the sanity waterline. They also take some real sophistication to interpret -- particularly understanding how to parse the volume, individual bet sizes (whale activity), participant selection effects, and resolution criteria. They're optimized for betti

4Linda Linsefors23d

Can you elaborate on this please?

9DirectedEvolution23d

In general, there's all kinds of insider trading apparently going on inside the Trump admin in prediction markets whose outcome is heavily determined by US gov actions. We also have of course the case of Emanuel Fabian, who's been receiving death threats for accurately reporting on a missile attack in ways that unfavorable to one faction of polymarket gamblers. There's an investigation into whether an airport thermometer used to resolve a polymarket bet waas tampered with. Obviously none of these are literal assassination markets, which is why I said "something approaching" rather than "precisely an assassination market." The general principle is people are taking destructive actions (tampering, indirectly leaking classified information through their betting behavior, threatening journalists) or are betting on while being directly involved in violent actions (the soldier) that are attached to prediction markets. And on a deeper level, we have to ask whether the fraud opportunities that prediction markets present are then going to become a systemic generator of fraud. Big picture, the combination of prediction markets, crypto, and gambling becomes an unregulatable, permanent fraud coordination mechanism. It incentivizes a set of bad social roles: * People who can come up with situations that sound outlandish to one segment of the population, who like to gamble and who believe there's another segment of the population that's just dumb. * People who realize that they're in a position to influence that outlandish situation into being. They'd never have done so if there wasn't an easy opportunity to make money on it, but there is, so they do. What these markets point toward is a future in which the world just becomes more chaotic, because for any outlandish situation you might consider and create a market for, you've now incentivized people to make that situation happen (or not happen), just because you asked the question and got people's attention. They become g

7ChristianKl22d

Insider training seems to me like a different category than assassination markets. The concept of assassination markets seem to me to suggest that someone takes harmful real world actions to make a prediction happen. A short seller releasing a dossier about fraud in a company for which he holds shorts looks to me more "assassination market" like than a soldier who has no choice about whether or not to capture Maduro because he has to follow the chain of command making an insider trade.

9AprilSR24d

This is a really compelling point.

3mabramov22d

I dont think its true that the news media is now more rational than it used to be. Outlandish nonsense is still said all the time. Its also not clear to me that it would matter that much, even if it were true

2speck144722d

Why do you attribute this largely to the rise of prediction markets? My perception is that news outlets started citing prediction markets roughly when they became an effective vehicle for hard-to-regulate sports gambling, I don't think this has ~anything to do with the 2016 election, and indeed, directly following that election, significantly prior to the rise in cultural salience of prediction markets, data scientists and pollsters were in crisis for several months trying to figure out why the polls were so wrong. They did a much better job in the 2018 midterms and this is certainly not attributable to prediction markets, rather to directly addressing the methodological gaps in polling that had recently become salient.

1casens23d

are you sure this isn't just the evolution of your own information diet and circle of friends? do you think if you asked a random american "do you know who nate silver is? -> do you think he got it mostly right or mostly wrong in the last few elections?" do you think they'd say "he was mostly right" or "nate silver is always wrong because he's [too woke]/[not woke enough]"? prediction markets are allegedly a way to bring empiricism to fields that had none before, and your best defense of them is "the vibes feel better now"

[-]habryka24d10241

FWIW, I refer to Manifold Markets and prediction-markets every week in my decision-making. My guess is this mundane utility generalizes. I am kind of confused why you think people don't use these for decision-making, they seem really useful in lots of circumstances.

Some random example markets I referred to recently:

That weird futures market on the Anthropic IPO price (can't find the link but saw it reference on Twitter a bunch)
This market on Anthropic making another big revision to their RSP
This market of mine on Anthropic security commitments
Basically all of the election betting odds markets
Markets on nuclear war

[-]MichaelStJules24d4918

What decisions do you think this has affected and what would you estimate the differences in outcomes to be as a result? Or, say, the most important impacts?

9Zack_M_Davis24d

You may be thinking of Ventuals. Best wishes, Less Wrong Reference Desk

7mabramov22d

Basically just +1 on what Michael said. How are you using markets on nuclear war in your decision making? Very concretely, can you name a decision you made differently due to these markets?

[-]habryka22d191

Yes, I used them to set a threshold for evacuation protocols at Lighthaven, together with decisions on emergency supplies, how many bugout bags to have, etc.

(I had also built a small website called "hasRussiaLaunchedNukesYet.com" which would send everyone who signed up a text message if the probability of a nuke being launched was above 90% according to the markets, which would then be a natural time to get out and escape)

8mabramov20d

FWIW, this does not change my mind on my OP, though this is interesting.

3Viliam10d

Cynically speaking, even if you do not see the value in this information per se, you should appreciate that people who care about these things can now look at one specific URL, say "okay", and not waste much more time trying to do their own research.

2Caleb Dixon14d

There is a fairly well known discussion point in quant trading circles that says you should always bet against the end of the world. If the logic is not immediately obvious to you, it goes something like this: if the world doesn't end, I make money, and if the world does end, it won't matter that I lost money. I don't know what the conditional probability of the world being "destroyed" is (defined here loosely as lots more nuclear weapons detonate, massive environmental damage, all world governments collapse, money is worthless) if one major city is destroyed by a nuclear weapon, but I would think its at least 25%. All this to say, if you are making any decisions based on the implied odds of that market, its probably rational to consider them as higher, since we are really looking at the odds of [nuclear weapon goes off AND the world doesn't end].

[-]elifland25d7526

What scope do you have in mind when you refer to forecasting? Is it specifically Tetlockian forecasting / prediction market style forecasting where most of the value is a forecasted number answering a well-defined question, and the methdology often involves aggregating a bunch of people’s views, each who didn’t spend much time?

If so, then I agree directionally and in particular agree the current track record isn’t great, though I think this sort of forecasting will be plausibly quite useful for AI stuff as we get more close to AGI/ASI, and thus it may be easier to operationalize important questions that don’t require long chains of conceptual thinking, there will be lots of important sub-questions to cover, some of which may be more answerable by superforecaster-like techniques as we have better trends / base rates to extrapolate since we are closer to the events we care about. And also having a bunch of AI labor might help.

But overall I am at least currently much more excited about stuff like AI 2027 or OP worldview investigations than Tetlockian forecasting, i.e. I’m excited about work involving deep thinking and for which the primary value doesn’t come from specific quantitative predictions but instead things like introducing new frameworks (which is why I switched what I was working on). I’m not sure if AI 2027 or OP worldview investigations work is meant to be included in your post.

[-]mabramov25d306

I am mostly talking about Tetlockian forecasting. I am talking about other versions of it too, though, including AI 2027.

I didn't want to argue against AI 2027 type stuff in this post but on net, I think AI 2027 made some very aggressive predictions, that will turn out to be wrong (even if you give double the time for them to occur) and I think that AI safety people will end up looking silly, like the boy who cried wolf.

For two concrete examples:

"By early 2030, the robot economy has filled up the old SEZs, the new SEZs, and large parts of the ocean. The only place left to go is the human-controlled areas.". This one is easy to operationalize. I would bet that by the end of 2032, less than 20% of the current Earth's oceans will be taken over by the "robot economy".

"June 2027: Most of the humans at OpenBrain can’t usefully contribute anymore."

8elifland25d

Yup, I'm also quite worried about this. I'm very uncertain though about the magnitude of the issue. e.g. if most humans at OpenBrain not contributing happens in 2030 (so taking a bit more than 2x longer to happen than predicted), I'd guess that many people will not discredit us / safety people because of AI 2027 and may still give some credit. Certainly not all people! But I've been pleasantly surprised by the discourse thus far on evaluating AI 2027, which (as far as I remember, might be wrong) has often focused on feeling like reality is unfolding in a way that is directionally toward AI 2027 compared to what the person previously thought, or whether AI 2027 is closer to reality than what the person had thought. And many people seemed to understand that it was not a confident prediction of any specific timeline. (I guess there was a blow up about Daniel updating his timelines later / having a median longer than AI 2027, but I'm talking about the reactions relating to how reality has compared to the scenario) (edit: You might worry that the reception has been good so far only because reality actually has looked pretty similar to the scenario, and that will change soon. That seems very reasonable. Also, to be clear, even if the crying wolf effect is large, I think there will remain large positive effects, especially if the takeoff looks recognizable relative to the takeoff in AI 2027 in terms of the overall dynamics even if it substantially later or slower.) [...] I'm also less than 50% on this, maybe ~33%? You can generally see my views at https://www.aifuturesmodel.com/forecast/eli-04-02-26, they're somewhat less aggressive than Daniel's. (Obviously I can't fully speak for Daniel but I think his response to your comment would be further in the direction of sticking by AI 2027's predictions are likely to be close to right.)

2mabramov20d

Yeah, I just don't agree that reality has played out like AI 2027 in any meaningful way that isn't very obvious. It's too early to say. Basically, no meaningful predictions are made until the end of 2026, so we are too early to say. It's just too early to claim victory. I have been meaning to write up my critiques of AI 2027 but I have too many of these kinds of posts to write up and I'm a slow writer.

5elifland20d

Makes sense. For what it's worth, we've had people tell us and seen people post on Twitter that they've taken scenarios like AI 2027 more seriously because so far reality has played out more like AI 2027 than they thought it would.

[-]Josh Rosenberg23d5418

[Relevant context/COI: I'm CEO at the Forecasting Research Institute (FRI), an organization which I co-founded with Phil Tetlock and others. Much of the below is my personal perspective, though it is informed by my work. I don't speak for others on my team. I’m sharing an initial reply now, and our team at FRI will share a larger post in future that offers a more comprehensive reflection on these topics.]

Thanks for the post — I think it's important to critically question the value of funds going to forecasting, and this post offers a good opportunity for reflection and discussion.

In brief, I share many of your concerns about forecasting and related research, but I'm also more positive on both its impact so far and its future expected impact.

A summary of some key points:

Much of the impact of forecasting research on specific decision-makers is not public. For example, FRI has informed decisions on frontier AI companies' capability scaling policies, has advised senior US national security decision-makers, and has informed research at key US and UK government agencies. But, we are not able to share many details of this work publicly. However, there is also public evidence that forecast

... (read more)

7mabramov20d

I'm not sure the norms here but I will just copy over my reply from the EA forum. Hi Josh, thanks for the response. I hate to do this, especially at the start, but I want to point out for you and others who have jobs related to forecasting that it's difficult to convince someone of something when their job relies on them not believing it. I think you should assume that you will think forecasting is more useful than it is. As for your points, I'll respond to some of them. 1. If you want to DM me, I can sign an NDA, and I may update my opinion depending on what these non-public uses of forecasting are. 2. I don't think this is all that relevant. I'm not sure what forecasting research has really elicited on AI timelines. I agree that talk about timelines creates a lot of "buzz" around AI but depending on your views, this is good or bad. 3. I agree that the impact of measurement-oriented research is difficult to measure, but importantly, not impossible. OWID for example should count how much their work is being cited and looked up. Conversely, I think it would be good to estimate, for FRI, how much $$ the change of the decision was worth and by what amount/percentage did FRI make that change more likely. I don't think you really gave a good reason that FRI should be funded over anything else that simply has very diffuse benefits. 4. When do you think it's reasonable, if ever, for the EA community to "give up" on funding more forecasting work? 5. If I'm being cynical, almost every field can say "AI will transform the field" though I'm not sure how much this is worth debating.

1dschwarz1d

I'd be curious if @mabramov you'd respond to @Josh Rosenberg's points about comparative evaluation of impact for other grant-based non-profits generating research and public information. How valuable do you think the four examples Josh gave: Epoch, Our World In Data, GovAI, and IAPS have been? Do you think the grants for these orgs have a good ROI compared to forecasting research?

[-]Kaj_Sotala25d3722

I liked and agreed with @Scott Alexander 's recent tweet on the benefits of prediction markets, though I would have a hard time saying how much of a monetary investment into them that justifies:

It's hard to say on a society-wide level because I don't know who's using them or how users are changing their minds/actions, but just for me they were very helpful during the beginning of the Iran war. People were saying things like "the regime will fall within days", and I didn't know enough about geopolitics to know whether this was reasonable or not, so it was helpful to be able to check p(regime falls). I also used them as a sanity check for oil price futures which seemed to be having weird herd dynamics in the actual markets. And I've used them to get some sense of the likelihood of various upcoming election-related violence and foul play and to discuss this with friends who are more worried than I am. Also, I've also used other markets (not Polymarket/Kalshi, before then) to know how worried I should be about upcoming potential natural disasters and COVID flareups.

I think a good comparison point would be the social utility of newspapers (let's say excluding investigative journalism, w

... (read more)

[-]Steffee24d2915

The Biden/Kamala example makes me think the value of prediction markets is not the uncovering of little-known info, but making things so blatantly obvious they can't be denied by those who are deceiving themselves.

6Daniel Kokotajlo23d

Yes, but there are also some examples of uncovering little-known info, e.g. insider trading giving advance warning of stuff. Consider: Suppose that someone tries to assassinate POTUS by running up with a suicide vest on. Suppose that it appears they were a random ideologue acting alone, but then analysis of the markets reveals a suspicious spike in bets on POTUS death starting a few hours before the attack. This suggests that this person didn't act alone; it suggests that at least one other person knew about it beforehand. Indeed it suggests that many people knew, because if it was just a few people then the odds that someone would leak are low. This is some interesting info that wouldn't otherwise have been uncovered! (And if the Secret Service is monitoring the markets, they might actually be able to protect POTUS from such attacks more effectively!)

1Steffee21d

I'm not disagreeing, but I do have a question - How would we distinguish new knowledge form random speculative spikes? E.g. some predictors might think that other predictors have a greater degree of knowledge than they actually do, and that creates a bubble.

3Daniel Kokotajlo21d

We probably don't have a foolproof way to distinguish but there would be various other bits of evidence we could look for, e.g. how capable are the AIs actually getting, what's the gossip in SF, how long and sustained has the spike been, etc.

8JenniferRM23d

Since I was cited, and since I updated some from followup conversation, I want to close the loop here... Buck read an entire book on this, and it sounds like the key movers and shakers were oblivious to the prediction markets but very interested in polls? But he didn't mention the authors of the book specifically ruling out influence. In general, I tend to think that staffers have a lot of collective cultural sway, and they care about "who can pull donor money to pay for staffers" a lot and George Clooney was both famous and a big donor and the author of this, and I would expect their shared Inner Ring's Overton Window to track things (1) like Clooney and also (2) prediction markets. Also that channel of information would plausibly not end up in a book (which is likely to focus on the narrative swoop of second order famous people, rather than the third and fourth tier staffers and their watercooler gossip and whether that gossip mentioned prediction markets or polls)? Anyway. Hearing from @Buck about the book lowered my credence on "Prediction Markets changed history there" from maybe 70% to maybe 42%? I was surprised, but it was surprise from hearing confident well-informed posteriors, so all I can do is reason via aumancy, from thin summaries of thick data.

1speck144722d

This explanation is plausible to me, and has the added benefit of explaining why @mabramov thinks prediction markets are less valuable than they are (if indeed they are less valuable than they are) - many people are prone to believing things that are obviously wrong, the main skill of good forecasters (beyond being generally well-informed) is that they are immune to this particular insanity, and so people who are not good forecasters benefit from access to insanity-immune opinions. This comment is the closest among existing comments to convincing me that prediction markets can have broad social utility. Still, I don't think this explanation really says that 40% chances are 40% chances, it says you can safely dismiss claims that 40% markets represent probabilities below like 10% or higher than like 80%. It's still possible that these markets are not particularly-good information aggregators and that superforecasters are not particularly good at producing actionable insights across domains - calibration is not the optimization target. Thus I still update, based on the original post, towards prediction markets being worse, and perhaps significantly worse, than advertised, in their current incarnations.

[-]elifland25d*3510

The most recent Tetlockian forecasting style thing I've spent substantial time on is the 2025 and 2026 AI forecasting surveys, in which hundreds of people each year have made predictions a year out on benchmarks, and other indicators such as revenue.

The theory of change is to (a) establish common knowledge about how fast things are going relative to people's expectations (and we collect data on people's overall views on when AGI will be reached so we can sort of see if we're "on track" for that), and (b) identify which people seem to be making the most accurate predictions. Importantly, it is not to elicit predictions that are directly useful for important decisions.

I've observed some evidence of this working, e.g. re: (a) establishing common knowedge, Anson of Epoch wrote an analysis that I've seen referenced a few times. I'm glad to have a data point against the common refrain of "people underpredict benchmark scores and overpredict real-world impact" from revenue outpacing people's predictions (though it is a narrow and single data point).

Re: (b) identifying who is making the most accurate predictions, I found it informative that in Anson's analysis (footnote 1), forecasters wit... (read more)

[-]TsviBT25d243

I've long had some sense like this, though not the expertise to make a claim like this.

My impression is that a lot of the conceit of forecasting and prediction markets boils down to

People can learn to think better via the real-world feedback of predictions;
You can find people or groups that are good at forecasting, e.g. through prediction markets;
Then you can somewhat rely on their predictions
And you can learn how they made their predictions and thereby get better world models in general

Have things like this happened? E.g.

Are there important strategic X-derisking decisions that have been made based on finding someone who's good at forecasting in general and then asking them to make predictions? (And do those seem like good decisions / for the right reasons?)
Have some people discovered through markets gone on to have a bunch of useful thoughts due to their forecasting skills / world models? Were they signal boosted in advance because of their known prediction market success?

[-]Stephen Fowler25d*2414

3 unrelated points:

I'd love to see this argument expanded further but also appreciate what you've written here.

You sort of mention this, but it strikes me that the argument doesn't need to be "are prediction markets useful for doing good" but just needs to be "does the improvements to prediction markets and infrastructure made by EA money and resources actually meaningfully increase the amount of good prediction markets do?"

Lastly, may I suggest cross-posting this to the EA forum?

1mabramov20d

Anything in particular you want expanded upon? I think this is most of what I have to say on the matter. I've been saying some form of this opinion for about 3 years now and I'm happy this is finally out there. Yea, my point is that the bar for EA money needs to be very very high. It's on the EA forum. Was posted at the same time!

[-]Bojadła25d2310

I find it hard to tell how much impact widespread forecasting has. You go into the lack of tangible impact in the post but it's hard to prove a negative (forecasting has had no impact). It's also hard to prove real but intangible impact. I trust your opinion as an expert forecaster more than mine but I'm confused here.

One personal forecasting impact I've observed is the ability to point to existing prediction market. For example, someone writes a post about China definitely invading Taiwan in 2026. It's hard for me to tell how good their argument is. With prediction markets I can find that market and get a more objective take. I can ask the post author how much money they put into the market, seeing they stand to make a lot of money correcting it.

This isn't knock down argument against your post. I'm just giving a specific example of less tangible impact. Multiplied over many people making slightly better decisions that might be high impact. It can both be true that this impact is real and EA effort should go elsewhere.

[-]Ben S.23d2216

I wrote a whole response to this :-)

Here's an excerpt:

As I understand Marcus's argument, his central thesis is that we haven't seen the benefits of this past forecasting funding, but I think the opposite is true! Here are just a few examples:

It's hard to measure the value of "epistemic infrastructure," not just for forecasting sites but also things like Wikipedia and OurWorldInData. That doesn't mean that value isn't there. Has Wikipedia been a good return on investment? Obviously! Manifold is far less impactful than Wikipedia, but Wikipedia gets about $200 million per year between returns on its endowment and donations. The return on investment in Manifold is probably still way higher than Marcus seems to believe. Hundreds of thousands of people have made incrementally better decisions; hundreds of thousands of people have learned to think about the world a little more concretely and quantitatively. I'm one active user of thousands on Manifold and I'd personally value its impact on my life quite highly, as I'd wager Marcus might too.
Giant companies like Kalshi and Polymarket have grown in part because of research around how to best leverage crowdsourced forecasting. Inasmuch as th

... (read more)

2mabramov20d

1. I'm not sure I understand the point. A lot of people have donated money to Wikipedia and now they have a big war chest. I agree Wikipedia has been valuable but I'm not sure how you are computing the value. I don't see any proof that hundreds of thousands of people are making incrementally better decisions though. My point is that this was the hope but it is sort of just asserted without evidence. I'm happy you've enjoyed Manifold, I don't think that means EA money should go to it. There is a very very high bar to clear for EA money. 2. I'm not sure I understand your point. I agree that Kalshi and Polymarket are big and have attracted users and investment. My point is that they haven't given this outpouring of social returns that people claim they are to have. They are just good businesses (charging fees on gambling). 3. I don't think you can claim millions of dollars from community growth but I'm sure this ROI would then be negative. 4. I agree that forecasting has been somewhat useful for identifying talent/nerd-sniping. I don't think very much of this has happened though. I think less than 25 people (to be conservative) have roles due to their forecasting prowess where they were unknown before. 5. This has been discussed elsewhere. 6. There is no proof here. Would you say today's epistemic environment is much better than 20 years ago?

1Ben S.19d

1. I guess I didn't bother to make a case for why something like Wikipedia provides value because I thought it was pretty obviously providing such high levels of societal surplus through its existence that the specific amount clearly dwarfs the costs of running it, irrespective of Wikimedia foundation stuff (not an argument for why one should donate money to it now but rather why whatever money it initially took to get it up and running was clearly very well spent). Same for Manifold. IIRC they probably got investments of somewhere in the ballpark of $2 million (I could be off by a lot)? This seems like a great amount to have paid to create the societal surplus that Manifold now provides! 2. Sure, but like... if you invested in Kalshi or Polymarket early on, then you'd have 100x-ed your return, just on a purely financial level, so that's clearly a "good investment" from the perspective of EA orgs, since now you can turn around and put that money towards EA goals. 3. ... why would growing the base of people making charitable donations to EA causes "negative ROI" from the perspective of EA orgs? 4. 25 is a lot of ppl! 5. - 6. Of course impossible to directly quantify but that doesn't mean it doesn't exist! Aren't you a forecaster :P

4mabramov18d

1. Manifold I think had $6M or so of investment and given the state of Manifold, clearly the market disagrees with you that this was money well spent. Perhaps they will turn things around but this is Manifold DAUs. 1. I agree that investing in Polymarket or Kalshi in the early days would have been a good investment. I think its really hard to make the case that they have been good for society; we basically got more sports betting. 2. I'm saying that if you were to compute the amount the EA community has spent on forecasting and the amount donated that got into the community through forecasting, I think the ROI would be very negative. 3. 25 is not that many people for this and I was being very generous saying 25. I think a more fair number is like 5-10. 4. . 5. I did the forecasting stuff to show that I could do it, yes. I remain unconvinced it has been very useful to the world. I feel like above all, I want to stress that EA dollars should meet a really high bar. You can save a child's life for about $5000. You can get a chicken out of a cage for 10 years for about $1. You can apparently build a play money prediction market website for about $6000/DAU.

2Ben S.17d

I guess I'd just like to point out that reducing the likelihood of any interstate conflict by 0.1% is probably valued at about $1 billion / year. I think the forecasting ecosystem as it stands probably does that. Unfortunately you can't measure this in an RCT so we'll never know I guess.

1pargui14d

At the heart of this discussion seems to be the issue of whether it makes sense to miss out on good "investment" opportunities just because you can't quite measure the impact. I'm an outsider so make of this what you will but it seems to me that EA is bound to have a measurability bias to it because, if not, then it starts to look like old school charity Obviously what you'd really want to do is give a lower bound of how much benefit society might be getting and see if the numbers work out when comparing to alternative uses This discussion is going nowhere without that last kind of analysis imo, and even then it might just be endless but I'd rather see someone try

[-]Eliezer Yudkowsky23d*184

I think a primary question I want an answer to here was what went so wrong with OpenPhil's attempt to fund superforecasters on AI questions -- why they were eg so much wronger than either of myself or Paul about the probability of a 2025 IMO gold medal win, as wrong (wronger?) than Holden Karnofsky on AGI timelines, etc. Do we know what went wrong? Is it fixable? Has it been fixed? If people with biases can get "superforecasts" that match their biases, and attempts to read the market entrails divine that markets in 2023 don't think AGI is on the way, and we can't get extinction-related prediction markets for settlement reasons, then there may not be much for AI people to do with prediction markets.

The rest of humanity should keep trying to get good at prediction markets in order to someday get a little closer to dath ilan, and I think non-real-money markets like Manifold are important for experimenting with that. (Manifold's brief ill-fated attempt to become a real-money market was unfortunate.)

9Ben Pace23d

Personally I had updated they were substantially doing reference class forecasting, and that most people (forecasters included) have historically sucked at picking a reference class for AGI.

7Eliezer Yudkowsky22d

I currently have no better historical account, but it's a sharp lesson about how all the bowing and scraping about deferring to superforecasters was just Modesty and "Outside View!"ing in disguise all over again, in the sense that the "superforecasters" who got hired somehow managed to end up being those without any inside view of AI.

2exmateriae22d

fwiw, I think on AI related questions, Metaculus has chosen Pros who are much more bullish than what I've seen superforecasters do. I don't want to say too much about private projects but some Pros were directly involved in the AI ecosystem. It's not a head-to-head but there is also a significant vibe difference between FRI's report on work and Metaculus Labor Hub where Pros have taken a much more dramatic position. When I'm wrong on AI forecasts it's more often due to things going a bit slower than I expected rather than too fast (although that has happened too) and I know I'm not the only one, there's at least Haiku among the regular Pros who's very "bullish". (as in, very worried)

6lc22d

The most well-funded prediction markets in human history did not seem to think either Nvidia or OpenAI was worth particularly much before 2022. Might these just be rare things to anticipate if you are not literally at the eye of the storm?

[-]lc25d*137

This is like if lebron james wrote a post about how basketball is stupid and should receive less attention

[-]Austin Chen25d135

Thanks for writing this! Some reasons I would steelman continued funding towards tetlockian or PM-style forecasting:

Source and screen for talent. There sure is some correlation between forecasting well and doing important things in EA. Just picking some people I know: Joel Becker, another former #1 Manifold trader, went on to join METR and then do their famous uplift studies. Eli Lifland went on to help make AI 2027. Peter Wildeford started Rethink Priorities, now IAPS. And some of your own track record in making good early-stage EA grants is here.

Beyond that, a bunch of smart and interesting people have expressed a lot of interest in forecasting, from banner bearers like Scott Alexander and Vitalik Buterin and Robin Hanson, to surprising cases like Anthony Giovanetti (of Slay the Spire) to <anon famous AI researcher who DM'd me> to Sam Altman. I do think there's some amount of intellectual fashion-ism going on here, but also, you should fish where the fish are.

When funding is abundant, one bottleneck becomes finding (and building consensus around) talent; if the only thing that a bunch of money spent on forecasting does is to identify good people, that may be worth it.
Fast,

... (read more)

[-]ike25d118

Do you have any reason to think those people succeeded in other areas as a result of the screen from forecasting success ? Did someone give them opportunities they wouldn't have had without being a top forecaster?

My guess is this is just positive selection.

5exmateriae22d

Sorry for the late reply but I have an anecdotal example: myself. I'm a lawyer by trade and there's no way I would have been able to get involved with Epoch AI had it not been for me being one of the best Metaculus forecasters. The most notable thing I did was writing How Fast Could Robotics Production Scale Up? with JS Denain. I know some of the top forecasters have worked for hedge funds although their backgrounds may have helped there? Not very knowledgeable about this and it's possible that you're right overall and it's anecdotal but there's at least one example.

7ike22d

No offense, but your example is using a track record in forecasting to get a job where you post forecasts. I asked about success in other areas.

1exmateriae22d

Did you look at the report? I know the title is very forecasting related but most of it is not forecasting unless you consider all of the research and data collection as purely forecasting. I don't think that's the same thing even if there's overlap in the skills, I wouldn't consider what OWID does with their data collection (not the opinion articles) as forecasting. Here we did mix both but the report has value in itself without the forecasting part, imo.

1exmateriae22d

Potential other example: the Bridgewater Contest on Metaculus. They're supposedly reaching out to top contestants. A lot of finance students are invited but apparently some from schools they wouldn't usually try to get interns from. I know a few of the top students have gotten internships but I have no idea if forecasting ends up being a matter in the recruitment process. They're going through the trouble of organizing it (and paying for) for the third year now, so I guess that must at least matter a little? To be clear, I'm not weighing in on whether this was a good argument or not but bringing examples that might fit the bill seems relevant.

9the gears to ascension25d

Does forecasting asymmetrically favor people trying to do good things? my impression is that it's a general fuel and what it ends up being useful for is highly contingent on who knows what.

2mabramov20d

I agree that forecasting is an ok way to find talent but not much of this has been done. I agree that in chaos, they are useful but I don't think they hit the bar for EA funding. Sure, it's speculative. If the AIs will use them, then they will also make them and we can just relax for now on the forecasting and let them do it in the future.

[-]jmh25d93

I liked this post. Strong upvote. I'm neutral on the funding/not funding issue but the points you make about not starting with the tool to solve some problem but with the problem and then selecting the tool(s) is so very important. I frequently see that as a problem in so much public debate and policy making.

[-]James Newport25d81

(Copied my comment from the EA Forum and related to my post)

I don’t disagree with some of the fundamentals of this post. Before diving into that, I want to correct a factual error:

“the Swift Centre have received millions of dollars for doing research and studies on forecasting and teaching others about forecasting”

The Swift Centre for Applied Forecasting has not received millions in funding. The majority of our earnings have been through direct projects with organisations who want to use forecasting to inform their decisions.

On your wider argument. I think... (read more)

[-]lilkim202524d65

I think part of the lack of use is institutional inertia. To use a concrete example, politicians (generally) believe that the things they are doing will be successful, popular, and beneficial to their reelection odds. In the past, the only immediate counter-evidence was polling, and this could be dismissed, often rightly, as push-polling intended to apply pressure rather than accurately predict results. Some of that skepticism has carried over to mechanisms that don't have that problem.

The age of our current political class, rather set in its ways, mean th... (read more)

2ChristianKl24d

I think politicians to plenty of things that they believe to be mostly ignored by most voters and not directly affect reelection odds. Dominics Cummings had a lot of trouble getting politicians in the UK to do the kind of things that would likely be good for reelection odds instead of campaigning on the pet issues of those politicians. When it comes to efforts of politicians that are targeted at convincing voters, focus groups are a key tool that's used in addition to polling.

1lilkim202523d

Focus groups have their own issues. Easy to subvert through any number of underhanded means, and also accidentally through errors in sampling. They also don't meaningfully indicate issue pertinence. Suppose I really like Trump's policy on road sign renaming, and I really hate his policy on highway median resizing. I might talk passionately about either in a focus group where I'm being paid for my opinion on those issues, but neither is likely to influence my voting behavior. Prediction markets are valuable in that they can cleanly separate issues that are popular/unpopular but electorally insignificant from ones that make or break a campaign.

1ChristianKl23d

I think you aren't factoring in that focus groups are lead by people that are not stupid. You can ask questions in a way to get answers about what people care about. Regime Change #2: A plea to Silicon Valley - start a project NOW to write the plan for the next GOP candidate by Dominics Cummings is a good read for what focus groups can do.

1lilkim202523d

At best, a focus group is directed by well-intentioned people who may not know every pertinent correlation value needed to ensure a representative sample of the public. The conventional wisdom is that focus groups are typically used and designed by people who are somewhat out of touch, and this isn't entirely false. At worst, a focus group is directed by people with an interest in showing a certain outcome to a certain set of people. Using the earlier example, AIPAC lobbyists might want to downplay the political damage caused by the Iran War, and can alter candidate selection and discussion framing in order to do so when given authority over study design. Moreover, participants often want to please the experimenters, and will, consciously or otherwise, speak differently depending on what they think they want. Put simply, you end up with all the same issues as polling. Potential for bias, design error, and unreliable subjects.

[-]AnthonyC25d61

People have a tool they want to use, whether that be cryptocurrency or forecasting, and then try to solve problems with it because they really believe in the solution, but I think this is misguided.

It is true that this (almost) never works, and has resulted in many, many wasted investment dollars.

Unfortunately it is also true that when a problem comes along, it is very convenient if someone else has already partially developed the underlying technology that can be repurposed to solve it. Cuts years off the timeline to a solution. Use of blockchain for crit... (read more)

7Petropolitan25d

The list of problems with mineral traceability has never really included someone tampering with the data already in an external database. What it does include is the data entered into the database being false to begin with, while many participants of the projects have economic incentives to cover it up, and there are indeed geochemical fingerprinting attempts to fix that problem but they are entirely orthogonal to the issue of the data storage and access. From what I know, I see no significant advantages of a blockchain against a public, well-audited relational database maintained by an independent NGO (like a special body under a UN mandate) besides maybe a geopolitical one (the NGO has to be based somewhere after all), but quite a few disadvantages like not being able to correct the fraud which has been discovered (very convenient for the fraudsters indeed, also no one to blame for that!), trickiness from an engineering/interoperability point of view etc.

3AnthonyC24d

This is a good point and that probably wasn't the best or cleanest example I could have picked. Last year I spoke with several blockchain company founders that pivoted to that application as part of a project at work. For the most part, they agreed that data entry is the biggest security hole in any such system. And the sense I got was that so many companies were dragging their heels on complying with passport mandates that have upcoming deadlines it would basically have to be a software product because there would not be time to implement the other options.

2mabramov20d

I think this is a good point. I don't think it makes up for the ~$125M or so that EA has put into forecasting.

3AnthonyC20d

That's fair. Totally reasonable to say EA should stop.

[-]tenthkrige24d50

I express no position on the object level, but you may be arguing for something that has already largely happened, see here:

As of March 30, the Forecasting Fund is no longer active, though we continue to make key forecasting grants through other funds, such as Navigating Transformative AI. This page will be maintained until the end of 2026 as a record of the fund’s work.

1mabramov20d

They will still be funding lots of forecasting, just not through a dedicated fund.

[-]elifland25d50

[-]XelaP24d31

Once, Robin Hanson made a post on his blog about what he'd do with $1M: try to get prediction markets used in companies. The theory of impact is that they are more likely to actually use the markets, and that it is more impactful than political discourse or prop bets. He suggested making a market of the form "If X CEO steps down and is replaced by Y, the stock price will go up by Z", then trying to get a company to take its advice and end up proving it right, and then to try to make it something shareholders demand of any company.

Current markets have lande... (read more)

2sanyer24d

Prediction markets, or at least judgemental forecasting, has been used in companies before though? At least Google had a pretty substantive internal forecasting platform at some point.

1XelaP24d

And they tended to be quite successful, yet not get used or get shutdown, which Hanson attributes to cognitive biases and upsetting social hierarchies. But it's possible for such things to get adopted - e.g. hospitals do hand washing now, and even checklists have spread. Thus the idea is to get some more high profile showcase of its usefulness, and for decisions that are worth much more.

[-]Martin Randall25d30

How do you rate the educational benefit to participating in prediction markets for about a year? You mention that trivial/gambling markets on short-term BTC movements don't sharpen skills, what about non-trivial markets? How does it compare to other educational/community activities like commenting on LessWrong or attending meetups?

7mabramov25d

I think from the time I started to the time I stopped, I didnt get any better. I was just as reasonable at both points in time

[-]Nathan Young19d20

[COI: I work at the Swift Centre as a forecaster, I have worked for a prediction market, I am very involved in forecasting. It is not my current work however, which is on community notes]

A few things points attempting to say things other commenters haven't, though I largely agree with the critical comments and the things they agree with Marcus on:

I agree that the $100M doesn't seem super well allocated. Not because forecasting is useless, but because the money flowed to big institutions and platforms rather than smaller, weirder, mechanism-design bets. I l... (read more)

3mabramov19d

I'll ask a different question than I did on the EA forum. Do you think you work in and are interested in prediction markets/forecasting because they are interesting or because they are the best thing you can do for the world?

4Nathan Young18d

I don't work primarily in prediction markets/forecasting. I work trying to build AI community notes. The forecasting work (maybe like 5-10hrs a week) I do is paid at a rate I am happy to work for it.

[-]alexjaniak22d20

I think you're measuring the right thing (decisions changed) but blaming the wrong cause. I think the field underperformed because:

The questions are at the wrong altitude. "P(AGI by 2027)" is fun to trade but hard to act on. The decision-relevant questions (e.g., will this research direction work, will this eval saturate first, will this intervention move its metric) rarely get asked because they're too narrow and poorly funded to attract pro-forecasters. Moreover, such narrow questions usually rely on internal information that is difficult to attain for t

... (read more)

[-]ejri9d10

There should be a parallel approach to forecasting research. Currently, it's all in silos, when parameters do not act independently

[-]Aryan6416d10

I am intrigued by the 'solution seeking a problem' framing. Unlike blockchain, there are a number of domains and decisions that would analytically benefit from more accurate predictions about the future (e.g. issue prioritisation, campaigning decisions in politics, domestic policy decisions, geopolitical actions) even if they have practically not adopted forecasting widely. Better forecasts can prevent worse counterfactual worlds, e.g. by preventing planning mistakes leading to rapid inflation, encouraging better resilience planning, etc.

The claim that 'F... (read more)

1mabramov15d

On the solution seeking a problem, I think proponents of blockchain say similar things about how useful it would be if our financial system were predominantly on the blockchain and how much benefit there would be, in particular to the unbanked. They do, in fact, say this. I never said that forecasting cannot be useful. I think it hasn't been useful to the tune of the money thrown at it, and I'm skeptical that further investment is going to pay off in usefulness. I was somewhat expecting the defenses of forecasting to be better than they have been. The best one was probably this one by @habryka or another reply by Eva Vivalt on the EA forum. It's possible we haven't been funding the right thing within forecasting. Maybe this weekend I will tally up every grant and investment made into forecasting, but I think it is ~$100m. That said, I think basically any poor funding decision will have people saying something similar to this: [...] When a startup says that their product hasn't taken off and all they really need is better marketing to do so, you should run. I think we should say the same thing here, too. If you need to focus on distribution and try to force your product into being used, it probably isn't that useful. When something is very useful, it gets adopted at extreme speed. The AI companies aren't spending a lot on marketing. For example, people adopted LLMs, chatbots, and other products extremely quickly, and they were more or less just announced. Furthermore, prediction markets have been adopted, so it's hard to say it's an issue of marketing. Almost everyone important has heard of Polymarket and Kalshi at this point and know how PMs work. And none of them has decided that they would be very useful. It just so happens that they are useful for sports gambling primarily and not for making better decisions.

5habryka15d

But... aren't prediction markets one of the fastest growing industries in the world right now?

1Aryan6415d

On the usefulness of blockchain, I think the analytical case for our financial system being on blockchain is significantly weaker than the analytical case for better forecasts being useful for policymaking. With my original comment, I was implicitly drawing a distinction between 'cannot be useful' and 'has not been useful': 1. If forecasting simply has not been useful, then it leaves us with two possibilities: (1) it cannot be and we should stop funding it; (2) it can be and we should redirect funding to efforts that figure out and solve why it hasn't been (e.g. distribution). I feel like the analytical case for at least attempting (2) is pretty strong. I will admit that this may validate skepticism to the tune of, 'We should reduce funding and seriously consider our theory of change'. 2. If the argument is that no amount of additional funding will justify the ~$100m we've already spent, I would argue that it doesn't need to. Any additional funding only needs to be useful enough to justify itself. Even if the overall forecasting program ends up being overfunded and unjustified, we should treat future funding as independent of any potential bad decisions in the past. For example, if the program is currently giving us $10m of value (for $100m of funding), and spending an additional $10m would increase that to $30m of value, then we should spend it. Even if the overall program remains a failure, our $10m has given us a 2x return. Wrt the startup analogy and LLMs, I am not sure it is reasonable to claim that all useful technologies get adopted at this extreme speed, re: seatbelts, childproof medical caps, vaccinations, drunk driving laws, helmets, indoor smoking bans, etc. which all required significant distribution efforts. To clarify 'distribution', I think there's a difference between 'everyone has heard of prediction markets' and 'we have the right tools to allow policy/decision-makers to adopt prediction markets in their decision-making

[-]Kavin20d10

Fair critique for many markets, but weather forecasting on Kalshi actually has tangible verification via NWS monitor data. I use Kumo (joinkumo.co/weather-analytics) to check multiple forecasts before betting. It provides real, measurable accuracy metrics not just vague "epistemics.

[+]HoVY20d-12-6

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