Prediction Markets for Participatory Budgeting | Devansh Mehta, Independent | ETHTaipei 2026

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Yep. Thanks for the intro, Shawn Woo. He's also the reason I'm here. He was one of our evaluators during our process to distribute funding and he was very good at his work. Um So, how many here have like either been a funder or evaluated projects?

Anyone has been like a funder? One. Two. How many have evaluated projects? You've been an evaluator for a funder.

One. How many have received funding from like grants and other things? One, two, three, four. Okay, slightly more. So, okay, we have a decent mix um thing.

How many of you know what prediction markets are and the basics of how they work? Okay, so around five. So, I'm going to assume like limited knowledge. Um Yeah, so the most basic form is what we are familiar with, which is like will Trump win or Harris? And whichever wins becomes 100 and whichever loses becomes zero.

So, that's the most basic form of prediction markets that we see. Slightly and I'm going to ask an exam at the end of this. So, like all of you pay attention because there's exam time coming up. Um this like the second form is what's called scalar markets and these markets don't resolve to zero or one. Uh they resolve to uh the percentage.

So, if we predict that Democrats will win 45% of the seats, but then they actually end up winning 50%. Your profit is only 5 cents because from 45% it goes to 50%. So, that's all that you actually end up making. So, unlike here, it's not zero or one. Here, it's a distribution.

45 becomes 50 or 55 54 becomes 50. So, that's kind of the second form of prediction markets. This is multi scalar markets, which is more applicable to some of us today here in Taiwan. Um like here you're trying to predict how much What is the percentage of seats that each party will win? So here you might say that EPP will win 188 seats, and your amount that you earn as a trader is based on how right you are.

How much how How much did you guess the percentage? If you guess it very correctly, then you get like and the market price is very wrong, then you get more. Um so that's multi-scalar. Now we start getting into the really interesting parts, which is conditional markets, where you're trying to decide who should the nominee be for my party to fight the election. Should my nominee be Trump, or should my nominee so so like should my nominee be Harris, or should it be Biden?

So this is a very difficult question to answer. Like I think the Democrats lost because they made the wrong decision over here. So this is where conditional markets help, where the market like people are like traders are giving their information about if Biden is the nominee, Democrats have a 30% chance of winning. If Harris is the nominee, Democrats have like a 25% chance of winning. So like with conditional markets, you can actually get information about what action you should take, about what decision you should make.

Um and this is now where we come to applying it to funding, where um here we are trying to see like what would a repo be worth. So we basically ask something like will the repo be evaluated? Shan Wu was an evaluator, but he has limited time. He can't look at every project and evaluate it. So we are saying that will it be evaluated?

And if it is evaluated, what score will it receive? So that like all of the traders are trying to predict the score of a project, and only some projects will actually get evaluated. And that's what determines their profit and loss, and that's what gives them an incentive to do it. Um so that's kind of how you can apply it to funding. There are many benefits to running a market, which I'll get into uh soon.

Um, yeah, the biggest benefit is this. This is like the entire Ethereum dependency graph. There are, as you can see, like 10,000 different edges or lines in the graph, and there are 4,000 different open-source repos. Like, no evaluator is able to look at all of these and then actually give like a number. It's like impossible for anyone to do that.

Um, so this is where we can run conditional markets, where we can run a market saying, "If an evaluator evaluated, what score would it receive?" And then we can only actually get evaluations for a few of them, but still put money and distribute to all of this. This was actually a prompt which uh Vitalik had given, which is, "Is it possible that I that you can just give me one public key, I put money in it, and it automatically distributes into the entire graph?" So, this is kind of a way to try making that happen. Um.

This is kind of the architecture as it works. Cool. Um, so the first level, second level, third level. It's better to see the example, so I'm not going to like run into this, but like quickly revision time because exam is next. So, all of you have to start answering questions.

Um, so yeah, you have the basic version, which I call binary markets. Binary markets are only two options. One option becomes 100, the other option becomes zero. That's binary. Scalar markets, where there are only two options, but it's not 100 or zero.

It's like it could be it 60 and 40, 50 50. It can it can resolve to anything. It doesn't have to be 100 and zero. Scalar markets. Uh multi-scalar markets, where uh there are more than two options.

So, that's kind of the And then you have conditional markets, which doesn't have to be resolved. So, that's the kind of just remember this framework, and now we'll start with exams. So, this is deep funding one, where we had 98 repos, and we asked people how important each repo is. So, here you can see that like go-ethereum, the market said it's worth 4.9%.

Uh Solidity is worth 4.67%. Consensus specs 4.3. And all of these have to add up to one.

All of these add up to 100. So, you can put money if you put money if you put $100 into this distribution, $4.9 will go to go-ethereum. So, that's kind of how the market works. So, question one is this a multi-scalar market, a scalar market, or a binary market?

You can just shout out the answer, yeah. Multi-scalar? Yeah, that's correct. It's a multi-scalar market. You're correct over here because there are many options.

Second question, this is a little harder. Is this a conditional market or a categorical market? Yeah.

[snorts]

It's a categorical market, right?

Okay, why?

Because the funding of one isn't dependent on the other.

The funding of one isn't dependent on the other.

Well, in some cases, yeah, but in this example, no.

Yeah. So, you got the answer right, but the reason wrong. The funding of one is dependent on the other because like they all have to add up to 100.

Yes.

So, because they add up to 100, if I if if one repo is getting 10%, that means the others can only compete for 90%, right? So, in a sense, so like we run into Arrows' impossibility theorem. If you try entering a new repo, then it upsets the weights of the others. So, this is not a conditional market because you have to evaluate every single repo. So, that's why you're right.

It is a categorical market. It is not a conditional market. I actually asked Vitalik that is it fine if you don't get answers on all 98 repos because it's super hard to get evaluators to cover all 98. And he said it actually creates more problems than it solves. You can't partially resolve a multi-scalar market.

You have to resolve every single outcome. So, this is a categorical multi-scalar market. So, that's question one. Uh we got one right answer and one right answer but but wrong reason here. Yeah.

Um Yeah. Um so, this is the second question now. So, this is like what's called the originality of a repo, which is uh how much money should stay with the repository versus get passed on to its dependencies. Um So, like the example here would be something like uh Brave browser. Brave browser is built on a fork of Chromium.

So, if you want to give accurate funding, you should actually not give the money to Brave browser or all 100% of it. You should give more to Chromium. Whereas, something like Solidity is very original. It has very few dependencies. It's built from zero.

So, they should keep all the money. So, that's what this market is. Uh here we are saying like supranational BLST, which is like a super important non-crypto repo, which we all depend on in the crypto world. So, here the market is saying that it is 74% original. So, 74% original means that 74% of the money stays with Blast and uh 26% gets passed on to its dependencies.

Um So, yeah. Second question is this uh simple scalar, multi-scalar, or binary market?

Still multi-scalar. Right?

Why?

Because it all adds up to one. Oh, no. It's a dependent.

Yeah. They're independent. Like if up value 74 and the down value is 26

Gotcha.

for that. So, do you want to change your answer?

No, no.

Okay, multi scalar is one answer. Anyone else have a I have another answer?

Uh simple scalar one.

Scalar?

Yeah.

Like simple scalar or multi

Simple scalar.

Simple scalar, yeah. Okay, that is the correct answer, simple scalar. The reason is that up plus down is equal to one. So, each of these are independent. There are just two options.

That's the main difference. A multi scalar has many options, right? So, when there are many options, then it's a multi scalar. This is a simple scalar because there are just two options. BLST 74% up, 26% down.

So, there are just two options per repo, which traders are actually like trying to predict. Um so, this is an origi- this is a simple scalar. Now, is it a conditional or a categorical market? By which I mean, do you need to resolve each of these repositories, or is it possible to just get a few answers and not get all of the other answers?

We only need to limited options, so this conditional

It's a conditional market. Yeah, that's also correct. Yeah, wow, you're really like an ace, you know, you aced it. This. Um so, it's a conditional market because um you don't actually have to get answers for all.

In our In our uh pilot, we only got answers for about 30% of the repos. So, the remaining 70% evaluators did not have time to actually get answers on it, but we did get answers for 30% of them, and that's what gave profit and loss to all the traders. So, the good news is that we got answers for all the repos, but we actually collected human data for only like a subset of like 30% of it. So that's kind of the advantage of running these markets. You can scale it up.

Um So this is the kind of the big level where we were trying to get weights to 4,677 dependencies. And so like this is an example of Viper. So you're saying uh Theta no boa, which is a repo repository of Viper language. Um we are saying that something like uh V VVM is worth 30% ETH account is worth 17.42%.

So this is like the different dependencies that make up the repo. Um if you look at the next repo, which is just Viper, not Theta no boa, there you can see CBAR is worth like 0.0005. So like this we can actually like get weights for the entire dependency graph, which is all 4,677. So is this a multi scalar, simple scalar, or binary market?

You've not got any answer from this side. So anyone here want to take a guess? Hm? Simple scalar. Simple scalar one answer.

Anyone have a different answer? So it is the wrong answer actually. They are um there's like um because this is not a simple because each of the values depend on the other. It's a multi scalar, many options. So here you can see all of the on the left-hand side you get all of these repos, um which have to add up to one.

So it's similar to the first market, which is a multi scalar because there are so many options. Is this a conditional market or a categorical? You can take another guess at it. Conditional or categorical? Categorical?

So, that's like partially correct because if we choose a repo, we have to evaluate all of its different dependencies. But, the difference is that it's also conditional because we don't have to choose every single repo. So, like if we choose Tietonobua, then we have to evaluate all of its dependencies, so it is categorical, but we can just choose Viper and not choose Tietonobua. In that case, it becomes a conditional market. So, for a lot of the like in this case, out of 98 repos and 4,600 dependencies, we only actually collected data for um like maybe 12 repos.

So, traders were trading at a 6x leverage in the market because their profit and loss just depended on those 12 repos that we actually collected data for. For the 98 repos and 3,000 dependencies of those 90 repos, we used their answers to distribute money, but we didn't actually resolve those markets. But, we only resolved these, so So, that's kind of the exam. Um this is like one part of the big exercise we ran, but something we realized is that we can actually like use this prediction markets in a more broader way that if there's any funding round happening, we can just run a prediction market on on that funding round. So, like right now there's a like there's a Zcash retro round where 37 projects have applied for funding, and it's very hard to look at 37 projects and evaluate them.

So, we just started a prediction market saying what is the odds that this project is going to be approved by the Zcash token voters. So, now when token voters have to actually vote on each project, they just go to our prediction market, they see the odds, and then they can actually do triage. They can see that, "Okay, this project like Frontier Compute Zcash or only 33%. Let me apply some extra scrutiny to this project." Or something like like a Iron Wood is 87%.

So, that's like a very high odds. So then they can calibrate their expectations and they can like spend less time reviewing it because the market is already assigning a high probability. And all of these values will convert. So like is this a binary market or a scalar or or is this a binary or a scalar? Yeah.

Binary. I heard someone shout binary and that's the correct answer because if the project gets funded, it converts to 100. If it doesn't get funded, it's zero. So that's a binary market. Um So yeah, like this is the last slides now.

This is like the big advantage of like what we got. So like for example, from deep funding, we looked at what the breakdown of each client distribution is for Ethereum. So according to the market, according to the votes of the jurors, and according to the winning model. So something which Vitalik noticed when we sent him this list, he said, "Oh, Teeku has a very high amount according to the juror votes. It's like 17%."

And then when we went back to the jurors, they said, "Yeah, yeah, it should definitely not be seven like 17%." Um it should be much less. So by actually running this market, you can identify discrepancies that the committee is saying this, but the market predicted another value. So let's ask the committee now. Hey, the market and your values are very off.

What is the reason for that? So that's kind of an advantage. Um this was an Octant. Octant is like a project. This is the last slide.

Um this is like the um Octant stakes ETH and they distribute money. So we ran a prediction market of who's going to get how much money. And actually the Octant organizers and others also said that they prefer the prediction market results. Like when we posted this on Twitter, everyone said, "Wow, the market weight like giving 10 like giving 10% to Solidity is like a good thing, you know? That's like how much they should get compared to the actual 7% that they that they received."

So we used the actual results of Octant to determine profit and loss for the traders, but we can use the traders score as an alternative to just the market thing. So, that's the thing. I won't get into this. This is like very technical in a paper we wrote, but thanks. Hope you enjoyed the talk.

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Uh anyone have a question? I really liked your talk and I was thinking when you mentioned the second last slide about the the one before the Yeah, this this one. Would if if I'm like a project like Tezos, wouldn't I be in my interest to like bump my prediction market to have like a self-fulfilling prophecy? Like it might be I might make more money betting on my own market because I might get more funding.

Yeah, and I think we saw those problems happen in quadratic funding especially where you could like vote for your own project. And I think the biggest difference is that in quadratic funding there's no cost to like manipulate. Like there's no cost to manipulation. I create many accounts. I can vote for my project and then I get more money.

And if I'm caught, the worst that happens is that my vote is not counted. So, there's no cost to like manipulation. Whereas here, if Tezos tries to manipulate the market to also move it up to 17% then any trader can counter trade them saying that look, Tezos is definitely not going to be worth this much. And we actually saw that happen in one of the markets where a repo called ACT from 1% they shot up to 10%, but then all the other traders are like this is definitely not worth 10% and they got it back down to 1% and it finally resolved the market at like 0.7%.

So, any trader who bought it like lost massive amounts of money, which is like good.

Yeah, and in this next slide after this, you have like for example, this is for stakers, right? And stakers might actually want Rodkey, right? Because Rodkey is a good product, but the market might not know what the hell it is.

Yep.

And the market just knows Solidity, so in this case the market isn't as smart as the people who want the what they want, right? Like they want Rodkey and they the market doesn't know what it is. Uh

[snorts]

so it it the it goes both ways, right? Like the market could influence the result, but the market could be naive to what the result really wants.

I think that's why it's also important to run repeated iterations. So in the next Octant round, the market is going to learn, right? Because everyone everyone made such a loss on Rodkey and all of that. So they're like, "Yeah, we don't want to make a loss again." So then the market ends up learning if you run it multiple times.

Um and even like just it's good to have an alternate set of weights. Like otherwise you're forced to accept one set of weights, but it's good to have an alternative that you can look at. And like the other thing about model builders is that a lot of them are building AI models which actually give which actually trade. So they don't look at each project, they just build an AI model which looks at the code base. So in this case, Rodkey's code base was looked at, Solidity's code base, and that's what their model outputs.

Because models can consider very different levels of information from a human. So that's like a way of using AI to kind of like a prediction market can just combine different AI outputs. It's called ensembling for those who know LLMs and all of that. So this is like just a method prediction markets are a method of ensembling different AIs. Any other question?

Hi. Uh I'm Yeah, I wonder can we go to the the previous slide? Yeah. Yeah, this slide. So, I'm curious to my understanding the jurors are the people who determine the payout to the gamblers.

Mhm. Right.

but but then I think you you mentioned that uh you you seem to uh suspect that maybe the jurors are wrong. But then I'm I'm curious like the jurors are sort of like the um umpires in a football game.

Mhm.

Or they are the oracles who give the ground truth

Right.

to us. So, yeah, like so I I wonder like who who makes sure the accountability of jurors?

Right. Um yeah, it's a very hard question because it's like even in the regular non-prediction market world, grant evaluators have very limited accountability. Right? Like if I'm a funder and I have have staff, if my staff make a wrong decision, it's very hard to hold them accountable and say you made a wrong decision. So, even in the regular world, evaluators have very low accountability.

Um in this case, the juror votes like the way we got the jury was uh Vitalik nominated two people and they did comparisons and they nominated two and they nominated two. So, we grew up our entire tree. So, you start with one person who's the funder, they nominate two people, they nominate two, they nominate two, and you can grow up your entire tree. So, that's how we built the juror votes over here. Um but in this case, I think like the jurors, it's not even a question of like them being right or wrong.

It's like you average the juror data because no it's not like one person is evaluating. There are many many people evaluating and you average the scores. So, once you average it, a lot of things break. A lot of jurors are like, "I don't agree with this score at all." because they were just one juror out of 50 jurors.

So, there's that also the level of averaging which like um which people don't agree with often. So there's a bunch of nuances here like when you average out people don't agree with the average results but they're all just giving one data point. So the market can be sometimes more consistent than the average of many judas votes because the market follows certain like different combined like combination laws.

Ti- times up. Sorry. Sorry times up but you guys can talk up. Yeah. Have a discussion later.

Okay. So uh thanks for the device this uh this wonderful uh talk. Yeah. So let's give him a a praise.

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