$350,000 is being allocated across 98 open source repositories and their 3,677 dependencies through Gitcoin GR24, with $175,000 from the Ethereum Foundation (EF) and $175,000 from Gitcoin. Rather than running another quadratic funding round, this time the round is being decided by an estimate of past contributions from a human jury, and a prediction market to trade those estimates.

The most interesting part of this round is that attempts to manipulate it become profit opportunities for others to capitalise on.

This is the most ambitious application of prediction markets to a real funding decision that’s ever been run on-chain, and the mechanism is worth understanding in detail because if it produces better outcomes, the same template can fork to any DAO, any protocol, and any community facing the same allocation problem.

Let’s discuss.

Why Markets Beat Committees for Funding Decisions

The case starts with the observation that committees don’t bear the cost of being wrong, and markets fix this by making accuracy expensive to abandon. Traders who misprice repos lose money to traders who reprice them correctly, and the system rewards being right rather than being ‘loud.’

Robin Hanson made this case for futarchy decades before the infrastructure existed to run it, and Vitalik continued the thread with ideas like Distilled Human Judgement, where he framed prediction markets as a way to solve the trust problem in scientific, political, and commercial decisions, especially where consensus on who to trust is nonexistent.

Deep Funding is Distilled Human Judgement applied to retroactive public-goods funding, a core engine of the Ethereum ecosystem. The round’s success or failure tests whether markets can scale the judgment of committees and help maintain better decision-making and greater impartiality.

Distilled Human Judgement: The Primitive That Makes This Possible.

The core primitive Deep Funding relies on is distilled human judgment, and understanding it is the difference between seeing the mechanism as “prediction markets for grants” and seeing why this particular shape can scale to problems committees can’t reach.

The setup is straightforward. You have some evaluation mechanism that produces high-quality judgments. In Deep Funding’s case, it’s human jurors at deepfundingjury.com who compare repos pairwise and score them based on contribution to Ethereum.

The problem is that this mechanism is expensive and can only evaluate a limited number of items, which means evaluating all 98 seed nodes pairwise, all 98 originality scores, and weights for all 3,677 dependencies would take an impossible amount of expert time. So instead of evaluating everything, you run a prediction market on the question “if this item is evaluated, what will the result be?” Most items never get evaluated by the underlying mechanism, but the market gives an estimate for all of them.

The items that do get evaluated serve to resolve the market and determine which traders profit and which lose, and because traders don’t know in advance which items will be selected for evaluation, they have an incentive to trade every item according to their genuine beliefs rather than gaming the subset they think will resolve.

The scaling benefit is the obvious one; expert judgment gets stretched across a much larger set than the experts could possibly evaluate directly. But the less obvious benefit is denoising. Evaluation is inherently noisy because it depends on the mood of the evaluator during evaluation, on which specific evaluator is chosen for a given item, and on countless other factors that vary from one assessment to the next. Evaluation can also be biased in ways that aren’t accessible to the broader pool of traders; an individual evaluator might have a personal grudge against a particular project, or be unusually enthusiastic about a niche technical area. Because traders can’t forecast the specific noise that will show up in any specific evaluation, that noise gets averaged out of the market price. And because traders can’t access or forecast biases held by individual evaluators, those biases also get washed out of the aggregate signal. The market price ends up cleaner than any single evaluation would be, even though it’s being calibrated against those individual evaluations.

This is why Deep Funding can credibly produce weights for 3,677 dependencies despite no committee in the world having the bandwidth to evaluate that many items directly. The market does the work of approximation, the jury does the work of resolution on a sample, and the combination produces estimates across the full set that are cleaner than either side could produce alone.

The Methodology in Depth: Three Levels of Allocation

The allocation works through three interconnected markets that together produce a complete weighted dependency graph from Ethereum down to its leaf-node libraries, and the cleanest way to understand how they fit together is to walk through what happens to a single repo.

Each level has its own Pond contest, its own prize pool, and its own market on Seer, which means model builders can compete at any of the three levels independently or build a unified model that produces predictions across all three.

Level 1- is the seed nodes market, which divides the full $350,000 across the 98 repos with weights that must sum to 1 against Ethereum as the target. If Solidity receives a 0.03 weight, that translates to $10,500 of the pool flowing to that node before any further distribution happens.

Level 2 — is the originality market, which assigns each repo a score between 0 and 1 representing how much of the credit belongs to the repo itself versus its dependencies. A score of 0.8 means the repo is mostly original work where dependencies are generic and replaceable, 0.5 means the repo has done substantial work but leans heavily on its dependencies, and 0.2 means the repo is essentially a fork or wrapper where most of the underlying value sits in the libraries it depends on. If Solidity’s originality score lands at 0.6, then $6,300 of its $10,500 stays at the seed node and $4,200 flows down to its dependencies.

You can see this demonstrated in Figure 2.

Level 3 — is the child nodes market, which divides that $4,200 across Solidity’s individual dependencies with weights that again sum to 1, so a dependency that catches a 0.05 weight at level 3 receives $210 from Solidity’s pass-through pool.

The whole structure is one weighted graph where each node’s allocation depends on the level above it, and resolution at every level happens against pairwise comparisons from the human jury at deepfundingjury.com.

Jurors submit judgments like “solidity is twice as important as geth,” the system takes logs of those ratios so they become differences, finds the values that best match the differences by minimizing Huber loss across all pairs, and then exponentiates the result to recover positive weights.

The scoring function used to grade model submissions is the same one that resolves the prediction markets, which means the Pond contest leaderboard and the Seer trading P&L are measuring the same thing through two different feedback channels.

How to Participate

There are two ways to engage with Deep Funding as a model builder, and both are live right now with no waiting period.

The first path is the Pond ML contest, which runs from March 9 through June 29 and offers $10,000 in total prize money split across the three level, $2,500 each at Level 1 and Level 2, $5,000 at Level 3, plus a separate $10,000 writeup prize pool for the best model documentation across all three levels.

The respective due dates for each level are:

Level 1 — June 30th, 23:59 UTC.

Level 2 — June 16th, 23:59 UTC.

Level 3 — June 2nd, 23:59 UTC.

Simply submit a CSV of predicted weights in the format {source, target, weight} where source and target are given to you, and you fill in the weight. The weights are scored against jury data, and the leaderboard updates as new jury data arrives during the round.

Submitting a writeup is mandatory for leaderboard prizes, which is how we ensure the round produces actual research rather than just a stack of opaque CSVs. The writeup needs to be submitted both alongside the model code and at the Gitcoin governance forum thread, and the username on both submissions has to match for prizes to land correctly.

The second path is trading on Seer directly at deep.seer.pm, where any model builder can upload the same CSV used in the Pond contest to take positions on the live markets. Market prices at any moment reflect the aggregate prediction of every model builder currently trading, which means even if you’re not running a model, you can profit by spotting where the consensus has drifted from where you think the jury will actually resolve. The same CSV that enters the Pond contest becomes a Seer trading position, which means one piece of work generates two revenue streams and validates against two different feedback signals simultaneously.

The Incentive Structure: Forgivable Loans and Trading Subsidies

The participation economics are designed so that the downside for any individual model builder is effectively zero.

Trading credits on Seer are structured as 20% pure grant and 80% forgivable loan. The grant portion is yours to keep regardless of how your model performs, and the loan portion only has to be returned out of whatever’s left after trading. If your model loses money, you only return what remains of the 80%, which means your downside from your own pocket is zero. If your model profits, you keep all the profits and return the original 80% loan, which means the upside is uncapped while the floor is protected.

This structure exists specifically to lower the barrier for builders who have never traded prediction markets before, because a model builder who has spent weeks on feature engineering shouldn’t also have to absorb the risk of losing personal capital on their first deployment.

Seer is providing additional trading subsidies to top performers throughout the round, which means model builders who climb the leaderboard receive ongoing capital to keep trading rather than running out of position size after early losses.

Round 1 participants who profited received a 1,500 sUSDS bonus on top of their trading profits, and participants who took losses still received 500 sUSDS for participating, which creates a positive expected value for engaging seriously with the mechanism even before considering the Pond contest prize pool.

Leaderboard position bonuses include:

• 1st 2000 sUSDS
• 2nd 1500s sUSDS
• 3rd 1000 sUSDS
• 4th-10th 500 sUSDS
• Score better than 9999: 200 sUSDS

The structure is deliberately overdetermined toward making participation rational, because the mechanism’s success depends on liquid markets and liquid markets depend on enough participants showing up.

Why Gaming Makes You Money

If a repo decides to inflate its own market price by buying UP tokens on the Level 2 originality market until the score sits at 100% and zero credit flows to dependencies, the human jury later evaluates that repo and lands on a more reasonable 80% originality score, and DOWN tokens that were trading between 0 and 20 cents redeem at 20 cents.

Anyone who bought DOWN against the inflated price profits the difference, and the repo that tried to game its own market loses money on every UP token it bought above the 80-cent mark. The same mechanic applies at Level 1 and Level 3; any attempt to push a market price away from where the jury will eventually resolve creates a profit opportunity for traders who price the repo correctly, and the more aggressively someone tries to manipulate, the more profit accumulates for the participants who spot the manipulation.

Deep Funding turns manipulation into a direct cash transfer from manipulators to the participants who price honestly.

If this works, the implications extend far beyond one Gitcoin round. Every DAO making funding decisions faces the same problem, where evaluation doesn’t scale, committees get captured, and voting optimizes for marketing rather than merit, and the same template that’s allocating $350,000 to Ethereum infrastructure right now can fork cleanly to any allocation problem where expert judgment is the bottleneck.

The contest is open. The markets are live.

What you build on top is up to you.

These instructions and examples are for Seer prediction markets on the Gnosis chain.

Adding Liquidity to markets

A) Use sDAI or xDAI to mint tokens. Plan to apply roughly 50% of the total collateral you plan to use for that specific market’s liquidity. Click “More” underneath the Trade (Buy and Sell) box and the option to “Mint” will appear. In this example we’re minting 500 sDAI worth of tokens.

B) Add liquidity to each individual outcome by clicking “Add Liquidity” for the respective outcome. In this example we’ll start the DOWN outcome (the “SY25_EDI_DO” outcome token) in this scalar market. Note how the 500 sDAI minted in step 1 created 500 outcome tokens for each outcome.

C) Clicking “Add Liquidity” will take you to Swapr where liquidity is managed for Seer markets.

• (1) Select a pair. The pair of tokens for the liquidity pool should be selected for you automatically. In the below example we see that SY25_EDI_DO and sDAI are chosen. Double-check before clicking “Set initial price >”

• (2) Set initial price. Set the initial value for the tokens. Remember that the value of all the outcome tokens for this market should add up to 1. This DOWN token is valued at 0.53. (UP will be 0.45 and INVALID will be 0.02). Double-check your values before clicking “Select a range >”

• (3) Select a range. You can set a custom price range or select a preset range. To minimize risk, particularly if you are inexperienced with providing liquidity, select “Safe” from the Preset ranges. Now click “Enter amounts >”

• (4) Enter amounts. First click “Approve” for Swapr to access each of the two tokens:

Click “MAX” for the amount representing outcome tokens

Adjust to “Token ratio” only if you understand the consequences on the price. Otherwise, double-check the token amounts and then click “Add liquidity”

Click “Go to pools” to check your positions in Swapr.

On the “Positions Overview” page you can toggle between “Active positions” (currently holding) and “All positions” (that you have ever held with this address in Swapr).

Below, you should see the position you just created:

D) Now repeat the process for the other outcomes in this particular market.

By providing liquidity to an outcome in a market, LPs will earn a small percentage of the transaction fees when the respective outcome is bought or sold.

And Seer has launched an Initial Airdrop Distribution where SEER tokens can be earned in proportion to the effective liquidity provided on designated liquidity pools. There will be a “Seer LP Point” token (SER-lpp) which will serve as a receipt and to allow liquidity providers to clearly understand how many points they are getting.

One of the most powerful features of Seer is permissionless market creation. Any user can create a market on Seer; check it out for yourself.

1. Click “Connect” in the upper right corner to connect your web3 wallet

Markets can be created on either the Gnosis or Ethereum blockchains; this example uses Gnosis.

2. Click “Create New Market”

You can also click “Create Market” in the navigation bar.

3. Select market type

There are four types of markets to choose from:

• Categorical markets have one correct outcome out of multiple options. This can include binary (Yes or No) outcomes.
• Scalar markets resolve to a numeric outcome along a scale.
• Muti Categorical markets allow for multiple correct outcomes. For example, ‘which teams will make the playoffs this year?’
• Multi Scalar markets allow for multiple numeric outcomes. For example, ‘how many seats will each party win in the Bundestag?’

4. Enter Question and Outcomes

The following example is for a Categorical market. Clicking on the “+” sign will allow more options to be added.

Optional: By default, outcome tokens are automatically named after their respective outcome. To customize the names of the outcome tokens, click “Set custom token name”. Letters must be uppercase and token names no more than 11 characters. For example, “BANNONPARDY” and “BANNONPARDN”

5. Select the “Opening Date”

This is the date the market opens for resolution and not the date it opens for trading. (All Seer markets are available for trading as soon as they are created.) Remember to use UTC time.

6. Review and click “Create Market”

There is a tiny transaction fee of under $0.01 in xDAI.

Optional: Verify the market

Because anyone can create a market, Seer allows for markets to be vetted through Kleros Curate so users know they can safely interact with a market. These markets have a green “Verified” badge:

Markets can be verified during market creation or after the market is already created by clicking “Verify it” in the bottom right of the market container. A refundable deposit of roughly $120 in xDAI is required to verify a market.

Please keep in mind

• All markets (even markets not verified) must meet Seer Market Rules or they will be invalidated. These can be found under “Policies” in the navigation bar.
• Seer uses an Automated Market Maker (AMM) so liquidity must still be added to the market in order to facilitate trading.
• To help traders discover markets, you can add your market to a category after it has been created.

Futarchy

In his seminal manifesto on the “futarchy” 25 years ago, Professor Robin Hanson devised a form of government using prediction markets to set public policy:

In “futarchy,” we would vote on values, but bet on beliefs. Elected representatives would formally define and manage an after-the-fact measurement of national welfare, while market speculators would say which policies they expect to raise national welfare.

…

[D]emocracy would continue to say what we want, but betting markets would now say how to get it.

Prediction markets have been proven superior to experts in a wide array of fields (politics, finance, economics, public health, meteorology, and more). Companies like Google are using them right now for forecasting performance. Rather than use experts to set policy, futarchy has elected officials set up prediction markets to decide the best course of action. And it makes sense: If prediction markets are more accurate than experts at forecasting, why not use markets to forecast public policy outcomes and automatically enact the best policy into law?

Decision Markets

Less rigid than Futarchy, “Decision Markets” use prediction markets to guide decision making. Nothing is automatically implemented. In “From prediction markets to info finance,“ Vitalik Buterin describes them as:

[Y]ou want to know whether decision A or decision B will produce a better outcome according to some metric M. To achieve this, you set up conditional markets: you ask people to bet on (i) which decision will be chosen, (ii) value of M if decision A is chosen, otherwise zero, (iii) value of M if decision B is chosen, otherwise zero. Given these three variables, you can figure out if the market thinks decision A or decision B is more bullish for the value of M.

Decision markets are used to provide information about competing courses of action. In the diagram above from Buterin, the Board of Directors for a company (“ExampleCorp”) is considering whether to fire its CEO. A parent market is created:

• Will ExampleCorp fire its current CEO? [Yes/No]

To measure the outcomes of fire or not firing the CEO, two conditional markets are created:

• Share price if CEO fired? [If parent market “Yes”]
• Share price if CEO not fired? [If parent market “No”]

In Buterin’s example, the company’s share price will be $32.70 if the CEO is fired, and $17.50 if the CEO is not fired. The markets signal to the board that the optimum decision in terms of share price is firing the CEO.

The CEO example only has one metric under consideration but other metrics could be used simultaneously with share price. Conditional markets could have also been created for revenue growth, earnings per share, profit margin, or any other forecast that would inform the board of the best course of action.

Info Finance

Coined by Buterin this past November, “Info Finance” is any use of finance to “align incentives in order to provide viewers with valuable information” where prediction markets and decision markets are mere subsets of this much broader category.

He describes the process as merely:

(i) start from a fact that you want to know, and then (ii) deliberately design a market to optimally elicit that information from market participants.

Buterin believes info finance can be applied to DAO governance, advertising, peer review and other use cases. AI will eventually “turbocharge” info finance by providing quality information to the markets but for less cost than is needed to motivate humans to trade in these markets.

Conditional Markets on Seer

In each of Futarchy, Decision Markets and Info Finance, there is the need for designing markets conditional on the outcome of other markets. Seer has built the infrastructure for these “conditional markets” using the Gnosis Conditional Token Framework. Now users can create a market for an event they want to know and design conditional markets to measure each outcome.

Let’s say we want information about how the South Korean constitutional crisis is affecting the Korean economy. We’ll design a market about whether South Korean President Yoon survives for one week after his martial law debacle:

The three possible outcomes — Yoon’s status in one week after martial law is lifted — are:

• Impeached & currently President, but powers suspended
• Actively serving as President
• Former President, resigned or removed from office

To measure the economic consequences of what happens to Yoon, we’ll create three conditional markets about the value of the South Korean KOSPI composite index one week after martial law was lifted for each of the three possible outcomes.

One day out from the Yoon market close, traders signaled different estimates for the KOSPI based on the outcome for Yoon:

Traders believe that Yoon leaving office (resigned or removed) would lead to the highest value for the KOSPI (2466) whereas Yoon remaining in office with full powers would lead to the worst economic outcome (2419). South Korean National Assembly members, leaders and citizens, could use these forecasts to inform their own personal decisions on how to proceed with President Yoon.

In Robin Hanson’s futarchy, Yoon’s fate would be tied to the conditional market with the highest KOSPI value. In decision markets and info finance, these prediction markets merely provide information that could be used in decision making.

The KOSPI is just one condition that could be used. There also could have been conditional markets on other metrics like the value of the Won or Samsung’s stock price. Categories other than financial could be measured; for example, what will be South Korea’s rating in The Economist Democracy Index for 2024?

Of interest, the actual value of the KOSPI index for 11 December 2024 was 2443, only one off from the market conditional on impeachment. However, while the impeachment process was in motion, Yoon wasn’t actually impeached until a few days later.

Links

• Seer prediction marketplace
• Seer documentation
• GitHub
• Gnosis Conditional Token Framework
• Futarchy: Vote Values, But Bet Beliefs (Hanson, Robin)
• An Introduction to Futarchy (Buterin, Vitalik)
• From prediction markets to info finance (Buterin, Vitalik)

Martial Law?

South Korean President Yoon Suk Yeol declared martial law on 3 December 2024. The National Assembly repealed it within a few hours, but the President and military initially resisted. Seer launched a market about martial law before President Yoon eventually backed down after mass protests and resistance from the assembly.

Seer traders were confident that martial law would not last one week:

Impeachment or Resignation? Or Neither?

Now that martial law has been lifted, there is uncertainty about what will happen to President Yoon. The impeachment vote is currently scheduled for Saturday, 7 December 2024.

The three distinct possibilities about Yoon’s status as President on 11 December 2024:

1. Impeachment. Yoon is impeached and his presidential powers are suspended until South Korea’s Constitutional Court hears the case. At that point, he would be reinstated or removed as President. The opposition party will need at least eight members from Yoon’s ruling party to vote for impeachment. Yoon’s People Power Party (PPP) was divided over martial law, and none voted for the impeachment motion, but the head of PPP is now calling for impeachment.
2. Actively serving. Yoon resists calls for his resignation and the impeachment vote fails. He continues to serve as President with full presidential powers.
3. Leaves office. Yoon is resisting demands from public protests and the opposition party to resign, for now. His defence minister has officially stepped down, but that has not quelled the public outrage over martial law.

Here is the Seer market about Yoon’s status at the end of 11 December 2024:

The market sees Yoon likely being impeached with a small chance of resignation.

Economic fallout?

The uncertainty over martial law, and now impeachment, has sent shockwaves through the South Korean financial system:

• Markets and stocks plunged.
• The finance ministry promised “unlimited” liquidity to stabilize financial markets.
• Massive crypto sell-off on local exchanges.

Seer created “conditional markets” on Yoon’s status as President at the end of 11 December 2024 to measure the economic consequences of what could happen to President Yoon.

There are three scalar markets forecasting the value of the KOSPI composite index dependent on Yoon’s status as President:

• Impeached & currently President, but powers suspended

• Former President, resigned or removed from office

• Actively serving as President

These markets generate information about the future, in this case the economic consequences (measured by the KOSPI index) of the different outcomes for Yoon. In turn, the market forecasts can help inform decisions on how to proceed, which is why conditional markets are often used as “decision markets.”

These markets are forecasting a dynamic situation, so please check the latest news on South Korea before trading.

Seer is a decentralized prediction marketplace. Users can buy and sell shares, create markets and provide liquidity. Markets are resolved using Reality.eth and arbitrated through Kleros Court. Seer offers different types of prediction markets beyond traditional binary Yes/No outcomes, including scalar, multi-scalar and multi-categorical markets. Implementing the Gnosis conditional token framework, Seer can offer “conditional markets.” These are prediction markets dependent on the outcomes of other prediction markets, allowing for futarchy, decision markets and info finance.

What professional pundits, and Nobel Prize winners like Paul Krugman, fail to understand about prediction markets is that if you think Trump is overpriced, then you should buy Harris shares. It’s really as simple as buying an undervalued asset.

Professor Robin Hanson describes prediction markets as being difficult to manipulate because they consist of “wolves” and “sheep”:

The wolves know something and go to the market to trade on what they know. The sheep don’t know anything and they’re there to trade for some other reason. The wolves want to trade against the sheep. When a wolf trades against a wolf, on average, neither of them can make money. But when a wolf trades against a sheep they can make money, so the wolves want to trade against sheep. Wolves are attracted to where the sheep go. So this means the more sheep there are, the more wolves are attracted, and therefore the price gets more accurate with more wolves. The key thing to know is that a manipulator, someone who wants to push the price somewhere, is basically a sheep. Manipulators don’t know something, they just have a goal they want to achieve by pushing the price. Expecting there to be manipulators attracts the wolves and makes the prices more accurate. [Edited for clarity]

In the campaign’s final month, and several weeks after, the media fixated on “Théo” the French Whale buying tens of millions in Trump shares on Polymarket as if this invalidated the market prices. If Théo was brazenly wrong, traders should have gobbled up Harris shares. (They didn’t.) Below we can see that prediction markets were generally in agreement on Trump’s chances, while both Seer prediction markets, the Smarkets betting exchange and the bookmaker Bovada were all within 1 cent of the Polymarket Trump price on Election Day:

How did Seer markets perform on Election night?

Below are four takeaways about the accuracy of Seer traders in the 2024 election:

1) In the final weeks before Election Day, Seer traders correctly saw Trump winning the Presidential election in markets on both the Ethereum and Gnosis chains.

2) The Congressional Control market correctly forecasted a Republican U.S. Senate (80%) but missed the narrow Republican majority in the U.S. House (as of this writing, Republicans have a one-seat majority).

3) Using conditional markets to trade on the price of Bitcoin in the event of either a Trump or Harris victory, Seer traders foresaw a BTC price spike in the event of a Trump victory relative to Harris.

4) Traders didn’t see Trump winning the popular vote, only giving him a 28.4% chance on Seer. This was roughly in line with Trump’s chances of winning the popular vote on other prediction markets like Polymarket, where he traded at 27.1%, and Nate Silver’s model at 23.8%. Seer’s Multi Scalar market for the Presidential election popular vote totals showed Harris receiving the most votes, but the forecasted percentages for both Trump and Harris were close to the final results (<2%).

Overall, traders on Seer illustrated the wisdom of crowds by traders ‘betting their beliefs’ in the presidential election despite sharp criticism from the media and pundits. While not perfect, these markets showed the value of aggregating human knowledge. Ethereum founder Vitalik Buterin has described this as “Info Finance” where market traders try to profit from predicting future events and information seekers rely on the probabilities created by traders.

Seer was intentionally built to bring these diverse actors together in one decentralized prediction marketplace:

• Traders: Buy and sell positions in markets.
• Market creators: Create markets and provide liquidity to facilitate trading.
• Information seekers: Pay to gather information about the possibility of an event and/or its impact.

The two Bitcoin price markets conditional on the Presidential election winner also illustrate the untapped potential for futarchy: given a single decision, like the Presidential election, what do the markets think about potential outcomes for each option? Seer conditional markets showed competing Bitcoin prices in the event of either a Trump or Harris victory, with traders indicating that Trump winning would be more favorable to Bitcoin than Harris winning, which we’re now watching play out in reality.

About Seer

Seer is a non-custodial and decentralized prediction market protocol existing on the Ethereum and Gnosis blockchains. Traders use DAI tokens to buy and sell outcome tokens on whether an event will happen. The prices for these outcome tokens can reflect the probabilities of that event happening. Seer allows for permissionless market creation and users to provide liquidity to markets.

Up to 1,000 people can claim free outcome tokens on the Seer Election Airdrop page right now. All Kleros jurors and people registered on Proof of Humanity (even if the registration is not currently valid) are eligible to the airdrop.

The airdrop page will tell you if you’re eligible and then you can claim your outcome tokens. No prior experience in prediction markets is required.

The outcome tokens listed below will be worth 1 sDAI if the respective market resolves to that outcome. The token will be worth nothing if that outcome does not happen.

For example, in the 2024 U.S. Presidential election market there are outcome tokens for both Harris and Trump. If Trump wins, the Trump outcome tokens are worth 1 sDAI each and the Harris outcome tokens are worth nothing. (Vice versa if Harris wins.) Regardless of what happens, the sDAI you win is yours to keep.

Each user will receive 10 sDAI worth of outcome tokens in the following Seer election markets:

• Who will win the 2024 U.S. Presidential Election?
  - Trump: 5 tokens
  - Harris: 5 tokens
• Who will win the popular vote in the 2024 U.S. Presidential election?
  - Trump: 1 token
  - Harris: 1 token
• Control of U.S. Congress after the 2024 election?
  - D House, D Senate: 1 token
  - D House, R Senate: 1 token
  - R House, D Senate: 1 token
  - R House, R Senate: 1 token
• How many popular votes will [candidate] receive in the 2024 U.S. Presidential election?
  - Harris: 1 token
  - Trump: 1 token
  - Others: 1 token

Plus tokens in two conditional markets that dependent on whether Trump or Harris win the election:

• During his second term as President, will Trump call on Congress to replace the income tax with tariffs? (if Trump wins)
  - Yes: 1 token
  - No: 1 token
• Will Donald Trump be sentenced to any time behind bars in 2024? (if Harris wins)
  - Yes: 1 token
  - No: 1 token

And tokens in two Scalar markets that resolve proportionally to the price of BTC at the start of 2025:

• The price of Bitcoin at the start of 2025? (if Trump wins)
  - UP: 1 token
  - DOWN: 1 token
• The price of Bitcoin at the start of 2025? (if Harris wins)
  - UP: 1 token
  - DOWN: 1 token

The rules for participating are simple:

1. For each market, sell the tokens of the outcome you believe is overvalued. (If you think Harris is probably going to win, then sell the Trump tokens.)
2. In order to fully qualify for further airdrops, each of the eight markets should have:
   - One token that you completely sold (balance of 0).
   - One token that you completely kept (balance of 1 or greater).

So for the main market, you just need to sell your Harris or Trump tokens. But for the ‘Control of US Congress’ market, you can sell 1, 2 or 3 of the outcomes (as long as you keep at least one).

Remember, this airdrop is limited to only the first 1,000 eligible people so claim now!

This is the perfect opportunity to trade in election markets on the Seer prediction marketplace without risking your own DAI.

If you have any questions, please reach out to us on Telegram or X/Twitter.

Seer gives users more options

There are dozens of prediction markets operating, but the overwhelming majority offer the same features and markets. Seer gives users more options by offering functionality unique from other prediction markets:

• Permissionless market creation where users can create markets at will.
• Yield-bearing tokens remove the cost of capital for traders.
• Fungible outcome tokens are not tied to the Seer front end and can be bought and sold on any ERC20 compatible exchange.
• Scalar markets allow traders to invest in specific numeric outcomes along a continuous scale. For example, “What will be Kamala Harris’ popular vote percentage in the 2024 Presidential election?” or “What will be the Euro Area Inflation Rate for October?”
• Contingent markets allow for markets to now be connected to other market outcomes. For example, “Will the price of Bitcoin drop below $50K?” could be dependent upon Kamala Harris winning the 2024 U.S. Presidential election.
• Yield farming opportunities, as users are the liquidity providers on Seer’s automated market maker (AMM). Providing liquidity to a market will earn a percentage of that market’s trading fees as well as token subsidies. Liquidity engines like Bunni incentivize long-term liquidity provision.

At the same time, Seer reduces risks for users

• Market verification system to notify traders that a user-created market was vetted for safety. “Unverified” markets are still open for trading but at the user’s own risk.
• “Invalid” outcome tokens offered on every market allow traders to hedge the risk of a market being invalidated.
• Market resolution through reality.eth combines crowdsourcing answers with incentives for accuracy.
• Dispute arbitration with Kleros Court. In the event of a dispute over a market resolution users can seek arbitration through Kleros Court. Instead of using token voting, a randomly selected jury would hear the case and be incentivized to vote for the correct answer.

Seer offers more types of markets

• Categorical markets: users buy and sell shares on whether an event will or will not happen, with a correct share redeeming for the full amount purchased, and losing shares receiving nothing. The share prices can reflect the probability of the event occurring.
• Scalar markets: users buy and sell shares for numbers along a continuous scale. This includes poll numbers, stock prices, and economic indicators. Shares redeem proportionally to where the outcome landed along the scale. Share prices reflect where traders expect the outcome to land.
• Contingent markets: users buy and sell shares in a market that is dependent on the outcome of another (parent) market. These can be categorical or scalar or a combination of the two. For example, “How many inches of snow in Kiev?” could be dependent on the “Yes” outcome for “Will it snow in Kiev, Ukraine on Friday, November 1, 2024?”

Launched with unique U.S. Presidential election markets

Seer launched with both categorical and scalar markets about the U.S. presidential election:

• Who will win the 2024 U.S. Presidential Election?
• How many popular votes will [candidate] receive in the 2024 U.S. Presidential election? [Multi Scalar]
• Who will win the popular vote in the 2024 U.S. Presidential election?
• Control of Congress after the 2024 election?

This includes three markets contingent on the outcome of the U.S. Presidential election:

• Contingent on Harris winning: Will Trump be sentenced to any time behind bars in 2024?
• Contingent on Harris winning: The price of Bitcoin at the start of 2025? [Scalar]
• Contingent on Trump winning: The price of Bitcoin at the start of 2025? [Scalar]

Since the launch Seer users have created additional markets. And with permissionless market creation anyone can create markets at will. To protect user safety, markets are tagged “Verified” after having gone through a strict vetting process.

Prediction markets have arrived

The prediction market space has exploded, with DefiLlama reporting TVL growing from $34 million to over $257 million in 2024. The primary U.S. Presidential election market on Polymarket, the largest prediction market in the world, has over $2 billion in trading volume.

Leveraging the Wisdom of Crowds

Prediction markets are an efficient way to quantify conventional wisdom (i.e., the ‘wisdom of crowds’) about future events. Their accuracy was first observed by Sir Francis Galton in 1907 and recently popularized by the book “The Wisdom of Crowds” by James Surowiecki. These ‘event futures’ markets have been outperforming the polls and experts in forecasting elections since 1988. Other successful use cases include forecasting U.S. and global economic indicators, movie box office, flu outbreaks, vaccine effectiveness, and hurricane tracking.

About Seer

Seer is a non-custodial and decentralized prediction market protocol existing on the Ethereum and Gnosis blockchains. Traders use DAI tokens to buy and sell outcome tokens on whether an event will happen. The prices for these outcome tokens can reflect the probabilities of that event happening. Built using the Gnosis conditional token framework, users can create conditional markets, merge outcome tokens, and create and trade in both categorical and scalar markets. Seer users are also the liquidity providers and can profit through yield farming.

Media Contact: info@seer.pm

Website: https://seer.pm
Documentation: https://seer-3.gitbook.io/seer-documentation
X/Twitter: https://x.com/seer_pm
Telegram: https://t.me/seer
GitHub: https://github.com/seer-pm
Medium: https://seer-pm.medium.com/
Images & Branding: https://shorturl.at/LbWtt

We are thrilled to share with you the Seer Initial Airdrop, a strategic token allocation to reward the people who’ve invested their time and assets in the Seer prediction market.

Here is everything you need to know about SEER tokens:

Governance

As specified in the whitepaper, Seer will be governed by the Seer DAO and further token emissions will be split between retroactive public good funding (for entities contributing to Seer) and liquidity providers as determined by governance.

Tokenomics

First, we need to allocate the initial supply of tokens. The total amount of tokens to be distributed will depend on the duration of the program with 200,000,000 SEER tokens per month (for context, once the DAO is launched, it’s expected to attribute 2,000,000,000 SEER tokens per year, half to liquidity provider, half in retro-PGF).

Airdrop Allocation

The proposed token allocation is the following:

• Liquidity providers (50%): In proportion to the effective liquidity provided on designated liquidity pools. There will be a “Seer LP Point” token (SER-lpp) which will serve as a receipt and to allow liquidity providers to clearly understand how many points they are getting. There currently a plan to incentivize on 2 exchanges (more may be added in the future):
  – Swapr (Gnosis Chain): For the liquidity to count, the position must be in range and the more concentrated the position, the higher the reward (the calculation is done similarly to Swapr farming). To get the maximum rewards, it’s better to periodically rebalance those positions.
  –Uniswap with Bunni (Ethereum): Bunni is a liquidity management tool for Uniswap. Here the specific range of positions eligible to rewards is predetermined. You just need to provide liquidity for the position and there is no need to rebalance.

• Outcome token holders (25%): In proportion to the value of outcome tokens. This counts both direct holding and indirect holding in designated liquidity positions.

• Proof of humanity verified users (25%): In proportion to the square root of the value held in outcome tokens. For those to count, the outcome tokens must be held at an address verified on Proof Of Humanity. This counts both direct holding and indirect holding in designated liquidity positions. The squaring means that someone holding 100 sDAI will accumulate 100=10pt per day while someone holding 1,000,000 sDAI will accumulate 1000000=1000pt per day (100 times more points for 10,000 times more value). This is made so that participants with small portfolios still get some decent airdrop. The Proof of Humanity requirement is to prevent Sybil attacks (making tons of bot accounts to game the system).

Next Steps

More information will be forthcoming. Follow us on X/Twitter, Telegram, or Medium to keep track of the latest airdrop details.