Reviewers: Jonas (Optimism), Cheeky Gorilla (Protocol Guild), James (Octant)

Smart contracts enable us to program commitment mechanisms that don't require force. This could fundamentally transform how societies fund and manage shared infrastructure. While states must use coercion to solve free-rider problems and communities struggle to enforce commitments through social norms, blockchain systems offer a third path: programmable mechanisms that align individual actions with collective needs.

This capability has sparked a wave of experimentation in public goods funding, with projects like Gitcoin directing over $60 million to open-source software and public health initiatives and Optimism allocating more than $100 million through its retroactive funding program.

To understand how these new mechanisms work, we need to break down the challenge they are solving. Public goods—like open-source software, scientific research, or clean air—are resources that anyone can use without reducing their availability to others. Their universal accessibility creates a "free-rider problem" where users can benefit from them without contributing to their creation or maintenance.

Any system that aims to solve this problem must perform three essential functions: gathering resources despite free-rider incentives, distributing those resources effectively, and capturing the impact of improvements in feedback loops that sustain the system. Each function presents distinct challenges.

Resource Pooling

• How do you create strong enough incentives to overcome free-riding?

• How do you ensure fair burden-sharing among beneficiaries?

• How do you maintain funding through market cycles?

Resource Allocation

• What mechanisms can aggregate collective knowledge about priorities?

• How do you balance expert judgment with community input?

• What safeguards prevent capture by special interests?

Impact Capture

• How do you measure and verify improvements in public goods?

• What feedback mechanisms connect outcomes to future funding?

• How can success generate sustainable funding cycles?

Grasping these functions helps us evaluate both traditional and new approaches to provisioning public goods. In the following sections, we will first examine how nation-states handle these challenges, establishing a baseline for comparison with onchain solutions.

The modern nation-state represents humanity's most advanced system for funding shared infrastructure at scale. Its approach relies on one core feature: the monopoly on force. This power to coerce enables solutions to all three core functions:

Resource Pooling

States solve the funding challenge through mandatory mechanisms:

• Taxation extracts wealth directly from citizens

• Monetary policy creates indirect taxation through inflation

• Sovereign debt enables borrowing against future tax revenue

Resource Allocation

Liberal constitutional democracies use a two-layer system for distribution:

• Democratic processes set high-level priorities through elected representatives

• Representatives vote on budgets that are administered through professional bureaucracies

Impact Capture

The state model creates several reinforcing feedback loops:

• Better infrastructure increases productivity, leading to an expanded tax base from economic growth

• Improved public services build citizen support, reinforcing the legitimacy of its coercive force (the so-called social contract)

• Economic growth also strengthens the creditworthiness of the State, enabling it to bower at lower rates of interest

While effective at national scale, this model faces several critical limitations:

• Scope Mismatch: State boundaries rarely align with infrastructure needs. They're too constrained for global challenges like climate change, yet too removed from local contexts to effectively incorporate community knowledge and participation.

• Bias and Capture: Political cycles create short-term biases, while special interests capture both democratic and bureaucratic processes for their benefit.

• Measurement Problems: Long delays between intervention and outcomes make impact hard to measure, while established bureaucracies resist potentially disruptive improvements.

• Misaligned Incentives: The people who pay, benefit, and decide are often different groups, creating conflicts of interest and inefficient resource allocation.

• Reactive Nature: Perhaps most critically, bureaucratic systems typically address problems only after they become crises, rather than proactively identifying and preventing emerging challenges.

Blockchains represent a new design space for addressing these limitations by enabling communities to deploy socially scalable commitment mechanisms.

By 2024, four approaches stand out:

• Gitcoin Grants: Quadratic matching rounds

• Optimism: Retrospective rewards

• Protocol Guild: Vested payment streams to individuals

• Octant: Community-based yield delegation

Unlike state systems that can rely on coercion, onchain approaches must solve the core challenge of voluntary participation.

Gitcoin Grants: Donations for Social Recognition

Gitcoin Grants combine social capital with matching incentives to drive funding. Their seasonal rounds create focused moments of community attention, where individual donors contribute to projects they value while large donors (often protocol foundations and DAOs) fund matching pools that amplify these contributions.

Optimism: Protocol-Native Funding

Optimism takes a protocol-native approach, embedding funding directly into its economic system. By allocating 20% of the OP token supply, they have created a substantial pool of resources that grows with the ecosystem’s success. OP Chains that together form the Superchain ecosystem, such as Base, Zora, and Unichain, contribute a set amount of their revenue from sequencer fees back to the Optimism Collective. This shows how protocol-native funding can evolve beyond initial token allocations.

Protocol Guild: Dependency Funding

Protocol Guild appeals to enlightened self-interest of Ethereum ecosystem projects, encouraging them to support the infrastructure they depend on. In January 2024, Guild members released a pledge encouraging Ethereum-related projects to donate 1% of their native tokens. Currently they have raised over $50m of donations in ETH and ERC-20 tokens.

Octant: Yield Delegation

Octant uses staking yields to create sustainable funding streams. Starting with 100,000 ETH from the Golem Foundation's 2017 ICO, the system allocates 35% of staking returns to a matching pool and another 35% to GLM stakers, who can choose between keeping the yield or donating it to projects. While this creates predictable funding, the total amount is partially dependent on the proportion of the GLM stakers that choose to donate their yield, and to fluctuations in the ETH price and staking yield.

Each system takes a different approach to the core tension between broad participation and manipulation resistance.

Gitcoin Grants: Quadratic Donation Matching

Quadratic funding aggregates community preferences, weighting many small contributions over few large ones. Passport provides sybil resistance, while the Connection-Oriented Cluster Match mechanism reduces funding for suspiciously collusive donation patterns.

Optimism: Impact-Driven Allocation

The Optimism Foundation selects a group of badgeholders who make allocation decisions. Rather than voting directly on projects, badgeholders weigh different impact metrics, creating a more objective framework. Data about the project’s impact is collected from various reputable sources. As of Round 4, the system uses median calculations to filter out extreme opinions and implements both minimum thresholds and maximum caps to ensure balanced distribution. Long term, the vision is that new badgeholders will be automatically eligible based on predefined reputation criteria and that impact evaluations will be driven by onchain metrics as well as human expertise.

Protocol Guild: Membership Registry + Time-Weighted Distribution + Vesting

Protocol Guild consists of a membership registry of Ethereum’s core protocol contributors, which is self-curated by the members themselves according to an eligibility framework, and ratified onchain via a 1p1v Moloch DAO. Distribution follows a retroactive time-weighting formula; the longer you have contributed, the larger your share. A vesting module further ensures funding goes to contributors that stick around long-term, or those that show up in the future — not just those that are around today.

Octant: 1p1v + Quadratic Funding

Community members first vote on Snapshot to create a project shortlist, with each member being able to spread their vote across as many projects as they like. To prevent long-tail projects from diluting the funds allocated to the most strongly supported initiatives, Octact has limited the number of grantees selected in each round. Qualified projects then enter an allocation window where GLM stakers can direct their staking rewards, with a matching pool amplifying contributions using quadratic funding. Like Gitcoin Grants, Octant relies on the Passport system for Sybil resistance, though with a lower threshold of 15 score to be eligible for participation.

Each system attempts to create programmatic links between impact and future funding, moving beyond simple goodwill to self-reinforcing cycles.

Gitcoin Grants: Network Effects

Turns social capital into sustainable funding through network effects. Successful projects gain ecosystem visibility and legitimacy, attracting more supporters and often leading to partnership opportunities beyond direct funding.

Optimism: Market-Driven Validation

Links funding directly to ecosystem performance metrics: OP token market cap, TVL, transaction volume, and OP stack adoption. These metrics provide tangible validation of funded projects' benefits, while badgeholder engagement demonstrates community buy-in.

Protocol Guild: Infrastructure as Impact

Ethereum’s core protocol development is a foundational public good that impacts every project in the ecosystem. Growing metrics like network adoption and liveness support the Guild’s mission to fund this work, while time-weighting and vesting align long-term contributor incentives with network success.

Octant: Network Adoption and Market Performance

Relies on the success of Ethereum's staking ecosystem and the GLM token economy. By tying funding to staking yields, they create direct alignment between ecosystem growth and funding capacity. Octant v2 will also incentivize other actors with large treasuries to contribute staking yields.

Drawing insights from both state systems and early protocol experiments, we can envision how onchain mechanisms might enable communities to sustainably provision their shared infrastructure. These mechanisms could serve municipalities, regional alliances, diaspora communities, professional associations, religious groups, decentralized protocols, and more. This section explores this design space, combining traditional financial instruments with new commitment mechanisms made possible by smart contracts.

(1) Elastic Token Supply

Tokens are programmatically minted in proportion to measurable, verifiable impact, creating an elastic money supply tied to the productivity or value of shared infrastructure. This contrasts with static token allocations by aligning token issuance with the system’s real growth. This mechanism mirrors how sovereign debt historically grew with national productive capacity, ensuring resources are allocated dynamically as the system scales.

(2) Bond Offerings

Projects issue bonds backed by anticipated future revenues, enabling them to raise capital upfront while committing to structured repayments. These instruments are particularly suited to large-scale infrastructure projects where immediate funding needs exceed current revenues. For instance, a local community might issue 3-year bonds to fund a local mesh network, repaying bondholders with fees from membership dues.

(3) Lottery

State-run lotteries have long funded public goods by combining entertainment value with social benefit. These systems typically direct 20-30% of ticket sales to public projects while maintaining prize pools that incentivize participation. Smart contracts enable programmable versions of this mechanism with deterministic prize distribution and transparent operation, as demonstrated by PoolTogether's no-loss lottery design. One example of this is LottoPGF.

(4) Conditional Funding Markets

Prediction markets are used to determine whether a project should receive funding, tying resource allocation to community confidence in its success. Participants bet on the likelihood of predefined success metrics, and funds are only unlocked if the market’s consensus exceeds a set threshold. For instance, a decentralized transportation initiative might set up a market predicting whether it will achieve a targeted ridership within two years. If the market reaches a threshold of confidence, funds are released; otherwise, contributions are returned, ensuring only high-confidence projects receive resources.

(5) Membership Dues

Religious institutions and membership organizations have long used regular dues or tithes to fund shared resources. These systems work by creating social commitments to contribute a fixed amount or percentage of income at regular intervals. Smart contracts enable programmable versions of these arrangements and transparent tracking of collective contribution goals; from subscription payments to real-time streaming.

(6) Harberger Tax

The Harberger tax model enables continuous resource pooling by requiring owners of assets to self-assess their value, pay a tax proportional to that assessment, and keep the assets available for purchase at their declared price. This ensures that a system's scarce resources are always productively used while pooling funds for shared infrastructure. For example, a decentralized domain naming system could adopt Harberger taxes for domain ownership. Owners would self-assess the value of their domain names, paying an annual fee based on that valuation. If someone values the domain more, they can purchase it at the declared price, ensuring domains remain efficiently allocated. The taxes collected could then be allocated to infrastructure provisioning.

Current allocation methods face multiple challenges: bias, cognitive overload, complex applications, popularity contests, and liquidity gaps. Several mechanisms show promise in addressing these issues.

(1) Pairwise voting

Rather than overwhelming voters with endless project lists, pairwise voting presents just two options at a time. This simple change transforms the cognitive load from paralysis to focused comparison. Pairwise produces more thoughtful, quality decisions by preventing decision fatigue. Opens source tools for pairwise voting include PairDrops by dOrg and Pairwise by General Magic.

(2) Random sampling (Sortition)

Random selection of evaluators for each project disrupts traditional popularity contests. When projects can’t target their outreach to specific voter blocks, the focus naturally shifts from marketing to substance. This mechanism specifically counters the tendency for well-networked projects to dominate funding rounds regardless of merit.

(3) Delegated domain allocation

Following Compound’s Grants Program 2.0 model, this approach puts allocation power in the hands of domain experts. These specialists bring deep understanding to their areas, whether it’s protocol security, user interface design, or community building. The result is a more nuanced evaluation and more efficient resource distribution.

(4) Continuous funding stream

Traditional grant rounds create feast-or-famine cycles that can cripple project development. Continuous funding protocols like Drips Network offer a solution: steady, predictable resource flows that match the actual rhythm of project development.

(5) Conviction Voting

Conviction voting introduces time as a key factor in allocation decisions. Rather than using one-time votes, participants continuously signal their support for proposals, with their influence growing stronger the longer they maintain their position. This mechanism naturally favors consistent, long-term contributors while protecting against short-term manipulation. If supporters change their minds or withdraw support, their conviction decreases gradually, creating a more stable and considered allocation process. The time-weighted nature of conviction voting helps communities distinguish between fleeting enthusiasm and genuine, sustained support for initiatives.

(6) Time-bound funding competition

Platforms like Jokerace and Prop House implement time-limited contests where participants compete for a predetermined prize pool. Unlike open-ended funding rounds, these competitions create focused periods of engagement with clear stakes. The fixed prize and timeline encourages participants to submit their strongest proposals upfront, while voters can compare all options simultaneously. This format is particularly effective for specific initiatives or themed challenges, as it creates momentum and attention around a particular funding goal.

Impact capture is perhaps the most challenging question of all. The aim is to create an effective link that ties successful resource allocation efforts into strengthening the system’s ability to fund itself.

(1) Hypercerts

Hypercerts are fractionalized, semi-fungible tokens that represent claims on the impact of specific work. They serve as a standardized method to capture and represent the positive outcomes of projects. By transparently documenting the impact of public goods projects, hypercerts enable retrospective funding, where funders reward projects based on observed outcomes. This approach incentivizes creators to undertake high-risk, high-reward initiatives, knowing they can receive compensation proportional to their impact.

(2) KPI tokens

Projects like Outcome proposed these tokens as a way to bridge the gap between pooled resources and impact capture by directly tying funding contributions to measurable outcomes. Issued either by the supported project or an onchain funding platform on its behalf, these tokens are distributed to contributors, community members, or stakeholders as a reflection of their support. Their value is tied to the achievement of predefined goals, ensuring that resources are only rewarded when impact is verifiably delivered. This creates a feedback loop where pooled resources drive progress toward specific targets, and token value mirrors the success of those efforts, aligning incentives between supporters and project teams.

(3) Token Burns

Token burning mechanisms create direct feedback loops between protocol usage and token value. When protocols burn tokens based on activity or impact metrics, they create a self-reinforcing cycle where successful public goods increase the value of remaining tokens. Burn mechanisms allow an ecosystem to capture impact without requiring explicit measurement or evaluation. Instead, they create automatic feedback loops where usage of public infrastructure translates directly into increased value for token holders.

(4) Impact Measurement

Transparent and standardized measurement tools can amplify the ability of a system to capture impact by making impact legible to the system’s participants and mechanisms. Some notable tools for this include Open Source Observer, Karma Gap, and Metrics Garden.

While these mechanisms offer novel ways to pool and allocate resources, the fundamental key to sustainable public goods funding is that the system itself generates real value for its participants. Only when a network, protocol, or community creates tangible benefits can it effectively capture and reinvest value through taxes, fees, or other mechanisms. The tools explored here simply make this reinvestment more efficient and aligned with collective needs, suggesting a future where any value-generating system can programmatically sustain its shared infrastructure.

Reviewers: Yosh Zlotogorski, Andy Tudhope

Crypto’s speculative nature is its greatest strength and most glaring weakness. On the one hand, it attracts fresh waves of capital to fuel the technology’s development and secure the underlying economic protocols. On the other hand, the volatile booms and busts distract from the technology’s underlying utility and lead many to conclude that permissionless blockchains are nothing more than unregulated casinos. The recent surge in onchain prediction markets exemplifies this duality perfectly. These platforms harness crypto’s speculative nature to power an engine to predict the future with unprecedented accuracy. In doing so, prediction markets have emerged as one of crypto’s most compelling and concrete use cases to date.

Prediction markets are nothing new. The earliest known forms around 500 years ago involved political betting on papal successors, while the first instance on Wall Street occurred around 1884. In the digital age, the Iowa Electronic Market (IEM), Predictit, and Manifold have had some traction as niche online platforms. Since the advent of smart contracts, onchain prediction markets have been pioneered by projects like Augur, Gnosis, and Omen, and recently started to have breakout success with Polymarket. Onchain prediction markets have grown more than 5x in volume since August 2023, with a current TVL of ~$130 million and $1 billion in cumulative volume since the start of 2024.

In this article, we will dive into the mechanisms behind onchain prediction markets to better understand why they exist, how they work (specifically Polymarket), and how they might be improved. 

Prediction markets allow people to directly bet on the outcomes of future events. By betting on the probability of some outcome, the private beliefs of participants are aggregated to indicate public sentiment regarding the likelihood of the event. 

A prediction market can take one of three forms: binary, categorical, or continuous:

Binary markets allow participants to bet YES/NO on an outcome. In a binary market, the perception of the likelihood of an event happening is expressed as a probability where the likelihoods of the positive and negative options are positive values that sum to 1. 

• Positive Outcome (Yes): A where A < 1

• Negative Outcome (No): B where B = 1-A

Categorical markets operate similarly, but instead of just YES/NO, participants can bet on a set of multiple predetermined outcomes. Again, each option is expressed as its probability where each value is positive and all options sum to 1.

• Outcome A: A where A = 1 - B - C

• Outcome B: B where B = 1- A - C

• Outcome C: C where C = 1 - A - B

Continuous markets account for many values across a range indicated by the participants’ predetermined constraints. These markets typically operate until a set expiration date, determining winners based on whether the outcome falls within the specified range.

• $Asset >= X on a set date

• Y < $Asset < X on a set date.

A common misconception is that prediction markets are more vulnerable to manipulation by wealthy participants than traditional forecasting institutions like weather services, government agencies, or public opinion polling. However, this isn't typically the case. Manipulators generally do not significantly impact information aggregation or prices due to the presence of noise traders—participants who trade for reasons unrelated to market information, such as errors or insurance needs. This random trading activity dilutes the impact of manipulative trades, preventing them from notably altering market trends. Additionally, rational traders often increase their trading activity in response to this noise, which can paradoxically enhance market accuracy and efficiency (see Robin Hanson 2004 and 2005).

Before prediction markets, traders would bet on future outcomes indirectly through derivatives markets. Derivatives markets use derivatives contracts such as futures, options, or swaps to bet on the future price of an asset indirectly related to that outcome. Let’s say a trader believes there will be a drought shortly. To profit off this prediction, the trader purchases a futures contract for wheat, locking in the lower price now and anticipating higher prices in the future. If the drought occurs and the price of wheat increases, the trader can activate their contract and arbitrage the commodity at the higher spot price for profit—a much more complex process than simply betting on the drought. 

While derivatives markets have historically facilitated indirect bets on outcomes through complex financial instruments, prediction markets offer a more direct and streamlined approach. Typically, traditional ones use a Continuous Double Auction (CDA) design. CDAs operate like an order book, where a centralized intermediary known as an operator matches buy orders and sell orders. This makes traders counterparties to each other and relies on market forces to incentivize traders to converge on accurate prices. CDAs are implemented in popular prediction markets such as the IEM and Predictit. 

Traditional prediction markets face several limitations (see Vitalik 2021):

• Single Point of Failure: Centralized systems are vulnerable to technical failures, cyberattacks, or operational issues that can disrupt market activity. If the central entity goes offline, the entire market is affected.

• Bias & Manipulation: Users must trust a single entity to maintain fairness and transparency. There is a risk of manipulation or bias by the central authority.

• Lack of Privacy: Participants in centralized markets often need to provide personal information, which can deter those who value privacy or are concerned about data breaches.

• High Costs: Centralized platforms often charge significant fees for participating in the market. These fees can include transaction costs, withdrawal fees, and other charges that eat into potential profits.

• Limited Access: Many centralized prediction markets restrict participation based on geographic location, limiting their accessibility. This can prevent a truly global and diverse set of opinions from being reflected in the market prices.

• Limited Range of Events: Centralized markets may offer a limited range of events for betting, often focusing on popular topics. This restricts the ability of participants to bet on niche or emerging events.

• Low Limits: Many centralized prediction markets impose low betting limits on participants, which can stifle serious trading and reduce users' ability to express strong confidence in a particular outcome.

These limitations reduce liquidity and participation, ultimately restricting their reach and accuracy. In contrast, onchain prediction markets utilize smart contracts, eliminating the need for trusted infrastructure and intermediaries, all carrying costs typically passed on to users. These protocols can operate autonomously, providing deterministic, transparent results and a highly accessible trading environment. Users can place bets of any amount at any time with minimal effort and reduced costs. 

Onchain prediction markets usually rely on Automated Market Makers (AMM), a blockchain-based alternative to CDA that allows liquidity pools to act as counterparties to all trades. In this design, pools managed by smart contracts automatically hold and distribute liquidity to traders. These pools maintain accurate prices by relying on an algorithm continuously adjusting pool quantities based on supply and demand.

However, AMMs come with their own set of challenges, including:

• Slippage: Slippage occurs when there is a difference between the expected price of a trade and the price at which the trade is executed. In AMMs, slippage tends to increase with the size of the trade, particularly in pools with lower liquidity. This means larger orders can significantly move the price, potentially leading to less favorable trade execution.

• Speed: Transaction speeds in AMMs can be affected by the underlying blockchain’s performance. During times of high demand, the network may become congested, leading to slower transaction confirmations. This delay can cause the prices on the AMM to become outdated compared to fast-moving markets, impacting trading strategies.

• Impermanent Loss: Liquidity providers in an AMM face this risk due to volatility in the price of assets within the pool. When the price of pooled assets changes after they are deposited, the proportional value of their stake in the pool changes compared to holding the assets outside the pool. This can result in a financial loss if the liquidity provider decides to withdraw their assets during a period of unfavorable price movement.

• MEV: AMMs can be vulnerable to Maximal Extractable Value (MEV), where validators prioritize, include, or exclude transactions to maximize profits. This can lead to front-running, where miners execute their trades before processing others already in the queue, taking advantage of price movements that are about to occur due to pending transactions. This undermines the fairness and efficiency of the market, potentially leading to losses for regular traders.

Despite these challenges, onchain prediction markets have seen a significant rise in recent months, with Polymarket becoming a leading indicator of sentiment surrounding the outcome of the US presidential election, according to the Wall Street Journal. Currently, Polymarket makes up about 75% of the total value currently locked in on-chain prediction markets. Below, we'll be able to explore its workings further. 

Polymarket, launched in 2020, is a non-custodial prediction market that runs on the Polygon blockchain. The platform provides users with diverse binary outcomes to bet on, known as ‘information markets.’ In these markets, users wager with the stablecoin USDC. By supplying USDC as collateral, users obtain outcome tokens representing either a market's positive or negative outcome. At the market’s conclusion, each outcome token on the winning side becomes redeemable for 1 USDC, while the outcome tokens of the losing side become worthless. In essence, the winners receive the collateral from the losers.

See the example below:

• Binary market

  • Token Y (Positive- Yes): $0.65 per share

  • Token N (Negative- No): $0.35 per share

• Outcome does occur, Market concludes

  • Positive bettors win

• Payouts

  • All Y tokens increase to $1

    • Winners earn $0.35 on each share

  • All N tokens fall to $0

    • Losers lose $0.35 on every share

The more likely an outcome is, the lower the expected payout, and vice versa. This dynamic creates a unique environment where the invisible hand of economics drives markets to converge on accurate prices. If an outcome is likely grossly overpriced or underpriced, a trader has a significant financial incentive to bet against or for it and turn a profit. This accuracy increases with more users and liquidity.

In its current design, the Polymarket team creates and deploys all markets on the platform. The Polymarket community suggests, discusses, and reviews new markets through the Polymarket Discord channel. This allows the Polymarket community's preferences to be reflected in the markets available on the platform. Below, we will explore some of its core mechanism implementations.

Conditional Tokens

Developed by Gnosis, the Conditional Token Framework (CTF) is a sophisticated way to create, manage, and settle bets on future outcomes. 

• Token Standard: It uses the ERC1155 standard, which allows for more efficient and flexible handling of multiple types of outcome tokens within a single contract and reduces gas costs compared to individual ERC20 tokens.

• Customization: The framework supports creating markets with various conditions and outcomes, making it adaptable to different prediction markets. It also allows for integration with various Oracle solutions. 

Polymarket’s CTF implementation allows for the following operations: 

• Splitting: Users can split one unit of collateral (USDC) into one unit of each outcome token, creating liquidity in the market.

• Merging: Users can merge one unit of each outcome token back into one unit of collateral. This allows for arbitrage opportunities and helps maintain price equilibrium.

• Redeeming: Users can redeem their winning outcome tokens for the underlying collateral after a market resolves.

Central Limit Order Book (CLOB)

The CLOB is Polymarket’s default system, which takes inspiration from the traditional Continuous Double Auction (CDA) but adapts it to work onchain. This mechanism operates with these key features:

• Order Matching: Orders to buy or sell outcome tokens are organized in the CLOB, where buy orders are matched with the lowest available sell orders and sell orders with the highest available buy orders, based on price priority. Trades are executed at the best available price, with earlier orders receiving priority when prices are equal.

• Hybrid On-chain/Off-chain System: Although order matching is done off-chain by Polymarket, the settlement (i.e., the actual exchange of tokens) is executed onchain. The exchange contract autonomously settles trades, tracks ownership, handles payouts, and issues/burns outcome tokens in exchange for USDC collateral.

• Order Types and Participant Roles: 

  • A ‘limit order’ is an instruction to buy or sell an outcome token at a specified price or better, which will only execute when the market price meets the price set by the trader. These orders enter the order book and wait for the market to reach their specified price. Traders who place limit orders are called ‘makers.’ Makers provide liquidity to the market, essentially ‘making’ the market for specific prices. 

  • A ‘market order’ is an instruction to buy or sell an outcome token at the best available current price. Traders placing market orders are called ‘takers.’ Takers provide the necessary market movement by executing trades, which helps discover the price and maintain market fluidity.

Fixed Product Market Maker (FPMM)

Also developed by Gnosis, the FPMM serves as an alternative to the CLOB, acting as a fallback mechanism in times of insufficient liquidity or order volume in the CLOB. It is an automated market maker (AMM) specific to prediction markets, which operates in the following way:

• Liquidity Pools: Each market has a liquidity pool comprising tokens representing possible outcomes. These pools facilitate trading by ensuring there is always a counterparty to take the opposite side of a bet.

• Price Adjustment: Prices within the liquidity pool automatically adjust based on trades, maintaining a constant product of the quantities of the two different tokens involved. This ensures that prices react dynamically to changes in supply and demand, stabilizing the market and encouraging balanced trading activity.

• No Operator: This mechanism does not require an active intermediary to match trades. The liquidity pool itself acts as the counterparty to all trades, adjusting prices algorithmically based on supply and demand.

Liquidity Rewards Program

Polymarket continuously runs a liquidity rewards program to reduce trading slippage within markets. This enables more participation, ultimately increasing the accuracy of predictions. Unlike typical liquidity mining programs incentivizing AMM liquidity, Polymarket adapts a design by dYdX and Blur to incentivize order book liquidity on its CLOB.  

• Daily Rewards: Anyone can earn money by placing limit orders on particular markets within a predefined spread that the Polymarket team sets to keep specific markets active and balanced. Rewards per market per day are fixed in USDC and are paid out to eligible Makers daily at midnight UTC based on their score relative to the other Makers in that market. 

• Scoring System: The program assesses the quality of liquidity provided by market makers based on factors like order size, duration, and proximity to the market’s current price, rewarding the best contributions to market health. The methodology adapts dYdX’s scoring mechanism, which is adjusted for binary contract markets. 

• Leaderboard Competitions: Polymarket plans to further incentivize liquidity through leaderboard competitions, which will likely provide an extra reward to the top Makers across all markets daily.

• Rewards Funding: The funding for the rewards in Polymarket’s Liquidity Rewards Program primarily comes from UMA tokens provided by the UMA DAO from the UMA treasury. These rewards are intended to stimulate trading activity and liquidity provision in markets that use UMA’s Oracle services. In March 2023, Polymarket requested 1,250,000 $UMA from the UMA DAO to allocate towards growing Polymarket’s new Order Book. The reliance on UMA tokens for Polymarket’s liquidity rewards began in 2022. In the 2023 forum post, Polymarket team members expressed that the platform may consider incorporating trading fees in the future, from which the rewards will be funded.   

UMA Oracle

Oracles are needed to resolve each market based on the real-world outcome of the corresponding event. Rather than relying on a single trusted party to feed this input to the smart contract, Polymarket leverages UMA’s decentralized oracle.

This mechanism works in the following way: 

• Optimistic Oracle Model: This model operates on the principle of “innocent until proven guilty.” Participants proposing data must post a bond, initiating a review period. During this time, if other participants suspect the data is incorrect, they can challenge it by posting a counter bond.

• Dispute Resolution: Challenges are resolved by the UMA Data Verification Mechanism (DVM), where UMA token holders vote to determine the validity of the disputed data. If the data is affirmed, the bond is returned to the proposer with additional rewards; if it is overturned, the bond is forfeited.

• Data Finalization: The data is considered final and actionable after the liveness period passes without disputes or after the DVM resolves any disputes.

Several mechanisms within the UMA Oracle and Polymarket process are designed to mitigate this risk and prevent incorrect data. 

• Bond Requirement: The necessity for proposers to bond their submissions incentivizes the accuracy of the data, as incorrect data risks the forfeiture of the bond.

• Tokenonomic Deterrents: The structure of UMA’s token economy makes it prohibitively expensive to influence outcomes through token-majority ownership, effectively deterring potential corruption by making the cost of dishonesty exceed possible gains.

• Bulletin Board: The Polymarket integration incorporates a Bulletin Board that allows market creators to directly question and communicate with the UMA Oracle regarding the accuracy and relevance of the data being fed into their markets. This direct communication channel helps ensure that the data used in market outcomes is reliable and transparent, allowing for corrections or clarifications when needed.

Beyond the case of the Polymarket, choosing to rely on CLOB versus FPMM as the primary mechanism in an onchain prediction market may arise from different outcome preferences and timelines. While the CLOB can perform better by being capital efficient and highly liquid, the FPMM is less prone to manipulation due to being intermediary-less, censorship-resistant, and end-to-end verifiable. If the short-term goal is to onboard as many users as possible, resorting to mechanisms such as the CLOB may be the best option. However, in the long term, and as the onchain prediction market grows and becomes more liquid, switching to manipulation-resistant mechanisms like FPMM may be a better choice. 

As mentioned, liquidity remains one of the most pressing challenges of onchain prediction markets. Below, we outline potential mechanisms to address this obstacle.

Leveraged Outcome Tokens

Leveraged outcome tokens can help solve liquidity challenges in onchain prediction markets. This approach allows traders to purchase YES/NO tokens with leverage, such as 2x or 3x, by paying a premium over the standard price. The premium paid for these leveraged positions contributes directly to the market’s liquidity pools, creating a unique mechanism that incentivizes liquidity provision while offering higher potential returns to traders.

Protocol-Owned Liquidity (PoL)

PoL involves the prediction market protocol retaining a significant portion of the liquidity rather than relying solely on external liquidity providers. Using a part of its treasury or collected fees to fund and maintain liquidity pools directly, the platform can ensure consistent liquidity availability and respond swiftly to market demands.

Global Liquidity Pool

A global liquidity pool (also referred to as “virtual funds” by Azuro) refers to using one or more liquidity pools to back multiple prediction markets rather than having a dedicated pool for each market. The system only locks the amount of liquidity needed to cover the maximum possible payout for each market, recalculating this amount with each new bet. This virtual liquidity accounting adjusts automatically as bets are placed, effectively acting as an automated market maker for betting odds.

Logarithmic Market Scoring Rule (LMSR)

Invented by prediction market pioneer Robert Hanson (2002) and implemented by Gnosis (2019), LMSR is an automated market maker mechanism for prediction markets that uses a cost function to determine the price of outcome shares based on the number of shares for each outcome and a liquidity parameter. 

Pythagorean Bonding Curve AMM

Developed by Obyte, the Pythagorean bonding curve AMM uses a mathematical formula inspired by the Pythagorean theorem to manage liquidity and price tokens in prediction markets while addressing some limitations of other liquidity solutions such as LMSR. An autonomous agent (AA) issues YES, NO, and optionally DRAW tokens for each market, with token prices determined by their relative supplies. Users can buy or sell tokens anytime, adjusting prices automatically as a function of each token’s supply. Token prices are calculated as partial derivatives of the reserve concerning token supply, ensuring that the total value of all tokens equals the reserve. After an event, holders of winning tokens can redeem their share of the total reserve, while other tokens become worthless. Trading fees are incorporated by adjusting the coefficient, which increases all token prices and allows every token holder to benefit from accumulated fees.

As these mechanisms evolve, prediction markets stand a chance to transform crypto’s speculative madness into a precision instrument for forecasting matters of public interest. This would open the door to a range of applications that prediction market nerds have prophesied for decades, including:

• Incentivized whistleblowing & intel gathering

• Decision markets for corporate governance and public policy

• Futarchy (also see MetaDAO)

• Hedging information security risks and other risk like weather events and economic crises

• Parametric insurance

• Funding science and academic research

In this way, prediction markets can move beyond predicting the future to actually facilitating coordination that improves the future.