Algorithmic Curation

Assets are selected based on immutable, transparent rules executed by smart contracts. Common criteria include:

• Total Value Locked (TVL)
• Transaction volume
• Liquidity depth
• Governance token holdings

This ensures the process is verifiable and non-custodial, as anyone can audit the code to confirm selections.

Algorithms assign real-time weights or scores to assets, creating a ranked list that updates automatically. This is foundational for:

• Automated Market Makers (AMMs) like Uniswap V3, where liquidity concentration is managed algorithmically.
• Lending protocol collateral tiers, where loan-to-value ratios adjust based on asset volatility.
• Yield aggregators that rank vaults by APY and risk scores.

Curation algorithms are often coupled with token-based incentive mechanisms to bootstrap and maintain quality. Examples include:

• Curve Finance's vote-escrowed CRV (veCRV) model, where locked tokens grant governance power to direct liquidity mining rewards.
• Olympus DAO's (OHM) bonding mechanism, which algorithmically curates the protocol-owned liquidity (POL) treasury.

This aligns long-term participant incentives with network health.

While the core logic is immutable, key parameters are often adjustable via governance, allowing the system to evolve. This includes:

• Fee structures (e.g., protocol take rates).
• Reward emission schedules.
• Risk parameters (e.g., collateral factors).

This creates a flexible yet secure framework where the community can steer the algorithm without compromising its deterministic nature.

Robust curation algorithms incorporate Sybil-resistance mechanisms to prevent gaming. Techniques include:

• Proof-of-Stake requirements for governance voting.
• Time-based locking (e.g., veToken models) to weight influence by commitment.
• Costly signaling mechanisms, where actions require burning or locking assets.

This ensures the curated output reflects genuine economic stake, not just the number of identities.

Algorithmic curation engines are designed as composable primitives that other protocols can integrate. For instance:

• An index fund protocol (like Index Coop) uses curation rules to select underlying assets.
• A yield optimizer uses algorithms to curate the best farming strategies across DeFi.
• Oracle networks like Chainlink use decentralized curation to select and reward high-quality data providers.

This turns curation into a lego block for the broader DeFi stack.

A curation model where the influence of a user's vote or signal is proportional to the amount of tokens they have staked or locked. This creates a cryptoeconomic alignment between curators and the platform's health.

• Example: In a content platform, users stake a platform token to upvote content. High-quality posts that receive more stake-weighted votes rise to the top.
• Mechanism: Implements Skin in the Game, as poor curation can lead to a loss of staked value.

Uses a bonding curve—a smart contract that defines a mathematical relationship between price and token supply—to algorithmically curate a list or registry. Adding an item (minting a token) becomes more expensive as more are added.

• Primary Use: Creating curated registries for tokens, NFTs, or data sets.
• Process: To list a new item, a curator deposits funds, minting a new token from the curve and raising the price for the next listing. This creates a crowdsourced economic filter.

A decentralized curation framework where token holders vote to add or remove items from a list. It's a specific application of stake-weighted voting for list maintenance.

• Workflow: 1) A proposer stakes tokens to submit an entry. 2) Token holders vote to challenge or accept it. 3) Disputes are resolved via voting or a dispute resolution system.
• Goal: To produce a trust-minimized, community-verified list of high-quality entities, like reputable DAOs or verified oracles.

Systems that generate a reputation score for users, content, or assets based on historical on-chain behavior and interactions. This score is then used for automated ranking.

• Data Sources: Transaction history, governance participation, successful predictions, content engagement metrics.
• Application: Sybil resistance in governance, sorting search results in marketplaces, or determining trust levels in social graphs. The algorithm defines which actions increase or decrease the score.

A combined economic model for curating dynamic assets. Assets are priced by their owners and listed at that price, but are subject to a Harberger tax (a periodic fee based on the self-assessed value). Anyone can purchase the asset at any time by paying the price.

• Curation Effect: The tax incentivizes owners to price assets honestly. Overvalued assets incur high fees, undervalued assets get quickly acquired.
• Use Case: Curating prime digital advertising space, domain names, or slots in a highly competitive list.

A mechanism where governance token holders vote to distribute rewards or liquidity across a set of options (pools, projects) according to their preferences. Voting power is often calculated using a voting escrow (ve) model.

• Primary Application: Directing liquidity mining incentives or protocol emissions in DeFi (e.g., Curve Finance's gauge system).
• Outcome: Creates an algorithmically curated distribution of capital and rewards, aligning incentives with community sentiment and strategic goals.

Even decentralized algorithms can lead to power concentration. Early adopters, large token holders (whales), or specialized bots can dominate the curation process, creating a feedback loop that marginalizes new entrants. This undermines the system's neutrality and can be addressed by:

• Progressive taxation on large stakers.
• Quadratic voting or funding to reduce whale power.
• Algorithmic randomness to break predictable outcomes.

The tokenomics and incentive structure must be secure and sustainable. Flaws can lead to:

• Inflation attacks: Farming rewards by submitting low-quality content.
• Stagnation: High barriers to entry reduce participation.
• Ponzi dynamics: Rewards are funded primarily by new entrants. Secure design uses bonding curves, challenge periods, and algorithmic adjustments to reward quality over quantity.

For users to trust an algorithmic system, its operations must be transparent and auditable. This requires:

• Fully on-chain logic where possible, verifiable by anyone.
• Open-source code for the algorithm and smart contracts.
• Public dashboards showing real-time inputs, parameters, and outcomes.
• Regular third-party audits of the code and economic model. Opaque systems are inherently higher risk.

A mathematical function that defines the relationship between a token's price and its supply. It is a core mechanism for algorithmic curation in bonding curve markets, where the price algorithmically adjusts based on buy/sell pressure.

• Continuous Liquidity: Provides a constant on-chain market.
• Automated Market Making (AMM): A common application for decentralized exchanges.
• Example: The xy=k invariant function used by Uniswap V2.

A list of vetted items (like tokens or smart contracts) maintained through a combination of algorithmic rules and human governance. The algorithm may enforce criteria, while token-weighted voting often determines inclusions.

• Purpose: Reduce spam and improve discoverability.
• Mechanism: Often uses a challenge period and staking for submissions.
• Example: The Kleros Curated Lists for token registries.

A specific implementation of a curated registry where the curation rights and governance are tied to ownership of a native curation token. Token holders stake to list, challenge, or vote on entries.

• Incentive Alignment: Token value is linked to registry quality.
• Curation Markets: A model for decentralized attention allocation.
• Key Process: Apply > Challenge > Vote > Resolve.

An economic mechanism where an asset owner pays a continuous tax (e.g., a percentage of a self-assessed value) while allowing anyone to purchase the asset at that price. This algorithmically enforces efficient allocation and valuation.

• Self-Assessment: Owner declares the asset's value.
• Continuous Auction: Asset is always for sale at the declared price.
• Use Case: Proposed for curating digital assets or ad space.

Decentralized networks that algorithmically rank, filter, or distribute content without central editorial control. The curation algorithm is defined by code and often influenced by user stake or engagement.

• Principle: Separation of infrastructure and content moderation.
• Mechanisms: Staking, upvote/downvote sybil-resistance, proof-of-human.
• Examples: Lens Protocol for social graphs, decentralized RSS aggregators.

A theoretical framework where tokens are used to signal value and allocate attention or resources within a network. The market itself performs the algorithmic curation based on collective token-weighted actions.

• Core Idea: "Token-curated everything."
• Signal-to-Noise: Aims to surface high-quality information.
• Application: Can govern lists, forecasts, or even software dependencies.