Fees & Rewards
In the Intuition system, interactions incur fees similar to gas costs in blockchain transactions. These fees serve critical roles in maintaining system integrity, incentivizing contributions, and fostering high-quality data.
Purpose of Feesβ
Preventing Abuse and Attacksβ
Decentralized systems face risks such as Sybil and DoS attacks. Intuition mitigates these through economic disincentives:
- Fees deter malicious activity by imposing costs
- Network resilience: attacks inadvertently benefit the system through fee payments
- Similar to how Ethereum benefits from all transaction fees
Encouraging Active Participationβ
Economic incentives motivate meaningful contributions:
- Historical challenge: Web2 platforms (Amazon, Yelp, Wikipedia) struggle with participation
- Intuition mirrors blockchain block rewards model
- Tangible incentives for creating valuable data
Promoting High-Quality Dataβ
Shifts focus from quantity to quality:
- Economic mechanisms discourage irrelevant data
- Rewards align with data quality and usage
- Reduces "junk data" proliferation
Establishing Standards Through Incentivesβ
Traditional standards creation is challenging ("standards hell"):
- Applies blockchain consensus principles to social consensus
- Financial rewards for distributed agreement
- Extends to data structures, schemas, formats, identifiers
Fee Typesβ
Every interaction with the knowledge graph involves two types of fees:
- Gas fees: Network transaction fees paid to the maintainers of the Intuition Network for processing transactions
- Protocol fees: Fees that flow through the Intuition protocol to reward data contributors and maintain the ecosystem
Because Intuition breaks data down into discrete, tokenized units, the system is aware of 'who owns what data' at any given point in time. Because of this, the system is able to programmatically flow value β such as these fees β through data as that data is interacted with.
Fee Allocationβ
When users interact with or create data, these combined fees support both network operations and data contributor rewards through the following mechanisms:
Purchasing Equity in the Data
To purchase tokens of an Atom or Triple, users deposit $TRUST (the native token of the Intuition Network and Protocol) into the Vault of the respective Atom or Triple. You pay a protocol fee proportional to your deposit amount. In return, you receive tokens of that specific Atom or Triple, entitling you to rewards generated by that data point proportional to your ownership percentage.
Rewarding Prior Contributors
When interacting with data, part of the protocol fee is distributed to all existing shareholders (prior contributors). This encourages early, meaningful contributions, as users who add valuable data will continue to be rewarded over time through protocol fees, while gas fees go to network validators.
Protocol Maintenance
A portion of the protocol fee is paid to the Intuition protocol for platform maintenance and development. This ensures Intuition can be self-sustaining and exist in perpetuity, without risk of shutting down.
Fee Structure Detailsβ
Entry Feesβ
- Charged when depositing into vaults
- Distributed to existing shareholders
- Incentivizes early discovery
- Amount varies by bonding curve position
Exit Feesβ
- May be charged on withdrawal
- Helps stabilize vaults
- Prevents rapid speculation
- Protects remaining stakers
Protocol Feesβ
- Portion goes to protocol treasury
- Funds development and maintenance
- Supports ecosystem growth
- Governed by community
Atom Deposit Fractionβ
- Special fee for Atom interactions
- Ensures Atom owners benefit when used in Triples
- Aligns incentives across primitives
- Creates value flow through graph structure
Fractals Data Structure Incentivesβ
The data model (Atoms, Triples, Signal) enables programmatic value distribution:
Example: YouTube Video Likeβ
- User creates Triple:
[User] [likes] [YouTube Video] - Creation fee rewards owners of component Atoms
- Initial deposit grants Triple ownership
- Future deposits reward this user
- Value flows through entire structure
This ensures:
- High-quality data remains prominent
- Incentives align with accuracy
- Meaningful contributions rewarded
- Natural quality filtering
Reward Distributionβ
Share-Proportional Rewardsβ
Rewards distributed based on ownership:
yourReward = totalFees * (yourShares / totalShares)
Temporal Advantagesβ
Early participants benefit more:
- Better share prices
- Longer fee accumulation period
- Compound growth effects
Usage-Based Generationβ
More useful data generates more fees:
- Popular Atoms referenced frequently
- Valuable Triples queried often
- Infrastructure data constantly used
Aligning Incentives with Data Structureβ
Economic model motivates users to:
- Converge on Entities: Consensus on key data points
- Adopt Effective References: Best ways to structure data
- Support Quality: Back accurate, useful information
- Create Standards: Emerge organically through use
Drives fractal consensus from individual Atoms to complex nested Triples.
Self-Regulating Ecosystemβ
Economic integration achieves:
- System Security: Fees deter attacks
- Meaningful Contributions: Rewards motivate quality
- Structured Consensus: Incentive-driven standardization
- Sustainable Growth: Value flows support development
Next Stepsβ
- Bonding Curves - Understand pricing mechanics
- Tokenomics - Learn about $TRUST token
- Incentive Design - How economics drive consensus