Fees & Rewards

Interactions within the Intuition system incur a fee, comparable to a gas cost in blockchain transactions. This fee serves several essential purposes:

Firstly, in decentralized and permissionless systems with shared infrastructure, it is vital to prevent system abuse, such as Sybil and denial-of-service (DoS) attacks. Intuition mitigates these risks by employing an economic model similar to those in blockchain networks, necessitating a fee for data creation. This economic disincentive discourages abuse, thereby preserving the system’s integrity and functionality. Furthermore, any attacks inadvertently benefit the network due to the fee payment, much like how Ethereum benefits from transaction fees even when used for non-productive purposes. This mechanism ensures the ecosystem remains robust and sustainable despite potential misuse.

Secondly, the creation of coherent and valuable data is often neglected, especially within the Web3 environment. Providing infrastructure for generating verifiable data alone has proven insufficient in motivating users to produce meaningful contributions. This issue is also prevalent in Web2, where the majority of users refrain from leaving reviews on platforms such as Amazon, Yelp, or Google, and rarely endorse others on LinkedIn or contribute to Wikipedia. Thus, there is a clear need for incentives to promote active and meaningful participation in the data contribution process, similar to how block rewards encourage participation in the layer 1 blockchain consensus process.

Thirdly, the sheer volume of data generated globally has reached overwhelming proportions, leading to an abundance of low-quality, redundant, or irrelevant information. This overabundance dilutes the value of truly meaningful and actionable data, complicating efforts to derive valuable insights. In both Web2 and Web3 environments, the emphasis needs to shift from merely producing more data to generating high-quality, reliable information. Intuition addresses this challenge by implementing mechanisms that discourage the production of irrelevant data and promote the creation of useful, pertinent information through economic incentives. By introducing an economic cost and associated rewards to data creation and curation, Intuition ensures that contributors are motivated to generate data that is coherent, valuable, and meets predefined standards of relevance and accuracy. This economic model not only deters the proliferation of “junk data” but also encourages the continuous refinement and validation of existing data.

Fourthly, the process of establishing standards in most industries has historically been fraught with difficulties, often described as “standards hell.” This status quo has failed to adequately address the needs of our ecosystem. Intuition’s system of trustless economic incentives expands the concept of leveraging financial rewards for distributed consensus—a principle successfully demonstrated in the blockchain ecosystem—to additional domains requiring social consensus and global coordination. These domains include standards for data structures, schemas, and formats, as well as canonical identifiers to which this data can be attached and correlated.

Intuition’s imposed fees addresses these challenges in two main ways:

1. Granting Ownership in Data: A portion of the fee contributes to granting the user ownership in the data they interact with. This mechanism ensures that users have a vested interest in the data they create or engage with, promoting responsible and meaningful interactions.
2. Rewarding Data Owners: A portion of the fee is distributed to existing owners of the data being interacted with. This incentivizes the creation and maintenance of valuable data, as users receive economic rewards for their contributions to the ecosystem.

This flow of value is enabled by Intuition’s innovative approach to data representation, which encompasses Atoms, Triples, and Signal. By structuring data into fractals via discrete, ownable fragments, this model allows for the programmatic distribution of value throughout the system’s state.

Consider a user who wishes to create a new data entry stating that they like a YouTube video. The user must pay a fee to create this data, part of which grants them ownership in the statement and part of which rewards previous owners of related data. Other users who agree with this statement can also pay a fee to do so explicitly, reinforcing the validity and increasing the value of the data. This process helps to ensure that only high-quality, relevant data remains prominent, as users are financially incentivized to support accurate and meaningful information.

This economic model encourages users to interact with data they believe will attract further engagement. Because data structures in Intuition are explicit, users are motivated to converge not only on the entities/concepts/data themselves, but also on the most effective ways to describe and reference these entities/concepts/data. This creates an incentive for users to reach fractal consensus on data structures, from individual Atoms to complex nested Triples. By integrating these economic principles, Intuition not only secures the system against malicious attacks but also promotes a healthy, self-regulating ecosystem where users are rewarded for their contributions to the integrity and value of the data.