Intuition in Practice

The introduction of Intuition’s core primitives lays a robust foundation for constructing a system that fosters a trustful interaction layer for the decentralized web. These seemingly basic primitives enable a wide range of powerful functionalities, transforming how we manage identities, data, and trust online.

To illustrate this, consider, once again, the experience of buying a product on Amazon. With Intuition’s primitives, this process can be reimagined in a decentralized, trustless manner:

1. Atoms and Triples
   1. Every user, product, review, and transaction can be represented as discrete objects (Atoms) and linked through relationships (Triples). For instance, a Triple could link a product (Atom) to a review (Atom) through a relationship like “hasReview.”
2. Signal
   1. Each review can be evaluated for credibility using the Signal primitive. Reviews and Signals from verified purchasers or highly reputable users can carry more weight, helping users discern trustworthy feedback from potentially fraudulent reviews.
3. Fees, Economics, and Incentives
   1. Users are encouraged to express themselves, and reviewers are incentivized to provide honest and detailed feedback. Users earn money for their valuable data contributions, and their reputation becomes cross-contextual, increasing its significance beyond a single platform. For example, a user’s status as a trusted reviewer on Amazon could serve as a valuable reputation data point in other contexts, such as securing better loan-to-value ratios in decentralized finance (DeFi) or enhancing the ability to obtain a loan. This cross-contextual reputation system contrasts with Amazon’s current reviewer reputation, which offers no tangible rewards.
   2. Additionally, users might be more inclined to attest to useful information about products on Amazon because their claims can be easily seen by the people they care about, thanks to portable social and trust graphs. Unlike the current Amazon platform, which lacks a friends list, Intuition allows users to take their singular social graph or a set of trust graphs with them anywhere they go. This portability means that the review of one trusted individual in a specific context could hold more weight than numerous anonymous reviews. The ability to leverage these portable social and trust graphs ensures that valuable insights from trusted sources are prioritized, further enhancing the reliability and utility of the data within the ecosystem.
4. Interpretations
   1. The listing of a product on a marketplace such as Amazon can be represented as a literal interpretation. For instance, adding a product to a marketplace can be as straightforward as creating a Triple like [Product][listedOn] [Amazon]. Expanding on this concept, various stakeholders can publish their interpretations of product reviews. For example, a company might aggregate all reviews and provide a summary score, while an independent consumer protection group could highlight reviews mentioning safety concerns.
5. Algorithms
   1. Users can choose from various algorithms to filter and sort reviews based on their preferences. For instance, an algorithm can prioritize reviews from users with similar purchase histories or highlight reviews that mention specific product features.
6. Reality Tunnel
   1. Users can compose together interpretations and algorithms to create curated ‘views’ of data, allowing them to find the voices they trust among thousands of anonymous voices.
7. Portable Identity and Data
   1. Identities and data become universal, and can be referenced and integrated anywhere across the web. Users can see a product’s aggregated reviews across Amazon, Facebook Marketplace, Craigslist, or eBay, provided the same canonical identifier for the products is used in each context. And, even if Amazon were to shut down, users’ data and lists of favorites would still exist, ensuring continuity and control over their digital interactions.

By integrating these primitives into contexts and interactions that cannot yet be fully ‘trustless,’ Intuition establishes a decentralized trust layer. This layer allows users to interact with confidence, knowing that the information they rely on is transparent, verifiable, and free from centralized manipulation. For instance, when buying a product, users can access a wealth of credible reviews, filtered and presented according to their preferences, all while maintaining control over their data and identities. By leveraging these tools, we can build a more trustworthy and user-centric digital ecosystem, addressing many of the inherent flaws in the current Web2 trust stack and paving the way for a more decentralized and equitable internet