Intuition 백서 전문

a lies in its comprehensibility—users’ ability to understand and derive meaningful insights from it. For instance, consider on-chain transactions. While these transactions contain implicit signals, the true meaning behind them is not always clear. Take the example of a user depositing money into a DeFi protocol. This action may or may not indicate trust in the protocol, belief in its long-term viability, or simply an attempt to achieve high yield in the short term. Understanding the true intent is impossible, and inferring even some relevant signal often requires sophisticated analysis tools and contextual knowledge, which many users lack. Data must also be presented within a semantic context to be comprehensible. Raw data without context is often meaningless. Structuring data using well-deﬁned schemas and ontologies helps convey relationships and signiﬁcance. For example, in a dataset of product reviews, it’s crucial to understand the relationship between the reviewer, the product, and the review content to extract meaningful insights. Relevance is another critical factor. In the decentralized web, users often encounter an overwhelming amount of information. Filtering and prioritizing data based on the user’s speciﬁc needs and context enhance comprehensibility. For example, a user seeking trustworthy product reviews should be able to ﬁlter reviews by criteria such as the reviewer’s reputation, the review date, and relevance to their query. Furthermore, user experience plays a crucial role in data comprehensibility. This involves designing user interfaces that make data easily accessible and interpretable. Eﬀective data visualization, straightforward navigation, and contextual information can signiﬁcantly enhance the user’s ability to understand and utilize the data. The focus should be on creating a seamless and intuitive experience that reduces cognitive load and helps users make sense of complex information. 5 Data Quality and Bias 5.0.1 Subjective Data Capture & Structuring Current decentralized identity, veriﬁable claim, and attestation systems predominantly focus on determin- istic truths and veriﬁable information, often neglecting the importance of subjective data. This emphasis on veriﬁable data means that subjective experiences, opinions, and nuanced information are frequently over- looked or undervalued. A comprehensive trust framework must accommodate subjective data, recognizing that not all valuable information can be objectively veriﬁed. These systems typically prioritize cryptographically veriﬁable data, such as identity credentials issued by trust anchors and transactional records. While this approach signiﬁcantly enhances security and reliability, it fails to capture the richness of subjective data essential for a more holistic understanding of trust and reputation. Subjective data, including personal experiences, opinions, and qualitative assessments, oﬀers critical insights that deterministic data alone cannot provide. Adapting these systems to accommodate subjective data requires signiﬁcant changes in data handling and processing methodologies. This includes developing advanced algorithms that can interpret and integrate qualitative data into existing data structures without compromising the integrity and reliability of the system. Moreover, there is a cultural aspect to consider. In many decentralized communities, there is a strong emphasis on veriﬁability and objectivity, often at the expense of subjective insights. Changing this culture to value and prioritize subjective data as equally important requires concerted eﬀorts in education and community engagement. 5.0.2 Platform-Induced Expression Bias Platforms often create environments that subtly coerce users into expressing speciﬁc things, driven by the design of the platform itself. Many platforms employ gamiﬁcation techniques to encourage behaviors such 31 as liking, sharing, or commenting. While these techniques can increase engagement, they often result in superﬁcial interactions that fail to capture the full depth of user expression. The platforms’ algorithms and interface designs are tailored to maximize user interaction and retention, but this frequently comes at the expense of genuine, nuanced expression. The reputation data and signals that arise from these interactions are thus ﬁltered through the platform’s design biases. For instance, users may be incentivized to present an overly positive or curated version of themselves to gain more likes or followers, leading to a representation that is not necessarily reﬂective of their true selves. Similarly, the pressure to conform to popular opinions or trending topics can stiﬂe authentic discourse and reduce the diversity of viewpoints shared on the platform. This dynamic is particularly evident in social media environments, where the pursuit of virality and en- gagement can overshadow meaningful communication. Users might prioritize content that is more likely to be liked or shared, rather than content that genuinely reﬂects their thoughts or values. As a result, the reputation signals generated on these platforms can be misleading, as they are heavily inﬂuenced by the platform’s incentives rather than the users’ authentic expressions. 5.0.3 Imposition of Rigid Models of T ruth Many existing systems attempt to impose a singular model of truth through scores and veriﬁcation mecha- nisms, which can be overly opinionated and inﬂexible. These systems often present information as deﬁnitive and unchallengeable, failing to accommodate diverse perspectives and the complexity of subjective reality. This approach can alienate users who do not align with the imposed standards and make it diﬃcult to achieve consensus within the community. Even platforms that strive for objectivity, such as Wikipedia, are fundamentally constrained by their editorial policies and community norms. While Wikipedia aims to present a neutral point of view, it often ends up enforcing a rigid model of truth that does not oﬀer a platform for diverse perspectives. The content on Wikipedia is subject to the biases of its contributors and editors, and consensus on contentious topics can reﬂect the prevailing views of a limited subset of users rather than a balanced representation of all perspectives. This imposition of a rigid model of truth can stiﬂe innovation and discourage meaningful discourse. In a decentralized and diverse digital landscape, it is crucial to create systems that can accommodate multiple viewpoints and recognize the validity of subjective experiences. Systems that enforce a single version of the truth fail to capture the richness of human knowledge and understanding, limiting their utility and relevance. This issue is compounded by the lack of robust reputation or reputation discovery mechanisms and the absence of veriﬁable attribution for most data. Without these tools, it becomes challenging to discern when information is biased or manipulated. 5.0.4 Complexity of User Interfaces Many decentralized applications (dApps) currently suﬀer from user interfaces (UIs) that are not intuitive or user-friendly, particularly for non-technical users. The complexity of these interfaces can be a signiﬁcant barrier to entry, limiting broader adoption and preventing mainstream users from engaging with decentralized technologies. One of the primary challenges is the prevalence of technical jargon and concepts that are unfamiliar to the average user. Terms like “private keys,” “gas fees,” “smart contracts,” and “staking” are commonplace in dApps but can be confusing and intimidating for those not well-versed in blockchain technology. This steep learning curve can discourage potential users from exploring and utilizing decentralized applications. Another issue is that the processes involved in executing transactions on blockchain platforms often require multiple steps, each with its own set of instructions and considerations. For example, to interact with a decentralized ﬁnance (DeFi) platform, a user might need to connect a wallet, approve transactions, manage gas fees, and ensure the security of their private keys. Each of these steps involves speciﬁc knowledge and careful attention, increasing the likelihood of user error and making the experience cumbersome. The complexity of dApp interfaces can also exacerbate security concerns. Users who do not fully understand the processes or technologies they are interacting with are more susceptible to phishing attacks, scams, and other malicious activities. Simplifying the UI and providing clear, easy-to-understand security warnings can help mitigate these risks, but this requires a concerted eﬀort in design and user education. 32 While wallet providers like MetaMask and Coinbase Wallet continue to enhance their interfaces to make them more user-friendly and secure, the persistence of hacks and scams demonstrates the ongoing immaturity of the crypto user experience. Despite advancements, the frequency with which users lose funds due to phishing, poor key management, and complex transaction processes highlights the critical need for improved UI/UX design and user education. 5.1 Authenticity , Security , and Privacy Challenges 5.1.1 Sybil Attacks Sybil attacks are a signiﬁcant threat to any decentralized system, particularly those where creating new identiﬁers involves minimal friction or cost. In a Sybil attack, an individual or entity creates multiple fake identities to manipulate or disrupt the network. These attacks can distort reputation systems, voting mechanisms, and other trust-based interactions, thereby undermining the reliability and fairness of the system. Preventing Sybil attacks requires robust mechanisms to ensure each identity within a network is unique and veriﬁable. To counter Sybil attacks, various mechanisms can be employed: • Proof of Work (PoW) and Proof of Stake (PoS) mechanisms increase the cost of creating multiple identities, making it economically unfeasible for attackers. • Proof of Humanity veriﬁes the uniqueness of individuals through social veriﬁcation, biometrics, or attestations, ensuring that each identity represents a real person. • W eb of Trust builds a network of trust through endorsements from veriﬁed users, leveraging social structures to validate identities. • Zero-Knowledge Proofs (ZKPs) allow for privacy-preserving veriﬁcation of identity attributes, ensuring uniqueness without revealing sensitive information. • Identity Veriﬁcation Services can provide additional security layers by verifying identities through oﬃcial documents and biometric data, though this may introduce centralization risks. However, each of these solutions comes with trade-oﬀs, and no perfect solution has yet been found. 5.1.2 Veriﬁable Bridging of Oﬀ-Chain Data to On-Chain Systems Decentralized systems often need to interact with data that originates oﬀ-chain. Bridging this data to trustless systems in a veriﬁable manner is crucial for maintaining trust and integrity. The main challenges include ensuring data integrity, conﬁdentiality, and availability during the transfer process. Numerous teams are actively working on addressing these issues, employing advanced techniques such as decentralized oracles and zero-knowledge proofs to enhance the reliability and security of data transfers- how- ever, despite these eﬀorts, solving the problem entirely remains a monumental task, demanding continuous innovation and collaboration across the entire ecosystem. 5.1.3 Data Provenance Data provenance, the ability to trace and verify the origins and history of data, is a signiﬁcant challenge across the web. Despite the existence of tools designed to address this issue, they have not fully delivered on their promises. In the traditional web, data is often siloed within proprietary platforms, making it diﬃcult to track its journey from creation to consumption. This fragmentation undermines the reliability and trustworthiness of information, as users have no way to verify the authenticity and integrity of the data they encounter. Misinformation and data manipulation become prevalent, eroding user trust. Various tools and frameworks have been developed to tackle data provenance. Blockchains, decentralized data storage solutions, and other cryptographic toolings oﬀer mechanisms to record and verify the history of data transactions in a tamper-proof manner. These solutions aim to provide transparency and accountability, ensuring that data can be traced back to its original source. However, the practical implementation and widespread adoption of these tools have been lacking. In an era where AI-generated content is frequently mistaken for human-created data, and deep fakes are increasingly 33 common, the failure to fully implement robust data provenance mechanisms leaves us vulnerable to rampant misinformation and deception. 5.1.4 Balancing T ransparency and Privacy The concept of “data as a public good” is highly beneﬁcial for enhancing our ability to make informed decisions, fostering transparency, and ultimately aiding in building a trustful interaction layer. The more accessible and transparent data we have, the better our perspectives and decisions can be. However, privacy remains a crucial concern. As we strive for greater transparency, it is equally important to ensure that individuals’ privacy is protected. Building systems that accommodate both public and private data is essential for creating a balanced and trustworthy digital ecosystem. Balancing transparency and privacy involves implementing selective disclosure mechanisms and privacy- preserving technologies. Solutions such as homomorphic encryption and Zero-Knowledge Proofs (ZKPs) enable users to reveal speciﬁc data attributes without exposing the entire dataset, bolstering the potential of selective disclosure systems. Homomorphic encryption allows data to be processed while still encrypted, ensuring conﬁdentiality throughout the process. ZKPs allow users to prove the validity of a statement without revealing the underlying data, maintaining privacy while ensuring authenticity. However, despite these advancements, balancing transparency and privacy in decentralized systems remains an unsolved problem. One of the primary challenges is the inherent tension between the two goals. Trans- parency requires data to be openly accessible and veriﬁable, while privacy necessitates keeping certain infor- mation conﬁdential. This tension is particularly pronounced in decentralized systems where the principle of transparency is foundational. 6 The Current State of Aﬀairs “Show me the incentive and I will show you the outcome” - Charlie Munger Despite signiﬁcant technological advancements that hold the promise of creating a trustful interaction layer, this issue remains largely unresolved. Eﬀective and eﬃcient decision-making depends on the availability of high-quality data. Although infrastructural tools such as platforms for creating veriﬁable claims exist and can aid in this process, they have yet to reach their full potential. This is evidenced by the frequent lack of access to critical data at crucial moments. Consequently, users often make poor decisions, leading to a suboptimal Web3 experience. Many individuals ﬁnd themselves in inappropriate communities, investing in unsuitable projects, or interacting with malicious contracts. This misalignment signiﬁcantly hampers the potential beneﬁts of decentralized systems, often making trustlessness more of a liability than an asset. Given these challenges and the fact that some users can navigate Web3 successfully (indicating that the necessary data exists somewhere), there is an urgent need for a system that leverages collective intelligence to enhance decision-making. Such a system would introduce greater trust in environments or situations where complete cryptographic veriﬁcation and trustlessness are not yet possible. In the examination of the host of aforementioned problems currently blocking the realization of a trustful interaction layer, each can be largely attributed to a breakdown in social participation, coordination, and consensus. For example, the lack of consensus on identity standards leads to fragmented digital identities across platforms, making it diﬃcult to establish cohesive online personas. Similarly, the absence of uni- ﬁed data standards and schemas results in incompatible data formats, hindering interoperability and data integration. Moreover, the scarcity of data itself hampers the ability to derive meaningful interpretations. Fortunately, the blockchain sector stands at the forefront of coordination technology, providing crucial in- sights for overcoming these challenges. Intuition harnesses the economic principles that currently secure and sustain Web3, applying them to incentivize participation and foster consensus on identiﬁers and informa- tion. This strategy seeks to establish a credibly neutral framework that leverages collective intelligence to inform and guide user interactions. By tapping into the wisdom of crowds, Intuition extends the capabilities of coordination technologies, creating a system that bolsters trust in areas that cannot yet be made fully trustless. 34 7 Core Guiding Principles of Intuition Through the examination of the current state and the persistent challenges of deriving trust in all our interac- tions, it becomes clear that the problem should be addressed through a composition of various decentralized identity, data, and ﬁnance primitives. Leveraging these tools, we can build robust systems that encourage better expression, curation, standardization, interpretation, and discoverability of data. By adhering to the following principles, we can create a framework that promotes fairness, inclusivity, and interoperability: Credibly Neutral The protocol should be credibly neutral, just as all protocols managing identities, data, and algorithms should. A credibly neutral system should operate impartially, without favoring any particular group, perspective, or outcome. This neutrality is essential for fostering trust and inclusivity within the ecosystem, ensuring equitable treatment for all participants. Unopinionated Protocol The protocol should remain unopinionated, imposing no restrictions or biases on the types of claims that can be made - just as the internet does not impose restrictions on the transfer of arbitrary data. This stance allows users to claim anything about anything, supporting diverse voices and perspectives. Higher- level middleware and applications can implement ﬁlters and moderation mechanisms to prevent abuse while preserving the core protocol’s neutrality and openness. No ‘T ruth Lock In’ The system should accommodate all viewpoints on any issue, rather than presenting only the prevailing opin- ion. This requires an absence of vendor lock-in. Users should be able to toggle between diﬀerent perspectives, ensuring a spectrum of views. This ﬂexibility prevents the platform from becoming a gatekeeper of infor- mation, maintaining the decentralization ethos. Moreover, developers should be able to create new methods for ﬁltering and presenting data without requiring permission. This permissionless innovation ensures that diverse methodologies for data distillation can ﬂourish, catering to the varied needs and preferences of the user base. Self-Sovereign Identity Users should be able to control their digital identities, managing, sharing, and protecting their information without relying on centralized authorities. This enhances privacy and security, fostering a user-centric digital ecosystem. Enabling self-sovereign identity promotes user autonomy and trust, allowing individuals to interact conﬁdently and securely across various platforms and services. Self-Sovereign Data Users should have sovereignty over their data. Decentralizing control mitigates risks such as censorship and data breaches, ensuring users retain full own