wtfipluralqf.decartography.com
For now, I am considering updating the WTF is Quadratic Funding? section, which includes information about DeCartography and the importance of Plurality. It also showcases real-life examples and our collaboration with Gitcoin. The concept of "Plurality" is becoming increasingly important in the blockchain space. DeCartography serves as a Relational Computation Oracle, providing data without fixed metrics for analysis. It combines complex systems with computational methods to generate social graphs, allowing us to quantify cooperation between contributors.
Plurality represents a shift in consensus systems, particularly in organizations within cyberspace like DAOs, from the traditional one-person-one-vote model to Quadratic Funding. However, there are risks associated with Ethereum addresses, such as the potential for civil attacks and collusion through smart contracts. To address these challenges, we provide social graph data generated by DeCartography as an oracle, enabling the adjustment of voting power based on Plurality Scores through Quadratic Funding.
Social Plurality aims to recognize and promote cooperation among diverse social and cultural ecosystems.
In the context of secondary funding ecosystems, it is important to consider the role of homogeneous and highly cooperative groups. Think about families, friends, colleagues, neighbors, and fellow citizens. Our identities are nested within rich social relationship graphs, where various individuals and organizations care about and cooperate with unique sets of people and organizations within that network. Quadratic Funding and Gitcoin Grants should significantly reward and amplify cooperation between family members, colleagues, and friendly neighbors. Technically, couples, companies, and geographical communities can request infinite matching funds through cooperation in Gitcoin Grants.
To address this reality, Gitcoin initially adopted the calculation of Pairwise coordination subsidies: a new quadratic funding design. One of the main features of this approach is that the administrator of Quadratic Funding can adjust the matching weight between any pair of contributors. Based on the spirit of plurality, the administrator can make this adjustment based on the perception of how much contributors have already cooperated. For example, two cooperative agents can receive lower matching weights compared to two agents with significantly different social backgrounds. Great. It is a formal mechanism for cooperation beyond differences. As far as I know, the evidence of cooperation among contributors captured by Gitcoin Grants so far is limited to the contribution behavior in the current funding round. Unfortunately, this evidence does not seem to fully recognize the complexity of contributors' cooperative reality. We aim to gradually increase cooperation beyond differences and mute contributions between homogeneous and more highly cooperative groups. This oracle makes it possible to quantify the cooperation between contributors.
Cooperation Across Differences
This oracle makes it possible to quantify the cooperation between contributors. It gradually increases cooperation across differences and mutes contributions between homogeneous and more highly cooperative groups.
Identity for plurality
Identity and policy modules for Plurality
DeCartography's data is generated through a social graph data generation process that combines complex systems and computational methods, using the NFTs and wallet transactions you possess. Human agents examine addresses, repeat the process, and generate data using collective wisdom and peer prediction methods. Unlike other social graph providers, DeCartography provides graph data without defining metrics (slicing) for social graph generation.
If you are interested in becoming a validator for the oracle and earning ETH in a matter of minutes, please sign up for the waiting list.
Regarding the wtfipluralqf.decartography.com website, I am considering how to present the samples. Specifically, I am thinking of including the following: - tkgshn.icon around this area: https://gyazo.com/c1400002f133c4203cf4aba94f182868
- This visualizes the following hypotheses:
- Phi is more supported by a wider community compared to StarkDeFi.
- Phi has more supporters, but StarkDeFi has a higher total donation amount.
- On average, the per-person donation amount is higher for StarkDeFi.
- In a normal Quadratic Funding scenario, Phi would receive more funding.
To achieve this, I need to reconsider the requirements and create a solution that addresses the end users' challenges. Specifically, I need to detect and showcase the fact that some projects receive a large amount of funding from similar communities. This may require analysis of end users, which presents an Egg Problem that needs to be resolved. To address this, I need to quickly generate graph data. It doesn't have to be on-chain data; a sample image should suffice. In terms of web development, this is almost 100% feasible, and the results can be displayed using the Graph API. In the Gitcoin and PoC section, I plan to provide the following API initially: 1. Specify an address and a project.
2. Return how "deviant" that address is compared to the list of donors.
To make DeCartography's functionality more understandable, I want to create a simulator site for the "adjustment of voting power based on social diversity."
https://gyazo.com/9e2297e88907896f4e51d15c7d97278e
- All points: People who have donated in the current round (GR3).
- Yellow points: People who have donated to the same project.
- Purple point: Yourself.
- The "number of deviation" represents how far away you are from the average position of people who have donated to the same project.
- The curve below represents the voting power adjustment based on this number of deviation.
- By becoming a different address, you can change this social graph-like representation.
https://gyazo.com/b0f7851e78ceb98ed4e0ac04079aa8d2
tkgshn.icon*5
Finding room for discussion
- By the way, have you considered using clustering techniques like k-means to group the data? I'm not an expert, but it seems like it could be a good fit. - In the case of k-means, it is an unsupervised learning method that clusters (groups) data based on their features. It depends on the data, so the clustering can vary. Although the hack resistance of this approach is a topic for discussion, it intuitively seems like it could work.
- There is room for exploration in terms of clustering, not limited to k-means.
- Clustering similar people to Address A: Possible
- Clustering bot-like addresses: Possible
- Clustering based on distance: Not possible
https://gyazo.com/64aebe6bdbdac280d11153039fbcbe46
https://gyazo.com/8b113ac16d48430e3d299ad7fdbf0015
glenweyl.icon
Review of QF
Quadratic Funding is a method of determining the distribution ratio for funding public goods based on the square root of the donation amount from end users. In simple terms, "public goods supported by many people" can receive a large amount from the Matching Pool. https://gyazo.com/fd4e315adbe6289a8e160063f091724e
Risks of QF
Quadratic Funding carries the risks of Civil Attack and Collusion. It is common for attackers to create multiple accounts and donate to their own projects, often resulting in the project receiving more funds from the matching pool than the cost of the attack (dividing the attack cost among multiple accounts). Even if we achieve 1Person, 1ID, the risk of attacks related to collusion and bribery in cooperative actions with other agents cannot be completely eliminated. https://gyazo.com/c3bf78db63e8a9f201565dba79f56ec0
To provide an actual analysis of Quadratic Funding, it is estimated that 26% of users in GR14 are engaging in civil attacks.
https://gyazo.com/c14e14d269565ad5eb8af261f2afca1a
https://gyazo.com/e127785e2cefe73672fdb8a4f2f17f44
Quantifying Cooperation Among Contributors
https://gyazo.com/bc23c4b4152a86bf6d5c34eecab34e75
https://gyazo.com/5a87d61c30d25134fa7f01848ca4db8d
https://gyazo.com/c667fd9dd37fe06a3e2fe633cd8c534f
What is Plurality?
https://gyazo.com/2782aa45b1957d9b16a3af5d685a7c29
For more information, please refer to:
- The interpretation of Kant's 'Perpetual Peace' as Plurality
DeCartography provides the necessary oracles for Plurality QF
Based on the academic research and thoughts of Glen Weyl, Joel Miller, Leon Erichsen, and others, DeCartography has started a proof-of-concept experiment with Gitcoin to provide social graph data as an oracle for Plural QF. DeCartography serves as a Relational Computation Oracle. This oracle enables the quantification of cooperation between addresses, gradually increasing cooperation across differences and muting contributions between homogeneous and more highly cooperative groups. The data provided by the oracle is generated by examining addresses using the NFTs and wallet transactions you possess, repeating the process, and using collective wisdom and peer prediction methods. This approach is the only way to provide graph data without defining metrics for social graph generation, distinguishing it from other social graph providers. @DeCartography: DeCartography makes it possible to quantify the cooperation between contributors. It gradually increases cooperation across differences and mutes contributions between homogeneous and more highly cooperative groups. https://pbs.twimg.com/media/Fp33VzMaAAA5Q1f.jpg
Simulation of Plural QF
Using the Plural Social Graph, we can adjust the voting power of Quadratic Funding.
https://gyazo.com/696fc8be00f2b0ce5e10a995c3a2be1c