Collaborative Design of Contribution Tracking Systems for Decentralized Organizations
Collaborative Design of Contribution Tracking Systems for Decentralized Organizations
Nicholas Vincent (Northwestern University); Christine Vandevoorde (GovrnHQ) https://gyazo.com/7c26a5a412c9e86d4a52754d736a5a08
This paper discusses the hypothesis of how to track and evaluate member contributions in decentralized organizations. The challenge lies in data acquisition. It is important to inform members that their actions may be evaluated and to consider the potential vulnerability to hacking as the organization grows. This is particularly relevant in the context of voting rates in DAOs, which ZkEther has shown interest in. In this short position paper, we propose the concept of "collaboratively designed contribution tracking systems" for recording the contributions of DAO members or contributors to open source projects. We highlight the trade-offs between recording contributions passively (via logging) or actively (via forms) and whether contributors are aware of how their contributions will be used. We suggest opinionated defaults for collaborative design and discuss our ongoing work to answer empirical questions in this field. To design a system for collaborative record keeping, we consider two scenarios. First, a team building software projects wants to log contributions, such as adding features, fixing tests, or writing documentation. Second, a group of friends wants to do collaborative filtering, where each member expresses their preferences. The key idea is to give each member options on how the data is structured, processed, and aggregated.
Can we discover "special will" (individual will) by collecting unconscious everyday behavior instead of relying on self-reporting, while making users aware that they are being analyzed? The goal is to identify Special Will and convert it into General Will within a DAO. This involves using tools to foster the assumption of contribution and determining the trade-offs between active and passive data collection, which depend on the context. https://gyazo.com/e53278cabcc3c7963d25f6af80d99597
There are four main aspects to consider:
1. Passive ⇄ Active: Choosing between user responses to surveys or user-generated data.
2. Unconscious ⇄ Conscious: Whether the user knows how their generated data will be used.
3. Development of tools to support active data creation and increase user awareness.
4. The correlation between active data creation and user interest.
https://gyazo.com/9ff8696cfceeac122ea1d3aff943b52e
The ideal scenario is to have a system where the collected data is always visible and users can see how it is used for decision-making. It should also allow users to trace back to the reasons behind the data collection. Discussions should be held separately on what data to collect and how to implement it.
The proposal components include a Records Table, where users provide observed results, and a Schema Cultivation Table, where users discuss the types of data to collect. The Application table focuses on how the collected data can be useful, such as for aggregation, transformation, filtering, or as training data. Preference Aggregation and its cultivation involve gathering user opinions on how to aggregate the collected data and resolve conflicts caused by schema changes.
Additionally, there are code examples and diagrams that illustrate the development process, test operation confirmation, and merging into a cohesive environment, which were discussed in CESC.