In our view, testing and experimenting with different sampling techniques and approaches over time is an important aspect of developing an effective and robust governance system. While it may garner various results, it turns theoretical models and assumptions into an empirical approach yielding tangible data.
By implementing and testing various sampling methods, the Collective can gather concrete data on how each technique performs in different scenarios and contexts. This hands-on experimentation allows for a much more nuanced understanding of the strengths and weaknesses of each approach.
Random sampling might prove highly effective in certain types of decisions but less so in others. Similarly, opt-in methods or full participation could show different levels of engagement and decision quality depending on the context. Nevertheless, these are still meaningful conclusions and outcomes worth finding that theories alone cannot offer.
This data-driven approach enables the Collective to make informed adjustments and refinements to its governance processes based on empirical evidence rather than assumptions.
Experimenting with various methods over time allows the Collective to adapt to changing circumstances and evolving community needs. What works well at one stage of the organization’s development may become less effective as the community grows or the nature of decisions changes. By maintaining a flexible approach and continuously testing new ideas, the governance system can remain agile and responsive.
Hence, despite understanding some of the feedback and criticism, we still deem the proposition to be viable and worth pursuing.
In my opinion, by opening social networks like Telegram, the necessary training can be given to the members, most of them do not have a correct understanding of these topics
Thank you to everyone who applied to become a Guest Voter in Round 5. We saw almost 100 applications from developers across the ecosystem. Experimentation is an essential component of growing the Collective’s knowledge around Citizen selection, and participation in these early iterations is especially appreciated.
Guest Voters for Round 5 were selected based on their GitHub contribution graph using a modified EigenTrust algorithm. More on that can be found in this forum post by OpenRank who collaborated with the Optimism Foundation and Open Source Observer to build the algorithm.
The following 30 individuals have been selected to participate as Guest Voters in Retro Round 5. If you are on the list, please stay tuned for an email from citizenship@optimism.io.
Hey @stas we asked folks to apply in order to create an initial filter for interest/motivation before selecting participants based on their GitHub contribution graph.
The GitHub contribution graph was not used to assess interest, but rather assess proximity to the OP Stack. You can read more about the algorithm here.