Suggested idea funding categories are easier to scale than the challenge setting process
Why is it important?
Scalable funding categorisation means ensuring that as the funding amount grows the funding process is still effective. Scalable categorisation will need to handle increasing funding, proposals and participants. Categorisation must be scalable so that it is sustainable over the long term and supports Cardano achieving global adoption and usage.
Requires recurring proposer, voter and assessor effort - Challenge settings are more difficult to effectively scale due to the amount of effort required by proposers, voters and assessors. Proposers are needed to define briefs, budgets and auditing metrics for each challenge (if they aren't repeating a pre-existing challenge). Proposers submitting into those challenges also need to read and learn about all the challenges again each funding round as they change. Assessors must review all the challenges and provide sufficient ratings on their quality. Voters must read challenges in each funding round to be informed on which categorisations make sense. Although this process could somewhat be feasibly scaled it comes at a high cost and effort from multiple stakeholders.
Complexity increases with scale - Challenge settings are flexible to all forms of categorisation. This causes the complexity that as the ecosystem grows and the funding is increased the more complex the justification is for deciding on categorisations and the budget weighting being used. A key concern for scaling complexity is that the needs of the community will increase in diversity over time. This complexity will get pushed into the challenge setting process and result in a more diverse selection of objectives and groupings that will be more difficult to handle for proposers, voters and assessors.
Difficult to automate - An issue with changing categorisation is the lack of consistency for what categorisation is used. Constant change will make it harder to automate how categorisation is defined and budgeted by requiring user input from proposers.
Only requires recurring voter effort - Funding categories focus on simplicity and remove effort where it isn't needed. As funding categories are recurring categorisation it results in no effort being needed to justify the categorisation each round which makes the categorisations more scalable. Another way recurring categorisation is more scalable is that it allows the voter to learn about a categorisation once and then focus on proposals which when scaled up could save a large amount of time for an increasing amount of voters. Increasing simplicity helps maximise scalability by reducing the time required for voters to participate. This is paramount for the governance process to effectively function at scale.
Lower complexity increases scalability - Funding categories can scale more effortlessly with minimal input. No effort is needed to justify categorisations each funding round as funding categories are inclusive and recurring. Using fewer broader categories also reduces complexity by reducing the number of decisions needed to apply budget weightings. This makes it easier to scale to large amounts of funds as there's less effort required to effectively allocate increasing amounts of capital. The other benefit for inclusive and recurring categorisation is that the community does not need the same depth in knowledge around the ecosystem and objectives to make sensible budget weighting decisions. A main reason for this is that funding categories are far more flexible to changing environments such as the type of proposals being submitted or for changing objectives. Less complexity and work for voters, assessors and proposers means that funding categories scales more effectively than other approaches like challenge settings which require continuous effort.
Can be automated in the future - Another massive benefit with funding categories is they are a recurring type of categorisation. More data can be collected from every round that they are used which helps to improve any created algorithms that could dynamically adjust or create suggestions for budget weightings that get used in each of the categories. Data could include how the categorisations are being used, the number and quality of proposals being submitted and then the amount of engagement from voters in each categorisation. All of this data with more established and recurring categorisation leads to automation opportunities that would make the funding process increasingly scalable.
Challenge settings are incredibly labour intensive for many stakeholders in the funding process. This required effort makes the process less scalable when coupled with all of the other difficulties in the funding process such as the increasing complexity from to a growing number of proposals.
Funding categories offer a far simpler approach to categorisation that has clear path to automating the process with algorithms that help suggest and potentially even determine what budget weightings should be applied to the funding categories.