Scaling Funding Categories

Exploring how the ecosystem will be able to further scale funding categories in the future

Initial funding categories implementation

An initial implementation of using funding categories could be to use the suggested categorisations in each funding round and simply add functionality to the voting app so that the community can vote on the percentage weighting to apply to each categorisation for the next funding round.

Funding categories are meant to be recurring and used in each round. Funding categories would still need to be changed over time to respond to how they’re actually used and be able to be updated based on changing requirements. A manual process through IOG or using a change proposal based approach could be used in the short term to make these amendments to any of the categories based on new data, insights or feedback.

This initial implementation is simple and also allows the community to start learning about how the community actually uses and votes on category budget weightings. After this initial implementation there are a number of ways that will help to scale the usage of funding categories.

1. Integrate approaches for the community to more effectively direct funding so they can handle an increasing number of proposals in each funding categorisation

Broad categorisations increase the number of proposals that can be submitted into a single categorisation. To be able to handle broader categorisations, or even just categorisations that receive a large amount of proposals, the processes and voting infrastructure being used in the ecosystem will need to help make it easy and efficient to identify, compare and vote on impactful initiatives.

Improving the information flow about priorities and historical ecosystem data will help provide more insights to the community that help with making well informed decisions. There are a wide number of approaches for the ecosystem to explore in how to improve how the community directs funding to high impact initiatives.

Some example approaches to help with directing funding include proposal tagging, community curated lists, proposal standards, previous funded proposal data, ecosystem data, community priority reports, community problem voting, community priority voting, proposal interaction and feedback data, proposal assessments, insight sharing and delegated representatives.

Community priority setting would especially help with ranking and comparing potential proposals. If the community has effective tooling for expressing what they believe is the highest priority right now this information can help influence the ranking of proposals during the voting stage. This data helps to inform the voter about which proposals are tackling certain priorities and then more easily compare similar proposals that address each priority when casting their vote.

Exploring and integrating as many approaches to effectively direct funding as possible will be important for scaling funding categories. These approaches will determine how effective the community becomes in handling a larger number of proposals in each categorisation.

2. Integrate data and insights to make it easier for the community to make budget weighting decisions

Data produced by some of the approaches mentioned above can help improve how the community directs funding. These approaches can also produce data that is relevant for helping the community make well informed budget weighting decisions for funding categories.

Data around the total amount of requested funding, number of funded and unfunded proposals and number of proposals that meet a certain quality threshold in each categorisation would all good examples of useful historical data. With this data the community would be able to see how popular each category is, how many proposals were not funded in each category and what number of proposals not funded met a certain quality threshold. The community can use this information when making considerations for the budget weightings to apply to each category in the next funding round.

Another area of data that will be useful for improving community set budget weighting decisions is information around the completion of previous projects. If the community can easily see the the amount or percentage of projects that have completed their proposals within different categories then this information can be useful to know which categories are performing better than others and also which ones are quicker in having their proposals completed.

Community priority setting with goals and objectives is another valuable source of information for the community in helping with budget weighting decisions. If community priorities are voted on then it would highlight which priorities are the most upvoted and important in the community. Some priorities will be more suited to certain solutions approaches to effectively address that priority. For the categories that invite solutions relevant to those priorities this information for the community would give an indication of which funding categorisations would benefit from further budget weighting in the next funding round.

3. Reduce the number of categorisations to reduce the budget weighting decision complexity

There are a few reasons the number of categorisations could be reduced over time. Less categorisations means less effort and voting time is needed for the community to set the budget weightings. This reduces complexity and time needed for the community to manage and vote on funding categorisations. Any consideration in the reduction of the number of categories also would need to take into account how this categorisation reduction might increase the complexity at the voting stage due to the voter needing to comparing proposals with even more focus areas and types in the same categorisation.

The first reason the number of categories may be reduced is the funding process will eventually have more tools and processes to make it easier for the community to effectively direct funding. In that event the community will be able to handle a larger amount of proposals in each funding categorisation. The better the community can handle a larger amount of proposals the more merit there is considering the reduction of the number of categories to reduce budget weighting complexity.

The second factor that may reduce the number of categorisations is that the funding categories which make sense in the present day may be less relevant or as widely used in the future. For instance governance & identity and development & infrastructure are both focussed more on the Cardano ecosystem. Once a larger amount of open source infrastructure and tooling is available these categorisations may be used less than other categorisations. In that event it may make sense to merge the categorisations together into a single category due to them being less relevant to the priorities of the ecosystem at the time.

4. Create an algorithm using priority and historical ecosystem data to help automate the funding category budget weighting decisions

Creating an algorithm would be possible when there is a sufficient amount of data within the funding process that is relevant and useful in making sufficient suggestions for what a good budget weighting could be for each category.

One example area that would be valuable for this algorithm could be the communities ability to set priorities. Community set priorities for the ecosystem would help create data that could influence an algorithm to determine the suggested category budget weightings. When certain priorities are more relevant to certain categories this would be a reason why a certain category may receive an increase amount of weighting. For instance, the community could believe that a Cardano node implementation written in different languages was an important priority. If this was the case it could result in influencing an increase of the budget available for the development & infrastructure category.

Another factor that can help to influence a budget weighting algorithm could be the historical data about proposals. For the last round of funding there is data available about the total amount of proposals submitted into each categorisation, the total amount requested, the overall quality of those proposals based on some quality threshold and how many of those proposals were funded and how many were unfunded that were tackling high priority areas. All of this data could be used in an algorithm to influence suggestions towards what budget weightings could be applied to the funding categories in the next funding round.

With sufficient data available from the funding process an algorithm could weight certain factors more than others. For instance if tackling priorities is the biggest preference of the community then an approach could be to weight the priorities of the community more heavily than the historical proposal data. Alternatively, if the community believes that funding high quality proposals is the most important factor then the algorithm could weight the historical proposal data more heavily based on the amount of high quality proposals that were not funded in each category from the previous round. Once useful data is available for this algorithm the community will be able to back test any parameter change to the algorithm to see how this influences the outcomes. This type of analysis would produce a stronger evidence based approach to proving the potential impact of the algorithm and the implications of any parameter changes.

In terms of actually implementing an algorithm and integrating it into the funding process an initial implementation could simply be just produce suggestions to the community to consider when casting their own budget weighting vote. If the community reach consensus that the output from the algorithm is to a sufficient standard then a vote could be made to allow the algorithm to replace the need for the community to vote on the budget weightings. If the algorithm was adopted fully then the community would then be able to save any effort from making a budget weighting decision and could instead direct all their attention on setting community priorities and identifying, comparing and voting on high impact proposals.

Reasons why the community setting the budget weightings could be a long term solution

There is no guarantee that the number of broad categorisations should be further reduced or that an algorithm will actually be effective for improving or automating budget weighting decisions. There are a number of reasons that a manual solution where the community sets the budget weightings may become the long term solution:

  • The community may have a preference of directly voting on the budgets for each category and want to have this direct influence rather than pushing this responsibility to an automated algorithm.

  • Even with more sophisticated tools and processes for identifying, comparing and voting on high impact initiatives it could still be very difficult for the community to direct funding effectively. In that event it would be more difficult to broaden the categorisations any further as that would mean increasing the number of proposals in each categorisation and be overly complex if the community is still struggling to effectively direct funding.

  • The suggested algorithm to help with automating the process could be too difficult to create and maintain due to the influence of external factors. It could be difficult to prevent bad actors influencing the algorithm in harmful ways or the algorithm could simply not be as effective for applying sensible budget weightings due to the amount of variables it needs to consider.


  • An initial implementation of allowing the community to directly vote on the budget weightings for categories is a simple and effective solution for the short term. It will allow the community to experience what it’s like to make considerations and their own choices towards how to effectively vote on budget weightings for categories to allocate more funding to certain groupings of focus areas over others.

  • Creating tools and processes to effectively direct funding and also integrating data and insights will both be needed to help initially scale funding categories. This will improve how easy it is to use funding categories and also improve how easy it is for the community to make budget weighting decisions.

  • Once these initial areas are explored further and the voting infrastructure improves the community will be able to explore the possibilities of trying to simplify the categorisations by either reducing the total amount of categorisations used and also potentially introducing an algorithm to make suggestions or automate the budget weighting process. There is no guarantee that either of these scaling approaches will be needed or even preferred. More data and analysis will be needed once the first two areas of improvement are completed.

  • Increasing the scalability and automation of funding categorisation will be important to reaching global adoption. Achieving better scalability for funding categorisation will mean more time and effort can be allocated to setting community priorities and identifying, comparing and voting on high impact initiatives that help to support, grow and improve the ecosystem.

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