Contribution verification factors for consideration

Listing the different verification factors that will be considered for each contribution verification approach

The following are some relevant contribution verification factors for consideration to help with comparing the different verification approaches. An approximate score will be added against each verification approach with a rationale behind the score for each of the different factors. The scoring for each area will range from 1 (very bad) to 5 (excellent). The importance of each factor is also rated out of 5, where 1 is not important and 5 is very important. This importance will be used to determine an overall score. As an example, a moderately important factor (3) could be represented as a 60% percentage value, multiplied by a score of 5 would result in an actual final score of 3 (0.6 importance * 5 score).

Contribution measurability

  • Description - Contribution measurability is concerned with how easy it is to quantify and compare the contributions outputs that get submitted.

  • Importance score - 5, Very important. Measurability is very important for making it easier to compare the contribution efforts of different contributors to more easily determine who the best and worst performers are, whether contribution outputs are improving or not over time and for making any comparisons with other ecosystems.

  • Scoring questions - How measurable are the contribution logs created through each approach? Is the time period the same each time? Is the scope of work fairly consistent?

  • Scoring - Higher measurability is good (Score - 5). Lower measurability is bad (Score - 1).

Contribution log accuracy

  • Description - Accuracy in contribution logs means it should be clear who is responsible for each of the contributions made when executing any given idea.

  • Importance score - 5, Very important. This is highly valuable for a growing ecosystem where many moving parts could be being worked on at any point in time. Making it clear who was responsible for working on a given area to the rest of the community makes it easier for people to be aware of this and then also direct questions to the relevant people immediately without any intermediary steps.

  • Scoring questions - How easily can a piece of contribution effort be tracked back to an individual who worked on that area? How accurate is a contribution log submission likely to be in covering this?

  • Scoring - Higher contribution log accuracy is good (Score - 5). Lower contribution log accuracy is bad (Score - 1).

Reputation building usability

  • Description - Contribution logs can help with building up an individual or team's reputation over time based on the quality of their contributions. Making it easier for people to build reputation in an ecosystem can help top performers with being identified and selected in future funding decisions.

  • Importance score - 5, Very important. Reputation building can be a powerful approach for identifying and rewarding top performers in an ecosystem. Ecosystems should benefit from being able to retain the best performing talent by enabling them to build up and showcase their reputation.

  • Scoring questions - How can a team and individual build up a reputation using the contribution recording approach? What value do the contribution logs have for reputation building when contributors start to move around and execute different ideas with different teams?

  • Scoring - Higher usability is good (Score - 5). Lower usability is bad (Score - 1).

Performance measurement usability

  • Description - Measuring the performance of contributors can help with determining the full value of someone's contribution efforts. Performance measurement can help to align the incentives with the contributors who have made the most effort and been the most performant at executing different ideas.

  • Importance score - 5, Very important. Performance measurement will be an important part of making sure that each contributor's compensation is fair and reasonable given the facts about their performance. If the incentives aren’t well aligned with rewarding top performers these actors could look towards other ecosystems that are more effective at identifying and rewarding the value they bring to the ecosystem.

  • Scoring questions - How can the performance of an individual contributor and team be better understood and measured using this contribution log approach? How could those contribution logs be used to accurately measure their performance?

  • Scoring - Higher usability is good (Score - 5). Lower usability is bad (Score - 1).

Future voting usability

  • Description - Contribution logs could become an increasingly useful source of information for voters to become more well informed on which contributors and teams have been the most performant and impactful in the ecosystem.

  • Importance score - 5, Very important. Ecosystems will benefit from being able to easily identify and retain the most performant and impactful talent. Informative contribution logs could represent an important information source for voters to identify and select this talent in future funding decisions.

  • Scoring questions - How could voters use the contribution logs to make future voting decisions? Does the information source become more insightful and useful over time?

  • Scoring - Higher usability is good (Score - 5). Lower usability is bad (Score - 1).

Game theory risks

  • Description - How contribution outputs are recorded and verified by a community will influence how easy it is for bad actors to try and game that system.

  • Importance score - 5, Very important. Bad actors can try to abuse any system that makes it easier enough for them to extract a larger amount of incentive than they should be receiving.

  • Scoring questions - How could contributors attempt to increase the amount of incentive they are receiving for doing less work? How could contributors lie or be deceitful about the amount of work they are actually doing?

  • Scoring - Lower risk is good (Score - 5). Higher risk is bad (Score - 1).

Verification time required

  • Description - Verification time is concerned with how long it would take for a community moderator to verify that contribution log submissions are correct.

  • Importance score - 3, Moderately important. Reducing the time it takes to verify a submission will help with reducing operational costs of moderating the disbursement process. The verification of contributions can be increasingly automated over time such as with code commits, designs created or documents written. The importance of verification time should reduce over time as more tools and processes are created and refined.

  • Scoring questions - How long would it take on average for a moderator to verify the contribution logs submitted by contributors?

  • Scoring - Lower verification time required is good (Score - 5). Higher verification time required is bad (Score - 1).

Submission time required

  • Description - Contributors will need to allocate a certain amount of time to record their contribution efforts so they can be submitted and verified by the community.

  • Importance score - 3, Moderately important. The time it takes to submit the contribution logs needs to be reduced where possible. Over a longer time period there are a number of areas that could be automatically gathered such as with code commits, written work and other forms of digitally submitted work. The importance of submission effort should reduce over time as these new tools and processes get developed.

  • Scoring questions - How long would it take on average for a contributor to make a submission? What total time would it take a group of contributors who are working on executing an idea?

  • Scoring - Lower submission time required is good (Score - 5). Higher submission time required is bad (Score - 1).

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