Knowledge process

Overview about the importance of a knowledge process for funding

Knowledge enables a community to understand what is happening inside and outside of an ecosystem. When knowledge is digested by a community it can then be applied to determine what priorities, ideas and contributors are the most important and suitable for that ecosystem to thrive. The knowledge process is concerned with identifying, creating, gathering, aggregating, presenting and selecting the most relevant and useful information that an ecosystem should consider to make well informed funding decisions.

Knowledge systems would be any system that helps with gathering, aggregating or presenting information. Knowledge systems can help with tackling the increasing challenge of handling a large amount of complex information that can exist across Web3 ecosystems. To scale an ecosystem's decision making ability it will be important that voters are able to quickly get access to and understand important information so they can become well informed before making governance decisions. Making voters more well informed can help with improving the communities decision making ability to identify the most important priorities for the ecosystem and how treasury assets could be disbursed towards impactful initiatives that help to address those priorities. The more informed a community becomes about what is happening both inside and outside the ecosystem the better they can become at deciding what priorities, ideas and contributors will be most suitable for making more impactful outcomes. Quality knowledge systems can provide a scalable solution for making it easier to participate in governance decisions. An outcome which could help with further decentralising these emerging networks and their governance processes.

Process steps

Identifying relevant information - Identify the most relevant areas of information that an ecosystem could benefit from knowing about when making funding decisions. It can be worthwhile to group this information together based on what sources it comes from. One example grouping could be any on-chain data, this grouping would include any information that can be retrieved directly from public ledgers and blockchains.
Gathering & aggregating information - Systems and processes can be created that help with collecting and verifying the relevant information from different sources. These systems will ideally be open sourced and publicly verifiable to improve the trust that end users have in using the information that has been gathered. Knowledge systems can help with the gathering and aggregation of different sources of information. If no knowledge systems exist then individuals in an ecosystem would themselves need to do any research to find relevant information to help with becoming more informed for their own decision making.
Presenting information - Systems that help with collecting knowledge can also be responsible for presenting this information in a way that is easy to understand and quick to interpret. How data is presented can impact the amount of insights and utility that information provides. Another factor is how the information being presented can be easily compared or intersected with other relevant information to identify new findings. The faster that voters can read and understand important ecosystem information the more scalable it becomes for many voters to become well informed in a short period of time before making important funding decisions. An ecosystem will benefit from ensuring the community is well informed on what knowledge systems exist and how that information can be most easily accessed and digested.
Digesting & selecting information - Community members will consciously and unconsciously use the information they digest to make future decisions. The more information that someone is able to digest and understand the more informed their opinions can become assuming the information sources are relevant and accurate. Community members will determine which pieces of information are most relevant and important when making their own funding decisions. Some ecosystems could optionally choose to also explore a knowledge selection system where community members openly select and vote on the most useful or important pieces of knowledge.
Updating information - Information can become outdated quickly if it is not maintained. Some information such as on-chain data can be gathered automatically and can more easily be always kept up to date. Research and analysis on what is happening in the ecosystem is one area that would need to be updated as time progresses so that any new trends and insights can be identified.

Desirable outcomes

Accuracy - The accuracy of any knowledge that gets created or aggregated will be an important outcome for any contribution efforts that bring new knowledge to an ecosystem. Available knowledge will be used by voters to make priority, idea and contributor selection decisions. Due to this the accuracy and correctness of any knowledge produced will need to be high.
Higher information quality - Achieving higher information quality will mean that all of the relevant areas of information are covered when a new piece of knowledge is created. As an example a transaction explorer would benefit from including all of the information about transactions so that it’s very clear what is happening instead of only a subset of the available transaction information. High quality knowledge sources should ideally provide full coverage of any relevant information.
Verifiable - Many knowledge sources can come from systems and processes that provide factual data about what happened during execution. Ensuring that any code and logic behind any knowledge sources is open source and verifiable can help with increasing the amount of trust in these knowledge sources. Making it easier for voters to determine what information is fact and opinion will also mean that voters will have an enhanced ability to better decide which pieces of information they believe is the most important and useful when making certain funding decisions.
Maintained information - Knowledge can quickly become out of date if it is not updated over time. A knowledge process will need to consider how it incentivises community members to keep important knowledge sources up to date so that the information remains relevant and useful.
Automated systems - Knowledge which can come from automated systems can often provide information as soon as it’s recorded. Opportunities to create any automated systems that gather, analyse and present data can be highly beneficial for voters to use when making their own funding decisions. These systems can also be foundational for deeper analysis that help with identifying important problems and opportunities. Automated knowledge systems also provide an opportunity for smart contracts that might want to rely on these sources of data to enforce certain rules that have been defined in the contract.
Timeliness of new information - Knowledge can impact any part of the funding process so having access to useful information as quickly as possible can make a big difference in how effectively assets can be disbursed and how fast existing contributors can respond to new information. Knowledge processes that are effective at encouraging the ongoing creation of insightful knowledge can be highly beneficial for an ecosystem to become more responsive to changes and when making any future decisions. Supporting knowledge sources that can become automated through software is one way in which the timeliness can be improved.
Diversity of information sources - Quality information that can be used for making well informed decisions can come from a multitude of sources. Using sources of knowledge that come from both inside and outside the ecosystem can be highly beneficial for making an ecosystem more well informed.
Voters become more well informed - Comprehensive knowledge systems that cover the different areas in an ecosystem can help voters become more well informed about what is happening inside and outside the ecosystem and across other different topic areas. This increased depth of knowledge can help with improving a voters decision making ability.
Reduced time required for voters - The better that information is structured and presented the less time it should take for voters to become well informed about different topic areas. Achieving this outcome can help with making governance processes more scalable as more voters will be able to quickly learn about the relevant information and then participate in governance decisions.
Decreased time for new information to propagate - Many knowledge systems could have live data being fed into them which could then be immediately sent to and digested by a community. A blockchain explorer that lists all the recent transactions is a good example of this type of real time information. The more that these services are made available and easy to use the faster a community will be able to receive and respond to new information.
Reduced information complexity - The quantity and depth of information relevant to these growing Web3 ecosystems is vast, often meaning it can be difficult to understand enough of the information available to be well informed. Knowledge systems could help with how information is presented and structured to pull out the most important facts and insights. Achieving this will make it more simple for people to understand and apply the most useful information.
Scalable solutions for informing voters - Creating systems and processes to better aggregate and present information relevant about an ecosystem is a more scalable solution than voters trying to complete this research and analytical work themselves independently.
Increased transparency & awareness - Information that may have otherwise been difficult to find or locate could now be made more publicly available through the usage of knowledge systems that help to aggregate and present a diversity of relevant information. This transparency and awareness of the available information can help with increasing the trust in these ecosystems and help with making it more open and accessible for community members to respond to new and important information.
Integration opportunities - Knowledge systems could be integrated into a range of different systems and processes. For instance, solutions building with AI technology could integrate the information provided by knowledge systems to then analyse or combine that information with other relevant information sources to generate new insights or an improved understanding of what is currently happening.

Concerns

Relevance - The true value of different sources of information may not be immediately obvious when deciding whether or not to support the efforts behind a certain knowledge initiative. Sometimes it is the intersection of multiple pieces of information that help bring more clarity or new understandings about what problems and opportunities exist. Due to this complexity there is a concern that potentially import and impactful knowledge sources are not supported or alternatively that too many knowledge initiatives get supported that do not generate large enough levels of impact with making an ecosystem more well informed.
Information complexity - Knowledge relevant to an ecosystem can cover a wide range of topics with different levels of complexity. Part of the challenge with the creation and maintenance of different information sources is ensuring that the outcomes of that work are not overly complex for others to digest and understand. Ecosystems will need to consider how they encourage contributors to spend sufficient time in increasing the ease of use and readability of any new pieces of knowledge that get created.
Correctness of information - As the scale and assets under management of Web3 ecosystems increase over time there is an increasing importance in making sure the information that is made available and that is relied upon is correct. If the information is biased, exaggerated or incorrect this could directly influence the outcomes of certain funding decisions. Knowledge sources would be an ongoing potential attack vector that will need to be constantly considered so these potential issues can be avoided.
Conciseness - The larger the amount of knowledge available the longer it will take for voters to become well informed about all of the different sectors and topic areas that are relevant to an ecosystem. The most important knowledge will need to be clear and concise due to the difficulty in expecting voters to digest vast amounts of information. The conciseness of knowledge will be an important factor for the scalability of participation in any funding decisions and governance more generally.
Complexity in aggregating information - Existing nation state and corporation treasury systems that have fewer people involved in decision making can more easily adopt more manual based processes for individually aggregating information they think is important before making well informed decisions. If this knowledge needs to be distributed between a much larger group of participants a manual approach becomes far too costly and difficult to scale. Web3 ecosystems will need to find efficient and effective ways to aggregate relevant information about the ecosystem so that participants in the funding process can review and understand what is happening. Considerations will be needed on how information can be most effectively presented to communicate the key points quickly and correctly so that the funding process can be scalable. If the ecosystem is unable to get access to the right information when making funding decisions it will be more difficult to make optimal decisions. This factor is one reason why it is easier to centralise decision making in existing systems due to the complexities involved with making a large group of people well informed about many different pieces of information.
Time cost of learning & knowledge sharing - It takes time for people to read and understand any information that has been shared to them and even more time to then apply that knowledge when making their own decisions with treasury funding. The more information there is available the more time it would take for each person that wants to participate. These factors makes it difficult to scale decentralized governance due to the cost of trying to be well informed when participating. One of the challenges for Web3 ecosystems is being able to identify what information is the most important for the different decisions that must be made and how can that information can be most accurately and easily presented to participants when making any decisions. Bringing the time cost down for sharing and understanding knowledge will make these emerging systems more easy to participate in scalable.
Ongoing attack vector - There would be an ongoing risk that malicious actors try to maliciously influence different knowledge systems to achieve personal gain or to compromise a system and its governance processes. These actors could try to insert biased or incorrect information into different knowledge systems in a multitude of ways. Knowledge systems will need to have an ongoing amount of moderation and verification to review how these systems are being used and how they get changed over time to identify where attack vectors may have been introduced or where problems have already emerged.
Dependency risks - As knowledge systems become more effective at gathering and presenting information there is an increasing risk that governance processes become over reliant and dependent on these systems. Having alternative options will be one approach to help reduce this risk. Adopting a mission critical mindset and development approach when creating these systems and processes will be another approach to try and reduce these dependency risks further.
Centralisation risks - Not all information gathered will come from informational systems, some information will not be easily verified and audited. A lot of information relevant to ecosystems can come from analytical sources and be created through the understanding, intersection and analysis of the current information available. This expert led analysis can create an ongoing risk where a centralised group of actors could establish a growing amount of influence over what analysis gets created and communicated across an ecosystem. Checks and balances will be needed to ensure any contributors with higher influence are not misleading people or sharing biased, incorrect or exaggerated information.

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Process steps

Identifying relevant information - Identify the most relevant areas of information that an ecosystem could benefit from knowing about when making funding decisions. It can be worthwhile to group this information together based on what sources it comes from. One example grouping could be any on-chain data, this grouping would include any information that can be retrieved directly from public ledgers and blockchains.

Gathering & aggregating information - Systems and processes can be created that help with collecting and verifying the relevant information from different sources. These systems will ideally be open sourced and publicly verifiable to improve the trust that end users have in using the information that has been gathered. Knowledge systems can help with the gathering and aggregation of different sources of information. If no knowledge systems exist then individuals in an ecosystem would themselves need to do any research to find relevant information to help with becoming more informed for their own decision making.

Presenting information - Systems that help with collecting knowledge can also be responsible for presenting this information in a way that is easy to understand and quick to interpret. How data is presented can impact the amount of insights and utility that information provides. Another factor is how the information being presented can be easily compared or intersected with other relevant information to identify new findings. The faster that voters can read and understand important ecosystem information the more scalable it becomes for many voters to become well informed in a short period of time before making important funding decisions. An ecosystem will benefit from ensuring the community is well informed on what knowledge systems exist and how that information can be most easily accessed and digested.

Digesting & selecting information - Community members will consciously and unconsciously use the information they digest to make future decisions. The more information that someone is able to digest and understand the more informed their opinions can become assuming the information sources are relevant and accurate. Community members will determine which pieces of information are most relevant and important when making their own funding decisions. Some ecosystems could optionally choose to also explore a knowledge selection system where community members openly select and vote on the most useful or important pieces of knowledge.

Updating information - Information can become outdated quickly if it is not maintained. Some information such as on-chain data can be gathered automatically and can more easily be always kept up to date. Research and analysis on what is happening in the ecosystem is one area that would need to be updated as time progresses so that any new trends and insights can be identified.

Desirable outcomes

Accuracy - The accuracy of any knowledge that gets created or aggregated will be an important outcome for any contribution efforts that bring new knowledge to an ecosystem. Available knowledge will be used by voters to make priority, idea and contributor selection decisions. Due to this the accuracy and correctness of any knowledge produced will need to be high.

Higher information quality - Achieving higher information quality will mean that all of the relevant areas of information are covered when a new piece of knowledge is created. As an example a transaction explorer would benefit from including all of the information about transactions so that it’s very clear what is happening instead of only a subset of the available transaction information. High quality knowledge sources should ideally provide full coverage of any relevant information.

Verifiable - Many knowledge sources can come from systems and processes that provide factual data about what happened during execution. Ensuring that any code and logic behind any knowledge sources is open source and verifiable can help with increasing the amount of trust in these knowledge sources. Making it easier for voters to determine what information is fact and opinion will also mean that voters will have an enhanced ability to better decide which pieces of information they believe is the most important and useful when making certain funding decisions.

Maintained information - Knowledge can quickly become out of date if it is not updated over time. A knowledge process will need to consider how it incentivises community members to keep important knowledge sources up to date so that the information remains relevant and useful.

Automated systems - Knowledge which can come from automated systems can often provide information as soon as it’s recorded. Opportunities to create any automated systems that gather, analyse and present data can be highly beneficial for voters to use when making their own funding decisions. These systems can also be foundational for deeper analysis that help with identifying important problems and opportunities. Automated knowledge systems also provide an opportunity for smart contracts that might want to rely on these sources of data to enforce certain rules that have been defined in the contract.

Timeliness of new information - Knowledge can impact any part of the funding process so having access to useful information as quickly as possible can make a big difference in how effectively assets can be disbursed and how fast existing contributors can respond to new information. Knowledge processes that are effective at encouraging the ongoing creation of insightful knowledge can be highly beneficial for an ecosystem to become more responsive to changes and when making any future decisions. Supporting knowledge sources that can become automated through software is one way in which the timeliness can be improved.

Diversity of information sources - Quality information that can be used for making well informed decisions can come from a multitude of sources. Using sources of knowledge that come from both inside and outside the ecosystem can be highly beneficial for making an ecosystem more well informed.

Voters become more well informed - Comprehensive knowledge systems that cover the different areas in an ecosystem can help voters become more well informed about what is happening inside and outside the ecosystem and across other different topic areas. This increased depth of knowledge can help with improving a voters decision making ability.

Reduced time required for voters - The better that information is structured and presented the less time it should take for voters to become well informed about different topic areas. Achieving this outcome can help with making governance processes more scalable as more voters will be able to quickly learn about the relevant information and then participate in governance decisions.

Decreased time for new information to propagate - Many knowledge systems could have live data being fed into them which could then be immediately sent to and digested by a community. A blockchain explorer that lists all the recent transactions is a good example of this type of real time information. The more that these services are made available and easy to use the faster a community will be able to receive and respond to new information.

Reduced information complexity - The quantity and depth of information relevant to these growing Web3 ecosystems is vast, often meaning it can be difficult to understand enough of the information available to be well informed. Knowledge systems could help with how information is presented and structured to pull out the most important facts and insights. Achieving this will make it more simple for people to understand and apply the most useful information.

Scalable solutions for informing voters - Creating systems and processes to better aggregate and present information relevant about an ecosystem is a more scalable solution than voters trying to complete this research and analytical work themselves independently.

Increased transparency & awareness - Information that may have otherwise been difficult to find or locate could now be made more publicly available through the usage of knowledge systems that help to aggregate and present a diversity of relevant information. This transparency and awareness of the available information can help with increasing the trust in these ecosystems and help with making it more open and accessible for community members to respond to new and important information.

Integration opportunities - Knowledge systems could be integrated into a range of different systems and processes. For instance, solutions building with AI technology could integrate the information provided by knowledge systems to then analyse or combine that information with other relevant information sources to generate new insights or an improved understanding of what is currently happening.

Concerns

Relevance - The true value of different sources of information may not be immediately obvious when deciding whether or not to support the efforts behind a certain knowledge initiative. Sometimes it is the intersection of multiple pieces of information that help bring more clarity or new understandings about what problems and opportunities exist. Due to this complexity there is a concern that potentially import and impactful knowledge sources are not supported or alternatively that too many knowledge initiatives get supported that do not generate large enough levels of impact with making an ecosystem more well informed.

Information complexity - Knowledge relevant to an ecosystem can cover a wide range of topics with different levels of complexity. Part of the challenge with the creation and maintenance of different information sources is ensuring that the outcomes of that work are not overly complex for others to digest and understand. Ecosystems will need to consider how they encourage contributors to spend sufficient time in increasing the ease of use and readability of any new pieces of knowledge that get created.

Correctness of information - As the scale and assets under management of Web3 ecosystems increase over time there is an increasing importance in making sure the information that is made available and that is relied upon is correct. If the information is biased, exaggerated or incorrect this could directly influence the outcomes of certain funding decisions. Knowledge sources would be an ongoing potential attack vector that will need to be constantly considered so these potential issues can be avoided.

Conciseness - The larger the amount of knowledge available the longer it will take for voters to become well informed about all of the different sectors and topic areas that are relevant to an ecosystem. The most important knowledge will need to be clear and concise due to the difficulty in expecting voters to digest vast amounts of information. The conciseness of knowledge will be an important factor for the scalability of participation in any funding decisions and governance more generally.

Complexity in aggregating information - Existing nation state and corporation treasury systems that have fewer people involved in decision making can more easily adopt more manual based processes for individually aggregating information they think is important before making well informed decisions. If this knowledge needs to be distributed between a much larger group of participants a manual approach becomes far too costly and difficult to scale. Web3 ecosystems will need to find efficient and effective ways to aggregate relevant information about the ecosystem so that participants in the funding process can review and understand what is happening. Considerations will be needed on how information can be most effectively presented to communicate the key points quickly and correctly so that the funding process can be scalable. If the ecosystem is unable to get access to the right information when making funding decisions it will be more difficult to make optimal decisions. This factor is one reason why it is easier to centralise decision making in existing systems due to the complexities involved with making a large group of people well informed about many different pieces of information.

Time cost of learning & knowledge sharing - It takes time for people to read and understand any information that has been shared to them and even more time to then apply that knowledge when making their own decisions with treasury funding. The more information there is available the more time it would take for each person that wants to participate. These factors makes it difficult to scale decentralized governance due to the cost of trying to be well informed when participating. One of the challenges for Web3 ecosystems is being able to identify what information is the most important for the different decisions that must be made and how can that information can be most accurately and easily presented to participants when making any decisions. Bringing the time cost down for sharing and understanding knowledge will make these emerging systems more easy to participate in scalable.

Ongoing attack vector - There would be an ongoing risk that malicious actors try to maliciously influence different knowledge systems to achieve personal gain or to compromise a system and its governance processes. These actors could try to insert biased or incorrect information into different knowledge systems in a multitude of ways. Knowledge systems will need to have an ongoing amount of moderation and verification to review how these systems are being used and how they get changed over time to identify where attack vectors may have been introduced or where problems have already emerged.

Dependency risks - As knowledge systems become more effective at gathering and presenting information there is an increasing risk that governance processes become over reliant and dependent on these systems. Having alternative options will be one approach to help reduce this risk. Adopting a mission critical mindset and development approach when creating these systems and processes will be another approach to try and reduce these dependency risks further.

Centralisation risks - Not all information gathered will come from informational systems, some information will not be easily verified and audited. A lot of information relevant to ecosystems can come from analytical sources and be created through the understanding, intersection and analysis of the current information available. This expert led analysis can create an ongoing risk where a centralised group of actors could establish a growing amount of influence over what analysis gets created and communicated across an ecosystem. Checks and balances will be needed to ensure any contributors with higher influence are not misleading people or sharing biased, incorrect or exaggerated information.