If I had only one word to describe a state in which a couple of friends from my generation and I find ourselves most of the time, that would be it - overwhelmed. This makes sense though, as the amount of information immediately accessible to us has increased dramatically and the Internet has played a significant role in it. Online news, YouTube, social networks compete for the attention of a modern human on daily basis, and, most of the time they succeed. Even if a person spent only one minute evaluating each of the 20 billion videos already uploaded to YouTube, the evaluation of videos would take approximately 38,000 years without sleep. We clearly don't have this time which makes me think that we can apply the laws of economics to content - attention is a scarce resource, and allocating it has an opportunity cost. Having limited time, we have to invest it in the most useful pieces of information, which raises the question - which information is more relevant to the current state of life and which is less.
That being said, we have to draw reasonable conclusions and decisions, which can be a complicated task as the external informational noise coming from media can become an internal noise that produces annoying questions and cyclic thoughts that don't lead anywhere.
Offloading the noise
The "second brain" tools like Obsidian, Roam Research, Logseq, and Tana can store information and partially offload it from the user's brain, thus the name. One of the most famous tools is Obsidian, which is a note taking tool that helps to store information in a form of notes. Writing down notes is an effective approach to archive knowledge, but it provides limited assistance in reasoning and the decision-making process. Though, there are techniques like Zettelkasten that can organize and structure Obsidian notes. The main idea of the Zettelkasten approach is that atomic notes can be linked to each other. The main drawback of Zettelkasten, in my opinion, is that it provides very limited semantics and the kind of relation between the notes is left to the user, which can introduce friction points when working with a large amount of notes. Externalizing information using "second brain" tools reduces the burden of remembering it, but does not solve the harder problem of deciding what matters, how ideas relate, and which unresolved questions deserve attention. Below is a depiction of the Zettelkasten-connected notes.
The semantics of notes live mostly in the author's head, which makes it hard to revisit or critique your own reasoning months later.
The experiment
In my attempts to fight the overwhelm I gave Obsidian and Zettelkasten a try, but, due to limited semantics the approach did not fit me. As a result, I have come up with an alternative approach to notes and links between them. The approach is simple - notes should have types and meaningful connections between them. Suddenly, general notes become ideas, conclusions, problems and hypotheses, and links between them acquire meaning - leads to, derived from, part of, supports etc. According to my assumption, by adding semantics to regular notes, we can get a technique that helps to reason efficiently, if used and interpreted correctly. This approach, in my opinion, can help organize thoughts "on paper" and visualize thoughts and conclusions they lead to. By having typed and visualized thoughts, we can prioritize the reasoning in one direction or another. Typed relationships do not determine what matters automatically, however, they can help to identify reasoning bottlenecks - unresolved questions on which several conclusions depend, weak hypotheses supporting important decisions, or unaddressed contradictions. The example of such graph can be as follows:
Problem: I feel overwhelmed
leads to
Question: What is the main cause?
leads to
Hypothesis A: The overwhelm is caused by external noise
Hypothesis B: It is caused by too many unresolved commitments
A leads to
Idea: Cut down the information originating from media
B leads to
Idea: Finish the tasks I can close by the end of the week
The graph does not answer the question for me. By forcing me to name a second hypothesis next to the first, it stops me from acting on the most obvious explanation before checking whether the competing one matters more.
Externalizing cyclic questions may help us distinguish between questions that require evidence, questions that require action, and questions that can remain unresolved for now. Though, there is a risk that additional complication of node types and their relations will cause excessive friction in the thought-documentation process. Below is a depiction of the same notes but with added semantics.
This is an experimental approach and its effectiveness is an open question. I built a small implementation of this idea called Arguma to see whether the representation is useful in practice. The main difference between my implementation and existing popular implementations like Kialo is that Kialo primarily models claims and their supporting or opposing reasons while Arguma experiments with a broader set of cognitive roles such as questions, problems, hypotheses, ideas, and conclusions and with relationships beyond support and opposition. Readers can see the proposed implementation by following the website https://arguma.io. The tool is in an early access state. One feature of Arguma that can help in collaborative reasoning is the ability to share the reasoning chain consisting of multiple logically interconnected thoughts. This is the example of a shared chain:
Anyone is welcome to try it out and leave feedback which I will be happy to receive.
Conclusion
Arguma is my attempt to test whether typed thoughts and explicit relationships between thoughts can make that structure easier to see, though, I do not yet know whether the structure improves reasoning or just creates additional friction.
Overwhelmed
If I had only one word to describe a state in which a couple of friends from my generation and I find ourselves most of the time, that would be it - overwhelmed. This makes sense though, as the amount of information immediately accessible to us has increased dramatically and the Internet has played a significant role in it. Online news, YouTube, social networks compete for the attention of a modern human on daily basis, and, most of the time they succeed. Even if a person spent only one minute evaluating each of the 20 billion videos already uploaded to YouTube, the evaluation of videos would take approximately 38,000 years without sleep. We clearly don't have this time which makes me think that we can apply the laws of economics to content - attention is a scarce resource, and allocating it has an opportunity cost. Having limited time, we have to invest it in the most useful pieces of information, which raises the question - which information is more relevant to the current state of life and which is less.
That being said, we have to draw reasonable conclusions and decisions, which can be a complicated task as the external informational noise coming from media can become an internal noise that produces annoying questions and cyclic thoughts that don't lead anywhere.
Offloading the noise
The "second brain" tools like Obsidian, Roam Research, Logseq, and Tana can store information and partially offload it from the user's brain, thus the name. One of the most famous tools is Obsidian, which is a note taking tool that helps to store information in a form of notes. Writing down notes is an effective approach to archive knowledge, but it provides limited assistance in reasoning and the decision-making process. Though, there are techniques like Zettelkasten that can organize and structure Obsidian notes. The main idea of the Zettelkasten approach is that atomic notes can be linked to each other. The main drawback of Zettelkasten, in my opinion, is that it provides very limited semantics and the kind of relation between the notes is left to the user, which can introduce friction points when working with a large amount of notes. Externalizing information using "second brain" tools reduces the burden of remembering it, but does not solve the harder problem of deciding what matters, how ideas relate, and which unresolved questions deserve attention. Below is a depiction of the Zettelkasten-connected notes.
The semantics of notes live mostly in the author's head, which makes it hard to revisit or critique your own reasoning months later.
The experiment
In my attempts to fight the overwhelm I gave Obsidian and Zettelkasten a try, but, due to limited semantics the approach did not fit me. As a result, I have come up with an alternative approach to notes and links between them. The approach is simple - notes should have types and meaningful connections between them. Suddenly, general notes become ideas, conclusions, problems and hypotheses, and links between them acquire meaning - leads to, derived from, part of, supports etc. According to my assumption, by adding semantics to regular notes, we can get a technique that helps to reason efficiently, if used and interpreted correctly. This approach, in my opinion, can help organize thoughts "on paper" and visualize thoughts and conclusions they lead to. By having typed and visualized thoughts, we can prioritize the reasoning in one direction or another. Typed relationships do not determine what matters automatically, however, they can help to identify reasoning bottlenecks - unresolved questions on which several conclusions depend, weak hypotheses supporting important decisions, or unaddressed contradictions. The example of such graph can be as follows:
Problem: I feel overwhelmed
leads to
Question: What is the main cause?
leads to
Hypothesis A: The overwhelm is caused by external noise
Hypothesis B: It is caused by too many unresolved commitments
A leads to
Idea: Cut down the information originating from media
B leads to
Idea: Finish the tasks I can close by the end of the week
The graph does not answer the question for me. By forcing me to name a second hypothesis next to the first, it stops me from acting on the most obvious explanation before checking whether the competing one matters more.
Externalizing cyclic questions may help us distinguish between questions that require evidence, questions that require action, and questions that can remain unresolved for now. Though, there is a risk that additional complication of node types and their relations will cause excessive friction in the thought-documentation process. Below is a depiction of the same notes but with added semantics.
This is an experimental approach and its effectiveness is an open question. I built a small implementation of this idea called Arguma to see whether the representation is useful in practice. The main difference between my implementation and existing popular implementations like Kialo is that Kialo primarily models claims and their supporting or opposing reasons while Arguma experiments with a broader set of cognitive roles such as questions, problems, hypotheses, ideas, and conclusions and with relationships beyond support and opposition. Readers can see the proposed implementation by following the website https://arguma.io. The tool is in an early access state. One feature of Arguma that can help in collaborative reasoning is the ability to share the reasoning chain consisting of multiple logically interconnected thoughts. This is the example of a shared chain:
https://arguma.io/share.html?t=3e29e336f7045dc29dfb1d1faa9e3d9c908e97c0ccabdfd3
Anyone is welcome to try it out and leave feedback which I will be happy to receive.
Conclusion
Arguma is my attempt to test whether typed thoughts and explicit relationships between thoughts can make that structure easier to see, though, I do not yet know whether the structure improves reasoning or just creates additional friction.
Thanks for reading!