Continuing the discussion from Task Management in Evernote:
Disclaimer 1: this post doesn’t mean that I think any app is better than any other app, nor that any kind of work is more or less complex, nor that complexity is a good thing. It’s just an explanation of the theory behind my practice.
Disclaimer 2: This is a bit of a mess, but I’m trying to put something out there ASAP rather than leave it for another day and never return to it.
Dangit, why do all the interesting questions get asked while I’m on a deadline? I don’t think you’re wrong, but that you’ve found a good fit between your system and your information needs. I have some ideas about that.
TL;DR: My suspicion is that there’s a sweet spot where complicated tools fit complicated work. If your work is simpler, a complicated tool is overkill. If your work is more complex, the conceptual model inherent to complicated tools may not fit, causing friction. If you aren’t frustrated by the conceptual models of the apps you use, keep using them!
Information systems & productivity systems
- An Information System (IS) is a tool that represents the state of some part of the real world. When well-designed, they maintain that representation perfectly, they effectively inform us about the state of the domain, and they allow us to act on the domain.
- A conceptual model is the understanding of the domain built into an information system. E.g., OmniFocus thinks that Projects have Tasks which have Due Dates. OmniFocus does not think Tasks have “do” dates.
- Our productivity systems are information systems. They represent the state of our work: the things we want/need to do and everything that might be involved in doing those things.
Knowledge work: simple, complicated, and complex
- All our work can be broken down into two things: the outcome we desire from their the work, and the next action required to progress on the work. If an outcome requires more than one next action to realize it, it’s a project. (Thanks, David Allen!)
- All our outcomes can also be broken down along two axes: the number of next actions required to realize the outcome, and the reproducibility of the outcome if the same steps are followed:
- Simple work is composed of fewer steps-per-outcome; following those steps always produces the same result.
- Complicated work has more actions-per-outcome; following those steps generally produces the same result, but because there are more steps, it is easier for errors to occur.
- Complex work has many actions-per-outcome; so many that interactions and dependencies between the actions become intrinsic to the work. Because of these interactions, it is rare that completing the same actions will produce the same result.
- Tying your shoelaces is simple. Launching a rocket is complicated. Raising a child is (extremely) complex.
- For a (reductive, abstract) knowledge work example: writing an email about something you’ve already understood/decided upon is simple. Making decisions about something for which the parameters are well-known is complicated. Generating novel solutions to unsolved/unanticipated problems is complex.
Cognitive capacity & cognitive load
Cognitive capacity is your ability to cognitively act on a bit of work.
- We only have so much cognitive capacity.
- Cognitive capacity is reduced by cognitive load. There are three types of cognitive load:
- Intrinsic: whatever the inherent complexity of the cognitive action actually is;
- Extrinsic: anything added to cognitive load by the system; and
- Germane: load involving recognizing and using patterns of cognitive action.
- There are four measures of data quality based on conceptual modelling:
- Completeness: the degree to which your system represents the total state of the work. If your system is incomplete, you’re missing information about your work. (e.g., you’ve captured only 50% of the projects you’re responsible for.)
- Unambiguousness: the degree to which there’s a one-to-one relationship between your representation of the work and the work itself. If your system’s ambiguous, the information in it can be interpreted in multiple ways. (e.g., A task has a due date later than the due date for its project.)
- Meaningfulness: the degree to which the system is easily mapped back to the real world. If your system is meaningless, then the information it contains is hard or impossible to interpret. (e.g., A task is described so vaguely that you don’t know what to do with it—think of writing “Mom” on your to-do list, when “Mom” represents “Plan mom’s birthday.”)
- Correctness: the degree to which the system contains an accurate representation of the work (e.g., tasks that are left incomplete even though they’re finished.)
Tying it all together
In my opinion, the goal of productivity systems is to minimize intrinsic load by effectively representing your work—while adding as little extrinsic cognitive load as possible, and enabling effective use of germane cognitive load.
The more complex a piece of work, the more important it is to represent it effectively in a system. However, there’s a threshold: once the work is past a certain point of complexity, it’s harder to represent. E.g., complexity determines how easily the work can be represented in a given system.
If your system’s conceptual model does not have the ability to fit all the important or relevant things you want to represent in it, then you have to find workarounds or you will introduce data quality issues. (Some folks do quite impressive things with tags, for instance.) However, I find workarounds introduce their own data quality issues and add to cognitive load to boot.
Any issues in data quality will contribute to increases in cognitive load. Further, managing a system with a conceptual model that does not match your view of your work leads to additional extrinsic and germane cognitive load.
Similarly, displacing your representation of the work from the work itself increases cognitive load. For instance, many folks represent tasks about writing in a todo app while writing in a writing app. This means you have to keep information about the writing in mind while looking at your tasks and vice versa. This is easily resolved by having two windows next to each other, but this solution can’t always be used.
The takeaway for me
I am a researcher (PhD candidate, technically) working in three or so disparate fields, and I am a management consultant supporting governments and NGOs in complex systems change work. I generally work alone, albeit often in service of others. My work is usually in the upper-right of the graph above.
The non-app solution to that problem is to simplify the work. Find ways to define it more clearly, reducing the volatility/uncertainty/complexity/ambiguity of it. I think the enlightened among us do this. Sadly, I’m not very enlightened.
As a result, apps like OmniFocus—and combinations of apps, like OmniFocus and DEVONthink—do not let me represent all the information I want to represent where and how I want to represent it. Obsidian provides a free-form conceptual model—plain text and files—that lets me establish my own conceptual model, through text, (embedded) search, and dataview-based lists and tables.
It would still be nice to see clean lists of tasks in an app like OmniFocus for some things I do. To that end, I’m thinking about building an integration between OF and Obsi that treats certain Obsidian notes as projects, syncing task items between them. Not sure how doable it’ll be but it would be fun.