you can usefully think of “men” as a group and make decisions based on considerations like “if we do this, men will do that”.
I agree with this, but a unit of action does not add anything to the concept; it is how marketing and advertising and politics all work currently. I want to capture something different: in particular the execution of plans or working towards a goal.
I feel like the value of an abstraction is that you can think about fewer objects. If you can only work with an abstraction by taking its component objects and breaking them down to their component objects, then it’s not clear in what sense you’re actually abstracting.
That’s interesting, and I am deeply sympathetic to this view. I do feel differently: my lens is that abstractions are for capturing the optimal amount of information. The most important thing is knowing what information is important, and then I want the most efficient way to capture it. My thinking gets muddy, though, when I don’t really know what is important. This biases me in favor of being able to capture more information if necessary because if the abstraction doesn’t capture the information I need then it is useless or, what is worse, misleading.
Short digression: a background assumption of mine is that there is always an algorithm or decision making process somewhere in which the abstraction will be employed. A concrete example of this which I reread from time to time is a blog post describing algorithmic efficiency in terms of problem information. The motivating example is Matlab, which is a ubiquitous numerical problem solver in engineering: the programming language is slow and wasn’t designed around performance, but they get pretty good performance when solving linear systems because their algorithms do a bunch of checks to see if specific kinds of algorithms can be applied that capture the information more efficiently. This is stuff like is the matrix square? or is the matrix triangular? which matters because in each of these cases they have a maximally efficient algorithm.
Returning to the example of the obstructed agency you gave, what I want is to be able to reason about the success case and about the failure case (which if I read you correctly, is where you think the unit of action breaks down). Rolling in the intuition about problem information, when we are thinking about the agency suing a company in a unit-of-action context:
If the lawsuit proceeds normally we have only the two objects:
[Agency, Company]
But suppose the blackmail gambit works. I still want to be able to describe what is happening, so I recurse on the agency to relevant sub-units, and we have:
[Head of the agency, Investigation team, Company]
We can imagine a scenario where the blackmail gambit is discovered and the agency responds, which probably means zeroing in on the company sub-units, like whichever VP ran the operation and his informant, which brings us to:
[Head of the agency, Investigation team, Company VP, VP’s informant]
And so on. The benefit is that I only need to go down into sub-units when the units I am currently looking at fail to capture the needed details. Further, I only need to look at the relevant sub-units, instead of committing to analyzing all agents/employees or all teams, which would capture all the information I need, but might be impossible (individuals) or hideously inefficient (teams).
I agree with this, but a unit of action does not add anything to the concept; it is how marketing and advertising and politics all work currently. I want to capture something different: in particular the execution of plans or working towards a goal.
That’s interesting, and I am deeply sympathetic to this view. I do feel differently: my lens is that abstractions are for capturing the optimal amount of information. The most important thing is knowing what information is important, and then I want the most efficient way to capture it. My thinking gets muddy, though, when I don’t really know what is important. This biases me in favor of being able to capture more information if necessary because if the abstraction doesn’t capture the information I need then it is useless or, what is worse, misleading.
Short digression: a background assumption of mine is that there is always an algorithm or decision making process somewhere in which the abstraction will be employed. A concrete example of this which I reread from time to time is a blog post describing algorithmic efficiency in terms of problem information. The motivating example is Matlab, which is a ubiquitous numerical problem solver in engineering: the programming language is slow and wasn’t designed around performance, but they get pretty good performance when solving linear systems because their algorithms do a bunch of checks to see if specific kinds of algorithms can be applied that capture the information more efficiently. This is stuff like is the matrix square? or is the matrix triangular? which matters because in each of these cases they have a maximally efficient algorithm.
Returning to the example of the obstructed agency you gave, what I want is to be able to reason about the success case and about the failure case (which if I read you correctly, is where you think the unit of action breaks down). Rolling in the intuition about problem information, when we are thinking about the agency suing a company in a unit-of-action context:
If the lawsuit proceeds normally we have only the two objects:
[Agency, Company]
But suppose the blackmail gambit works. I still want to be able to describe what is happening, so I recurse on the agency to relevant sub-units, and we have:
[Head of the agency, Investigation team, Company]
We can imagine a scenario where the blackmail gambit is discovered and the agency responds, which probably means zeroing in on the company sub-units, like whichever VP ran the operation and his informant, which brings us to:
[Head of the agency, Investigation team, Company VP, VP’s informant]
And so on. The benefit is that I only need to go down into sub-units when the units I am currently looking at fail to capture the needed details. Further, I only need to look at the relevant sub-units, instead of committing to analyzing all agents/employees or all teams, which would capture all the information I need, but might be impossible (individuals) or hideously inefficient (teams).