The Arch. of Complexity, the points that resonate

The Arch. of Complexity, the points that resonate
Art by Wu Guanzhong

Herbert Simon’s The Architecture of Complexity is the text I keep returning to when reflecting backwards and forwards.

For a concise summary of that essay, check out these Text Notes. For a brief snapshot of how one applies these ideas to one’s own thinking and learning (a la MY thinking and learning), read on.

The points that resonate

Simon breaks down his description of complexity into 4 key points:

  1. Complex systems are Hierarchic
  2. Hierarchic systems evolve from Stable subsystems
  3. And have the property of Near Decomposability
  4. Which simplifies the Description of complex systems

Within each of these subsystems, there are key points that resonate

(1) Complex systems are Hierarchic

Hierarchic systems are subsystems within subsystems, parts within parts. The content may change but the theme is the same.

Personally, this reflects my own projects of: how to structure teams, how to manage that which I find of value, and how to think about the next thing.

(2) Hierarchic systems evolve from Stable subsystems

Herb Simon writes of stable subsystems boosting evolution, and of problem solving as natural selection. Problem solving is selective trial and error. Cues signalling progress play the same role as stable intermediate forms. The indication of progress spurs further search in a specific direction.

This is a strong reminder to self when feeling mired in error. Don’t worry about it so much; it’s just a trial for the next bigger and better thing.

(3) Hierarchic systems have the property of Near Decomposability

Near Decomposability is a property of hierarchic systems where interactions are weak but not negligible. For greater stability, relationships *within* subsystems should be stronger than those *among*. In the short run, the behavior of each component is approx. independent of the others. In the long run, the behavior of any one component depends only in an aggregate way on the behavior of the others.

How might today’s / this month’s / this year’s efforts be more successful, if I paid attention to the structure of among vs. within?

One’s frame of reference is critical when assessing short-run vs. long-run outcomes. Are you looking at the system from the perspective of an atom, or from the perspective of the greater whole?

(4) Near Decomposibility simplifies the description of complex systems

Near Decomposability also simplifies the description of complex systems; only aggregate properties enter the description of interaction. This ↘️ of redundancy helps find the simple pattern: Process descriptions stand in for state descriptions.

By substituting process descriptions for state ones, what new clues might I find?

“By recoding, the redundancy that is present but unobvious in the structure can be made patent… Replacing a description of the time path with a description of a differential law that generates that path.”


I was combing through blog drafts when I found this. Originally written at the end of 2019. How times of changed! It is now Fall of 2020. And yet, the approach still crisply resonates.

This essay continues to fuel my thinking, even through 2019, 2020. How?

A. Determining a project’s proper composition of teams or cadence of communication benefits from Near Decomposability (ND). How to orchestrate projects– among vs within– to ensure broader projects are most successful.

B. I may not be able to do everything I can dream of (I dream a lot!)– but if I can make some progress in the span of the ND components that carry meaning for me, that creates stability that feels satisfying.

C. When thinking about new projects– what is redundant in the system? Which details don’t matter in the aggregate? (And which definitely do?)

Note to self– Always ask: Which simple patterns generate the desired path?