Knowledge resides in teams

The magic of a gelled team is that they know how to work together, and together, they know how to do particular work. The members don’t know how to work together; the team does.

These learnings don’t reside in the members individually, the learnings are in the interrelations.

A shared know-how is jointly constructed between the participants. This shared know-how does not amount to the sum of the individuals’ know-hows nor does it strictly “belong” to any of the participants…. It involves instead the practice of coordinating sensorimotor schemes together, navigating breakdowns, and it belongs to the system the participants bring forth together: the dyad, the group, the family, the community, and so on.

“Linguistic Bodies The Continuity between Life and Language” Ezequiel A. Di
Paolo, Elena Clare Cuffari, and Hanne De Jaegher, quoted by @theblub

If you’re a director who gets to decide which teams stay together and which break apart, you have a lot of power — and very little control. Power can bust up symmathesies, but not build them or repair them. Other levels of hierarchy can set up conditions for success, but teams grow from within.

Leaving a company is scary because we know how to be in that company. Our own knowing-how-to-be exists partly in our interrelations there. Finding a new job means discovering a new way, a new self, to be in the new place. With families, even more so – this is part of what makes divorce so scary. If you’re in an unhealthy system and can’t imagine anything else, this is normal.

When you do get to be part of a healthy team, or a healthy family, appreciate it. Cherish it and nourish it.

Tiny dramas, tiny deploys

It is better to practice risky things often and in small chunks with a limited blast radius, rather than to avoid risky things.

Charity Majors, “Test in production? Yes

Charity is writing about deploys. Not-deploying may be safer for tonight, but in the medium term it leads to larger deploys and bigger, trickier failures.

In the long term, slow change means losing relevance and going out of business.

In relationships, the same applies. If I have some feeling or fact that my partner might not like, I can say it or not. It never feels like the right time to say it. There is no “right time,” there is only now. There is positive reinforcement for holding back, because then our evening continues pleasantly. No drama.

This leads to an accumulation of feelings and facts they don’t know about. Then when it does become urgent to talk about those, they react with feelings of betrayal: Why didn’t you tell me about this sooner?

In the long term, lack of sharing means growing apart and breaking up.

My new strategy in relationships is: tiny dramas, all the time. The more tiny dramas we have, the fewer big dramas. Also we get practice at handling drama in a way that is safe, because it’s minor. I take any mental question of “should I say this?” as a clue, an opportunity! Yes, say it. Unless it’s a really bad time, it’s the best time.

And the complementary strategy: whenever my partner tells me something scary, like something I did that they don’t like or some feeling they had that might upset me, my first response is “Thank you.” Usually it is not a drama anyway, it’s fine. When I do have feelings about it, we can talk about them. Reassurance helps a lot, especially when I recognize and appreciate the risk they took by telling me in this moment.

If a small deploy causes failure, please respond with “Thank you for not making this part of a bigger deploy.”

We have built a glass castle, where we ought to have a playground.

Charity again, on our lack of safe tooling and therefore fear of production

Reductionism with Command and Control

In hard sciences, we aim to describe causality from the bottom up, from elementary particles. Atoms form molecules, molecules form objects, and the reason objects bounce off each other is reduced to electromagnetic interactions between the molecules in their surfaces.

Molecules in DNA determine production of proteins which result in cell operations which construct organisms.

This is reductionism, and it’s valuable. The elementary particle interactions follow universal laws. They are predictable and deterministic (to the omits of quantum mechanics). From this level we learn fundamental constraints and abilities that are extremely useful. We can build objects that are magnetic or low friction or super extra hard. We can build plants immune to a herbicide.

Bottom-up causality. It’s science!

In Dynamics in Action, Juarrero spends pages and pages asserting and justifying that causality in systems is not only bottom-up; the whole impacts the parts. Causality goes both ways.

Why is it foreign to us that causality is also top-down?

In business, the classic model is all top-down. Command and control hierarchies are all about the big dog at the top telling the next level down what to do. Intention flows from larger (company) levels to smaller (division), and on down to the elementary humans at the sharp end of work.

Forces push upward from particles to objects; intentions flow downward through an org chart

Of course when life is involved, there is top-down causality as well as bottom-up. Somehow we try to deny that in the hard sciences.

Juarrero illustrates how top-down and bottom-up causality interact more intimately than we usually imagine. In systems as small as a forming snowflake, levels of organization influence each adjacent level.

We see this in software development, where our intention (design) is influenced by what is possible given available building blocks (implementation). A healthy development process tightens this interplay to short time scales, like daily.

Software design in our heads learns from what happens in the real world implementation

Now that I think about how obviously human (and organization) intention flows downward, impacted by limitations and human psychology pushing upward; and physical causality flows upward, impacted by what is near what and what moves together mattering downward; why is it even strange to us that causality moves both ways?

Five levels of learning

Gregory Bateson talks about distinct levels of learning. From behavior to enlightenment, each level represents change in the previous level.

Zero Learning: this is behavior, responding in the way you always do. The bell rings, oh it’s lunchtime, eat. This does not surprise you, so you just do the usual thing.

Learning I: this is change in behavior. Different response to the same stimulus in a given context. Rote learning is here, because it is training for a response to a prompt. Forming or removing habits.

Learning II: this is change in Learning I; so it’s learning to learn. It can be a change in the way we approach situations, problems, relationships. Character traits are formed here: are you bold, hostile, curious?

For example — you know me, so when you see me you say “Hi, Jess” — zero learning. Then you meet Avdi, so next time you can greet him by name — Learning I. Lately at meetups Avdi is working on learning everyone’s names as introductions are happening, a new strategy for him: Learning II.

Bateson sees learning in every changing system, from cells to societies.

In code — a stateless service processes a request: zero learning. A stateful application retains information and recognizes that user next time: Learning I. We change the app so it retains different data: Learning II.

Learning III: This is change in Learning II, so it is change in how character is formed. Bateson says this is rare in humans. It can happen in psychotherapy or religious conversions. “Self” is no longer a constant, nor independent of the world.

Letting go of major assumptions about life, changing worldviews, this makes me feel alive. The important shift is going from one to two, and accepting that both are cromulent: my model is, there are many models. It is OK when a new model changes me; I’m not important (for whatever version of “I” is referenced).

Learning IV: would be a change in Learning III. Evolution achieves this. It doesn’t happen in individual humans, but in a culture it could. Maybe this is development of a new religion?

I wonder where team and organizational changes fall in this.

  • Zero learning: “A bug came in, so we fixed it.”
  • Learning 1: “Now when bugs come in, we make sure there is a test to catch regressions.”
  • Learning II: “When a bug comes in, we ask: how could we change the way we work so that this kind of bug doesn’t happen?”
  • Learning III: “Bugs will always happen, so we continually improve our monitoring and observability in production, and we refine our delivery pipeline so rolling forward is smoother and easier all the time.”
  • Learning IV: a framework for agile transformation! hahahahahaha

Mission Statement

“Code, as a medium, is unlike anything humans have worked with before. You can almost design right into it.”

me, in my Camerata keynote


But not totally, because we always find surprises. Complex systems are always full of surprises. That is their frustration and their beauty. 

We live in complex systems. From biology up through cultures and nations and economies, we breathe complexity. And yet in school we learned science as reductive.’

In software, we now have seriously complex systems that we can play with on a time scale that helps us learn. We have incidents we can learn from, with many clues to the real events, to the rich causalities, and sometimes we can trace those back to social pressures in the human half of our software systems. What is more, we can introduce new clues. We can add tracing, and we can make better tools that help the humans (and also provide a trail of what we did). So we have access to complex systems that are (1) malleable and (2) observable. 

My work in automating delivery increases that malleability. My speaking about collaborative automation aims to increase observability.

My quest is: as people, let’s create software systems that are complex and malleable and observable enough that we learn how to work with and within complex systems. That we develop instincts and sciences to change systems from the inside, in ways that benefit the whole system as well as ourselves. And that we apply that learning to the systems we live and breathe in: biology, ecology, economy, culture.

That’s my mission as a symmathecist.