Don’t build Cities of Nowhere

I just finished reading Seeing Like A State: How Certain Schemes to Improve the Human Condition Have Failed by James C. Scott (full text online). I highly recommend it. Through examples ranging from Soviet collectivization to the construction of Brasilia, the book argues that grand, centralized planning efforts in the high modernist tradition are all doomed to failure. One simply can’t substitute pure reason – no matter how beautiful and internally consistent – for local human decision-making informed by direct experience.

To take one striking anecdote, Le Corbusier spent some time lobbying Soviet intelligentsia to implement his redesign of Moscow. However:

Stalin’s commissars found his plans for Moscow as well as his project for the Palace of Soviets too radical. The Soviet modernist El Lissitzky attacked Le Corbusier’s Moscow as a “city of nowhere, … [a city] that is neither capitalist, nor proletarian, nor socialist, … a city on paper, extraneous to living nature, located in a desert through which not even a river must be allowed to pass (since a curve would contradict the style).” As if to confirm El Lissitzky’s charge that he had designed a “city of nowhere,” Le Corbusier recycled his design virtually intact—aside from removing all references to Moscow—and presented it as La ville radieuse, suitable for central Paris.

Seeing Like A State – James C. Scott
Le Corbusier's vision for the ideal Moscow. Symmetric, vertical, orderly.
A map of actual Moscow. Asymmetric, spread out across either side of the Moskva, disorderly.

In Scott’s book, this pattern plays out over and over. Planners, relying heavily on what they imagine are universal principles, produce designs for human life that are nevertheless completely at odds with how humans actually live and work. These designed spaces possess a symmetric, holistic beauty which blinds their creators to the needs of the infinitely complex human communities that are meant to populate them. The planned city ultimately fails to improve the human condition, since improving the human condition is one of the many considerations which must bow to the planner’s aesthetic.

Toward the end of the book – although this is only a short passage and certainly not the thrust – Scott gives 4 rules of thumb for development planning. Building up a SaaS product is clearly different in many ways (not least of which is the stakes) from planning human development. But the parallels got me thinking in engineering terms, and I find that these rules also work quite well as rules of thumb for making changes to a complex software system. By following them, we can mostly avoid wasting effort on huge endeavors that end up being Cities of Nowhere.

1. Take small steps

In an experimental approach to social change, presume that we cannot know the consequences of our interventions in advance. Given this postulate of ignorance, prefer wherever possible to take a small step, stand back, observe, and then plan the next small move.

In software, taking small steps is a challenge of discipline. We work with pure thought-stuff. In principle, we can build whatever we can imagine, so it’s always tempting to solve more of the problem.

But taking small steps has by now become the common wisdom in our industry. Single-feature pull requests are encouraged over massive, multifaceted ones. We roll out features to small groups of users before ramping up. Prototypes and MVPs abound.

Where we still have much to learn from Scott is the “stand back, observe” part. Often, we’re tempted to simply let the machine do the observing for us: if there’s anything wrong with our change, the integration tests will fail, or the deploy will fail, or we’ll get an alert. While such automated signals are indispensable, they’re not sufficient. To understand the real-world effects of our small changes, we have to exercise the further discipline of curiosity. With our particular change in mind, we have to search diligently for evidence of its effects, both intended and unintended, direct and indirect. Observability is not enough – we must actively observe.

2. Favor reversibility

Prefer interventions that can easily be undone if they turn out to be mistakes. Irreversible interventions have irreversible consequences. Interventions into ecosystems require particular care in this respect, given our great ignorance about how they interact. Aldo Leopold captured the spirit of caution required: “The first rule of intelligent tinkering is to keep all the parts.”

It’s pretty clear how this reversibility consideration applies to deploying software and infrastructure. Most changes should be trivially reversible by “rolling back” the deploy. Where this is impossible (such as in certain classes of database migrations and infrastructure changes), we come up with more case-specific back-out plans, or we end up inventing reversible patterns despite ourselves. This amounts to an implicit recognition that our changes can always have unexpected consequences. Which is good!

But, in a socio-technical system, the technology isn’t the only thing that gets altered over time. We must also favor reversibility with respect to the social elements – with respect to procedures, policies, and organizational structures.

One pattern I like for this is an experiment ledger. As a team, you keep a running register (e.g. in a spreadsheet) of the different experiments you’re trying. These can be anything from a new recurring meeting to a new on-call rotation to a rearrangement of your kanban columns. Each experiment in the ledger has one or more check-in dates, when the team will discuss the results of the experiment and decide whether to keep going or abandon the course.

Of course, for many reasons, not every change can be reversible. Not least because even after you reverse something, the taste stays in people’s mouths. But taken together with the rest of Scott’s advice, reversibility is a sensible attribute to strive for.

3. Plan on surprises

Choose plans that allow the largest accommodation to the unforeseen. In agricultural schemes this may mean choosing and preparing land so that it can grow any of several crops. In planning housing, it would mean “designing in” flexibility for accommodating changes in family structures or living styles. In a factory it may mean selecting a location, layout, or piece of machinery that allows for new processes, materials, or product lines down the road.

No matter how much time and sweat you put into the design of a system – no matter how much of the problem you try to solve a priorithere will always be surprises. It’s just the nature of a complex system, and even more so for a system with inputs you can’t control (e.g. customer traffic patterns).

Therefore, watch carefully for both expected and unexpected results. That’s what “plan on surprises” means to me: make small, reversible changes, and in the meantime look closely for new unexpected behaviors that you can investigate and understand. This will give you much more insight into your system’s abilities and constraints than any application of pure thought.

4. Plan on human inventiveness

Always plan under the assumption that those who become involved in the project later will have or will develop the experience and insight to improve on the design.

Write with clarity and humility on the motivations for your designs. Explain what you did and what you chose not to do, and why. The reasons for a particular design are never self-evident, no matter what cosmic beauty they may have in your head.

Taken together, Scott’s rules sketch out a pragmatic philosophy for managing the evolution of complex systems. In favor of grand redesigns that attempt to solve all problems at once, one should prefer targeted, reversible changes. We should change significant things about the system only we can fully explain why it’s necessary, and afterward we should exercise diligence and curiosity in making sure we understand what we changed.

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