You might not know this but I have a newsletter that I publish every Saturday morning called The University of Winds. This week’s issue covers library-related and library-adjacent topics and so I thought I’d republish part of it here.
Good morning. I have survived my first week of being back at work. Yesterday was an extraordinary day as external internet access returned to my workplace at almost the exact same time when half the country lost their Internet access due to an system-wide outage of the Rogers telecommunications system.
Suddenly we had a massive collective understanding that many of our infrastructure systems don’t have redundancies or off-grid alternatives.
In order for us to properly invest in the work that will ensure that systems are more robust, we are going to need more outages. It’s a lesson that I’m borrowing from the essay, Keeping Some of the Lights On: Redefining Energy Security [ht]
Because demand and supply influence each other, we come to a counter-intuitive conclusion: to improve energy security, we need to make the power grid less reliable. This would encourage resilience and substitution, and thus make industrial societies less vulnerable to supply interruptions.
At work, I’m in a role that is new to me. For the next twelve months, I’m the Acting Head of our Systems Department. I very much like the fact that most libraries use this wording of ‘Systems’ and not ‘Information Technology’. This level of abstraction is a good thing.
It is good to remember that services run on processes.
I’m currently reading Donella H. Meadow’s much-recommended work, Thinking in Systems. As an graduate of an environmental science program, I’m a little embarrassed that to admit that I haven’t read this work yet as it is crucial to so much its thinking and understanding.
Some of the biggest problems facing the world—war, hunger, poverty, and environmental degradation—are essentially system failures. They cannot be solved by fixing one piece in isolation from the others, because even seemingly minor details have enormous power to undermine the best efforts of too-narrow thinking.
While readers will learn the conceptual tools and methods of systems thinking, the heart of the book is grander than methodology. Donella Meadows was known as much for nurturing positive outcomes as she was for delving into the science behind global dilemmas. She reminds readers to pay attention to what is important, not just what is quantifiable, to stay humble, and to stay a learner.
Here’s a passage from the work that I’ve kept for future reference:
The human mind seems to focus more easily on stocks than on flows. On top of that, when we do focus on flows, we tend to focus on inflows more easily than on outflows. Therefore, we sometimes miss seeing that we can fill a bathtub not only by increasing the inflow rate, but also by decreasing the outflow rate. Everyone understands that you can prolong the life of an oil-based economy by discovering new oil deposits. It seems harder to understand that the same result can be achieved by burning less oil. A breakthrough and energy efficiency is equivalent, in its effect on the stock of available oil, to the discovery of a new oil field – although different people profit from it.
Systems thinking can produce all sorts of counter-intuitive insights, such as the one that one suggests that more outages will create more robust systems. But systems thinking does so at the cost of confusing the tidy relationships between cause and effect. Recognizing feedback loops, like vicious circles, is systems thinking.
Ursula K. Le Guin was a systems thinker:
Do you see how an act is not, as young men think, like a rock that one picks up and throws, and it hits or misses, and that’s the end of it. When that rock is lifted, the earth is lighter; the hand that bears it heavier. When it is thrown, the circuits of the stars respond, and where it strikes or falls, the universe is changed. …
… But we, insofar as we have power over the world and over one another, we must learn to do what the leaf and the whale and the wind do of their own nature. We must learn to keep the balance. Having intelligence, we must not act in ignorance. Having choice, we must not act without responsibility.
― The Farthest Shore
Who needs to know more about systems thinking?
Would you guess, UK civil servants? This week, thanks to the Just Two Things newsletter, I learned that the UK’s Government Office for Science (2022) produced this remarkable set of documents called, Introduction to systems thinking for civil servants.
To be honest, this toolkit is a pretty overwhelming introduction to systems thinking at first glance. But I’m going to spend time with it because there are some interesting models in the toolkit that I have not heard of before.
The pig model: understanding stakeholder views of the system
The project team know there is a significant diversity of opinion and even conflict amongst stakeholders and experts about their problem area. The team want to map this out to help discuss the different views of their problem. This model will play a large role in how the team interact with stakeholders as the project progresses and help them spot areas of friction early on. The team create a ‘pig model’ to depict the different stakeholders who ‘see’ the team’s problem area or system, and also map how those stakeholders perceive the team’s problem area or system…
… It is called the pig model based on an original example provided by Gareth Morgan (1997)and adapted by systems thinking experts in the Defense Science and Technology Laboratory (DSTL). The simple example is, consider how a pig might be seen by others, for example a wolf, farmer, poet and veterinarian (see figure 5). Each one will see the pig differently, as food, income, inspiration or a patient respectively. The example aims to illustrate how our view of the world, and the significance we give to parts of the system, will vary according to our frame of reference, which in turn is influenced by the direction we are given by senior civil servants and politicians, but also our background, beliefs, values et cetera. Thus the problem or system that we are interested in can be many things at once and the simple question of ‘What is the Pig?’ is often difficult as ‘the pig’ is many things at once.
I want to use this model in a real-world context just so I can say these words out-loud : “What is The Pig? This simple question is difficult to answer because The Pig is many things at once.”