Last week I started a training course to become a Warm Data Lab host with Nora Bateson and the International Bateson Institute. I want to start a series of blog articles in which I reflect on the different concepts and ideas that I take away from the course. The series is intended as much an opportunity for me to reflect and deepen my understanding of the concepts as it is intended to be for the readers of my blog to get an introduction to Warm Data and connected concepts. The first article in the series is about defining Warm Data and the connected concept of transcontextuality.
Most fundamentally, Warm Data is about changing how we perceive the world and make sense of it. It is not a process or any other type of thing. It goes far deeper than that. For me, it is a coherent world view that ties in well with other schools of thought I have been following like anthro-complexity. Yet there are also differences between these different schools of thought, which are important interfaces where learning can happen (the idea that learning happens at interfaces between different context is in itself an idea from Warm Data, but more on that later).
In order to be able to explain Warm Data, another concept needs to be explained first: transcontextuality. The idea of transcontextuality is that there are multiple different contexts that are interconnected and interdependent behind any single question, issue or thing we look at. Or as Nora Bateson puts it: “Whatever we are talking about, it is never just that and nothing more.” Let’s take a simple example of a subsistence farmer and the use of more productive seed. A simple linear logic would argue that once the farmer sees that she would get more crop with the new seed, she will adopt it. A transcontextual perspective would first look at the intersection of different contexts that play a role in such farmers’ decision: the history or the farmer and how she learned to farm, the family she is part of, the community she is part of, the financial system, the education system, the culture, the economy around the crop (both in terms of where she buys the seed – or if she can propagate it herself – but also where she sells the produce, linking her to longer value chains and possibly even global supply chains), politics (which includes taxes but also things like corruption or even extortion) and so on. One would also look at traditional knowledge that is available to the farmer and to habitual practice. And of course one would look at the particular ecosystem of which the farm is part of, with its own specificities like other species that are there like pests or beneficial species, the quality of the soil, the amount of rainfall and how that changes, and so on, and so forth. As we can see in this simple example, the discussion cannot be just about the improved seed and nothing more.
Warm Data is about opening up this transcontextual perspective whenever we approach a complex living system. Nora uses the following definition of Warm Data:
Warm Data is transcontextual information about the interrelationships that integrate a complex system.
The need for a Warm Data approach has grown out of the realisation that in complex living systems, you cannot fully understand what is happening if you pull something out of its context and study it in isolation. While this seems like a trivial insight, Warm Data is the first approach that genuinely expresses what that means if one thinks it through to the end.
Of course our scientific approach has made great progress on understanding how things work in isolation. This is the way most science works – take something, isolate it, study it and put it back into the context. And we have gained a massive amount of knowledge like that. But it is static knowledge, it is necessarily limited as it excludes the interrelationships and interdependencies the ‘thing’ exhibits in its natural environment.
Another important thought – maybe even more important – is that once you isolate a thing and describe it in isolation outside of the multiple contexts it forms part of, you can exploit it. For example, once trees are isolated as independent individual plants and studied and described as such, they become an object in themselves. An object that can be planted, cultivated in an ‘optimal’ way and then cut down. Or better even cut down from natural (rain) forests. However, in Warm Data one would ask: what are the multiple contexts a tree forms part of and how does it interrelate with these contexts? What interdependencies are trees part of? And, eventually, when thinking about this for long enough, one inevitably has to ask the question: where does the individual tree stop and the forest start? Each tree is interwoven with the forest for example through other organisms like mycelia, both while it is alive but also over time as part of a never ending cycle of growth and decay. Now ask the question again: what does it mean to cut down a tree or a whole lot of trees? We now see that this will influence a whole lot of contexts in ways that we cannot predict. And we now know that cutting down large parts of the Amazon rain forest, for example, is influencing global patterns of rain and draught.
There is nothing wrong with a reductionist approach to scientific inquiry. It has given us lots of knowledge and comfort. But we have to understand its limits. Unfortunately, we have elevated the reductionist approach to become our main lens through which we see the world. Because we have extrapolated the reductionist view on the whole world, indeed into every aspect of our culture, education, politics, families, lives, we have also as a human species brought our planet to the brink of collapse. Because we have disconnected trees from forests, animals from ecosystems, crops from the soil, work from family, school from learning for life, mental health from nutrition, etc. we have ignored the interrelations and interdependencies of all of these contexts. It is time now to put this back together again.
Title photo by veeterzy on Unsplash