Monthly Archives: September 2020

numbers

Holistic worldview and cold data

This is the second article in my Warm Data Series. In my last post, I talked about the basic understanding of what Warm Data is and how it is based on a transcontextual understanding of complexity. Today I want to start with responding to two comments / questions that I have received as a reaction to the last post on my website. The first one is from Ulrich Harmes-Liedtke, wanting to know how a world view based on Warm Data and transcontextuality is the same or different to a holistic worldview. The second is a comment by Shawn Cunningham about the difference between warm data and cold data.

First, let’s look at the question on holism. The worldview informed by Warm Data and transcontextuality has certainly some similar aspects to having a holistic worldview, yet it is still distinct. This is how I understand it (and as you know I’m still exploring this). A holistic worldview implies that there are parts that interact and together build a grater whole, the system. This separation of parts and wholes can be problematic. Firstly, when defining the parts, you need draw boundaries around them. But if you look closer and go down to what you think might be such a part, the part turns out to be a whole itself (yet not always). So where do we draw the boundary around a part? This inevitably leads to the question of finding the elemental part(s) that build up everything else, which is a reductionist perspective and not immediately helpful when looking at complex living systems. Secondly, finding parts and isolate them also implies that they can be acted upon. In this way, talking about parts leads to a mechanistic view on complex systems. There is always a tendency to isolate the parts, fix them. Or optimise the system for some parts (like optimising the economy to serve the poor). We know this does not work, as optimising the system for one type of parts will lead to unintended consequences in another part of the system. In a complex living system, nothing can change without everything else changing (this is actually how William Bateson conceptualised a system, if I paraphrase him correctly). In reality, all is entangled. To take my example of the tree and the forest: where does the part we call tree end and the whole we call forest start? Are the insects that live inside a tree in a symbiotic relationship as much part of the tree as the microbes that live in our gut are part of us? While we can draw boundaries around elements, like the skin being the boundary of the human body, these boundaries are not always helpful — for example if we look at behaviour, my behaviour forms the community as much as the community forms my behaviour – where is the boundary, where do I end and the community start? 

This is from Nora:

The way in which we have culturally been trained to explain and study our world is laced with habits of thinking in terms of parts and wholes and the way they “work” together. The connotations of this systemic functional arrangement are mechanistic; which does not lend itself to an understanding of the messy contextual and mutual learning/evolution of the living world.

Reductionism lurks around every corner; mocking the complexity of the living world we are part of. It is not easy to maintain a discourse in which the topic of study is both in detail, and in context. The tendency is to draw categories, and to assign correlations between them.

Bateson, N. (2016). Symmathesy–A Word in Progress. Proceedings of the 59th Annual Meeting of the ISSS – 2015 Berlin, Germany, 1(1). Retrieved from https://journals.isss.org/index.php/proceedings59th/article/view/2720

The way I understand this is not to completely loose the notion of parts but rather to hold the distinction of whole and part as a kind of paradox – as Nora puts it to appreciate that the study is both in detail and in context. The one requires the other and is dependent on the other. For example, we become ourselves because there are other selves. So the others are part of why I view myself as myself. At the same time, learning happens between the different selves through interaction. As Chris Mowles writes [2]: 

Human beings and what they are doing, thinking and acting is what causes social evolution.

Mowles, C. (2015). Managing Uncertainty. Complexity and the paradoxes of everyday organizational life. Routledge: Oxon and New York.

So in that sense, human society builds a learning whole.

Now to Shawn’s comment. He wrote:

Hi Marcus, thank you for sharing your thoughts from the Warm Data lab. From my understanding of warm data, understanding what cold data is also makes sense. If I recall correctly, cold data can be captured as exact numbers, it can be quantified. It could even be statistically analysed without understanding the context. In our field, it means that an index to assess global competitiveness might consist largely of cold data. Understanding why some people resent being benchmarked, or why they feel that important aspects are not reflected in the dataset would require a warm data approach.

Yes, cold data is statistics, numbers, measures. Particularly, it is data that is taken out of its context, which often happens when you quantify aspects of complex living systems. There are important applications for cold data and we can learn many things when looking at it. But it does not give us the full picture. And more importantly, it often doesn’t tell us anything new about the interfaces between contexts and how learning can happen there (I hope to post more about that soon). Cold data is generally focusing exclusively on one context – economy,  education, ecology, politics; or at best it draws simple correlations between measures from two contexts like the economic achievement of students from socially challenged backgrounds. Warm Data is not primarily about creating data, it is about changing perception, about creating a new understanding of why things are happening the way they happen and shifting the way learning is happening between contexts and through that hopefully how things are done. Warm Data is not to inform and analyse in a way as if you are outside of the system and can make an objective plan. It is about understanding how things are connected and what our part and our role is in these interconnected contexts.

Now I need to head into the next session of the Warm Data course.

Featured photo by Mika Baumeister on Unsplash

tree in a forest

Exploring Warm Data

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