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Holistic worldview and cold data

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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

Holism vs. targeting in complex systems

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Yesterday, I had a discussion with a friend about the question whether any form of targeting of our interventions towards a specific group of people or topic is already limiting our ability to come up with solutions that are fully adapted to the system. The concrete issues we were talking about were the poverty orientation of the development sector in general and as an example the focus on women economic empowerment as a form of development in particular. The hypothesis we had is basically that if we enter a system with predefined clients (e.g. ‘the poor’ or ‘poor women’) in the first place, our solution will always by biased in order to directly and quickly cater those clients’ needs. This argument goes into the direction of the silo thesis, i.e., that development organizations basically have their topics, such as human health, water and sanitation, markets, etc., and that, regardless of the system or problem they encounter, their solution always has something to do with their topic.

In contrast to that, systems theory tells us that we need to take a holistic view on the system and not limit ourselves to one specific domain if we want to really understand how a system works. So if we only look at the problem of how women get their water and come up with the solution of digging a well in each village, we might miss the whole actual problem women are facing. As a consequence, the entry point for us to support the women might be somewhere completely else, for example local governments, traditional structures, etc.

Then again, what we as development practitioners want to achieve – be it in the short or the long run – is to reduce poverty and also to improve the situation of women, which, in many instances, suffer an even more dire fate as their male counterparts. Not to talk about the incentive structure of the funding of international development which clearly favors quick wins with specific target groups.

I did some more thinking on the topic and I guess the important differentiation we need to make is between what we look at during the system analysis and what we define as target state of the system. In the first, i.e., the system analysis, we have to be open and holistic and take into account all kinds of influences. The only thing we need to do is to set the appropriate system boundaries to frame the system and the level of aggregation that is useful for our work. But only once we have analyzed the system, we should hone in on our target variables, e.g., the poverty or the economic empowerment of women and see how we can influence the system so these variables change in a way that seems favorable to us.

Hence, holism and targeted interventions don’t have to be a trade-off per se. The trade-off, in my view, starts when we are designing the interventions. At that point we have to choose between interventions that have a short-term effect on our target variables or we have to appreciate the dynamics of the system and choose interventions that work with the system. The latter often have the price tag of only showing results after longer periods of time – although that is not necessarily always the case.

I’m curious about your thoughts on this topic and whether it makes sense what I am writing here.