Dealing with complex problems through warm data labs

Problem-solving | New ways of working

tl;dr A warm data lab facilitates an experience of mutual learning, exploring of complex questions that generates new ways of thinking and learning together.

When I heard about warm data for the first time on Douglas Rushkoff’s Team Human podcast a bit more than a year ago, I was thrilled. Looking for ways to create an environment, in which groups could think together about the wicked problems of our times without jumping to premature conclusions, I had immersed myself in various group facilitation methods.

Open space technology, world café, the circle way, and Bohmian dialogue were among the more obvious choices, while Stafford Beer’s elaborated syntegration might count as a lesser-known option.

However intrigued by the pertinence of these methods, even some 30 or 40 years after their first inception, I was still somewhat unsatisfied with their ability to handle the complexity of many of the issues we are facing especially with the current environmental crisis.

Now, in Rushkoff’s interview Nora Bateson, filmmaker, author and youngest daughter of the eminent systems theorist Gregory Bateson, talks about the so-called warm data labs she has developed to explore complex questions in ways that would avoid reductive conclusions, leaving their multidimensional contextual entanglements intact.

Warm data labs? My curiosity was sparked, so I enrolled in a one-week workshop with Nora last summer to be trained as a warm data lab host. And warm it was, considering not only the hot temperatures at the Northern Italian town of Abano Terme where the workshop was held, but first and foremost in terms of the personal and inspiring conversations I had with the other people attending.

The concept of warm data

To understand the concept of warm data, it may be helpful to start with what they are not: Anything that we know as quantitatively measurable facts or isolated information about „things“.

There is nothing wrong with „cold data“ per se (especially in times of so-called „alternative facts“ aka propaganda one learns to appreciate a minimal consensus around sheer numbers), but their reductionism leaves much to be desired when it comes to understand, experience, and appreciate the multiple contexts in which many of today’s complex issues unfold.

Warm data, on the other hand, focus less on the qualities attributed to individual elements in a complex system (e.g. a particular car’s CO2 emissions), but rather on the patterns and dynamics that drive the interrelationships between elements that make up such a system (to modify the well-known quote by Gregory Bateson: „What are the patterns that connect the way we foster technological development, the way we organize our working lives, the teaching of science in schools, and the ecological impact of human mobility?“)

What is the pattern that connects?

Warm Data criteria

Nora Bateson lists several criteria for warm data:

1. Observing the observer: An all-time classic of systemic thinking and second-order cybernetics: Every observation is made from a particular, and hence limited standpoint of a specific observer. Being aware of the way I observe helps me to understand the multiple contexts that inform my perception of the world. Take the example image above. This is a woodland garden, I planted this fall. Being the optimistic gardener I am, I see lush bushes fruiting with berries and nuts, where other people just see a barren patch of mud and woody sticks.
2. Multiple descriptions: Multiple observers imply multiple descriptions ― none of which are better or more legitimate than others. Where I see a garden bringing joy to its inhabitants, my partner may see a lot of work. Where a real estate agent sees the monetary value, a biologist may see a diverse environment for various species to thrive. Where the mice and squirrels living in the nearby woodland may find an unexpected source of food, the garden could also be described in terms of its capacity to convert carbon dioxide into oxygen, the amount of food it produces, or its horticultural sophistication.
3. Fluid patterning: The patterns that connect the individual members of a living system are not static but change with every variation in the interrelationships between them. The individual plants in the garden, the fungi and microorganisms in the ground, the composition of the soil, the surrounding trees with their canopies, and ultimately the climatic and meteorological circumstances make up a complex interrelated network of interactions, whose patterning changes if one of its constituents begins to change.
4. Paradox, inconsistency, and time: Non-reductive approaches to complex systems acknowledge their inherent inconsistencies and paradoxes. A woodland garden is both natural and highly artificial, both ecologically beneficial and harmful (considering the transportation of the plants), both subject to my willful intervention and hopelessly unpredictable as any gardener might confirm.
5. Holism and reductionism: Reductionism and holism go side by side and are rather different modes of observing a complex system. I may thus, reductively, describe the crippled state of the Robinia tree in the back of the garden, measure its height or even determine its age by counting the growth rings in its wood, while a more holistic approach would see its shape as the result of its shaded environment, the previous dominance of an oak tree now gone, the sandy soil and limited water supply in the summer.
6. Cultural responsibility: An important variation of the observer/observed problem. Although historically, we tend to universalize our modern western view of the world as a non-disputable given, other cultures hold different, and sometimes more viable views, for example when it comes to our understanding of„nature“. Where I as a western gardener see plants mainly as insentient producers of food, the perspectivist animism of some indigenous peoples in the Amazon, attribute to them not only sentience but even something like an inner self that needs to be treated with respect.
7. Aesthetic, mood, rhythm: The way patterns emerge in the interrelationships between entities in a complex system may be described, like a piece of music, in terms of its aesthetic qualities and its effects on an observer. Think of any gathering of living beings and the particular mood that arises from their interactions. In the case of my garden this might be rather dull in the wintertime, and more vibrant in the spring.

While all this may sound overly abstract and theoretical, actually doing a warm data lab facilitates an experience of mutual learning, exploring, and leaning into the complexity of a given question that generates new ways of thinking and learning together in an at least temporal community of people. For example, in a warm data lab, I hosted with around 70 people at the Bon Courage Conference at Dark Horse we were exploring the meaning of courage in a changing world in contexts as diverse as politics, education, science, family, ecology, health, culture, technology, spirituality, aesthetics, and the body. Our conversations around our topic flowed in patterns between the very concrete and personal to the fundamental and abstract. Overlaps between contexts, reinforcing their patterns, but also gaps, inconsistencies, and fissures became apparent, opening new spaces for thought and courageous action.

Warm data labs are and remain non-reductive, meaning that there is no summing up or drawing of reassuringly clear conclusions at the end. Instead, they allow for mutual learning in the living interaction between people. While an actual change in the way we think and interrelate may happen, for sure every single participant, as well as the group as a whole will emerge more attuned to the transcontextual dimensions of our realities. Not a bad way to start, when we try to create and innovate for a better future.