Warm Data

Recognizing that complex problems are not susceptible to predetermined solutions, the International Bateson Institute has taken up the task of generating a category of information specifically dedicated to description of contextual relational interaction, calling it “Warm Data”. The units of knowledge by which reasoning and calculations are made namely, data, information, and facts, suggest processes of research into which we place our hopes for better understanding of the world we inhabit. But the subject being perceived must dictate the necessity of understanding in different ways, therefore producing different kinds of information. Warm data is the product of a form of study specifically concentrated on (trans)contextual understanding of complex systems. Utilizing information obtained through a subject’s removal from context and frozen in time can create error when working with complex (living)systems. Warm data presents another order of exploration in the process of discerning vital contextual interrelationships, and another species of information.

“Warm Data” can be defined as: Transcontextual information about the interrelationships that integrate a complex system.

Information can come in many forms, depending on what is being studied. There is a need now for a way to gather and impart relational information when what we need to study is relational in nature. Warm Data is a category of information to develop in tandem to existing forms of data. This kind of information is a slippery mess of variables, changes, and ambiguities. It does not sit nicely in graphs or models, and it takes longer to produce. Since Warm Data describes relational interdependencies it must also include the necessary contradictions, binds (double-binds and more), and inconsistencies that occur in interrelational processes over time. Warm Data is the delivery of these multiple descriptions in active comparison, usually in a form that permits and even encourages the subjectivity of the observer within which it is possible to make meta connections.

This essay is an exploration of how and why Warm Data is developing as a form of documentation of relational and contextual information. Here the discussion does not go into the ways in which Warm Data are produced, or what do with Warm Data in terms of “actions”.

Making Sense & Science:

In early 2012, I was at a session on Big Data at the SAS Institute in North Carolina when I declared to my colleagues that there is a need for “contextual information” and first referred to it as “Warm Data.” That moment marked the beginning of conceptualizing and exploring the possibilities of producing an approach to research, and then imagining what the delivery of “Warm Data” research might look like.

I was not clear what it was yet, I was only certain of one thing: that a new kind of information was needed to balance the information produced by research that decontextualized its subjects of inquiry. In short, the way in which we make sense of the world has everything to do with the way we behave in it, so I saw a need for another sense-making as a necessary component of shifting behavior. Five years later the International Bateson Institute is now utilizing “Warm Data” in our research and findings on addiction, how systems learn, health and ecology.

Science, the systematic pursuit of knowledge, is at the center of the post-trust, post-truth meltdown. It appears that information as a product of knowledge, is now on trial. Every research study it would appear, is perforated with holes of distrust and met with counter evidence. Research bias and funding-based conflicts of interest have undermined public confidence in scientific research results, even though good research remains the best (and only) possible way forward as we look for ways to reduce our negative impact on the world around us. Subsequent public confusion and division is resulting in binary argument fueled by information that has been derived without the complexity required to actually make sense of it contextually. The damage is vast.

In this era, it is nearly impossible to get through a day without contributing to the destruction of our world. By lunchtime most people have participated in: further disruption to the ecology, an increase in the wealth gap, the demise of social justice, and the vengeful division between cultures. Breakfast cereals are laced with chemical pesticides that are known to be toxic both to our bodies and to the soil. They also contain damagingly high amounts of refined sugar. Yet these harmful practices have been approved by the institutional authorities of science and society. How has it come to this? And how can new patterns of interaction in our societies be encouraged to emerge? Our social deference to authorized institutions in the interest of collective safety has evolved over centuries. But that safety has been contaminated, along with our trust in the institutions that are supposed to provide truth and justice. How can science evolve to contribute to greater trustworthiness of our socio-economic institutions? How can sense be made of this tangle?

Part of the problem is that, globally, nationally and personally, we face crises that can be described as “complex” or “wicked” problems. Complexity is recognizable in situations which have multiple variables in ever shifting contexts of interdependency. Some examples of complex living systems are oceans, cities, families, economic systems, culture, the health of our own bodies, and the medical systems we expect to support them.

In each of these systems, vitality is produced by multiple processes in contextual interaction. To study a jungle is to recognize that the jungle itself is not an isolated “thing” but instead exists in the interrelationship between soil, foliage, animals, weather patterns, bacteria and so on. The same contextual linkings can be found in all living systems; approaching the system without an understanding of this holism will create short circuits in the complexity and countless unintended consequences. Making sense of the vitality of a complex system is an inquiry into its way of making contact. A study of the relational patterns gives entirely different understanding of the way in which a system is cohering.

“At present there is no existing science whose special interest is the combining of pieces of information. But I shall argue that the evolutionary process must depend upon such double increments of information.

Every evolutionary step is an addition of information to an already existing system. Because this is so, the combinations, harmonies, and discords between successive pieces and layers of information will present many problems of survival and determine many directions of change.”

Gregory Bateson, Mind & Nature, 1979

Why Warm Data?

Although statistical data is useful, it is also limited by the common practice it often accompanies: decontextualizing the focus of inquiry. To study something is usually to pull it out of context and examine it in isolation. Rarely is the study re-contextualized to look at the complexity of its larger web of relationships. Warm Data circumnavigates the limitations inherent to statistical analysis by engaging a transcontextual research methodology, bringing not only context, but multiple contexts into the inquiry process. In order to interface with any complex system without disrupting the cohesion of the interdependencies that give it integrity, we must look at the spread of relationships that make the system robust. Simply using analytic methods focused on parsing statistical (cold) data will often point to conclusions that disregard the complexity of the situation at hand. Moreover, information that does not take into account the full scope of interrelationality in a system is likely to inspire misguided decision-making, which compounds already “wicked” problems. Warm Data is not meant to replace or in any way diminish other data, but rather it is meant to keep data of certain sorts “warm” — with a nest of relations intact.

Photo by Jeff Bloom Photo by Jeff Bloom

Transcontextual Research & the Rigor of Ambiguity:

Warm Data provides cross-sector interrelational information because it is the outcome of a research approach premised upon the transcontextual interaction inherent in any system. This sort of inquiry is daunting and perpetually in its pioneering stages. This research is has as its basis humility for the un-knowability and ambiguity inherent in these forms of study. However, these inevitable uncertainties we recognize do not lead us to an abandonment of deep study. On the contrary, studying relational information from multiple contextual perspectives, produces more work, takes more time, and requires larger teams. The rigor of this research is not to be underestimated. For example, if one wants to study the ways in which food impacts our lives, a multifaceted study of ecology, culture, agriculture, economy, cross-generational communication, and media is needed. This transcontextual platform provides a wider contextual framework for further inquiry into what forms and constitutes certain international contemporary issues such as eating disorders, starvation, and other health problems associated with diet. Or in a family study, to better understand an individual family member the Warm Data of the family culture, and other contextual information is enormously productive. The meanings of behavior differ greatly from family to family and from culture to culture, therefore contextual information can provide needed insight into otherwise line item analysis, diagnostics and understandings of causation. People migration, the changing banking industry, and the challenges facing mechanical and civil engineering are all topics that would benefit from an increase in contextual, relational information. Warm Data is generated through a Batesonian[1] approach of comparing interrelating processes in a given system. This approach needs us to reconsider our prevailing epistemology, to foreground the study of interdependency, to observe the observer and to look for “the pattern that connects”.

Pattern:

What is the pattern that connects? This question, famously posed by Gregory Bateson (father of the author), draws the inquirer and researcher to another level of description. It is an invitation to reach behind the perceived separations of knowledge to get to the contextual knitting together of definitively inseparable processes.

Epistemology:

A majority of current scientific research tools and methodologies pull “subjects” from their contexts in order to derive detailed, specialized, quantifiable information. To complement, and yet support, this specialized type of science, a wider practice of science in the future might develop ways to utilize information derived from both detail and interdependency. However, for now, the cultural habit of decontextualizing information, or, reductionism, is the standardized, authorized, and empirical norm.

An evolution in the realm of science is needed to foreground, and find ways to communicate (and “deliver”) another form of information, one that is less likely to be riddled with errors deriving from hidden contextual consequences. But it will require a significant shift in epistemology to begin to perceive the interrelationality in addition to the parts and wholes of any given system. A shift in the way information is derived will, in turn, inform the actions we take to protect society and ecology.

The Scientific Revolution of the 1600s brought us the scientific method and the bounty of mechanistic thinking. It brought us the notions of induction, empiricism, hard evidence, quantitative measurement, and objectivity. All of these have been enormously useful: skyscrapers, aircraft, computers and EKG machines are all manifestations of this form of scientific research and development. But not all studies are served by the empirical and inductive quantitative method. There are some forms of understanding that resist measurement and elude objectivity: these can include understanding of what is necessary for raising children well or understanding the ramifications of culture on climate change. Reductionism, or the habit of isolating information from its context(s), has been good to us, and it has been deadly.

Interdependency:

If there is a concerted effort and demand from the scientific community and from society at large, we may witness a shift in scientific practices to include another form of research that will deliver information that includes the interdependency within complex systems. But this is not an easy shift. The habits of studying things through silo-ed disciplines is deeply entrenched in our culture.

In the mid 1950s the beginnings of a new way of understanding systems emerged in the study of “cybernetics”. Cybernetics offered the tools to look at how the “parts” of systems came together. But this tool was not easy to bring into the fold of scientific notions of isolating objective truth. The logic of “cause and effect” within the study of complex living systems defies the confines of existing methodologies. Interrelationships whose combined processes create the conditions for a particular consequence, such as an addiction, or economic wealth gap, or racism are impossible to quantify without distortion.

Observing the observer:

It takes a team of people to study in this new way. It also takes an open-ended declaration of “outcome” because this form of scientific research will produce only unforeseen “deliverables”. Stabilized, standardized “objective” science is fine for the study of some subjects, but not all. In the case of living systems and complex problems the “facts” are not always enough. The facts according to whom? Through what cultural, and methodological lens were they looking?

The observer matters, and teams of observers matter. Since data are always derived through the particular lens of the researchers, descriptions of their filters of perception are vital information and must not be sterilized out of findings.

As we currently witness the melting of trust in science, politics, law, medicine, social systems and economics, it is clear that this era will require a reclaiming of trustworthiness. Lamenting the postmodern condition of multiple relative truths and impossible clarity is only partially useful in regaining trustworthiness. Beyond the cynicism that the postmodern dilemma delivers is the practical need for better questions, and more rigorous inquiry into complexity.

We can remember that at the same time as the scientific revolution of the 1600s there was a corresponding period in art in which the techniques for rendering perfect replication of still life were constantly improved. Yet in the ensuing centuries it became clear that perfection was not enough. Baroque still lifes may have pursued a photographic realism, but later art movements such as Impressionism, Expressionism, Surrealism, Modernism and Postmodernism were responses to a need for a different envisioning of information, as not only “about the chair” but about who is seeing the chair, and how it might be possible to perceive “chair-ness.” Now perhaps it is time for science in its turn to adopt a parallel course of discovery around what perception and information are.

Historical antecedents:

Warm Data is not really new. Throughout history people have used aggregated information toward a natural history approach. Often these findings were often metabolized into other forms of information more readily recognized by the society. It would seem that devolution of natural history is part of the natural history, as it gets sucked back into the cultural paradigms of credibility. The limits of rationale hold particular sense-making habits in place, while others remain threatening. However, there has been a stream of inquiry that has always existed in which relational information was produced. This introduction of the notion of Warm Data is offered here in response to the need for this type of information to be strengthened and honored.

The alchemists, Leonardo da Vinci, Goethe, William Blake, people producing case law, Pythagoras, anthropologists, and artists like Shakespeare, Hokosai, Bjork and countless others have used their own means of bringing together multiple forms of information and rubbing them together to find new perspectives. Currently qualitative research teams and methods are becoming increasingly popular. They may benefit from the naming of another form of information in which to place their findings.

Characteristics of Warm Data:

To more effectively meet the challenges of this new sort of rigor, we require studies that generate understanding of contextual systemic data. The information generated makes a difference not only in scientific research, but also in the contextual influences considered in decision-making. Here are six characteristics of Warm Data.

1. Multiple description: This is a way to illustrate processes and contexts of interdependency. Multiple description both blurs the distinctions between contexts, and describes them through difference, comparison and relational perception. While it might appear that this process would lead to an untenable and infinite collection of perspectives, the Batesonian notion of information as “difference that makes a difference” is way to study the relation between perspectives, through contrasting qualitative characteristics. The information is not located but diffused into the contextual contacts and boundaries.

2. Looking for pattern: We compare findings from one context with findings of similar patterns in other contexts, to generate hybrid information. This is very much in keeping with Pierce’s Abduction. The findings from pattern comparison across contexts are conceptual, and indirect. For example, the patterns of ecological relationships in a tide pool can be compared to the patterns of relationship in a family, but the needs of survival for the tide pool are clearly different in detail than those of the family. Understanding the patterns comparing ecological systems is useful for studies of other systems, even though the systems may be not be alike in their details.

3. Paradox, inconsistency and time: Scientific research premised upon the complexity of a system in relation to its environment will produce paradox and inconsistency, by necessity. In order to keep the complexity intact, results should feature these dilemmas without resolving them. In fact these instabilities are sources of information about the relationships that are highly generative. Relationships over time change, and aggregated relationship such as a forest or society must produce responses to responses that are disruptive. The disruptions are rich with Warm Data.

4. Holism and reductionism: Information derived by zooming out to study context is as important as the information derived by zooming in on detail. These two forms of information are not alike. One is relational and overlapping, the other is isolated and (sometimes) linear. Both are needed in relation even when they produce contradictions. Smaller and larger contexts are tangled up mutually calibrating interactions. They are not concentric nor are they separable; rather they are steeped in interdependency.

5. Cultural epistemological responsibility: Science and culture are deeply entwined. Development of inquiry that is simultaneously inclusive of multiple generations, cultures, and sectors is useful to keep observers’ frames relevant. Information is only as perceivable as the sensorial limits of the observer. A variety of perceptions lessens blind spots.

6. Aesthetic/mood/rhythm: In any inquiry of life, the aesthetic matters — perhaps above all else. This vital condition of any interrelational context is often ignored in favor of misplaced rationality. Given that complex systems are interrelational, the nature of the relationships needs to be noticed. The aesthetic is the conduit through which relation occurs. While the aesthetic need not be valuated, it must be noticed to better assess relational information. Keeping in mind that the opposite of aesthetic is anesthetic, it is clear that increasing sensitivity is preferable to numbness as it increases receivable information.

Just as the methodology for generating Warm Data is characterized by transcontextual research, the end-product and delivery of this information will be characterized by multiple description (though all aspects of Warm Data involves both transcontextual research and multiple description). I need look no further than my own hand for an illustration of how multiple description can increase the scope of understanding within a system and between systems of understanding. To illustrate this, let us ask, “What is a hand for?” Different contexts provide contrasting contexts for understanding. A violinist’s hands hold the muscle memory and learning of a lifetime of practice. But a sculptor’s hands know weight and texture and pressure in another way. People who use sign language express not only words but also emotion through their hands. In this sense, the contexts that the hand exists within, (anatomy, music, memory, language, cognition) each provide a realm of relational data to be explored. This is just one simple example of the possibility of transcontextual research. At present the International Bateson Institute is currently researching the Warm Data of: Addiction, Health Care Systems, Education, Climate Change, Emergency Population Relocation, Double Binds within Political Discourse and more. The Theory:

There are several theories at work within this process. Here are a few:

Patterns that connect

2. Difference that makes a difference

3. Multiple description

4. Symmathesy: Contextual Mutual learning and calibration

5. Autopoiesis, and Mind (Maturana, Varela, Thompson and Bateson)

6. Systems and Complexity Theory

7. Ecology of communication

8. Double binds

9. Conscious purpose

10. Epistemological frames

11. Change in complex systems

12. Interdependency

13. Abduction (Pierce)

14. Transcontextual Research

Meet the Hydra:

Beyond the conventional problem solving techniques of reducing and resolving, problem solving in complexity further requires an understanding of the interdependencies that are generating the issues. We must address these even in addition to our ever more acute and urgent responses to rising situations. Like the heads of the mythological Hydra our crises are many now. But in our silo-ed world the crises that we perceive and address are also silo-ed, as is the habit of finding silo-ed solutions. Much like chopping off the Hydra’s heads, the resulting solutions that do not address the complexity seem only to generate more consequences.

The most serious problems facing us now are not in any particular institution, but rather in the relationship between them. If change is made it is a consequence of a shift not only in the problematized part, but in the combined conditions in which the system exists, be it a person, organization, forest, or society. Like an ecosystem the interdependencies of the institutional systems are interlinked and steeped together in patterns that make it difficult to create whole systems change. To address our socio-economic and ecological crisis now requires a level of contextual comprehension, wiggly though it may be to grok the inconsistencies and paradoxes of interrelational process. Far from solving these dilemmas or resolving the conflicting patterns, Warm Data utilizes these characteristics as its most important resources of inquiry.

nb3.png T

he Hydra grows new heads every time one is chopped off.

References: Bateson, Gregory. Steps to an Ecology of Mind. Chicago: U of Chicago, 2000. Print.

Bateson, Gregory. Mind and Nature: A Necessary Unity. Cresskill: Hampton, 2002. Print.

Bateson, Nora, “Warm Data.” The International Bateson Institute. N.p., n.d. Web. 10 Apr. 2017.