As part of the Cambridge Culture, Politics and Global Justice group on the Digital Condition, I made a video response which sought to bring a cybernetic perspective to Margaret Archer’s views on the “Practical domain” as pivotal in the relations between nature and the social. I remember challenging Archer on this many years ago when she gave a talk in London about her work on reflexivity and I suggested that Maturana and Varela’s concept of “structural coupling” provided a clearer explanation of what she was trying to articulate in terms of the relations between people, practices and things. She brushed the point aside at the time, although more recently I heard her talk more approvingly of autopoietic theory, so I’d be interested to know what she thinks now. This is my video:
One of the things about making a video like this is that it is a very different kind of thing from Archer’s paper that we were all reading…
Enlarge/ Vannevar Bush seated at his desk, circa 1940-1944. During President Franklin Roosevelt’s administration, Bush built a national science policy based on a new structure for innovating quickly and effectively.
There’s rarely time to write about every cool science-y story that comes our way. So this year, we’re once again running a special Twelve Days of Christmas series of posts, highlighting one science story that fell through the cracks each day, from December 25 through January 5. Today: One of the most intriguing books of 2019 explored how the science of phase transitions can foster innovation.
Few people these days are familiar with the name Vannevar Bush, an engineer who played a significant role in fostering the developing of key technologies that helped the Allied Forces win World War II. He also spearheaded a highly influential federal report, Science: The Endless Frontier. Presented to President Franklin Roosevelt in 1945, the report famously argued for federal funding of basic research in science, calling it “the pacemaker of technological progress.” It shaped national science policy in the US for decades, and helped usher in an unprecedented explosion of economy-boosting scientific and technological innovation. (On the downside, Bush took a very dim view of the humanities—including science history—and social sciences.)
Physicist Safi Bahcall first learned about Vannevar Bush when he joined the President’s Council of Advisers on Science Technology in 2011, charged with producing a version of that 1945 report for the 21st century. The experience dovetailed nicely with his longstanding interest in the arc of human thought over the course of history, and his background as both a physicist and a biotech entrepreneur. (Bahcall comes by his physics bona fides naturally: his father is the late John Bahcall, best known for helping to solve the solar neutrino problem.) The result: an intriguing new theory about fostering innovation, based on the physics of phase transitions, that led to his first popular science book: Loonshots: How To Nurture the Crazy Ideas that Win Wars, Cure Diseases, and Transform Industries.
“I think business people are really tired of the thousands of more or less identical business books produced every year, saying more of less the same stuff,” Bahcall told Ars about his fresh approach to the topic. “And most economists have never seen the inside of a real company, so their models have no connection to reality. I happen to be in the middle of a very weird Venn Diagram of someone with condensed matter physics experience, someone with business experience, someone who likes to tell stories, and likes to think about history.”
According to Bahcall, the most significant breakthroughs comes from what he calls “loonshots,” as opposed to “franchises”: ideas that seem a bit crazy and are hence often dismissed outright, with anyone championing it labeled unhinged. There are two types. An S-type loonshot introduces a novel strategy or business model that no one believes can ever make money. When Sam Walton founded Walmart in 1962, for instance, he did so in a small town far away from major cities, bucking conventional thinking about the best locations for major retail. Walmart is now the largest corporation in the world by revenue, per the Fortune Global 500 list.
Enlarge/ Sam Walton’s original Walton’s Five and Dime Store in Bentonville, Arkansas, now serves as The Walmart Museum
A P-type loonshot introduces a new product or technology that nobody believes will work. Business leaders once thought the telephone was little more than a toy, and foolishly passed on investing in what would become the Bell Telephone Company. Similarly, physicist Robert H. Goddard‘s design for a liquid-fuel rocket in the 1920s was dismissed by academic and military experts at the time. Decades later, his invention helped usher in the era of spaceflight.
Understanding the science of phase transitions can nurture loonshots faster and better, according to Bahcall, so groups can achieve a harmonious balance between radical innovation (loonshots) and “operational excellence” (stable franchises). Instead of trying to change corporate culture, he maintains that small changes in structure can help transform group behavior, much like making tiny structural changes in a material can change its phase (water freezes into ice, or boils away as vapor). That was the secret to Vannevar Bush’s success: US military culture was resistant to taking risks on radical new ideas, so instead of trying to change the culture, he changed the structure, creating a separate research branch (eventually leading to the establishment of DARPA), where those radical “high risk, high gain” ideas could find a home.
Bahcall’s theory rests on three fundamental concepts familiar to any condensed matter physicist: phase separation, dynamic equilibrium, and critical mass. No two phases can co-exist in an organization—say, being good at loonshots (eg, original independent films) versus excelling at franchises (eg the Marvel Cinematic Universe)—unless they are poised right at the critical edge of a phase transition. “At the cusp of a phase transition, blocks of ice co-exist with pockets of liquid,” he writes. The phases break apart but stay connected, cycling back and forth to maintain a state of dynamic equilibrium—teetering on the edge of chaos. Ars sat down with Bahcall to learn a bit more about his intriguing new theory.
Ars Technica: Most people associate the critical threshold of a phase transition with Malcolm Gladwell’s 2000 bestseller The Tipping Point. How does your book differ from Gladwell’s take, almost 20 years later?
Bahcall: The Tipping Point is simply a qualitative discussion of the concept that the spread of ideas is governed by a phase transition. That’s well known in the literature, and [Gladwell’s book] weaves popular stories around the concept that the spread of ideas is like the spread of a virus. Gladwell pioneered the idea of translating academic science through compelling personal stories for a popular audience. That field, which was N=1 when he did it, is now N=5,000 people imitating him.
Enlarge/ Robert H. Goddard, bundled against the cold weather of March 16, 1926, holds the launching frame of his most notable invention — the first liquid-fueled rocket, an example of a “P-type” loonshot.
NASA/Public domain
But there’s no underlying new theory there. Loonshots is written by a scientist, and it’s based on an underlying original theory that hasn’t existed in the world of economics. No one has ever suggested the concept of an organization having a phase transition based on underlying incentives. People have been working on this problem literally for 200 years, since Adam Smith first asked, “Well, how might incentives affect behavior in organizations?” There’s a straightforward underlying academic paper I could write, which is essentially appendix B of the book. Here’s the model. Here’s why it’s reasonable. Here’s why I’m making these approximations, and here’s how you analyze this model. And here’s what you extract from it.
Ars: Let’s talk a moment about “disruptive innovation” versus “loonshots,” because you draw a distinction between these two concepts.
Bahcall: So many people are sick of hearing about disruptive innovation. The flaw with that is that it’s a hindsight problem. Disruptive innovation is all about the effects of something on a market. If you’re talking about a new idea, the market might be two years, five years, ten years, or 20 years away. The even bigger flaw is that if it’s a very early stage idea, any experienced entrepreneur knows you have no idea where it’s going to be not only a year from now, but even next week. It could morph into something totally different. So you talk about disruptive innovation to analyze history. Otherwise, we should rip that word out of the dictionary.
A loonshot is about testing an embedded belief, those things that you are sure—as a manager, or a business leader running any kind of group—are absolutely true about your world, your market, your products. But what if you’re wrong? Do you want to hear about that new idea about as much as a bullet coming at your head, or do you want to nurture loonshots to challenge your beliefs?
Ars: How does the concept of phase transitions in physics translate into a viable model for human organizations?
Bahcall: The underlying idea is that there are phases of human organization driven by the underlying interactions. So whenever you organize people into a group, the only precondition you need is that there is a mission for that group and a reward system, meaning an incentive system tied to that mission.
It can seem to a general audience to be a little crazy. How could you possibly apply physics to people? But it really is no different than economics. A market is just people interacting with incentives. The buyers want to get the lowest price, and the sellers want to get the highest price. Those are the interaction rules. The laws of supply and demand, or the “invisible hand” of the market, emerge simply from those rules. I’m just taking economics and applying it to a different system: an organization.
“The underlying idea is that there are phases of human organization driven by the underlying interactions.”
If you’re interacting within an organization, it’s not buyers and sellers, it’s employees and managers. And instead of buying and selling goods, wanting higher or lower prices, they want to maximize their incentives, their reward systems. It’s really the intersection of three things: organizational behavior, economics, and physics. Physics is only brought in as a way of thinking about collective behavior in language that economists don’t usually think about. Ultimately this is a sub discipline of economics, organizational economics, which is understanding the influence of incentives inside an organization.
The tools, or techniques, of phase transitions are a simple set of shortcuts to extract useful insights from a system. You have people in this construct of an organization. You make a simple model. The goal is to make a model that keeps it simple, but not simplistic. You want to capture enough of the underlying interaction so that you can probe the features of the system you’re interested in, but not so much that the problem becomes intractable. So that’s what I did. I found a not very complicated model of an organization and the incentives inside of an organization that was tractable.
Ars: Can you explain how organizational structure changes and a phase transition might occur as a company grows in size?
Bahcall: Whenever you organize people into a group you instantly create two forces on any individual member of that group. One is their stake in the outcome of the project that they’re working on. And the other is the perks of rank within the hierarchy. So if you’re a two-person group, let’s say each person has 50 percent stake in the outcome. Whether you call A, or B, the captain, and the co-captain, is irrelevant. If the project works, everybody is happy, and if it fails, they’re depressed and unemployed. With four people, you’re now at 25 percent stake. You’re probably going to have a team captain and three team members, but it still doesn’t matter very much.
But when you’ve a hundred people, your stake becomes, let’s say, 1 percent. You can’t have one person with 99 people reporting to them. That just doesn’t work. So you have one CEO, five VPs, 25 SVPs, and the rest are the associates or worker bees. Now, if your stake is 1 percent, what’s your reward for getting promoted? It’s probably more than 1 percent. All of a sudden we’ve had a shift. Somewhere between four and a hundred, there is a shift in balance between these two forces.
That’s the phase transition. That’s the qualitative aspect. You can write down what that looks like more mathematically, with realistic incentives. You get cash: that’s how much your base salary goes up in the hierarchy. And you get equity. That’s your stake. You write those two terms down, and then you see what the break even point is, where the derivative is zero. That gives you the equivalent of the critical point. It also tells you what controls that size. Those are the dials that you can adjust. By cranking up the size, you’re effectively making a more innovative group.
Enlarge/ Safi Bahcall applies the tools and techniques of phase transitions to making science and technological breakthroughs.
St Martin’s Press
Ars: You also talk about how having a “system mindset,” versus an “outcome mindset,” can help an organization maintain the balance between radical innovation and operational excellence.
Bahcall: I took that idea from one of [chess grandmaster] Gary Kasparov‘s books. The process that helped him achieve world champion status is that, when he lost a game, he wouldn’t just analyze why a particular move was a bad one. That’s an outcome strategy. Why did my outcome not achieve what I wanted? A more interesting level is one step up, where you look at the process behind the decision. Why did you make that decision? What set of rules were you following?? That has leverage far beyond that one move. It could apply to hundreds or thousands of games in the future.
Now let’s translate that to teams, groups, or companies. A team launches a product. The product flops in the marketplace. Some teams will say, “All right, let’s sit down and figure out what happened here,” post mortem. “Well, this product didn’t have this feature. Our competitor clearly had that feature. It was superior. So let’s make sure next time we launch a product we look at these features and we don’t launch until it’s at least as good as our competitor.” That’s the lazy, lower level, outcome mindset.
The more sophisticated meta level is, how did we as a group arrive at the decision? If you use that as an opportunity to analyze your decision-making system inside a company, you can gain far more leverage. If you want to maintain this delicate balance between loonshots and franchises, you want to understand the process by which you make those decisions. Key to the success of that system is maintaining life at the edge—maintaining balance between these two groups. To maintain life on the edge, on the cusp of the phase transition, you need to be constantly probing your system.
Jennifer Ouellette is a senior writer at Ars Technica with a particular focus on where science meets culture, covering everything from physics and related interdisciplinary topics to her favorite films and TV series. Jennifer lives in Los Angeles.
The focus of this paper is observation, self-observation, and enactive management of organizational conflict situations whereby a community, an organization, or a human being has the possibility of recognizing their resources and generating changes in their practices if they so desire, and making fresh decisions, in the sense that different ontological dimensions are involved. We show how considering Body¹- Language- Emotions- History- Eros- Silence can configure a nurturing technology call CLEHES. This tool has been applied for diverse people, groups, communities, and organizations that need and wish to develop their own skills to inquire conflict practice resolutions, in order to learn as a human decision support system. Conflict situations are understood as interactions, a breakdown in-between CLEHES from the individual or social standpoints. This tool allows observing the boundaries of conflict situations and building an observer system with the ability to manage, solve, or attenuate the situation, enabling fresh decision-making attending to the context in which the organization moves. This learning process happens in a constructed place called an Enactive Laboratory where strategies are developed to cope with the domains and context in the perceived individual and human activities systems. We present a case study focusing on a Learning Family Mediators System.
1Philosophy of Science Graduate Program, National Autonomous University of Mexico (UNAM), Mexico City, Mexico
2Institute for Philosophical Research (IIF), National Autonomous University of Mexico (UNAM), Mexico City, Mexico
3Institute for Applied Mathematics and Systems Research (IIMAS), National Autonomous University of Mexico (UNAM), Mexico City, Mexico
4Center for the Sciences of Complexity (C3), UNAM, Mexico City, Mexico
Habits are the topic of a venerable history of research that extends back to antiquity, yet they were originally disregarded by the cognitive sciences. They started to become the focus of interdisciplinary research in the 1990s, but since then there has been a stalemate between those who approach habits as a kind of bodily automatism or as a kind of mindful action. This implicit mind-body dualism is ready to be overcome with the rise of interest in embodied, embedded, extended, and enactive (4E) cognition. We review the enactive approach and highlight how it moves beyond the traditional stalemate by integrating both autonomy and sense-making into its theory of agency. It defines a habit as an adaptive, precarious, and self-sustaining network of neural, bodily, and interactive processes that generate dynamical sensorimotor patterns. Habits constitute a central source of normativity for the agent. We identify a potential shortcoming of this enactive account with respect to bad habits, since self-maintenance of a habit would always be intrinsically good. Nevertheless, this is only a problem if, following the mainstream perspective on habits, we treat habits as isolated modules. The enactive approach replaces this atomism with a view of habits as constituting an interdependent whole on whose overall viability the individual habits depend. Accordingly, we propose to define a bad habit as one whose expression, while positive for itself, significantly impairs a person’s well-being by overruling the expression of other situationally relevant habits. We conclude by considering implications of this concept of bad habit for psychological and psychiatric research, particularly with respect to addiction research.
To quote from https://meaningness.com/fluidity-preview/comments:
[I distinguish] three sources of “nebulosity”: linguistic ambiguity, epistemological uncertainty, and ontological indefiniteness. The first two are “problems in the map” and the third is “problems in the territory.”
Generally, it seems rationalism tries to deal with the map problems, and ignores the territory problems. (The “Guide to Words” is about linguistic ambiguity; and Bayes/decision theory/etc. are about epistemological uncertainty.)
“Fluidity” or “meta-rationality” is about territory “problems.” That is, the world is inherently fluid/mushy/vague, independent of any being’s beliefs about it.
I don’t know of any discussion by rationalists of ontological indefiniteness. The unstated background assumption seems to be that the world is perfectly well-behaved: facts are definitely true or false. It is just stuff in our brains (language and beliefs) that are imperfect.
Ontological remodelling – it’s not just that ‘the map is not the territory’, it’s that the territory is inherently fluid/mushy/vague: https://meaningness.com/eggplant/remodeling
How do we know? https://meaningness.com/metablog/meta-systematic-judgement
Nebulosity and pattern: https://meaningness.com/monism-dualism-recursion
Monism and dualism are opposites. But because each is obviously wrong, each turns into the other when cornered. A devious trick!
Monism is the stance that fixates sameness and connections, and denies differences and boundaries. Dualism is just the other way around: it denies sameness and connections, and fixates differences and boundaries.
Both these confused stances sometimes show themselves to be obviously wrong. The complete stance of participation recognizes that samenesses and differences, boundaries and connections, are all real, but also always somewhat nebulous: ambiguous and fluid. This is obviously accurate, but usually less convenient. Monism and dualism are simpler, and deliver particular emotional payoffs—some of the time.
not eternalism or nihilism but meaningness, not monism or dualism but participation, not causality or chaos but flow: https://meaningness.com/all-dimensions-schematic-overview
Pattern: https://meaningness.com/pattern
Nebulosity: https://meaningness.com/nebulosity
Terminology: emptiness and form, nebulosity and pattern: https://meaningness.com/terminology/emptiness-form-nebulosity-pattern
Pattern and nebulosity on the Deconstructing Yourself podcast: https://meaningness.com/metablog/deconstructing-yourself-6
This post sketches a hypothetical curriculum for developing these meta-systematic capabilities. It’s preliminary; perhaps even premature. There is no existing presentation of this subject that I know of, which makes it more difficult than it should be. My understanding of the topic draws on a dozen academic disciplines, each written in its own unnecessarily obscure code. Both my understanding, and the pedagogical structure I’m proposing, are tentative and incomplete.
Partly this presentation hopes to inspire some readers to pursue meta-systematicity; partly it is a plan for a large project that I hope to pursue myself; partly I hope you will give feedback, make suggestions, or contribute ideas to the project too!
Goal and audience
The overall goal is to take you from systematic rationality to meta-rationality as quickly and painlessly as possible. The curriculum should re-present insights I’ve found in many semi-relevant fields, as clearly and simply as possible, in STEM-friendly terms, in a structured, sequential format.
Learning meta-systematic skills shouldn’t be so hard, and meta-systematic understanding is particularly valuable in STEM. It is inherently somewhat conceptually difficult; but probably not as difficult as, say, senior-year undergraduate physics. However, it does have cognitive prerequisites.
This curriculum is for people who have mastered systematic rationality, specifically in a STEM framework. For the most part, you have to have a thorough understanding of how to work within systems before it’s feasible to step up and out of them, to manipulate them from above. There are other routes to mastering systematic rationality—through experience as a manager in a bureaucratic organization, for instance—but this curriculum will assume a STEM background.
The minimum requirement might be an undergraduate STEM degree; but research experience at the graduate level may be needed. You have to have seen how many different systems work, and—more importantly—how they fail. At the undergraduate level, you are mainly shielded from the failures, and systems get presented as though they were Absolute Truth. Or, at least, they are taught as though Absolute Truth lurks somewhere in the vicinity, obscured only by complex details. Recognizing that there is no Absolute Truth anywhere is a small downpayment on the price of entry to meta-systematicity.
That may already have set off warning bells. Woomeisters and postmodernists say things like that—and if you think they are horribly wrong, I agree!
This curriculum is about how to do STEM better. It is not about taking you out of a STEM worldview into some alternative. Everything here is on top of that view. It addresses limitations in the way STEM is typically taught and practiced, but does not contradict any of its content. There is no woo involved—including no STEM-flavored woo, such as neurobabble or quantum or Gödel woo.
In fact, a critical step is letting go of some of STEM’s own woo—quasi-religious beliefs about the ability of rationality to deliver certainty, understanding, and control. For that letting-go, the meta-systematic mode demands that one develop an additional cognitive style. Routine STEM is easy for those who are precise and rigid of mind, and so find promises of certainty, understanding, and control particularly comforting. Meta-systematicity requires openness, flexibility, daring, and uncommonly realistic common sense—as well as technical precision.
I’ll begin with some preliminary definitions, and provide a brief overview of the curriculum. Then most of the page goes through the syllabus, organized into ten modules, in more detail. That is still just a summary, which may be difficult to make sense of on its own. I’ve included in it links to resources that provide more explanation; some of my own web pages, and articles and books by others. At this stage in the project, even these leave many holes, which I hope to fill gradually. Many of the books are seriously difficult reading; the hypothetical curriculum would extract and explain clearly their relevant points.
Some loose definitions
By system, I mean, roughly, a collection of related concepts and rules that can be printed in a book of less than 10kg and followed consciously. A rational system is one that is “good” in some way. There are many different conceptions of what makes a system rational. Logical consistency is one; decision-theoretic criteria can form another. The details don’t matter here, because we are going to take rationality for granted.
Meta-systematic cognition is reasoning about, and acting on, systems from outside them, without using a system to do so. (Reasoning about systems using another system is systematic, and meta, but not “meta-systematic” in this sense.1) Meta-rationality, then, is “good” meta-systematic cognition. Mostly I use the terms interchangeably.
One field I draw on is the empirical psychology of adult development, as investigated by Robert Kegan particularly. This framework describes systematic rationality as stage 4 in the developmental path. Stage 5 is meta-systematic. However, as far as I know, no one from this discipline has applied the stage theory to STEM competence specifically. Empirical study of cognitive development in graduate-level STEM students would be helpful,2 but in the absence of that I’m working from a combination of first principles, bits of theory taken from many apparently-unrelated disciplines, anecdata, and personal experience.
According to this framework, there is also a stage 4.5, in which you lose the quasi-religious belief in systems, but haven’t yet developed the meta-systematic understanding that can replace blind faith. Stage 4.5 leaves you vulnerable to nihilism, including ontological despair (nothing seems true), epistemological anxiety (nothing seems knowable), and existential depression (nothing seems meaningful). It’s common to get stuck at 4.5, which is awful.
Abstract “Critical Systems Heuristics,” also just called “Critical Heuristics” or “CSH,” is a framework for reflective practice based on practical philosophy and systems thinking. The basic idea of CSH is to support boundary critique – a systematic effort of handling boundary judgments critically. Boundary judgments determine which empirical observations and value considerations count as relevant and which others are left out or are considered less important. Because they condition both “facts” and “values,” boundary judgments play an essential role when it comes to assessing the meaning and merits of a claim. Their systematic discussion can help bridge differences of perspectives across disciplines and between experts and non-experts. They also lend themselves to a specific critical employment, calledemancipatory boundary critique, against claims that do not uncover their underlying boundary assumptions. CSH can thus serve as a tool for coproducing knowledge as well as for critical and emancipatory purposes on the part of people concerned by, but not necessarily involved in, the definition of relevant facts and values.
Critical systems heuristics (Ulrich 1983) represents the first systematic attempt at providing both a philosophical foundation and a practical framework for critical systems thinking. The Greek verb heurisk-ein means to find or to discover; heuristics is the art (or practice) of discovery. In management science and other applied disciplines, heuristic procedures serve to identify and explore relevant problem aspects, questions, or solution strategies, in distinction to deductive (algorithmic) procedures, which serve to solve problems that are logically and mathematically well defined. Professional practice cannot do without heuristics, as it usually starts from ‘soft’ (ill-defined, qualitative) issues such as what is the problem to be solved and what kind of change would represent an improvement.
A critical approach is required since there is no single right way to decide such issues; answers will depend on personal interests and views, value assumptions, and so on. A critical approach does not yield any single right answers either; but it can support processes of reflection and debate about alternative assumptions. Sound professional practice is critical practice.
CSH aims to support reflective professional practice through a critical employment of the systems idea. The methodological core idea is that all problem definitions, proposals for improvement, and evaluations of outcomes depend on prior judgments about the relevant whole system to be looked at. Improvement, for instance, is an eminently systemic concept, for unless it is defined with reference to the entire relevant system, suboptimization will occur. CSH calls these underpinning judgments boundary judgments, as they define the boundaries of the reference system (the situation or context considered relevant) to which a proposition refers and for which it is valid.
Accordingly, the methodological core idea of CSH is to support systematic processes of boundary critique. To this end, CSH offers (among other concepts) a table of boundary categories (Figure 1) that translates into a checklist of twelve critical boundary questions (Ulrich 1987, 1996, 2000). These can be used:
To identify boundary judgments systematically;
To analyze alternative reference systems for defining a problem or assessing a solution proposal; and
To challenge in a compelling way any claims to knowledge, rationality, or ‘improvement’ that rely on hidden boundary judgments or take them for granted.
The first two applications are basic for dealing with multiple perspectives in basically cooperative settings. They can help people understand why in respect to one and the same situation, their considerations of “fact” and “value” differ. They can thus help to bridge such differences or at least, to promote mutual understanding and cooperation in handling them. The third application, by contrast, leads to an emancipatory employment of systems thinking called emancipatory boundary critique. It offers both those involved in and those affected by professional practice an opportunity to develop a new kind of critical competence, a competence that will not depend on any special theoretical knowledge or expertise with respect to the problem or situation in question that would reach beyond what is available to ordinary citizens.
In short, CSH can be defined as a critical methodology for identifying and debating boundary judgements.
The model of hierarchical complexity (MHC) is a framework for scoring how complex a behavior is, such as verbal reasoning or other cognitive tasks.[1] It quantifies the order of hierarchical complexity of a task based on mathematical principles of how the information is organized, in terms of information science.[2] This model has been developed by Michael Commons and others since the 1980s.
Perspectiva is developing a new project called The Transformative Education Alliance, or TEA. A major inspiration for it is Education in a Time Between Worlds, a book by the philosopher of education Zachary Stein. Zachary is now helping Perspectiva to develop TEA. Here he talks to Caspar Henderson about major themes in the book.
Caspar Henderson: Let’s start with what you call “the central importance of education as an aspect of the global meta crisis.” Tell me something about what that means.
Zachary Stein: We’re in a situation where it seems like in order to have a viable future for humanity the challenge is one of technical and scientific problems to be solved such as the future of computing and ways to tackle environmental problems such as carbon emissions. There is some truth to this. But it’s also the case that none of those problems will be…
How to Cite the Online Version of One Family’s Story
Kerr, Michael E. “One Family’s Story: A Primer on Bowen Theory.” The Bowen Center for the Study of the Family. 2000. http://www.thebowencenter.org.
The eight concepts presented here are available in printed form on the online store. One Family’s Story: A Primer on Bowen Theory is available in single copies and at a discount for bulk purchases.
Preface
Bowen family systems theory is a theory of human behavior that views the family as an emotional unit and uses systems thinking to describe the complex interactions in the unit. It is the nature of a family that its members are intensely connected emotionally. Often people feel distant or disconnected from their families, but this is more feeling than fact. Families so profoundly affect their members’ thoughts, feelings, and actions that it often seems as if people are living under the same “emotional skin.” People solicit each other’s attention, approval, and support and react to each other’s needs, expectations, and upsets. The connectedness and reactivity make the functioning of family members interdependent. A change in one person’s functioning is predictably followed by reciprocal changes in the functioning of others. Families differ somewhat in the degree of interdependence, but it is always present to some degree.
The emotional interdependence presumably evolved to promote the cohesiveness and cooperation families require to protect, shelter, and feed their members. Heightened tension, however, can intensify these processes that promote unity and teamwork, and this can lead to problems. When family members get anxious, the anxiety can escalate by spreading infectiously among them. As anxiety goes up, the emotional connectedness of family members becomes more stressful than comforting. Eventually, one or more members feels overwhelmed, isolated, or out of control. These are the people who accommodate the most to reduce tension in others. It is a reciprocal interaction. For example, a person takes too much responsibility for the distress of others in relationship to their unrealistic expectations of him, or a person gives up too much control of his thinking and decision-making in relationship to others anxiously telling him what to do. The one who does the most accommodating literally “absorbs” system anxiety and thus is the family member most vulnerable to problems such as depression, alcoholism, affairs, or physical illness.
Dr. Murray Bowen, a psychiatrist, originated this theory and its eight interlocking concepts. He formulated the theory by using systems thinking to integrate knowledge of the human species as a product of evolution with knowledge from family research. A core assumption is that an emotional system that evolved over several billion years governs human relationship systems. People have a “thinking brain,” language, a complex psychology and culture, but people still do all the ordinary things other forms of life do. The emotional system affects most human activity and is the principal driving force in the development of clinical problems. Knowledge of how the emotional system operates in one’s family, work, and social systems reveals new and more effective options for solving problems in each of these areas.
This is a fantastic, simple, necessary, and powerful article.
It also encapsulates quite neatly my thoughts including reservations about the complexity ‘brand’. This is about purely scientific-realist level reductionist done better, at a more appropriate scale, with technical solutions. It’s about analysis which doesn’t recognise hierarchically-interacting effects or emergence. It mentions the microbiome, and differences between humans, which is good, but doesn’t even nod to the real layers of complexity in the system of human health related to food – I can understand not mentioning any of the effects of farming, supply chains, sustainability etc, since they operate at at least one remove – but the health effects of food cannot merely be reduced to interactions of molecules. Context, environment, epigenetics, attitude of mind, exercise are all a completely interacting part of the picture.
Maybe all that just isn’t appropriate for this kind of article, maybe that’s just the way science has to work – and the final long paragraph does suggest it’s not far from the authors minds:
To appreciate the transformative potential of a deeper quantitative understanding of the nutritional dark matter, we must realize that our genetic predispositions to specific phenotypes and pathophenotypes can conceivably be modified by these food-based molecules. Indeed, while we cannot currently change the genetic basis for disease, we regularly modulate the activity of our subcellular networks through the food we eat, diminishing the impact of some mutations and enhancing the role of others. This differential modulation of subcellular networks explains why individuals with strong genetic predispositions to heart disease can lower the chance of developing the disease by up to 70% with proper lifestyle choices56, within which dietary changes play a dominant role56,57. This finding implies that an accurate mapping of our full chemical exposure through our diet could lead to actionable information to improve health. Recent trends in nutrition research, aiming to explore the synergies, competitions and interactions among the entire matrix of what constitutes a food product, increasingly acknowledge the complexity of the problem, and the need for new tools to address it58. We must embrace this irreducible complexity to be able to integrate changes in the food supply, the role of the microbiome and personalized dietary patterns, so that we can eventually offer individually tailored food-based therapies and appropriate lifestyle choices for disease prevention and lifespan optimization.
But I can’t help having a ‘so near, and yet so far’ response to this. It’s great, it’s essential, but it isn’t enough, it isn’t systemic enough, and it risks closing eyes to true complexity.
One of my highlights of 2019 was the putting together of a what is beginning to look like a mathematical theory of evolutionary biology, with John Torday of UCLA, Peter Rowlands in Liverpool university, using the work Loet Leydesdorff and Daniel Dubois on anticipatory systems. The downside of 2019 has been that things have seemed to fall apart – “all coherence gone” as John Donne put it at the beginning of the scientific revolution (in “An Anatomy of the world”):
And new philosophy calls all in doubt,
The element of fire is quite put out,
The sun is lost, and th’earth, and no man’s wit
Can well direct him where to look for it.
And freely men confess that this world’s spent,
When in the planets and the firmament
They seek so many new; they see that this
Is crumbled out again to his atomies.
‘Tis all in pieces, all coherence gone,
All just supply, and all relation;
Prince, subject, father, son, are things forgot,
For every man alone thinks he hath got
To be a phoenix, and that then can be
None of that kind, of which he is, but he.
The keyword in all of this (and a word which got me into trouble this year because people didn’t understand it) is “Coherence”. Coherence, fundamentally, is a mathematical idea belonging to fractals and self-referential systems. It is through coherence that systems can anticipate future changes to their environment and adapt appropriately, and the fundamental driver for this capacity is the creation of fractal structures, which by definition, are self-similar at different scales.
In work I’ve done on music this year with Leydesdorff, this coherent anticipatory model combines both synchronic (structural) and diachronic (time-based) events into a single pattern. This is in line with the physics of David Bohm, but it also coincides with the physics of Peter Rowlands.
When people talk of a “mathematical theory” we tend to think of something deterministic, or calculative. But this is not at all why maths is important (indeed it is a misunderstanding). Maths is important because it is a richly generative product of human consciousness which provides consciousness with tangible natural phenomena upon which its presuppositions can be explored and developed. It is a search for abstract principles which are generative not only of biological or social phenomena, but of our narrative capacities for accounting for them and our empirical faculties for testing them. Consciousness is entangled with evolutionary biology, and logical abstraction is the purest product of consciousness we can conceive. In its most abstract form, an evolutionary biology or a theory of learning must be mathematical, generative and predictive. In other words, we can use maths to explore the fundamental circularity existing between mind and nature, and this circularity extends beyond biology, to phenomena of education, institutional organisation and human relations.
When human organisations, human relations, learning conversations, artworks, stories or architectural spaces “work”, they exhibit coherence between their structural and temporal relations with an observer. “Not working” is the label we give to something which manifests itself as incoherent. This coherence is at a deep level: it is fractal in the sense that the pattern expressed by these things are recapitulations of deeper patterns that exist in cells and in atoms.
These fractal patterns exist between the “dancing” variables involved in multiple perceptions – what Alfred Schutz called a “spectrum of vividness” of perception. The dancing of observed variables may have a similar structure to deeper patterns within biology or physics, and data processing can allow some glimpse into what these patterns might look like.
Fractal structures can immediately be seen to exhibit coherence or disorder. Different variables may be tried within the structure to see which displays the deepest coherence. When we look for the “sense” or “meaning” of things, it is a search for those variables, and those models which produce a sense of coherence. It is as true for spiritual practice as it is for practical things like learning (and indeed those things are related).
2019 has been a deeply incoherent year – both for me personally, and for the world. Incoherence is a spur to finding a deeper coherence. I doubt that we will find it by doing more of the same stuff. What is required is a new level of pattern-making, which recapitulates the deeper patterns of existence that will gradually bring things back into order.
A quick editorial note — lately, I’ve been referring to my work as ‘structural memetics’ — with the intent of expanding a concept of knowledge generation with memes along the same line as genetics — laying out general principles to follow about how humans generate knowledge. Much of this material has already been created on this blog, but I wanted to consolidate and summarize it in one place.
Bored, and seeking the never-ending references, I Googled up Melvin Conway, whose famous law serves as the backbone for most of my developed insights. Turns out he’s still alive — and on Twitter. So.. I tweeted back at him. And he responded, saying he’d take a look at my work.
Short version of a longer story — I hurried up with this post so he wouldn’t have to dig. I think it’s pretty complete. So…
Thanks to a tweet from @pezeshkicharles (also https://empathy.guru/) for reminding me of this piece, via posting the ‘Big Think’ article at https://bigthink.com/ideafeed/mit-physicist-proposes-new-meaning-of-life (I don’t recommend you click on this, loads of adware etc and a promised video which I can’t get).
“This is (a) Alan Watts’ ‘ah yes, the rock in space is peopling’ (like a tree ‘fruits’), and (b) I *think* it’s the piece I’ve been trying to re-find for some time which essentially makes the point that negentropy / complexity is actually a mechanism of longer-term entropy.
“(And we must remember that the unfolding complexity is essentially ‘hierarchical’ as well as networked in nature)”
While the statement ‘everything that develops in nature is necessarily in the nature of nature’ might seem a truism of the ‘Eureka! You’ve discovered water’ type, it’s the kind of thing I get excited about.
Content I took from the BigThink piece when I originally posted this at https://model.report/s/4eliay/mit_physicist_proposes_new_meaning_of_life_big_think
MIT Physicist Proposes New “Meaning of Life”
MIT physicist Jeremy England claims that life may not be so mysterious after all, despite the fact it is apparently derived from non-living matter. In a new paper, England explains how simple physical laws make complex life more likely than not. In other words, it would be more surprising to find no life in the universe than a buzzing place like planet Earth.
What does all matter—rocks, plants, animals, and humans—have in common? We all absorb and dissipate energy. While a rock absorbs a small amount of energy before releasing what it doesn’t use back into the universe, life takes in more energy and releases less. This makes life better at redistributing energy, and the process of converting and dissipating energy is simply a fundamental characteristic of the universe.
[S]imple physical laws make complex life more likely than not.
According to England, the second law of thermodynamics gives life its meaning. The law states that entropy, i.e. decay, will continuously increase. Imagine a hot cup of coffee sitting at room temperature. Eventually, the cup of coffee will reach room temperature and stay there: its energy will have dissipated. Now imagine molecules swimming in a warm primordial ocean. England claims that matter will slowly but inevitably reorganize itself into forms that better dissipate the warm oceanic energy.
[T]he second law of thermodynamics gives life its meaning.
The strength of England’s theory is that it provides an underlying physical basis for Darwin’s theory of evolution and helps explain some evolutionary tendencies that evolution cannot. Adaptations that don’t clearly benefit a species in terms of survivability can be explained thusly: “the reason that an organism shows characteristic X rather than Y may not be because X is more fit than Y, but because physical constraints make it easier for X to evolve than for Y to evolve.”
Osvaldo García De la Cerda
Patrick Christie Humphreys
Maria Saavedra
Susana Ramírez-Vizcaya
Tom Froese
Below Kanab Creek, Grand Canyon, 2003
Systems Community of Inquiry 
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