Monthly Archives: April 2019
Adaptive social learning for systemic leadership – Catherine Hobbs
Integration and Implementation Insights
Community member post by Catherine Hobbs
Catherine Hobbs (biography)
What’s involved in developing human capacity to address complexity, taking a mid- to longer-term viewpoint than is usual? How can we create the conditions in which people can cope with the daily challenges of living in a complex world and flourish? What form of leadership is required to inspire and catalyse this transformation?
Framework for adaptive social learning
The need for systems thinking is often referred to, but rarely considered, as a rich and comprehensive resource which could be developed further and applied. A critical systems thinking approach suggests that a variety of approaches should be drawn upon, in a manner of methodological pluralism, being aware of the strengths and weaknesses of different approaches and applying them adaptively using synthesis as well as analysis.
In the spirit of such an approach, I’ve developed a learning pathway for systemic…
View original post 782 more words
Mind, Body, Quantum Mechanics – Stuart Kauffman, April 2019
I’d be interested in opinions on this! V good or has he gone ‘late career’ and mystical?! 😀
Source: Mind, Body, Quantum Mechanics | SpringerLink
Mind, Body, Quantum Mechanics
Abstract
I discuss the following: The causal closure of classical physics implies that consciousness in a classical physics brain can at best be epiphenomenal. Quantum mechanics can break the causal closure of classical physics in two ways: measurement and a newly discovered Poised Realm. Conscious experience may be associated with quantum measurement. Here quantum mind has acausal consequences for the classical brain. I propose genetic experiments to test this. Entanglement may solve the “binding problem.” I believe these proposals unite mind and body in a new way and answer Descartes after 350 years of the Stalemate introduced by his dualism of Res cogitans and Res extensa.
Keywords
Causal closure Quantum mechanics Poised realm Mind body
1960: The Year The Singularity Was Cancelled | Slate Star Codex
There should be more cybernetics on Slate Star Codex; as with Meaningness, it seems like a good fit. Look how many comments in a single day!
Source: 1960: The Year The Singularity Was Cancelled | Slate Star Codex
1960: THE YEAR THE SINGULARITY WAS CANCELLED
[Epistemic status: Very speculative, especially Parts 3 and 4. Like many good things, this post is based on a conversation with Paul Christiano; most of the good ideas are his, any errors are mine.]
I.
In the 1950s, an Austrian scientist discovered a series of equations that he claimed could model history. They matched past data with startling accuracy. But when extended into the future, they predicted the world would end on November 13, 2026.
This sounds like the plot of a sci-fi book. But it’s also the story of Heinz von Foerster, a mid-century physicist, cybernetician, cognitive scientist, and philosopher.
His problems started when he became interested in human population dynamics.
(the rest of this section is loosely adapted from his Science paper “Doomsday: Friday, 13 November, A.D. 2026”)
Assume a perfect paradisiacal Garden of Eden with infinite resources. Start with two people – Adam and Eve – and assume the population doubles every generation. In the second generation there are 4 people; in the third, 8. This is that old riddle about the grains of rice on the chessboard again. By the 64th generation (ie after about 1500 years) there will be 18,446,744,073,709,551,616 people – ie about about a billion times the number of people who have ever lived in all the eons of human history. So one of our assumptions must be wrong. Probably it’s the one about the perfect paradise with unlimited resources.
Okay, new plan. Assume a world with a limited food supply / limited carrying capacity. If you want, imagine it as an island where everyone eats coconuts. But there are only enough coconuts to support 100 people. If the population reproduces beyond 100 people, some of them will starve, until they’re back at 100 people. In the second generation, there are 100 people. In the third generation, still 100 people. And so on to infinity. Here the population never grows at all. But that doesn’t match real life either.
But von Foerster knew that technological advance can change the carrying capacity of an area of land. If our hypothetical islanders discover new coconut-tree-farming techniques, they may be able to get twice as much food, increasing the maximum population to 200. If they learn to fish, they might open up entirely new realms of food production, increasing population into the thousands.
So the rate of population growth is neither the double-per-generation of a perfect paradise, nor the zero-per-generation of a stagnant island. Rather, it depends on the rate of economic and technological growth. In particular, in a closed system that is already at its carrying capacity and with zero marginal return to extra labor, population growth equals productivity growth.
What causes productivity growth? Technological advance. What causes technological advance? Lots of things, but von Foerster’s model reduced it to one: people. Each person has a certain percent chance of coming up with a new discovery that improves the economy, so productivity growth will be a function of population.
So in the model, the first generation will come up with some small number of technological advances. This allows them to spawn a slightly bigger second generation. This new slightly larger population will generate slightly more technological advances. So each generation, the population will grow at a slightly faster rate than the generation before.
This matches reality. The world population barely increased at all in the millennium from 2000 BC to 1000 BC. But it doubled in the fifty years from 1910 to 1960. In fact, using his model, von Foerster was able to come up with an equation that predicted the population near-perfectly from the Stone Age until his own day.
But his equations corresponded to something called hyperbolic growth. In hyperbolic growth, a feedback cycle – in this case population causes technology causes more population causes more technology – leads to growth increasing rapidly and finally shooting to infinity. Imagine a simplified version of Foerster’s system where the world starts with 100 million people in 1 AD and a doubling time of 1000 years, and the doubling time decreases by half after each doubling. It might predict something like this:
1 AD: 100 million people
1000 AD: 200 million people
1500 AD: 400 million people
1750 AD: 800 million people
1875 AD: 1600 million people
…and so on. This system reaches infinite population in finite time (ie before the year 2000). The real model that von Foerster got after analyzing real population growth was pretty similar to this, except that it reached infinite population in 2026, give or take a few years (his pinpointing of Friday November 13 was mostly a joke; the equations were not really that precise).
What went wrong? Two things.
First, as von Foerster knew (again, it
Continues in source: 1960: The Year The Singularity Was Cancelled | Slate Star Codex
Taxonomies of the unknown – Kerwin’s Map of Ignorance (1983)
Along with many other ignorance taxonomies in the first link. H/t David Ing, I came to this from his ISSS Presidential inaugural presentation, just linked.
Main source: Taxonomies of the unknown [Andreas’ Notes]
Other sources:
http://web.archive.org/web/20120310033139/https://ignorance.medicine.arizona.edu/ignorance.html
https://www.researchgate.net/publication/323585599_From_Ignorance_Map_to_Informing_PKM4E_Framework_Personal_Knowledge_Management_for_Empowerment/figures?lo=1
Taxonomies of the unknown
A compilation with references of some classifications, systematics and other orders of what is not known.
The Map of Ignorance (Kerwin, 1983-)
Domains of Ignorance
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Known Unknowns: All the things you know you don’t know
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Unknown Unknowns: All the things you don’t know you don’t know
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Errors: All the things you think you know but don’t
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Unknown Knowns: All the things you don’t know you know
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Taboos: Dangerous, polluting or forbidden knowledge
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Denials: All the things too painful to know, so you don’t
By Ann Kerwin and Marlys Witte (Q-cubed Programs: What Is Ignorance?). According to Ann Kerwin the Map of Ignorance was developed by her circa 1983. It has later been presented in 1985 and 1986 together with Marlys Witte.
This little map has traveled the globe. On its clones have scribbled Nobel Laureates, U.N. delegates, educators, physicians, artists, students, politicians, inventors, scientists, poets and ponderers from many walks of life. It’s just a prop, a cosmic swerve, a silly prompt for exploration and celebration of the fertile home territory of learning. (Ann Kerwin)
References
Ann Kerwin: None Too Solid. Medical Ignorance. Knowledge: Creation, Diffusion, Utilization 15 (December 1993) 2: 166–185.
Abstract
Our ignorance encompasses, at least, all the things we know we do not know (known unknowns); all the things we do not know we do not know (unknown unknowns); all the things we think we know but do not (error); all the things we do not know we know (tacit knowing); all taboos (forbidden knowledge); and all denial (things to painful to know, so we suppress them). Medical ignorance seems especially threatening to many of us. If, however, we are to cope with our vast ignorance of the human body, its powers and processes, we must learn to acknowledge our nescience and optimize it. To do so, we need to rethink the nature and interrelations between knowledge and ignorance. We need to expand our capacities for self-learning and refine abilities to map our complex experience.
Recommended reading
Ann Kerwin: On no other planet. 2 essays, 47 pages.
MORE in source: Taxonomies of the unknown [Andreas’ Notes]
2011/07/22 ISSS Incoming Presidential Address | Coevolving Innovations – David Ing
A really brilliant meta-overview of some key issues in and about systems thinking from syscoi.com co-host David Ing (from 2011)
Source: 2011/07/22 ISSS Incoming Presidential Address | Coevolving Innovations
2011/07/22 ISSS Incoming Presidential Address
Service Systems, Natural Systems: Sciences in Synthesis — An Outline for a Presidential Address
David Ing, International Society for the Systems Sciences, President, 2011-2012
isss@daviding.com
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This written outline is a complement to the presentation slides presented in the incoming presidential address at 55th Annual Meeting of the ISSS on July 22, 2011. Leading up to the 56th Annual Meeting scheduled for July 2012, members of the society are encouraged to look for towards opportunities where the systems approach can support the development of new perspectives on service science and natural science.
[jump to the presentation preview (at the bottom of the page)]
[view/download the presentation slides as ODP] (3 MB)
[view/download the presentation slides as PDF] (2.2 MB)
jump to part [1 on the systems approach] [2 on service systems] [3 on natural systems] [4 on frames] [5 on learning and knowing]
0: Introduction — Synthesis across the sciences of service systems and natural systems in a systems approach is a promising way to deal with complexity in our world
As we look forward into 2012, I encourage members of the ISSS to continue the development of sciences in synthesis. Synthesis means putting things together, rather than taking them apart. Synthesis leads to emergence: properties of a whole that are not in its parts. The research communities centered on service systems and on natural systems may benefit from a synthesis through a systems approach.
This presidential address has 6 parts.
- 1. Challenges where the systems approach can make a contribution
- 2. Research into service systems
- 3. Research into natural systems
- 4. Some frames brought with a systems approach
- 5. Learning and knowing
The address concludes with a call for participation at the 56th annual meeting of the ISSS in San Jose, California, in July 2012.
Continues in source: 2011/07/22 ISSS Incoming Presidential Address | Coevolving Innovations
Workshop: Complexity Methods – Neil Johnson – YouTube
BEING EMERGENCE VS. PATTERN EMERGENCE (2019) Complexity, control and goal-directedness in biological systems – Jason Winning and William Bechtel
source (pdf) https://philpapers.org/archive/WINBEV.pdf
BEING EMERGENCE VS. PATTERN EMERGENCE (2019)
Complexity, control and goal-directedness in biological systems
Jason Winning and William Bechtel
Emergence is much discussed by both philosophers and scientists. But, as noted by Mitchell (2012), there is a significant gulf; philosophers and scientists talk past each other. We contend that this is because philosophers and scientists typically mean different things by emergence, leading us to distinguish being emergence and pattern emergence. While related to distinctions offered by others between, for example, strong/weak emergence or epistemic/ontological emergence (Clayton, 2004, pp. 9–11), we argue that the being vs. pattern distinction better captures what the two groups are addressing. In identifying pattern emergence as the central concern of scientists, however, we do not mean that pattern emergence is of no interest to philosophers. Rather, we argue that philosophers should attend to, and even contribute to, discussions of pattern emergence. But it is important that this discussion be distinguished, not conflated, with discussions of being emergence. In the following section we explicate the notion of being emergence and show how it has been the focus of many philosophical discussions, historical and contemporary. In section 3 we turn to pattern emergence, briefly presenting a few of the ways it figures in the discussions of scientists (and philosophers of science who contribute to these discussions in science). Finally, in sections 4 and 5, we consider the relevance of pattern emergence to several central topics in philosophy of biology: the emergence of complexity, of control, and of goal-directedness in biological systems
Designing with Society: A Capabilities Approach to Design, Systems Thinking and Social Innovation
Scott Boylston is professor and graduate coordinator of the Design for Sustainability program at SCAD (Savannah College of Art and Design), author of three books, and founder of Emergent Structures. I would like to congratulate to Scott on the launch of his new book Designing with Society. He introduces his book in the following way:
“This is not a design book. It’s a book for designers. Specifically, it’s a book for designers who want to aim the design dictum, “what’s next,” directly at the heart of our own practice. To do so requires an honest look at what might be holding us back. Many say the barriers we still have to transcend exist within our ability to authentically incorporate other disciplines such as anthropology and nanotechnology.
Digging deeper, however, lies the question, “for what purpose?” This question suggests we look inward before looking further outward. It requires we tap into…
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The sociocybernetics of observation and reflexivity – Bernard Scott, 2019
No open access version available yet, AFAIK
Source: The sociocybernetics of observation and reflexivity – Bernard Scott, 2019
On Becoming a Cybernetician: Highlights and Milestones: World Futures: Vol 75, No 1-2 – Bernard Scott, March 2019
Recursivity and Contingency | Yuk Hui – Academia.edu
Preface available for download
Source: (99+) (PDF) Recursivity and Contingency | Yuk Hui – Academia.edu
Cybernetics in Late Soviet Culture: • Kultur • Osteuropa-Institut, Freie Universitat Berlin, 17 June 2019
Not much detail yet!
Source: Cybernetics in Late Soviet Culture: • Kultur • Osteuropa-Institut

Cybernetics in Late Soviet Culture:
17.06.2019
Workshop: Cybernetics in Late Soviet Culture
News vom 10.04.2019
The discourse on cybernetics is one of the most inspiring and thought-provoking intellectual currents in post-war science. As a truly interdisciplinary approach linking physics, psychology, computer science, sociology, philosophy and many disciplines more, its influence is topical in current epistemological understandings of “systemic” thinking. Although the history of cybernetics has been studied for the Western and Latin American context, cybernetics in the East European context has been brought to our fore only to some extent. Our workshop on June 17th aims to address cybernetics from an interdisciplinary point of view, linking historical and philosophical approaches with insights from literary and cultural studies. It is centered on the period from the early 1950s to the late 1970s and wants to draw attention to the peculiarities of Soviet and Socialist cybernetic thinking.
Programme and speakers t.b.a.
Killer robots are not science fiction – they have been part of military defence for a while | The Independent
Killer robots are not science fiction – they have been part of military defence for a while
They are just one of the fears with developing technology, but such bots have been here for much longer than you think, writes Mike Ryder
Humans will always make the final decision on whether armed robots can shoot, according to a statement by the US Department of Defence. Their clarification comes amid fears about a new advanced targeting system, known as Atlas, that will use artificial intelligencein combat vehicles to target and execute threats. While the public may feel uneasy about so-called “killer robots”, the concept is nothing new – machine-gun wielding “Swords” robots were deployed in Iraq as early as 2007.
Our relationship with military robots goes back even further than that. This is because when people say “robot”, they can mean any technology with some form of autonomous element that allows it to perform a task without the need for direct human intervention.
These technologies have existed for a very long time. During the Second World War, the proximity fuse was developed to explode artillery shells at a predetermined distance from their target. This made the shells far more effective than they would otherwise have been by augmenting human decision making and, in some cases, taking the human out of the loop completely.
So the question is not so much whether we should use autonomous weapon systems in battle – we already use them, and they take many forms. Rather, we should focus on how we use them, why we use them, and what form – if any – human intervention should take.
The birth of cybernetics
My research explores the philosophy of human-machine relations, with a particular focus on military ethics, and the way we distinguish between humans and machines. During the Second World War, mathematician Norbert Wiener laid the groundwork of cybernetics – the study of the interface between humans, animals and machines – in his work on the control of anti-aircraft fire. By studying the deviations between an aircraft’s predicted motion, and its actual motion, Wiener and his colleague Julian Bigelow came up with the concept of the “feedback loop”, where deviations could be fed back into the system in order to correct further predictions.
Wiener’s theory therefore went far beyond mere augmentation, for cybernetic technology could be used to pre-empt human decisions – removing the fallible human from the loop, in order to make better, quicker decisions and make weapons systems more effective.
In the years since the Second World War, the computer has emerged to sit alongside cybernetic theory to form a central pillar of military thinking, from the laser-guided “smart bombs” of the Vietnam era to cruise missiles and Reaper drones.
It’s no longer enough to merely augment the human warrior as it was in the early days. The next phase is to remove the human completely – “maximising” military outcomes while minimising the political cost associated with the loss of allied lives. This has led to the widespread use of military drones by the US and its allies. While these missions are highly controversial, in political terms they have proved to be preferable by far to the public outcry caused by military deaths.
Continues in source: Killer robots are not science fiction – they have been part of military defence for a while | The Independent
Pace Layering: How Complex Systems Learn and Keep Learning – Stewart BRand
Source: Pace Layering: How Complex Systems Learn and Keep Learning
Pace Layering: How Complex Systems Learn and Keep Learning
“Civilizations with long nows look after things better,” says Brian Eno. “In those places you feel a very strong but flexible structure which is built to absorb shocks and in fact incorporate them.”
You can imagine how such a process could evolve—all civilizations suffer shocks; only the ones that absorb the shocks survive. That still doesn’t explain the mechanism.In recent years a few scientists (such as R. V. O’Neill and C. S. Holling) have been probing the same issue in ecological systems: how do they manage change, how do they absorb and incorporate shocks? The answer appears to lie in the relationship between components in a system that have different change-rates and different scales of size. Instead of breaking under stress like something brittle, these systems yield as if they were soft. Some parts respond quickly to the shock, allowing slower parts to ignore the shock and maintain their steady duties of system continuity.
Consider the differently paced components to be layers. Each layer is functionally different from the others and operates somewhat independently, but each layer influences and responds to the layers closest to it in a way that makes the whole system resilient.
From the fastest layers to the slowest layers in the system, the relationship can be described as follows:
Fast learns, slow remembers. Fast proposes, slow disposes. Fast is discontinuous, slow is continuous. Fast and small instructs slow and big by accrued innovation and by occasional revolution. Slow and big controls small and fast by constraint and constancy. Fast gets all our attention, slow has all the power.
All durable dynamic systems have this sort of structure. It is what makes them adaptable and robust.
Take a coniferous forest. The hierarchy in scale of pine needle, tree crown, patch, stand, whole forest, and biome is also a time hierarchy. The needle changes within a year, the crown over several years, the patch over many decades, the stand over a couple of centuries, the forest over a thousand years, and the biome over ten thousand years. The range of what the needle may do is constrained by the crown, which is constrained by the patch and stand, which are controlled by the forest, which is controlled by the biome. Nevertheless, innovation percolates throughout the system via evolutionary competition among lineages of individual trees dealing with the stresses of crowding, parasites, predation, and weather. Occasionally, large shocks such as fire or disease or human predation can suddenly upset the whole system, sometimes all the way down to the biome level.
The mathematician and physicist Freeman Dyson makes a similar observation about human society:
The destiny of our species is shaped by the imperatives of survival on six distinct time scales. To survive means to compete successfully on all six time scales. But the unit of survival is different at each of the six time scales. On a time scale of years, the unit is the individual. On a time scale of decades, the unit is the family. On a time scale of centuries, the unit is the tribe or nation. On a time scale of millennia, the unit is the culture. On a time scale of tens of millennia, the unit is the species. On a time scale of eons, the unit is the whole web of life on our planet. Every human being is the product of adaptation to the demands of all six time scales. That is why conflicting loyalties are deep in our nature. In order to survive, we have needed to be loyal to ourselves, to our families, to our tribes, to our cultures, to our species, to our planet. If our psychological impulses are complicated, it is because they were shaped by complicated and conflicting demands.
2
In terms of quantity, there are a great many pine needles and a great many humans, many forests and nations, only a few biomes and cultures, and but one planet. The hierarchy also underlies much of causation and explanation. On any subject, ask a four-year-old’s sequence of annoying “Why?”s five times and you get to deep structure. “Why are you married, Mommy?” “That’s how you make a family.” “Why make a family?” “It’s the only way people have found to civilize children.” “Why civilize children?” “If we didn’t, the world would be nothing but nasty gangs.” “Why?” “Because gangs can’t make farms and cities and universities.” “Why not?” “Because they don’t care about anything larger than themselves.”
I propose six significant levels of pace and size in the working structure of a robust and adaptable civilization. From fast to slow the levels are:
- Fashion/art
- Commerce
- Infrastructure
- Governance
- Culture
- Nature
In a durable society, each level is allowed to operate at its own pace, safely sustained by the slower levels below and kept invigorated by the livelier levels above. “Every form of civilization is a wise equilibrium between firm substructure and soaring liberty,” wrote the historian Eugen Rosenstock-Huessy.
Each layer must respect the different pace of the others. If commerce, for example, is allowed by governance and culture to push nature at a commercial pace, then all-supporting natural forests, fisheries, and aquifers will be lost. If governance is changed suddenly instead of gradually, you get the catastrophic French and Russian revolutions. In the Soviet Union, governance tried to ignore the constraints of culture and nature while forcing a five-year-plan infrastructure pace on commerce and art. Thus cutting itself off from both support and innovation, it was doomed.We can examine the array layer by layer, working down from the fast and attention-getting to the slow and powerful. Note that as people get older, their interests tend to migrate to the slower parts of the continuum. Culture is invisible to adolescents but a matter of great concern to elders. Adolescents are obsessed with fashion while elders are bored by it.
The job of fashion and art is to be froth—quick, irrelevant, engaging, self-preoccupied, and cruel. Try this! No, no, try this! It is culture cut free to experiment as creatively and irresponsibly as the society can bear. From all that variety comes driving energy for commerce (the annual model change in automobiles) and the occasional good idea or practice that sifts down to improve deeper levels, such as governance becoming responsive to opinion polls, or culture gradually accepting “multiculturalism” as structure instead of just entertainment.
If commerce is completely unfettered and unsupported by watchful governance and culture, it easily becomes crime, as in some nations after Communism fell. Likewise, commerce may instruct but must not control the levels below it, because it’s too short-sighted. One of the stresses of our time is the way commerce is being accelerated by global markets and the digital and network revolutions. The proper role of commerce is to both exploit and absorb those shocks, passing some of the velocity and wealth on to the development of new infrastructure, but respecting the deeper rhythms of governance and culture.
Infrastructure, essential as it is, can’t be justified in strictly commercial terms. The payback period for things such as transportation and communication systems is too long for standard investment, so you get government-guaranteed instruments like bonds or government-guaranteed monopolies. Governance and culture have to be willing to take on the huge costs and prolonged disruption of constructing sewer systems, roads, and communication systems, all the while bearing in mind the health of even slower “natural” infrastructure—water, climate, etc.
Education is intellectual infrastructure. So is science. They have very high yield, but delayed payback. Hasty societies that can’t span those delays will lose out over time to societies that can. On the other hand, cultures too hidebound to allow education to advance at infrastructural pace also lose out.
In the realm of governance, the most interesting trend in current times—besides the worldwide proliferation of democracy and the rule of law——is the rise of what is coming to be called the “social sector.” The public sector is government, the private sector is business, and the social sector is the nongovernmental, nonprofit do-good organizations. Supported by philanthropy and the toil of volunteers, they range from church charities, local land trusts, and disease support groups to global players like the International Red Cross and World Wildlife Fund. What they have in common is that they serve the larger, slower good.
The social sector acts on culture-level concerns in the domain of governance. One example is the sudden mid-20th-century dominance of “historic preservation” of buildings, pushed by organizations like the National Trust for Historic Preservation in America and English Heritage and the National Trust in Britain. Through them, culture declared that it was okay to change clothing at fashion pace, but not buildings; okay to change tenants at commercial pace, but not buildings; okay to change transportation at infrastructure pace, but not neighborhoods. “If some parts of our society are going to speed up,” the organizations seemed to say, “then other parts are going to have to slow way down, just to keep balance.” Even New York City, once the most demolition-driven metropolis in America, now is preserving its downtown.
Culture’s vast slow-motion dance keeps century and millennium time. Slower than political and economic history, it moves at the pace of language and religion. Culture is the work of whole peoples. In Asia you surrender to culture when you leave the city and hike back into the mountains, traveling back in time into remote village culture, where change is century-paced. In Europe you can see it in terminology, where the names of months (governance) have varied radically since 1500, but the names of signs of the Zodiac (culture) are unchanged in millennia. Europe’s most intractable wars have been religious wars.
As for nature, its vast power, inexorable and implacable, just keeps surprising us. The world’s first empire, the Akkadian in the Tigris-Euphrates valley, lasted only a hundred years, from 2300 BCE to 2200 BCE. It was wiped out by a drought that went on for three hundred years. Europe’s first empire, the Minoan civilization, fell to earthquakes and a volcanic eruption in the 15th century BCE. When we disturb nature at its own scale, such as with our “extinction engine” and greenhouse gases, we risk triggering apocalyptic forces. Like it or not, we have to comprehend and engage the longest now of nature.
The division of powers among the layers of civilization lets us relax about a few of our worries. We don’t have to deplore technology and business changing rapidly while government controls, cultural mores, and “wisdom” change slowly. That’s their job. Also, we don’t have to fear destabilizing positive-feedback loops (such as the Singularity) crashing the whole system. Such disruption can usually be isolated and absorbed. The total effect of the pace layers is that they provide a many-leveled corrective, stabilizing feedback throughout the system. It is precisely in the apparent contradictions between the pace layers that civilization finds its surest health.
Acknowledgements
The idea of pace layering has a history. The text above is a slightly edited version of a chapter in my 1999 book The Clock of the Long Now: Time and Responsibility. I first created the healthy-civilization diagram with Brian Eno at his studio in London in 1996. Earlier still, in the early 1970s, the English architect Frank Duffy wrote, “A building properly conceived is several layers of longevity of built components.” He identified four layers in commercial buildings—Shell (lasts maybe 50 years), Services (swapped out every 15 years or so), Scenery (interior walls, etc. move every 5 to 7 years), and Set (furniture, moving sometimes monthly.) For my 1994 book How Buildings Learn: What Happens After They’re Built I expanded Duffy’s four layers to six: Site, Structure, Skin, Services, Space Plan, and Stuff. The chapter on how the components play out in a healthy building I titled “Shearing Layers.” Some reviewers of the book on Amazon claim that How Buildings Learn is really about software and systems design.







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