Recursivity and Contingency | Yuk Hui – Academia.edu 

Preface available for download

 

Source: (99+) (PDF) Recursivity and Contingency | Yuk Hui – Academia.edu

 

Recursivity and Contingency
2019
Yuk Hui
Yuk Hui

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

 

Source: 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

Killer robots may seem new, but they've been around for a long time
Killer robots may seem new, but they’ve been around for a long time ( Getty/iStock )

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.

Military drones are widely used by the US and its allies (Getty Images)

 

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

Pace layers provide many-leveled corrective, stabilizing feedback throughout the system. It is in the contradictions between these layers that civilization finds its surest health. I propose six significant levels of pace and size in a robust and adaptable civilization.
Updated Feb 04, 2018 (1 Older Version)
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.”

1

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
The order of a healthy civilization.  The fast layers innovate; the slow layers stabilize.  The whole combines learning with continuity.
The order of a healthy civilization.  The fast layers innovate; the slow layers stabilize.  The whole combines learning with continuity.

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.

3

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.

This is the diagram on which How Buildings Learn (1994) is based.
This is the diagram on which How Buildings Learn (1994) is based.

Interactions and complexity – Gerry McGovern

 

Source: Interactions and complexity – Gerry McGovern – Customer experience keynote speaker; user experience keynote speaker

 

Interactions and complexity

“Networks are an essential ingredient in any complex adaptive system,” Eric Beinhocker writes in The Origin of Wealth. “Without interactions between agents, there can be no complexity.”

Think of a printed page in a book for a moment. It may contain complex ideas, but it is relatively simple. It is hard-linked to the page that came before and the one after. It may contain references which leads you to an appendix. However, in many ways, it is what you may call “finished” or “published”.

Webpages have a whole other level of potential. They may in fact be just like that print page if an organization has merely digitized print content and uploaded it on the Web. However, it is usually surrounded by an architecture of links. Sometimes, it may contain live data that gets updated as and when change occurs. Sometimes, it will change based on an action by the one accessing it. That is the true Web. And when it is at its most powerful, it is also at its most complex.

However, this is generally a hidden complexity. Think of Google. It couldn’t be simpler to use: a search box and a click. Its interface has become even more minimal over the years. Up until sometime in 2015, it had a link beside the search box that read, “Advanced Search.” It no longer has that link, even though, year after year, the search algorithm has been refined to become more and more complex.

Google removed the link “Advanced Search” simply because most people didn’t want to do an “advanced” search. “Why, little old me, I’d never be able to do any advanced search. Sounds very complex. Do you need a degree for that?”

Google realized it needed to perform the advanced and complex work for the customer. Thus, instead of the customer going to the advanced section and selecting an option that indicated they wanted to conduct geographical searches, Google sought to identify words in their search behavior that would indicate whether they were searching for a place rather than a thing or person. If Google noticed such “geographic” words, it would include a map in its search results.

We all want the fruits of complexity. Only a few want to undertake the labors of complexity to make things simpler for others. If you consider the history of retail – from barter to Amazon – we see an inexorable shift of complexity away from the buyer and towards the seller. The seller is constantly and relentlessly making it easier to purchase because sellers have done the math. They understand the return on investment when investing in simplicity.

Most other organizations do not invest in simplicity. They constantly calculate the costs of complexity they will incur, ignoring the returns on simplicity. They seek the cheapest ways to get stuff up on the Web (which is why we see so much digitized print content in the form of bulky PDFs on websites).

The Web is not print. It’s a much more complex, networked environment. Those who are investing in complexity, while also investing in simplifying interactions with such complex systems, are reaping the bountiful rewards.

Online Courses – CC Modeling Systems

Paid courses for teachers (I think)

Source: Online Courses – CC Modeling Systems

 

Online Courses


REGISTER NOW

1st Course in System Dynamics Modeling:
Basic Models

(3 Graduate CE Credits, option)

The first online course in the sequence of System Dynamics (SD) modeling courses is intended for any instructor who does not have experience with SD modeling. The participant will learn to build the models presented using lessons that they could adapt for their own students. The model-building lessons have been used in math and/or science classes, some with students as young as 15 years.  The course is asynchronous.

Prerequisites: 1. Comfort with basic secondary school algebra concepts;  2. Comfort with the content of the course being taught by the participant (within which model-building activities will be added); 3. An interest in expanding the hands-on experiences of the participants’ students; 4. An interest in learning the value that System Dynamics modeling can bring to the learning environment.

The sequence of session topics are listed below:

  • Session 1:  Digital Communication Tools
  • Session 2:  Analyzing Simple Generic Behavior Characteristics
  • Session 3:  Building Simple Models
  • Session 4:  Learning about Feedback and the Importance of Unit Consistency
  • Session 5:  Building Drug Models
  • Session 6:  Building an Epidemic Model
  • Session 7:  Building Models that Produce Oscillations
  • Session 8:  Building Rocket Models and Models for other Trajectories
  • Session 9:  Constructing Your First Model-Building Lessons
  • Session 10:  National Curriculum Standards and Creating Assessments

Each session is divided into sections that involve exploring the new concepts, practicing the new concepts, an assessment, reading and discussion questions, and numerous web resources.  (Maximum 15 participants.)


REGISTER NOW

2nd Course in System Dynamics Modeling:
More Advanced Models

(4 Graduate CE Credits, option)

The second online course in the sequence of System Dynamics (SD) modeling courses is intended for instructors who want to go the next step, building models dealing with more advanced system dynamics concepts. The participant will learn to build the models presented using lessons that they could adapt for their own students. The model-building lessons have been used in math and/or science classes, some with students as young as 15 years.  The course is asynchronous.

Prerequisites: 1st Course in System Dynamics Modeling: Basic Models. Or, participants need: 1. experience building small System Dynamics (SD) models (especially containing exponential structure); 2. the ability to include appropriate, consistent units in an SD model; 3. the ability to recognize and explain reinforcing and balancing feedback loops in an SD model.

The sequence of session topics are listed below:

  • Session 1:  Review of Digital Communication Tools and Review of Course 1
  • Session 2:  Some Scenarios from the “Shape of Change” Book
  • Session 3:  Building Population Models
  • Session 4:  Introduction Dimensionless Multiplier Components
  • Session 5:  Keystone Species and More About Feedback
  • Session 6:  Euler’s Method
  • Session 7:  How Differential Equations Relate to System Dynamics Models
  • Session 8:  Three Model-Building Lesson Structures
  • Session 9:  Constructing Another (3rd) Model-Building Lesson
  • Session 10:  Creating Assessments for Model-Building Lessons, Viewing the Design of a Year-Long Modeling Course for Students; Using Story-Telling feature of Stella

Each session is divided into sections that involve exploring the new concepts, practicing the new concepts, an assessment, reading and discussion questions, and numerous web resources.  (Maximum 15 participants.)


REGISTER NOW

3rd Course in System Dynamics Modeling:
Building Models From the News

(5 Graduate CE Credits, option)

The third online course in the sequence of System Dynamics (SD) modeling courses is intended for instructors who want to learn to build original models whose inspiration arises from news articles or from course content for which there are currently no available SD models. The participant will learn even more advanced SD techniques and practice building some pre-designed models as well as original models. There is a modeling project required for this course that will take the participants through the entire SD modeling method. The model-building lessons have been used in math, science, social science, and/or economics classes, some with students as young as 15 years of age.  Most of the course is asynchronous.  There are parts of two lessons for which a time will be synchronized for group model building.

Prerequisites: 2nd Course in System Dynamics Modeling: More Advanced Models. Or, participants need: 1. experience building System Dynamics (SD) models containing at least 2 – 3 stocks; 2. the ability to design and apply a dimensionless multiplier in an SD model; 3. the ability to define consistent units and identify and explain feedback in an SD model; 4. to understand the meaning of transfer of loop dominance.

The sequence of session topics are listed below:

  • Session 1:  Review of Digital Communication Tools and Review of Course 1 and Course 2 Concepts
  • Session 2:  Information and Material Delays; Supply and Demand Model
  • Session 3:  Verification and Validation; Pollution Model
  • Session 4:  Building a Stock/Flow Diagram From a News Article
  • Session 5:  Starting the Research for Your Team Project; Policy Testing; Urban Growth Model
  • Session 6:  Building the Model for Your Team Project; Start Fishbanks Activity
  • Session 7:  Draft of Project Model Due; Finish Fishbanks Activity
  • Session 8:  Correct Project Model; Complete Model testing; Build Fishbanks Model
  • Session 9:  Determine Potential Successful Policies for Project Model; Policy Testing with Fishbanks; Video Presentation Specified
  • Session 10: Video Presentation Due; Demonstration of C-Roads Climate Change Simulation

Each session is divided into sections that involve exploring the new concepts, practicing the new concepts, an assessment, reading and discussion questions, and numerous web resources.  (Maximum 15 participants.)


Instructors for Course 1:

Anne LaVigne: In collaboration with the Creative Learning Exchange, create materials and experiences that engage people in basic modeling concepts and connect educators to real-world curricular systems being studied in pre-college education.

Diana Fisher: Winner of the Lifetime Achievement Award (2011 – System Dynamics Society); Presidential Award (1995); Intel Innovation in Teaching Award (1996). Teacher of system dynamics modeling at the pre-college and university level for over 20 years.

Instructor for Course 2 and Course 3:

Diana Fisher

Release | The open theory and its enemy

Dr. Steffen Roth

Roth S. (2019), The open theory and its enemy: Implicit moralisation as epistemological obstacle for general systems theory, Systems Research and Behavioral Science, Vol. 36 No. 3, 1-8 [SSCI .860, Scopus, CABS**].

Article available for download here.

Abstract: Ludwig von Bertalanffy decisively shaped open systems theory as challenge and alternative to the then‐dominant theories of closed systems. This strategic positioning and its success have abetted frequent and frequently implicit moralisations of openness and closeness. In this article, we draw on the concept of autopoietically closed systems to show that the prevailing affirmative bias to openness constitutes an epistemological obstacle to the advancement of general systems theory. We demonstrate how this obstacle can be removed by tetralemmatic decision programmes that facilitate the management of dilemmatic co‐occurrences of and trade‐offs between openness and closeness.

Keywords: autopoietic systems, Bertalanffy, epistemological obstacles, general systems theory, Luhmann, open systems, tetralemma

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