Guiding the Self-organization of Cyber-Physical Systems

cxdig's avatarComplexity Digest

Self-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention real-world examples of systems where self-organization has managed to provide solutions that outperform classical approaches, in particular related to urban mobility. Finally, I identify when a centralized, distributed, or self-organizing control is more appropriate.

 

Guiding the Self-organization of Cyber-Physical Systems
Carlos Gershenson

Source: arxiv.org

View original post

The Threshold Concept – an introduction and overview to the concept

 

Tonnes more in the source

 

Source: The Threshold Concept

Threshold Concepts: Undergraduate Teaching, Postgraduate Training, Professional Development and School Education
A Short Introduction and a Bibliography from 2003 to 2018

The Meyer and Land Threshold Concept

“The idea of threshold concepts emerged from a UK national research project into the possible characteristics of strong teaching and learning environments in the disciplines for undergraduate education (Enhancing Teaching-Learning Environments in Undergraduate Courses). In pursuing this research in the field of economics, it became clear to Erik Meyer and Ray Land [1-7], that certain concepts were held by economists to be central to the mastery of their subject. These concepts, Meyer and Land argued, could be described as ‘threshold’ ones because they have certain features in common.”
Glynis Cousin, An introduction to threshold concepts

Over the past decade this concept has been embraced by many disciplines outside economics; indeed the above quote is from Glynis Cousin’s excellent short introduction to the concept written for earth scientists. The threshold concept has been seen as a valuable tool, not only in facilitating students’ understanding of their subject, but in aiding the rational development of curricula in rapidly expanding arenas where there is a strong tendency to overload the curriculum (Cousin, [2008, 2006]). This web page will describe, briefly, the characteristics of a threshold concept and list selected references to the work of those examining its value in a broad range of disciplines.

Features of a Threshold Concept

Portal Picture

‘Threshold Concepts’ may

be considered to be “akin

to passing through a portal”

or “conceptual gateway”

that opens up “previously

inaccessible way[s] of

thinking about something”

(Meyer and Land [1]).

  • Transformative: Once understood, a threshold concept changes the way in which the student views the discipline.  >  More …
  • Troublesome: Threshold concepts are likely to be troublesome for the student. Perkins [1999, 2006] has suggested that knowledge can be troublesome e.g. when it is counter-intuitive, alien or seemingly incoherent.   >  More …
  • Irreversible: Given their transformative potential, threshold concepts are also likely to be irreversible, i.e. they are difficult to unlearn.  >  More …
  • Integrative: Threshold concepts, once learned, are likely to bring together different aspects of the subject that previously did not appear, to the student, to be related.  >  More …
  • Bounded: A threshold concept will probably delineate a particular conceptual space, serving a specific and limited purpose.  >  More …
  • Discursive: Meyer and Land [2] suggest that the crossing of a threshold will incorporate an enhanced and extended use of language.  >  More …
  • Reconstitutive: “Understanding a threshold concept may entail a shift in learner subjectivity, which is implied through the transformative and discursive aspects already noted. Such reconstitution is, perhaps, more likely to be recognised initially by others, and also to take place over time (Smith)”.  >  More …
  • Liminality: Meyer and Land [4] have likened the crossing of the pedagogic threshold to a ‘rite of passage’ (drawing on the ethnographical studies of Gennep and of Turner in which a transitional or liminal space has to be traversed; “in short, there is no simple passage in learning from ‘easy’ to ‘difficult’; mastery of a threshold concept often involves messy journeys back, forth and across conceptual terrain. (Cousin [2006])”.  >  More …

Source: The Threshold Concept

 

 

What is powerful knowledge? | Eddie Playfair (and general links for the concept, developed by Michael Young and others)

couple of good videos:

 

 

 

Source: What is powerful knowledge? | Eddie Playfair

What is powerful knowledge?

Knoweldge and the future schoolIn Knowledge and the Future School (2014) the sociologist of education Michael Young proposes a ‘return to knowledge’ following what he regards as the ‘turn away from knowledge’ taken by some progressives including Young himself in his earlier work. This book, co-authored with David Lambert, Carolyn Roberts and Martin Roberts, makes a powerful case for a curriculum and a pedagogy based on what the authors call ‘powerful knowledge’. This is part of a kind of ‘third way’ approach; a synthesis of two clashing perspectives on the school curriculum which can be characterised broadly as ‘traditionalist’ and ‘progressive’.

The authors distinguish between three alternative futures or ways of thinking about the school curriculum:

A Future 1 curriculum is the curriculum inherited from the 19th century which assumes that knowledge is a given and is beyond debate. The future is seen as an extension of the past.

A Future 2 approach acknowledges that knowledge has social and historical roots. It is defined in terms of particular needs and interests, often those which are dominant in society. It was a response to the rigidity and elitism of the Future 1 model but it was based on a misguided theory of knowledge. The fact that knowledge is socially constructed does not necessarily mean that it is inherently biased or that some knowledge is not better; more valuable, more truthful or more universally applicable.

Future 3 already exists in parts of the curriculum despite the pressure to lean towards Futures 1 or 2. In contrast to Future 1, it locates knowledge as the creation of specialist communities of researchers rather than simply treating it as given. It acknowledges that knowledge is fallible, contestable, provisional and subject to change. But in contrast to Future 2 it does not see it as an arbitrary response to a particular challenge; it is bound by epistemic rules about what makes things likely to be true.

Future 3 treats subjects as the most reliable tools we have to help students acquire powerful knowledge and make sense of the world. Subjects are a resource to take students beyond their experience, to challenge their existing ideas.

“We want schools to give children access to knowledge that takes them beyond their experience in a way that their parents can trust and value, they they will find challenging and which prepares them for the next step in their education.”

Powerful knowledge starts from the idea of equal citizens with an equal entitlement to knowledge; an entitlement which should not be limited on grounds of assumed ability or motivation, ethnicity, class or gender. The curriculum should be seen as a guarantor of equality based on the best knowledge we have, or at least a staged approach towards acquiring it.

According to Young, skills cannot be an adequate basis for a curriculum:

“Skills have their place in the curriculum but skills on their own limit the student to tackling ‘how’ questions and not ‘what’ questions. It is only ‘what’ questions that take students beyond their experience and enable them to engage with and grasp alternatives.”

The authors propose 3 criteria for defining powerful knowledge:

  1. It is distinct from ‘common sense’ knowledge acquired through everyday experience and therefore context-specific and limited.
  2. It is systematic. Its concepts are related to each as part of a discipline with its specific rules and conventions. It can be the basis for generalisations and predictions beyond specific cases or contexts.
  3. It is specialized; developed by specialists within defined fields of expertise and enquiry.

Powerful knowledge embodies values of objectivity, openness to challenge, rationalism and respect for all humans. These criteria are concerned with truth rather than with valuing different belief systems people may hold to.

What would a shift to a powerful knowledge curriculum mean?

It would require a major reassessment of all curriculum programmes as well as changes to pedagogy. The approach advocated in this book requires schools to see a knowledge-led curriculum as an entitlement for all and as a starting point for a more equal, fair and just society. It would set us on a path of pretty radical pedagogic and curriculum innovation.

“Our approach is not to start by assuming different ‘types’ of children but by wanting to give all children access to the foundations of powerful knowledge.”

“There is no good argument for comprehensive secondary schools if they are not based on a comprehensive curriculum…If we are serious about educational equality we have to be serious about curricular justice.”

The authors have no time for a traditional, old-fashioned, backward-looking view of knowledge but they do agree with a strong emphasis on knowledge:

“Denying access to some in the name of diversity, however linked to a concern for the welfare of students, is not about promoting equality or social justice.”

In conclusion

This is an important contribution to contemporary debates about educational equality and entitlement as well as the central place of knowledge in the curriculum. It is a useful starting point for those of us who support a broad non-elitist knowledge-rich ‘Future 3’ type of curriculum for all young people.

Following on from this, I would want us to have a more thorough discussion of the place of skills and skill-development in the curriculum as well as of the concept of ‘usefulness’. Skill acquisition plays a big part in helping students go beyond their experience and surely those ‘how’ and ‘what’ questions are in constant dialogue with each other, neither of them necessarily more or less challenging than the other.

The book doesn’t explicitly address the idea of ‘useful education’, although at one point, the ‘power’ in ‘powerful knowledge’ is described as referring to ‘what it can do’ for those who have access to it; a fairly major, and welcome, concession to the notion of utility. Question: Is there any difference between what knowledge can do for us and what we can do with it…?

I think there is also a need for more consideration of how academic disciplines change and evolve over time and how they translate into taught subjects. What are the benefits of interdisciplinarity as well as disciplinarity?

And finally, the authors seem to assume that a curriculum entitlement only applies up to 16. I’m not sure that there is any good reason for such an early or sudden cut-off point. I think the idea of a broad liberal studies curriculum with room for specialisation and interdisciplinarity can extend fruitfully into further and higher education.

The book does not claim to be the last word on any of these questions and it should be seen as a solid and clear basis for further exploration. As such it deserves to be widely read and widely discussed.

See also:

Progs and trads: is a synthesis possible? (March 2014)

Gramsci’s grammar and Dewey’s dialectic (December 2014)

Learning to love liberal education (October 2014)

Debating the liberal arts (October 2014)

Complexity theory and leadership practice: A review, a critique, and some recommendations (2019)

Sadly not on open access – yet – but if you get access (or maybe drop me a line), I would value other views on this!

 

Source: Complexity theory and leadership practice: A review, a critique, and some recommendations – ScienceDirect

Full length article

Complexity theory and leadership practice: A review, a critique, and some recommendations

Abstract

There is an extensive literature on complexity theory authored by natural scientists writing about research fields in which they are themselves active. There is also a growing literature that draws on this work to address leadership concerns and practices, but whose authors are experienced in leadership education rather than in the substantive scientific fields whose findings they report and interpret. We shall refer to this arena as complexity leadership. The initial burst of enthusiasm for complexity management and leadership in the 1990s, as a conceptual framework for informing organisational practice, has not been sustained at its early intensity. However, the field continues to attract interest. The purpose of this paper is to contribute to a discussion of the validity and significance of these ideas for the leadership of organisations. We enable this through a review of the literature, a critique, and some recommendations. The type of questions which we will be raising are: (1) What failings in current leadership theory or practice are claimed to be corrected? (2) How novel, and how plausible, are the leadership prescriptions which are derived from complexity theory? (3) Does complexity theory provide scientific authority for these prescriptions? We find a paradox in the complexity leadership message which, on the one hand, claims to be rooted in complexity theory, but at the same time, rejects key denominators of the hard sciences. Finally, we offer suggestions on how to constructively handle the apparent paradox.

Keywords

Complexity, Leadership, Management, Analogy, Metaphor

Source: Complexity theory and leadership practice: A review, a critique, and some recommendations – ScienceDirect

Why the Bronx burned (Joe Flood 2010) – and what theoretical basis did RAND Corp use in their modelling?

A fellow traveller on Twitter referenced “systems approaches regularly overreach and/or underachieve and therefore become discredited. But because they are the way the world actually works, we always come back to them.”, then in evidence talked about “(Eg overreach of crap 60s cybernetic computer modelling and Cybersyn, under-achievement of early Blair-era systems approaches)”. When I inquired more into this, he gave two fascinating examples – James Kahn modelling the Vietnam war (and geo-thermonuclear warfare), and “the disastrous modelling done for the NYC fire department which removed stations from poorer areas.”

Both of these are basically RAND Corporation work, early systems engineering? Or some variant of systems dynamics, operations research, or similar? But don’t seem to be notably ‘cybernetic’ if you ask me. Wanted to shared these examples – and the reminder that what works in systems/cybernetics/complexity can so easily get bound up with anything that doesn’t work – and ask more experienced colleagues if they know the theoretical basis of this work?

cheers

Benjamin

 

 

(An) original RAND report:

Click to access R632.pdf

Review paper (1980): https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.26.4.418

NY Times piece (1980): https://www.nytimes.com/2010/05/30/nyregion/30books.html

Joe Flood’s website: http://www.joe-flood.com/about-the-fires/

 

Source: Why the Bronx burned

Why the Bronx burned

It was game two of the 1977 World Series, a chilly, blustery October night in the South Bronx. The Yanks were already down 2-0 in the bottom of the first inning when ABC’s aerial camera panned a few blocks over from Yankee Stadium to give the world its first live glimpse of a real Bronx Cookout. “There it is, ladies and gentlemen,” Howard Cosell intoned. “The Bronx is burning.”

The scene quickly became a defining image of New York in the 1970s, a fitting summation of the decade perfect in every way but one: It never happened. Cosell, tapes of the game show, never said, “The Bronx is burning.”

“It’s a great quote, if it had been a real one,” says Gordon Greisman, who co-wrote and produced ESPN’s “The Bronx is Burning” mini-series based on the Jonathan Mahler book. “But we got all of this footage from Major League Baseball, including the entire broadcast of that game, and we went through all of it and it’s not there, because God knows if it was there we would have used it.”

More likely, the phrase was invented by New Yorkers — what the broadcaster should have said — and spun by credulous journalists.

But Cosell’s “Play It Again, Sam” moment is hardly the only myth that has sprung out of one of the darkest chapters of New York City history.

The South Bronx (along with Brooklyn’s Brownsville, Bushwick, and Bedford-Stuyvesant neighborhoods, and Manhattan’s Harlem and Lower East Side) was indeed burning. Seven different census tracts in The Bronx lost more than 97% of their buildings to fire and abandonment between 1970 and 1980; 44 tracts (out of 289 in the borough) lost more than 50%. “The smell is one thing I remember,” says retired Bronx firefighter Tom Henderson. “That smell of burning — it was always there, through the whole borough almost.”

But many of these fires were not — as was suggested then and is popular opinion now — caused by a rash of arsons. In fact, there’s a good chance that not even the World Series blaze was intentional. That fire was in an abandoned schoolhouse, there was no insurance policy for anyone to cash in on.

Hoodlums did not burn The Bronx. The bureaucrats did.

IN 1971, Mayor John Lindsay asked the FDNY’s chief of department, John O’Hagan, for a few million dollars in savings to help close a budget deficit. O’Hagan turned to a team of statistical whiz kids from the New York City-RAND Institute, a joint endeavor of the mayor’s office and the Santa Monica-based defense think tank famous for all but inventing the fields of game theory, systems analysis and nuclear strategy (and for devising a series of spectacular strategic failures in Vietnam).

NYC-RAND’s goal was nothing less than a new way of administering cities: use the mathematical brilliance of the computer modelers and systems analysts who had revolutionized military strategy to turn Gotham’s corrupt, insular and unresponsive bureaucracy into a streamlined, non-partisan technocracy.

For O’Hagan’s fire department, RAND built computer models that replicated when, where, and how often fires broke out in the city, and then predicted how quickly fire companies could respond to them. By showing which areas received faster and slower responses, RAND determined which companies could be closed with the least impact. In 1972, RAND recommended closing 13 companies, oddly including some of the busiest in the fire-prone South Bronx, and opening seven new ones, including units in suburban neighborhoods of Staten Island and the North Bronx.

RAND’s first mistake was assuming that response time — a mediocre measure of firefighting operations as a whole, but the only aspect that can be easily quantified — was the only factor necessary for determining where companies should be opened and closed. To calculate these theoretical response times, RAND needed to gather real ones. But their sample was so small, unrepresentative and poorly compiled that the data indicated that traffic played no role in how quickly a fire company responded.

The models themselves were also full of mistakes and omissions. One assumed that fire companies were always available to respond to fires from their firehouse — true enough on Staten Island, but a rarity in places like The Bronx, where every company in a neighborhood, sometimes in the entire borough, could be out fighting fires at the same time. Numerous corners were cut, with RAND reports routinely dismissing crucial legwork as “too laborious,” and analysts writing that data discrepancies could “be ignored for many planning purposes.”

Finally, the models fell prey to the very thing that technocracies are supposed to prevent, political manipulation. At the outset the RAND studies didn’t need to be manipulated — they provided what the politicians wanted without prompting. The models’ flaws all tended to make it appear that poor, fire-prone (and generally black and Puerto Rican) neighborhoods were actually over-served by the fire department, and recommended the cuts be focused in these politically weak areas. But as the cuts deepened, the models began recommending closings in wealthier, more politically active communities, an untenable development for the ambitious chief O’Hagan, who was well-connected in the Democratic clubs of Brooklyn and Queens and was later appointed fire commissioner.

“There was no question that where the commissioner kept his car was not a house that was going to be closed,” says RAND’s Rae Archibald, who was later hired as an assistant fire commissioner. “If the models came back saying one thing and [O’Hagan] didn’t like it, he would make you run it again and check, run it again and check.”

When the results still didn’t come back to his liking, O’Hagan’s men handled the problem. “Mostly we used [the RAND models] for the cuts, but if they came back saying to close a house in a certain neighborhood, well . . . if you try to close a firehouse down the block from where a judge lived, you couldn’t get away with it,” says retired chief Elmer Chapman, who ran the department’s Bureau of Planning and Operations Research. In those cases, continues Chapman, you could simply skip down the list of closings to a company in a poorer neighborhood. The models said there were less painful cuts to be made, “[b]ut the people in those [poorer] neighborhoods didn’t have a very big voice.”

As the city’s budget deficit ballooned, the RAND studies were used to close dozens more companies; in all, 50 fire units were shuttered or moved.

Fire inspections were cut by 70%; the fire marshal program was gutted; ancient rigs with outmoded safety features and rickety wooden ladders were pressed into service, and fire alarm boxes broke down by the score.

“I’d say a quarter to a third of the hydrants didn’t work,” says Jerry DiRazzo, who fought fires in the Bushwick section of Brooklyn. “You can see the way an area changes when they don’t repair a neighborhood. Every day I drove over the border from Queens to Brooklyn to go to work, and it was like this imaginary line was crossed. Almost like suddenly the sun wasn’t shining, like it was darker somehow . . . People would ask me, ‘How can you deal with this, seeing that every day?’ And I’d tell them, ‘I have a front row seat to the greatest show on earth.’ This was history being made, a city collapsing.”

DESPITE the models’ predictions of minimal impact, response times shot up and the number of fires that nearby companies fought as much as quadrupled. Citizens who lost their neighborhood firehouses protested. But by citing the supposed statistical infallibility of RAND and its computer models, City Hall was able to mollify the constituencies that really mattered. When the firefighters’ union filed a lawsuit to stop the closings, the department trotted out the models and convinced a U.S. District Court judge to threw the case out, and convinced New York Times editorial page to come out in favor of the closings and to credulously cite one RAND analyst who said the cuts would have no serious impact on coverage.

With fire rates already rising thanks to poverty, family dysfunction and an overcrowded, aging housing stock, the closings helped turn the fire problem into a scourge, consuming block after block of once densely populated, viable neighborhoods.

Thanks in large part to technological innovations like smoke detectors and fire-retardant building materials — O’Hagan’s own pet projects — the country at large experienced a 40% drop in fire fatalities from the mid 1960s to late 1970s. In the city O’Hagan was charged with protecting, though, fire fatality rates more than doubled.

Despite the conventional wisdom that arson was to blame, it was ordinary fires, caused by things like faulty wiring, errant cigarettes, and space heaters that drove the destruction. During the 1950s, city fire marshals attributed less than 1% of fires to arson. Until 1975, when the final round of fire cuts went into effect, that ratio never rose above 1.1%.

Where arson was a problem, it was largely the consequence of government intervention intended to mitigate the social consequences of the fires, namely no-questions-asked fire insurance for landlords in fire prone neighborhoods, and special welfare payments made to fire victims.

But even at its peak in the late 1970s, arson made up less than 7% of fires, and occurred primarily in already burned-out, abandoned buildings.

The fire cuts even helped lead to the Son of Sam shootings. In the mid-1970s, fire marshal Mike DiMarco was staking out David Berkowitz’s Bronx home after his yellow Ford Galaxy was spotted fleeing the scene of two trash fires set on City Island in the Bronx. “We had him under surveillance for months, watching his car late at night when we didn’t have any fires to run off to,” says DiMarco. But when Berkowitz moved to Brooklyn, the cut-to-the-bone fire marshal division dropped the tail, Berkowitz forgotten until he was arrested for the Son of Sam murders.

AS New York City faces its worst budget shortfall since it almost went bankrupt in 1975, some shadows of the RAND fire closings loom. The mayor’s initial budget plan calls for closing 20 fire companies by July 1, with more closings likely to come if other savings aren’t realized. The fire units up for closing will be announced this week.

Once again, the fire department is making cuts with computer models based on data of questionable validity, releasing incomplete and misleading statistics when it suits the department’s purposes, and refusing to release raw data so that their claims can be verified by anyone outside the department.

But FDNY spokesperson Frank Gribbon says this time will be different.

“The chiefs are looking at other factors as well,” as the models, he says. “”There’s a whole host of criteria, and then it’s the expertise of the chief officers who have to consider all of the facts and all of the data.””

Gribbon says the department doesn’t share the data behind the models, nor will it discuss the specifics of how the models work. “The public doesn’t understand,” Gribbon continued. “In terms of what the criteria [for closings] are, we’re not going to convince anybody by discussing, you know, the facts. We’re not going to convince anybody.”

Fire Commissioner Sal Cassano finds himself is in a difficult spot. On the one hand is an understaffed fire department going on as many calls as it ever has (building fires are down 50% from the 1970s, but the department now responds to more 200,000 medical emergencies every year). On the other hand is the man who Cassano, who was the chief of department before being promoted last year, owes his last two jobs to, a mayor intent on closing a looming budget gap.

Like the 1970s, firehouses are being closed while futuristic technology projects, outside consultants and computer models are still being funded. Last year the department paid computer consultants from Hewlett-Packard $3.5 million, about as much as it costs to keep two firehouses open and fully staffed for a year, to continue fine-tuning the Automatic Vehicle Locator (AVL) system they’d already installed. AVL is part of a new dispatch modeling system built by Deccan International (the same company that built the computer models being used to close fire companies), which in turn is part of a $2 billion overhaul of the city’s emergency dispatch system.

That the department needs to maintain a modern communication and dispatch system is clear, but the usefulness of spending millions to update street-corner fire alarm boxes that the department is planning on shutting down anyway, and equipping 911 operators with special software programs to receive live video feeds from callers, is questionable when basic city services are being slashed.

In a move strangely reminiscent of Rudy Giuliani’s ill fated decision to put all of his Office of Emergency Management eggs in a 7 World Trade Center-housed basket, the department is spending more than $300 million consolidate each borough’s fire dispatch office into one unit at the department’s Metrotech headquarters, and hundreds of millions more to build backup dispatch unit in The Bronx in case the Metrotech unit breaks down or is attacked.

The city has spent more than $20 million on a new Unified Call Taker (UCT) system that lets 911 call takers write down fire information and send it directly to fire dispatchers, instead of simply passing the caller along to more experienced fire call takers. Firefighters have taken to calling UCT the “U Can’t Tell” system after being sent to a series of incorrect addresses by the 911 call takers. And fire call takers are now playing a larger role in the call taking process — eliminating much of the reason for building the UCT system in the first place — after 911 call takers sent fire crews to the wrong addresses for fires in Brooklyn and Queens last November, and three people died in each fire.

A month after the fatal fires, Deputy Mayor Skyler praised UCT in testimony before the city council, saying that it “lowers response times in an effort to save lives.” But according to fire union critics, those lower response times are only true on paper, not in reality. Unlike most fire departments, the FDNY does not count the time a caller spends on the phone with a 911 operator in its response time calculations. And now that 911 operators are taking down fire information, that time is more than a minute, according the Uniformed Firefighters Association. This means that while the FDNY is reporting faster response times, the amount of time it actually takes fire crews to arrive might actually be longer.

THERE is little in New York’s current criminal, economic or building fire trends to indicate that the city will be returning to the ashen anarchy of the 1970s any time soon, but some of the management lessons to be learned from that era are clear: Whiz Kid consultants with plans to save the city through technology have their place, but shouldn’t come at the cost of basic services.

And while numbers can sometimes cut through the fog of government decision-making, they can just as easily be mistaken or manipulated.

“The models might be able to help you a little bit with closing fire companies,” says former fire commissioner Thomas von Essen, who led the department through the terrorist attacks of 9/11. “But there are so many other parts to those decisions, not just response time but the effectiveness of the unit, the political response from the neighborhood, what kind of buildings are nearby, whether there are schools or hospitals or terrorist targets.

“There’s no question that there are neighborhoods where if the firehouse is removed, it will have a minor impact. But there are also many communities that need additional fire units. It should be an ongoing process, not just something to scare the public in a fiscal crisis.”

Joe Flood is the author of “The Fires: How a Computer Formula, Big Ideas, and the Best of Intentions Burned Down New York City — and Determined the Future of Cities” (Riverhead), in stores on May 27.

 

Tweet stream from @conways_law on “the importance of systems thinking in education (and a lot of other things)”

Purpose of a System in Light of VSM:

Harish's avatarHarish's Notebook - My notes... Lean, Cybernetics, Quality & Data Science.

Varieties 2

In today’s post, I am looking at the concept of POSIWID (“Purpose Of a System Is What It Does”) Please note that VSM stands for “Viable System Model” and not “Value Stream Mapping”.

The idea of POSIWID was put forth by the father of Management Cybernetics, Stafford Beer. As Beer puts it: [1]A good observer will impute the purpose of a system from its actions… There is, after all, no point in claiming that the purpose of a system is to do what it consistently fails to do.

An organization is a sociotechnical and complex system. This means that it cannot be controlled by simple edicts that are put top down from the management. We should not go by what the “designer” of the system says it does, we should impute the purpose from what the system actually does.

A good explanation comes from Dan Lockton: [2]

View original post 1,439 more words

SCiO Open Meeting – January 20, 2020, London UK, 09:30-17:00, just £20

book at https://www.eventbrite.co.uk/e/scio-open-day-winter-2019-london-all-welcome-tickets-83713257607

 

Source: Open Meeting – Winter 2019/20 | SCiO

Open Meeting – Winter 2019/20

London, UK
£20
Monday, January 20, 2020
09:30 – 17:00, London

A packed and exciting-looking SCiO open meeting where a series of presentations of general interest regarding systems practice will be given – this will include ‘craft’ and active sessions, as well as introductions to theory. More information and book on Eventbrite at: https://www.eventbrite.co.uk/e/scio-open-day-winter-2019-london-all-welc…

Starts at 09:30 – ‘introduction to the viable system model’. Main presentations start at 10:00 with …

Session 1 (Gareth Evans) – Thinking in Systems – Friend or Foe

Systems have formed a significant part of science over many-a-year… scholars such as; Ludwig von Bertalanffy, Peter Checkland, Ross Ashby, Russell Ackoff, Stafford Beer and many more have discussed, debated and placed front and centre the importance of not just thinking ‘systemically’ but also being, acting and doing ‘systemically’. Many have revealed and evidenced the magic and impact of ‘Thinking Systemically’ across industry, albeit some have also found it less than accessible for the wider community. What I’m curious to explore: Is ‘Systems Thinking’ too bound in academic theory to the extent that it is either too widely misunderstood, misinterpreted or just purely too impractical to adopt across a wider field of professional practice due to the levels of understanding and practical wisdom that currently exists. Therefore, Is ‘Systems Thinking’, a friend or foe?

Session 2 (Angus Jenkinson) – Are Viable Companies Alive? Does it Matter?

The Viable System Model is one of the key capabilities that SCiO has focused on. It’s an implementation of cybernetics. “Viable system” suggests living system — and vibrant systems feel alive. Are they? Can organisations be organisms? And what difference would that make? This questions our questions and stimulates provocations. At a time when science is regenerating, does management need to as the same? If we start thinking organically, how many of our mechanistic systems assumptions do we have to challenge? What happens to the design of change or strategy or control` if an organisation is organic? What does it mean for identity, policy, and policies?

Lunch, then …

Session 3 (Rowena Davis) – Systems-Centered® – Working with Differences Differently

In common with all living human systems, organisations need differences to develop and transform. And yet, in organisations, as in all living human systems, we often dismiss, attack or try to convert differences. Indeed, we are primed neurologically to do this – our Flight, Fight, Freeze responses. Agazarian’s systems-centered method of functional subgrouping offers a way to lower our reactivity to differences, and to increase our capacity to stay open and curious in the face of the unknown and problem-solve. Rowena Davis will give an overview of Agazarian’s Theory of Living Human Systems (TLHS), including how boundaries open to similarity and close to difference and how the context we are part of impacts on our ability to work functionally in our roles. We will practise the core Systems-centered method of functional subgrouping and review the systems-centered map of phases of team development to make sense of organisational dynamics.

Session 4 (Patrick Hoverstadt) – Systems and Strategy War Rooms

The talk will look at the underlying concepts, design and practice of War Rooms as decision environments for dealing with complex and fast moving situations. Starting with Blackett’s invention of the War Room, through Beer’s Cybersyn to the work we are currently engaged on and its use with client in tackling complex strategic issues. We’ll talk through the difference current technology offers and the different ways our modern War Rooms can be used.

Source: Open Meeting – Winter 2019/20 | SCiO

WOSC 17th Congress 2017 | World Organisation of Systems and Cybernetics

With the 20th WOSC coming up, some interesting presentations from 2017

Source: WOSC 17th Congress 2017 | World Organisation of Systems and Cybernetics

 

Category Archives: WOSC 17th Congress 2017

The Brain of the Future by Alexandre Pérez Casares

The ‘Age of the Cognitive Machines’ is the most drastic economic transition since the Second Industrial Revolution. This transition is driven by the confluence of multiple technological innovations –such as advanced robotics, machine learning, and the exponential growth of computation … Continue reading →

Posted in WOSC 17th Congress 2017 | Comments Offon The Brain of the Future by Alexandre Pérez Casares

From precision medicine to systems medicine by Christian Pristipino

“In humans, very strong interactions between quantitative and qualitative dimensions occur, in which psychological, emotional, cognitive and cultural variables invariably influence disparate biological processes within every bodily system. The result is the need for a combined bio-psycho-social/environmental approach to complex … Continue reading →

Posted in WOSC 17th Congress 2017 | Comments Offon From precision medicine to systems medicine by Christian Pristipino

Artificial intelligence and law: what perspective? by Daniele Bourcier

The law is based on a certain idea of man as the subject responsible for his actions, AI devices can influence the responsibility of those who create and use them or even replace total human activities and decisions by machines. … Continue reading →

Posted in WOSC 17th Congress 2017 | Comments Offon Artificial intelligence and law: what perspective? by Daniele Bourcier

Recognizing the Dangers of Simplicity Addiction by Michael Lissack

We are seldom taught that simplification has a high risk of failure. In truth, it only works up to a point, after which all that lies ahead is failure. To examine the limits of simplicity is to look at what … Continue reading →

Posted in WOSC 17th Congress 2017 | Comments Offon Recognizing the Dangers of Simplicity Addiction by Michael Lissack

Smart growth strategies by Elias G. Carayannis

The future and sustained peace, prosperity and security of the WORLD require that we pursue and accomplish a reasonable modicum of BOTH of those visions and Knowledge for Development (K4Dev) and its related proposed roadmap (K4Dev__Vision 2030) based on the … Continue reading →

Posted in WOSC 17th Congress 2017 | Comments Offon Smart growth strategies by Elias G. Carayannis

Design of Regional System by Alfonso Reyes

A Keynote providing real life evidence of invoking new technologies to support cooperation and direct production concepts in a region. Design of Regional System

More Galleries | Comments Offon Design of Regional System by Alfonso Reyes

Governance in the Anthropocene: cybersystemic possibilities? by Ray Ison

eye-opening: The “Anthropocene” is a term formulated by Earth scientists to claim that we have entered a new geological epoch: human influences have become so great that they are affecting “whole Earth dynamics” through a range of biophysical and social … Continue reading →

More Galleries | Comments Off

Frontiers | Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms | Robotics and AI (2019)

via complexity digest

Without reference to this article, my instant thought was ‘swarms… aren’t really very complex, are they?’

 

Source: Frontiers | Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms | Robotics and AI

PERSPECTIVE ARTICLE

Front. Robot. AI, 26 November 2019 | https://doi.org/10.3389/frobt.2019.00130

Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms

  • 1Department of Computer Science and Engineering, Campus of Cesena, Alma Mater Studiorum Università di Bologna, Bologna, Italy
  • 2IRIDIA, Université libre de Bruxelles, Brussels, Belgium

Complexity measures and information theory metrics in general have recently been attracting the interest of multi-agent and robotics communities, owing to their capability of capturing relevant features of robot behaviors, while abstracting from implementation details. We believe that theories and tools from complex systems science and information theory may be fruitfully applied in the near future to support the automatic design of robot swarms and the analysis of their dynamics. In this paper we discuss opportunities and open questions in this scenario.

1. Introduction

Metrics that quantify the complexity of a system and measure information processing are used in a wide range of scientific areas, including neuroscience, physics, and computer science. In the scientific literature, the word complexity is overloaded, as it may refer to the amount of effort needed to describe a system, or to create it, or also to quantify its structure both in terms of components and dynamical relations among its parts. For example, let us consider a swarm of robots: we may ask what is the complexity of a function describing the overall behavior of the swarm, or what is the complexity of the problem of optimally assigning tasks to the robots, or what is the complexity of each of the tasks. These objectives require different measures, each addressing a specific question. As a consequence, there is no unique and all-encompassing complexity measure: a plethora of metrics are available. Most come from information theory, which abstracts from specific system’s details and focuses on information processing. While notable results have been attained, we believe that the potential of these methods has still to be fully exploited in the automatic design of robot swarms and in the analysis of their behaviors.

In automatic design methods, the design problem is cast into an optimization problem that is solved either off-line or on-line, i.e., either before the swarm is deployed in its target environment or while the swarm is operating in it. A prominent example of automatic design is evolutionary robotics (ER), where the control software—typically an artificial neural network (ANN)—is optimized by means of an evolutionary algorithm (Nolfi and Floreano, 2000). A number of alternative methods depart from the classical ER by employing control software architectures other than ANNs and/or optimization techniques other than evolutionary computation (Watson et al., 2002; Hecker et al., 2012; Francesca et al., 2014; Gauci et al., 2014). A review of the main studies on automatic design of robot swarms—both off-line and on-line—is provided by Francesca and Birattari (2016).

The aim of this paper is to outline what we think are the most important open questions and to describe opportunities to use complexity measures for supporting the automatic design of swarms of robots and the analysis of their behaviors. In section 2, we provide an introduction to complexity measures. In section 3, we highlight the main contributions to the robotics field. In section 4, we illustrate our perspective and outline relevant open questions.

2. A Capsule Introduction to Complexity Measures

The notion of complexity is multifaceted. If, by the term “complex,” one means “difficult to predict,” then a suitable metric is provided by information theory with Shannon entropy (Shannon, 1948). Let us consider a simple system of which we observe the state at a given time. The observations can be modeled as a random variable X, which can assume values from a finite and discrete domain XX. If the observation is x∈Xx∈X, which has a probability P(x), then the amount of information carried by the observation of x is defined as 1logP(x)=−logP(x)1logP(x)=-logP(x)1. Shannon entropy is defined as the expected value of the information of all symbols: H(X)=−∑x∈XP(x)logP(x)H(X)=-∑x∈XP(x)logP(x). Intuitively, H(X) measures the amount of surprise—or, equivalently, the lack of knowledge—about the system; we may also observe that Shannon entropy measures the degree of disorder in a system or process. Many complexity measures are based on Shannon entropy. For example, the reciprocal influence between two parts of a system can be estimated by computing their mutual information, defined as I(X; Y) = H(X) + H(Y) − H(X, Y), where H(X, Y) is the joint entropy of the variables X and Y, defined on the basis of the joint probability P(x, y). I(X; Y) provides a measure of the information we can gain on a variable, by observing the other. Information-theoretic metrics are currently widely applied, as they have the property of being model independent and able to capture non-linear relations. In practice, probabilities are usually estimated through the observed frequencies.

When the objective is to measure the complexity of the description of a system, then algorithmic complexity may be used, as proposed by Kolmogorov (1965): the complexity of a string of symbols is defined as the length of the shortest program producing it. This measure is not computable in general, but approximations are available, such as the ones based on compression algorithms (Lempel and Ziv, 1976). Shannon entropy and Kolmogorov complexity are conceptually different (Teixeira et al., 2011). The former measures the average uncertainty of a random variable X, and so it estimates the difficulty of predicting the next symbol of a sequence received from a source. Conversely, Kolmogorov complexity measures the length of the minimal (algorithmic) description of a given sequence of symbols σ, therefore it estimates the difficulty of describing or reconstructing the sequence. However, they both capture the notion of compressibility of a signal and, in particular, they are null when X (resp. σ) is constant and maximal when X (resp. σ) is random.

Kolmogorov complexity also provides a theoretical framework for the principle known as Occam’s razor that states that among all the possible explanations of a set of data, the simplest one is preferable. A similar argument supports the notion of stochastic complexity, proposed by Rissanen (1986), which is the shortest description of the data with respect to a given probabilistic model.

The term “complex” is often used for capturing the notion of structure or pattern observed in data or in the dynamics of a system, once random elements are discarded. This concept is also related to the extent to which correlations distribute across the parts of the system observed (Grassberger, 1986a). The intuition is that high complexity should be associated to conditions characterized by a mixture of order and disorder, structure and randomness, easily predictable dynamics and novelty. Along this line, several measures have been proposed (Grassberger, 1986a; Lindgren and Nordahl, 1988; Li, 1991; Crutchfield, 1994; Gell-Mann and Lloyd, 1996; Shalizi and Crutchfield, 2001). A survey on complexity metrics is out of the scope of this contribution and we refer the interested reader to prominent works on the subject (Grassberger, 1986a; Lindgren and Nordahl, 1988; Badii and Politi, 1999; Lloyd, 2001; Prokopenko et al., 2009; Lizier, 2013; Moore et al., 2018; Thurner et al., 2018; Valentini et al., 2018).

Continued in source: Frontiers | Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms | Robotics and AI

Bonnitta Roy – Six Ways to Go Meta — Emerge: Making Sense of What’s Next — Overcast

Bonnitta Roy worth listening to, and reading:

View at Medium.com

 

Today on the show I’m speaking with Bonnitta Roy about her presentation ‘Six Ways to Go Meta’. We cover such topics as what it mean to ‘go meta’, why the anthropocene is driving humans to discover new ways of ‘going meta’, how deconstructing our experience through meditation creates a clean palette to experiment with new ways of going meta, how previous guests like Adam Robbert, Jordan Greenhall, Nora Bateson, and Rob Burbea fit into Bonnitta’s meta-meta-model, and why it’s vital that we create new educational forms that help create and discover new human minds. Six Ways to Go Meta Presentation

Support this podcast: https://anchor.fm/emerge/support

Source: Bonnitta Roy – Six Ways to Go Meta — Emerge: Making Sense of What’s Next — Overcast

Four Kinds of Thinking: 2. Systems Thinking

comments sought.

Steven Shorrock's avatarHumanistic Systems

Several fields of study and spheres of professional activity aim to improve system performance or human wellbeing. Some focus on both objectives (e.g., human factors and ergonomics, organisational psychology), while others focus significantly on one or the other. Disciplines and professions operating in these areas have a focus on both understanding and intervention. For each discipline, the focus of understanding and method of intervention will differ. For instance, for human factors and ergonomics, understanding is focused on system interactions, while intervention is via design. Understanding alone, when intervention is required, may be interesting, but not terribly useful. Intervening without understanding may have unintended consequences (and indeed it often does). With appropriate understanding and intervention, both system performance and human wellbeing have a chance of being improved.

Understanding and intervention for system performance and human wellbeing is rooted – to some extent – in four kinds of thinking. In this short…

View original post 2,086 more words

Inquiry

Recently, I sent a question (to ‘contact’) about the appropriateness of a potential series of posts for comments)e — never got an answer. Should I take this as ‘no interest’ or did that letter not get through?

The NNT, Explained – The Number Needed to Treat

(via the always-excellent https://medium.com/gentlyserious, to which you should subscribe)

Quick summaries of evidence-based medicine.

Source: The NNT, Explained – TheNNTTheNNT

and:

Number Needed to Treat (NNT): A tool to analyze harms and benefits

 

Diagram categorizing ways meta-rationality can improve the operation of a rational system – David Chapman, @meaningness

s
search
c
compose new post
r
reply
e
edit
t
go to top
j
go to the next post or comment
k
go to the previous post or comment
o
toggle comment visibility
esc
cancel edit post or comment