1Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
2Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
3Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
4Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
5Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
Complex biological systems, by definition, are composed of multiple components that interact non-linearly. The human brain constitutes, arguably, the most complex biological system known. Yet most investigation of the brain and its function is carried out using assumptions appropriate for simple systems—univariate design and linear statistical approaches. This heuristic must change before we can hope to discover and test interventions to improve the lives of individuals with complex disorders of brain development and function. Indeed, a movement away from simplistic models of biological systems will benefit essentially all domains of biology and medicine. The present brief essay lays the foundation for this argument.
Introduction
Non-invasive neuroimaging has invigorated a deep and abiding interest in understanding the human brain, the most complex biological system, in health and disease. This burgeoning research focus has impelled technological innovation in neuroimaging and application of a growing number of mathematical/computational approaches to analysis, which help visualize the complexity of the brain in greater depth than previously possible. From our current vantage point we are compelled to ask whether our capabilities have outstripped the paradigms we use for scientific research, and whether our conceptual and analytical frameworks have become a barrier to understanding complex systems.
A deep understanding of complex biological systems requires conceptual and analytical strategies that respect that complexity. Yet, there continues to be a dominating focus in experimental design and analysis on univariate, linear, and narrowly defined relationships. These approaches, including multivariate linear regression (which is an elaboration on the univariate linear framework), are gratifying because they are conceptually simple and align neatly with the traditional scientific method, in which emphasis is placed on a single isolatable dependent variable. However, the univariate/linear approach will necessarily fail when tasked with providing the basis for deep explanations for complex biological systems.
This essay highlights the need to recognize the fallacy of the univariate conceptual framework with respect to complex systems and to embrace complexity so as to align the problem to be solved with the approach taken. We contend that there are some effective ways to study complex systems through care in study design and sample ascertainment, deep phenotyping, and statistical approaches. However, the shift to individual-level analysis, the basis for personalized medicine, will require both methodological advances and a readiness for investigators and reviewers to eschew biologically implausible reductionist models of complex biology.
Do Brains Operate at a Tipping Point? New Clues and Complications
New experimental results simultaneously advance and challenge the theory that the brain’s network of neurons balances on the knife-edge between two phases.
A tray of rat cortex slice cultures in John Beggs’ lab at Indiana University. Electrodes record cascades of activity known as neuronal avalanches.
Eric Rudd/Indiana University
A team of Brazilian physicists analyzing the brains of rats and other animals has found the strongest evidence yet that the brain balances at the brink between two modes of operation, in a precarious yet versatile state known as criticality. At the same time, the findings challenge some of the original assumptions of this controversial “critical brain” hypothesis.
Understanding how the huge networks of neurons that comprise our thinking organs process information about the world is a daunting mystery for neuroscientists. One part of that broad puzzle is how a single physical structure can be primed to deal with life’s myriad demands. “If the brain is completely disordered, it cannot process information,” explained Mauro Copelli, a physicist at the Federal University of Pernambuco in Brazil and a coauthor of the new research. “If it’s too ordered, it’s too rigid to cope with the variability of the environment.”
In the 1990s, the physicist Per Bak hypothesized that the brain derives its bag of tricks from criticality. The concept originates in the world of statistical mechanics, where it describes a system of many parts teetering between stability and mayhem. Consider a snowy slope in winter. Early-season snow slides are small, while blizzards late in the season may set off avalanches. Somewhere between these phases of order and catastrophe lies a particular snowpack where anything goes: The next disturbance could set off a trickle, an avalanche or something in between. These events don’t happen with equal likelihood; rather, small cascades occur exponentially more often than larger cascades, which occur exponentially more often than those larger still, and so on. But at the “critical point,” as physicists call the configuration, the sizes and frequencies of events have a simple exponential relationship. Bak argued that tuning to just such a sweet spot would make the brain a capable and flexible information processor.
The idea has had its ups and downs. The first empirical evidence for it came from rat brain slices in 2003. John Beggs, a biophysicist at Indiana University, found that chain reactions of firing neurons, termed “neuronal avalanches,” came in particular arrays of sizes characteristic of criticality. Namely, any size was possible, but — like a snowy slope at its critical point — the frequency of avalanches exponentially depended on their size. Beggs argued that this “power law” relationship meant the brain slice was critical, triggering a flood of follow-up research. Critics, however, eventually showed that the claim was premature, as power laws show up in random systems, too — such as the word frequencies produced by a monkey at a typewriter.
Proponents faced two other conundrums: The so-called critical exponent defining a power law — the number indicating, for example, how many smaller avalanches occur relative to the larger ones — varied by setup, belying the notion of a universal mechanism behind the brain’s responses. Moreover, experimentalists found stronger signs of criticality in synchronized neural waves, which occur most often during deep sleep, than in the more scattershot firing patterns of alert animal brains. This difference puzzled researchers, who didn’t predict a relationship between criticality and synchronicity.
To address these challenges, Copelli and his collaborators drugged rats using a particular anesthetic that lets brains swing between the extremes of synchronization, sometimes firing in a synchronized manner typical of sleep and other times resembling the random static of awake brains. Recording the swells of neural activity in the primary visual cortex with dozens of metal probes, the group found that the sizes and durations, and the relationships between the sizes and durations, of neuronal avalanches all fit power law distributions with varying critical exponents — similar to Beggs’ 2003 findings in brain slices of dead rats.
Going further, though, they showed that when neurons fired with a certain moderate level of synchronicity, these three exponents fit together according to a simple equation. This relationship among the exponents satisfied a more stringent test for criticality suggested by critics in 2017. The brains of the anesthetized rats spent most of their time close to this state, seemingly hovering near the dividing line between two phases.
“It’s a smoking gun; you can’t escape it anymore,” said Beggs, who was not involved in the research. “It’s very hard to say that this is random.”
When the team looked in detail at where the critical point fell, however, they found that the rat brains weren’t balanced between phases of low and high neuronal activity, as predicted by the original critical brain hypothesis; rather, the critical point separated a phase in which neurons fired synchronously and a phase characterized by largely incoherent firing of neurons. This distinction may explain the hit-or-miss nature of past criticality searches. “The fact that we have reconciled the data from earlier research really points to something more general,” said Pedro Carelli, Copelli’s colleague and a coauthor of the research, which appeared in Physical Review Letters in late May.
But an anesthetized brain is not natural, so the scientists repeated their analysis on public data describing neural activity in free-roaming mice. They again found evidence that the animals’ brains sometimes experienced criticality satisfying the new gold standard from 2017. However, unlike with the anesthetized rats, neurons in the mice brains spent most of their time firing asynchronously — away from the alleged critical point of semi-synchronicity.
Copelli and Carelli acknowledge that this observation poses a challenge to the notion that the brain prefers to be in the vicinity of the critical point. But they also stress that without running the awake-animal experiment themselves (which is prohibitively expensive), they can’t conclusively interpret the mouse data. Poor sleep during the experiment, for instance, could have biased the animals’ brains away from criticality, Copelli said.
They and their colleagues also analyzed public data on monkeys and turtles. Although the data sets were too limited to confirm criticality with the full three-exponent relationship, the team calculated the ratio between two different power-law exponents indicating the distributions of avalanche sizes and durations. This ratio — which represents how quickly avalanches spread out — was always the same, regardless of species and whether the animal was under anesthesia. “To a physicist, this suggests some kind of universal mechanism,” Copelli said.
Alain Destexhe of the National Center for Scientific Research (CNRS) in France, the critic who proposed the equation relating the three exponents as a test of criticality, called the universality of the results “astonishing,” but said he isn’t sure if it means what critical brain proponents say. He points out that because avalanches in alert brains scale similarly to those in brains under deep anesthesia — when they have no sensory input — criticality may have nothing to do with how the brain processes information, and could be due to some other aspect of brain dynamics.
Next, the Brazilian team hopes to study how the rats’ synchronous and asynchronous brain phases relate to behavior, a puzzle compounded by the fact that synchronized bursts are common during sleep, but also occur in awake brains.
Other research has linked sleep with restoring a destabilized brain to its critical point, and Beggs thinks further studies may someday establish deeper connections between mental health and the physics of the brain. But first, Copelli says, the criticality field needs to address more basic questions. “The current theory can’t explain the result,” he said, meaning his and his colleagues’ new findings, “so it opens the race again for models.
1ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona, Spain
2Institute of Evolutionary Biology (UPF-CSIC), Barcelona, Spain
3European Centre for Living Technology, University Ca’ Foscari, Venezia, Italy
The presence of modular organization is a common property of a wide range of complex systems, from cellular or brain networks to technological graphs. Modularity allows some degree of segregation between different parts of the network and has been suggested to be a prerequisite for the evolvability of biological systems. In technology, modularity defines a clear division of tasks and it is an explicit design target. However, many natural and artificial systems experience a breakdown in their modular pattern of connections, which has been associated with failures in hub nodes or the activation of global stress responses. In spite of its importance, no general theory of the breakdown of modularity and its implications has been advanced yet. Here we propose a new, simple model of network landscape where it is possible to exhaustively characterize the breakdown of modularity in a well-defined way. Specifically, by considering the space of minimal Boolean feed-forward networks implementing the 256 Boolean functions with 3 inputs, we were able to relate functional characteristics with the breakdown of modularity. We found that evolution cannot reach maximally modular networks under the presence of functional and cost constraints, implying the breakdown of modularity is an adaptive feature.
Aha! Here’s a good scientific argument why identification of ‘non-deterministic systems’ is dependent on perspective, level of analysis and (importantly) good old-fashioned accuracy – ‘indeterminacy’ is not an act of fiat at all levels in every sense based on certain systems conditions. I am pleased to see this in so concise a form.
as the author says:
“Of course, this doesn’t mean that there aren’t interesting discussions to be had on chaos and prediction. I’ve written before on computer science on prediction and the edge of chaos and on how stochasticity, chaos and computations can limit prediction. But we shouldn’t use chaos or complexity to stop ourselves from asking questions or making predictions. Instead, we should use apparent complexity as motivation to find the limits of more linear theories. And whatever system we work with, we should look for overarching global principles like the conservation of energy that we can use to abstract over the chaotic microdynamics.”
If you want a visual intuition for just how unpredictable chaotic dynamics can be then the go-to toy model is the double pendulum. There are lots of great simulations (and some physical implementations) of the double pendulum online. Recently, /u/abraxasknister posted such a simulation on the /r/physics subreddit and quickly attracted a lot of attention.
In their simulation, /u/abraxasknister has a fixed center (block dot) that the first mass (red dot) is attached to (by an invisible rigid massless bar). The second mass (blue dot) is then attached to the first mass (also by an invisible rigid massless bar). They then release these two masses from rest at some initial height and watch what happens.
The resulting dynamics are at right.
It is certainly unpredictable and complicated. Chaotic? Most importantly, it is obviously wrong.
But because the double pendulum is a famous chaotic system, some people did not want to acknowledge that there is an obvious mistake. They wanted to hide behind chaos: they claimed that for a complex system, we cannot possibly have intuitions about how the system should behave.
In this post, I want to discuss the error of hiding behind chaos, and how the distinction between microdynamics and global properties lets us catch /u/abraxasknister’s mistake.
A number of people on Reddit noticed the error with /u/abraxasknister’s simulation right away. But the interesting part for me was how other people then jumped in to argue that the correctors could not possibly know what they were talking about.
That’s possible, but also double pendulums involve chaos theory. It’s likely this is just a frictionless simulation.
These detractors were trying to hide behind complexity. They thought that unpredictable microdynamics meant that nothing about the system is knowable. Of course, they were wrong.
But their error is an interesting one. This seems like an unfortunately common misuse of chaos in some corners of complexology. We say that a some system (say the economy) is complex. Thus it is unknowable. Thus, people offering liner theories (say economists) cannot possibly know what they are talking about. They cannot possibly be right.
Have you encountered variants of this argument, dear reader?
You can’t just slap the word chaos on something and expect the conservation of energy to no longer apply
So let us use the conservation of energy to explain why the simulation is wrong.
From the initial conditions, we can get an estimate of the system’s energy. This is particularly easy in this case since the masses start at rest at some height — thus all energy is potential energy. From this — due to the time-invariance of the Hamiltonian specifying the double pendulum — we know by Noether’s theorem that this initial energy will be conserved. In this particular case, this means that we cannot ever have both of the masses above their initial position at the same time. If that happened then (just) the potential energy of this configuration will be strictly higher than the total initial energy. Since we see both of the masses simultaneously above their initial position in the gif, we can conclude that there is an error in /u/abraxasknister simulation.
I enjoy this kind of use of global abstract argument to reason without knowing the details of microdynamics. For me, this is the heart of theoretical computer science.
Based on this violation of energy conservation, many theories were discussed for what the error in the simulation might have been. And the possibility of energy-pumping from finite step size was a particularly exciting candidate. A ‘bug’ (that can be a ‘feature’) that I’ll discuss another day in the context of replicator dynamics.
The actual main mistake turned out to be much less exciting: a typo in the code. A psy instead of phi in one equation.
I’m sure that all of us that have coded simulations can relate. If only we always had something as nice as the conservation of energy to help us debug.
Of course, this doesn’t mean that there aren’t interesting discussions to be had on chaos and prediction. I’ve written before on computer science on prediction and the edge of chaos and on how stochasticity, chaos and computations can limit prediction. But we shouldn’t use chaos or complexity to stop ourselves from asking questions or making predictions. Instead, we should use apparent complexity as motivation to find the limits of more linear theories. And whatever system we work with, we should look for overarching global principles like the conservation of energy that we can use to abstract over the chaotic microdynamics.
About Artem Kaznatcheev
From the Department of Computer Science at Oxford University and Department of Translational Hematology & Oncology Research at Cleveland Clinic, I marvel at the world through algorithmic lenses. My mind is drawn to evolutionary dynamics, theoretical computer science, mathematical oncology, computational learning theory, and philosophy of science. Previously I was at the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, and the School of Computer Science and Department of Psychology at McGill University. In a past life, I worried about quantum queries at the Institute for Quantum Computing and Department of Combinatorics & Optimization at University of Waterloo and as a visitor to the Centre for Quantum Technologies at National University of Singapore. Meander with me on Google+and Twitter.
During the past month or so I have been ruminating over my post of May 10, 2019, which was about my latest effort to come to a better understanding of the workings of a systems approach described in a workbook that I am co-writer of. Wicked Solutions, as it is called, uses three operable systems concepts to explain systems thinking in a nutshell and encourages learners to apply them directly on a ‘wicked’ problem of their own so as to gain a direct, hands-on experience of their usefulness. The three concepts are: inter-relationships, perspectives, and boundaries. Last week I had a discussion with two members of staff of Australia’s Southern Cross University, Ken Doust and Andrew Swan, who has used Wicked Solutions in one of his courses. They had several critical observations that set me thinking. One idea was to focus the dialectical systems approach of Wicked…
GBSKIP NAVIGATION9+ 0:11 / 4:06BRIEF HISTORY OF THE CYBERNETIC PARADIGM46 views50SHARESAVEJavier Livas CantuPublished on 12 Jun 2019SUBSCRIBED 1.8KFrom the Bible to the Newtonian Paradigm, knowledge has finally come to depend on the CYBERNETIC PARADIGM. Most science today is done using models.
Katherine Long and Giles Hutchins are hosting a unique nature-immersion retreat, an opportunity for profound reflection which will renew, re-energise and regenerate – a day that will support you to deeply re- connect with your sense of purpose, the wider systems you are a part of, and the future you seek to co-create.
Together with other change practitioners (leaders, coaches, organization design and development practitioners, activists) we will explore questions such as:
What shifts in ourselves, our work, and in our professional communities are needed to respond to the scale of threat this planet faces?
What can we learn from living-systems about resilience, adaptability, collective intelligence and change?
How do we support healing and wholeness in a fragmented world?
What can we do we resource ourselves and each other to stay aligned to ‘deep purpose’ whilst…
“Adaptive learning” might be one of the latest education technology buzzwords, one that’s often uttered alongside that other popular adjective “personalized.” But, like much in ed-tech, the concept is not new. (And like much in ed-tech, the “History” section for the Wikipedia entry on “adaptive learning” is woefully incomplete.)
The earliest teaching machines – those built by B. F. Skinner and Sidney Pressey, for example – were not adaptive. They did promise “personalization” of sorts by allowing students to move at their own pace through the lessons, but that path was quite rigidly scripted. The machines only responded to right or wrong, allowing students to proceed to the next question if they got the previous question right. And the point, particularly of machines designed around Skinner’s theory of “operant conditioning,” was for the student to get it right, that is to maximize the positive reinforcement. As Paul Saettler writes in his 1968 book, A History of Instructional Technology, “Effective Skinnerian programming requires instructional sequences so simple that the learner hardly ever makes an error. If the learner makes too many errors – more than 5 to 10 percent – the program is considered in need of revision.” These machines could not diagnose why a student got an answer wrong or right; again, according to behaviorist theory, the machines were designed so to make sure students got it right.
Despite initial excitement of learning with a new technology like one of Skinner’s teaching machines, many students found these devices to be quite boring. “The biggest problem with programmed instruction was simply that kids hated it,” writes Bob Johnstone in Never Mind the Laptops. “In fact, it drove them nuts – especially the brighter ones. The rigidity of the seemingly endless, tiny-steps, one-word-answer format bored clever students to tears. They soon found ingenious ways of circumventing the programs and even, in some cases, of sabotaging the machines. A well-placed wad of chewing gum could throw a whole terminal out of whack.”
Adaptive Teaching Machines
Best known for Conversation Theory, the British cybernetician Gordon Pask designed a different sort of teaching machine – an adaptiveteaching machine – patenting it in 1956. This patent provides the basis for the self-adaptive keyboard instructor (SAKI), which the theorist Stafford Beer described as “possibly the first truly cybernetic device (in the full sense) to rise above the status of a ‘toy’ and reach the market as a useful machine.”
The SAKI was designed to train people to use a Hollerith key punch, a manual device used to punch holes in cards used in turn for data processing. There was at the time quite a significant demand for keypunch operators – mostly women – as this was, until the 1970s, a common method for data entry.
Image credits: Gordon Pask, “SAKI: Twenty-five years of adaptive training into the microprocessor era”
Like many teaching machines (then and now), SAKI purported to function like a human tutor. But unlike earlier teaching machines, the adaptive component of Pask’s devices offers more than just an assessment of right or wrong: they identify and measure a student’s answers – accuracy, response time – and adjust the next question accordingly. That is, the difficulty of the questions are not pre-programmed or pre-ordained.
Teaching Machines: An American Story (And the Case for Gordon Pask)
Audrey Watters
on
6 min read
One of the criticisms I get about my work is that it is too focused on education technology in the US. I typically hear this every December, when I publish my year-end review of the field. Although I recognize that Americans are prone to self-centeredness, I don’t purposefully overlook the rest of the world’s experiences out of any sense of nationalism. Rather, I believe that education technology is imagined, developed, and implemented in a particular context. And that context is shaped by a country’s school systems, educational policies, and larger social, economic, and political forces.
(I often say: if you want to write an annual ten-part series about how your country has experienced education technology, please do.)
As I’ve written previously, many histories of education technology have been written as though this context is irrelevant. They spend little time talking about what was happening in education (as an institution, for example). As such, new technologies seem to appear out of nowhere – a creation of a genius inventor, rather than a reflection some larger cultural forces.
Teaching Machines will be limited in its scope to a particular time period in a particular country – that is, to the mid–1920s thru the late 1960s in the US. I want to be able to contextualize the work of Sidney Pressey, B. F. Skinner, Norman Crowder, and others by addressing how their machines coincided with developments in educational psychology and standardized testing; how they were responses to changes in student demographics and to the launch of Sputnik; how these machines reflected a twentieth-century fascination with gadgetry and automation; how they were part of a much larger push by businesses to sell curriculum products to schools; how they underscored that most American of values, individualism, with their proponents calling for instruction to become more “individualized.”
Education technology is not solely an American story. But the one I’m writing will be.
There is (I think) one possible exception to the American setting and American cast of characters, and that’s the British cybernetician Gordon Pask.
This webinar was hosted by Deeanna Burleson, Ph.D. in the ‘Topics in the field of systems science’ series intended as a contribution to the International Society for Systems Science (here is a link to the original announcement of the webinar).
The webinar (video link below) starts with a 45 minute presentation offering reflections on nearly 20 years of experience in trying to apply whole systems thinking to the field of design for sustainability and more recently regenerative development practice.
I explore the limitations of a quantity focussed science and the need for a new ‘science of qualities’. I explore how the work and thinking of Donella Meadows evolved from ‘leverage points 1.0’ to ‘leverage points 2.0’ and on to ‘Dancing with systems. Nora Bateson’s ‘warm data’ approach is mentioned as a related example of this relationships and qualities focussed work with systems from the inside. Katia Laszlo’s framing from ‘systems thinking to systems being’ is also briefly addressed.
I go on to explore some of the most common mistakes made in intervening in complex adaptive systems and highlight the need for embracing the fundamental unpredictability of such systems as we move from being detached observers to being engaged participants of such systems. In this context I also speak to the power that lies in asking the right questions and asking questions rather than offering a list of principles to follow.
To ground these theoretical considerations and paradigmatic shifts in a practical example, I offer a brief summary of my work for Gaia Education on the ‘SDG Flashcards’, the ‘SDG Project Canvas’, the ‘SDG Training of Multipliers’ and the ‘Multipliers Handbook’ as an example of ‘designing for positive emergence’ and taking Bucky Fuller’s advice that “to change the way people think, don’t tell them what to think, give them a tool the use of which will change the way they think”. The flashcards by Gaia Education and UNESCO have been used successfully on 5 continents and translated into 6 languages.
… after the presentation there is another 45 minuter conversation or Q&A session that addresses a wide range of topics related to ‘Designing Regenerative Culture’ … enjoy and share!
You can watch the slides with animations here: http://www.strategicstructures.com/?p=1511
Short description
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It is often said that organisations are full of paradoxes. But this refers to contradictions and tensions. It is understood as something that needs to be taken care of. When organisations are looked at as social systems, however, it becomes clear that they are only possible because of paradoxes, and particularly paradoxes of self-reference. Understanding how these paradoxes create and maintain organisations is an important skill for practitioners trying to make sense of what’s going on and improve it. The basic generative organisational paradox is that of decisions. It brings new light not only on decision patterns and dependencies, but also on understanding the nature of objectives, power, and relations with clients.
CECAN Webinar: The Human, Learning, Systems approach to managing in complexity
CECAN Webinar: The Human, Learning, Systems approach to managing in complexity
Tuesday 1st October 2019, 13:00 – 14:00 BST
Presenter: Dr Toby Lowe, Senior Lecturer in Public Leadership and Management, Newcastle Business School
You are warmly invited to join us for the following CECAN Webinar…
Webinar Overview:
The webinar views the challenge of creating complexity-informed evaluation by seeing it as a public management challenge. How can public management adopt a more complexity-informed approach? The session will outline an emerging complexity-informed approach to public management: the Human, Learning, Systems (HLS) approach. The HLS approach involves public services responding to the variety of human need through bespoke service provision, using learning as the engine for performance improvement and stewarding the health of the systems which produce social outcomes.
Human
One of the three key tasks of managing work in an HLS way (including funding and commissioning of work) means creating the conditions in which people can build effective human relationships.
This means understanding human variety, using empathy to understand the lives of others, recognising people’s strengths, and trusting those who do the work. Variety, Empathy, Strengths and Trust (VEST).
Learning
In complex environments people are required to learn continuously in order to adapt to the dynamic, ever-changing nature of the work. In complex environments, there is no simple interventions which “works” to tackle a problem. “What works” is an on-going process of learning and adaptation.
It is the job of managers to enable staff to learn continuously as the tool for performance improvement. This means using measures to learn, not for reward/punishment. It means creating the conditions where people can be honest about their mistakes and uncertainties. It means creating reflective practice environments between and across peer groups.
This requires funders/commissioners to fund for learning and adaptation, not for “results”.
Systems
The outcomes we care about are not delivered by organisations. They are produced by whole systems – by hundreds of different factors working together. The final job of managers is therefore to act as Systems Stewards – to enable actors in the system to co-ordinate and collaborate effectively – because that it was will enable positive outcomes to emerge.
The HLS approach has recently been outlined in this report.
Presenter Biography (Dr Toby Lowe, Senior Lecturer in Public Leadership and Management, Newcastle Business School):
My purpose as an academic is to help improve the funding, commissioning and performance management of social interventions (across the public, private and voluntary sectors). My research team has used complexity theory to create a critique of New Public Management approaches, particularly highlighting the problems created by attempts to use Outcome-Based Performance Management (e.g. Payment by Results) in complex environments.
We have also developed a new complexity-informed paradigm for the funding, commissioning and performance management of social interventions, and are undertaking action research programmes with public and voluntary sector funders and delivery organisations to explore how this paradigm is implemented in practice, and to support the development of a Community of Practice around this new paradigm.
My team is also conducting working as a Learning Partner for the Lankelly Chase Foundation’s inquiry into place-based system change. In this context we are exploring how learning functions as a mechanism for system change.
I began my academic life as a political philosopher (my PhD is in the concept of community in political theory). After completing my PhD I worked in both the public and voluntary sectors for 15 years. My previous job before returning to academia was as Chief Executive of Helix Arts, a North East charity specialising in participatory arts practice with marginalised groups.
From 2015-2018 I worked at Newcastle University Business School, and Open Lab. In this context I also worked with PhD students at Open Lab exploring the role of digital technology in enabling people and organisations to reflect on their performance, and to contribute to learning in complex systems.
How to Join:
This talk will take place via a Zoom Webinar – please click hereto register for a place.
After registering, you will receive a confirmation email containing information about joining the webinar. In case you are unable to attend, a recording of the webinar will be uploaded to our website following the event.
pdf direct link https://www.researchgate.net/profile/Polinpapilinho_Katina/publication/323202439_Systems_theory-based_construct_for_identifying_metasystem_pathologies_for_complex_system_governance/links/5b365aa0aca2720785f69eff/Systems-theory-based-construct-for-identifying-metasystem-pathologies-for-complex-system-governance.pdf
The theoretical construct definitive of the manner of operation of that class of systems that includes living systems. This term, combined from the Greek auto- (self) and poiesis(creation/production), was coined by Maturana in (approximately) 1972 (Cf. Maturana & Varela, 1980, p. xvii). Often loosely translated as ‘self-creation’ or ‘self-production’, the term connotes the process or dynamic by which an autopoietic machine / system maintains its autopoietic organization (via intrinsic processes of production of components realizing this particular organization). More specifically, autopoiesis is attributed to a machine (delineated as a a network of processes) which through that network of processes produces the components that:
“(1) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and(2) constitute it (the machine) as a concrete unity in the space in which they [the components] exist by specifying the topological domain of its realization as such a network.”
(Varela, 1979, p. 13)
In the primary literature, autopoiesis is not directly defined as a process. Instead it is defined indirectly, on the basis of how an ‘autopoietic machine’ operates. There are, in fact, very few instances in the primary literature where ‘autopoiesis’ is substantively treated in and of itself, and then only as a process characteristic of ‘self-production’ or ‘homeostatic organization’ — constructs themselves framed mechanicistically with respect to the subject system’s architectonics. For example, Varela (1979, pp. 24-26) comes closest to addressing ‘autopoiesis’ directly in the course of discussing productions of relations in a given system:
“What makes this system a unity with identity and individuality is that all the relations of production are coordinated in a system describable as having an invariant organization. In such a system any deformation at any place is compensated for …by keeping its organization constant as defined by the relation of the productions that constitute autopoiesis. The only thing that defines the cell as a unity (as an individual) is its autopoiesis, and thus, the only restriction put on the existence of the cell is the maintenance of autopoiesis.”(Varela, 1979, p. 26, emphasis in the original)
“…[A]utopoiesis may arise in a molecular system if the relations of production are concatenated in such a way that they produce components specifying the system as a unity that exists only while it is actively produced by such concatenation of processes. This is to say that autopoiesis arises in a molecular system only when the relation that concatenates these relations is produced and maintained constant through the production of the molecular components that constitute the system through this concatenation.”
(Varela, 1979, pp. 26-27)
NOTE: Given the above distinctions and qualifications about the nature and origin of the construct ‘autopoiesis’, the details on what makes a composite unity (system) ‘autopoietic’ are therefore to be found under the entries for autopoietic machine and autopoietic organization.
The strict, though indirect, definition of autopoiesis proposed in the early papers was intended to provide a basis for overcoming vague or problematical characterizations of living systems — particularly those which represented vitalistic explanation of biological phenomena. As Maturana (1980a, p. 45) put it, the construct of autopoiesis:
“…resulted from the direct attempt … to provide a complete characterization of the organization that makes living systems self-contained autonomous unities, and that makes explicit the relations among their components which must remain invariant under a continuous structural transformation and material turnover.”
This passage reinforces the viewpoint that it is the constitutive organization of an autopoietic system which is primary in delineating autopoiesis. This is reflected even in the less formal popular account given in The Tree of Knowledge (Maturana & Varela, 1987, 1992):
“When we speak of living beings, we presuppose something in common between them; otherwise we wouldn’t put them in the same class we designate with the name ‘living.’ What has not been said, however, is: what is the organization that defines them as a class? Our proposition is that living beings are characterized in that, literally, they are continually self-producing. We indicate this process when we call the organization that defined them an autopoietic organization.“(Maturana & Varela, 1992, p. 43, emphasis added)
Having said that, Maturana and Varela proceed (as they have consistently done in the more formal literature) to delineate the autopoietic organization as the basis for ‘indicating’ the process of ‘autopoiesis.’
These last quotations illustrate a point which has proven somewhat problematical over the years. As mentioned at the outset, ‘autopoiesis’ has in fact been delineated and formally defined in terms of the constitution and operational character of an autopoietic machine or system. This definitional approach was entirely consistent with the mechanicistic perspective from which Maturana and Varela initially proceeded. To have invoked an ephemeral ‘autopoiesis’ (e.g., as a processual or qualitative referent) would have arguably entailed sliding into the sort of vitalistic explanation which they explicitly opposed and stringently avoided.
In other words, ‘autopoiesis’ is an abstract construct known solely in relation to a machine / system of a particular constitution which maintains its key constitutive character over time. Strictly speaking, autopoiesis has not been positively defined as a type of process in and of itself, even though it is clear in the context of its primary literature (e.g., Maturana & Varela, 1980) that it is the dynamic or process evidenced by, and reciprocally preservative of, the autopoietic organization / autopoietic machine. Nonetheless, it became common practice (even on occasion by Maturana and Varela themselves) to allude to ‘autopoiesis’ as a rhetorical shorthand connoting (in terms of process) the constitutive and operational details of a particular system. This is most evident when addressing the dynamics of an autopoietic system — i.e., when the processes manifest in the autopoietic network comprise the referential foreground, and the mechanics of the network itself are relegated to the background.
Given the above-cited conditions, it is possibly understandable, though definitely somewhat ironic, that this indirectly- or allusively-defined shorthand term should become the de facto label for the essence of Maturana and Varela’s work, as well as a common label for that work itself (Cf. 2. below). So long as such invocations retain (or at least can be linked to) the sort of mechanicistic context in which the process ‘autopoiesis’ is definitively framed, this is not problematical. What is problematical is explanatory invocation (and reliance upon) the process or dynamic of ‘autopoiesis’ absent this context. To invoke ‘autopoiesis’ (e.g., as ‘self-production’) without concomitantly explaining the constitutive elements of the system(s) for which such invocation is made, is to deny any basis for evaluating the applicability of the construct (as it was defined originally). The most well-known example of such an invocation would be that of German sociologist Niklas Luhmann, who adopted ‘autopoiesis’ as a processual construct in analyzing social systems, yet never (to date) bothered to explain what in his view are the key constitutive elements (e.g., ‘organization’, ‘structure’) by which such an application might be assessed in terms of Maturana and Varela’s clear-cut definitional criteria.
The explanatory risk in invoking ‘autopoiesis’ absent attention to the machine / system manifesting it has two distinguishable (but admittedly intertwined) components. The first is that an observer may simplistically project the feature ‘autopoiesis’ onto a unity with which she has insufficient or imperfect observational engagement upon which to base its ascription. Phrased another way, stripping the processual construct away from the machine manifesting it opens the possibility of its mistaken attribution to something only partially or indirectly observed. Varela (1979) provides some illustration for this type of risk in writing of recognizing an autopoietic system (as distinct from autonomous systems in general):
“In general, the actual recognition of an autopoietic system poses a cognitive problem that has to do both with the capacity of the observer to recognize the relations that define the system as a unity, and with his capacity to distinguish the boundaries that delimit this unity in the space in which it is realized (his criteria of distinction). Since it is a defining feature of an autopoietic system that it should specify its own boundaries, a proper recognition of an autopoietic system as a unity requires that the observer perform an operation of distinction that defines the limits of the system in the same domain in which it specifies them through its autopoiesis. If this is not the case, he does not observe the autopoietic system as a unity, even though he may conceive it.”(Varela, 1979, p. 54)
The second, but related, explanatory risk has to do with ascribing autopoiesis to systems with which the observer / explainer may have ‘proper’ observational engagement, but for which the observer ignores addressing the key features of the autopoietic organization by which the process of autopoiesis is defined. Varela (1979) also addresses this issue in passing, during his discussion of ascribing autopoiesis to other (autonomous) systems (i.e., systems of similar apparent constitution or apparent mode of operation, but not ‘living systems’). Varela notes that other systems, being autonomous, entail:
“…assertion of the system’s identity through its functioning in such a way that observation proceeds through the coupling between the observer and the unit in the domain in which the unity’s operation occurs.What is unsatisfactory about autopoiesis for the characterization of other unities … is also apparent from this very description. The relations that characterize autopoiesis are relations of productions of components. … Given this notion of production of components, it follows that the cases of autopoiesis we can actually exhibit, such as living systems or model cases …, have as a criterion of distinction a topological boundary, and the processes that define them occur in a physical-like space…
Thus, the idea of autopoiesis is, by definition, restricted to relations of productions of some kind, and refers to topological boundaries. These two conditions are clearly unsatisfactory for other systems exhibiting autonomy.” […of which Varela specifically mentions animal societies and human social institutions — Ed.]
(Varela, 1979, p. 54, emphasis in the original)
The difference between autonomy and autopoiesis is that autopoietic systems must produce their own components in addition to conserving their organization . Autonomous machines need only exhibit organizational closure, and they are not required to produce their own components as part of their operation.
A term invoked by Maturana (1978) in summarily characterizing autopoietic systems. He states autopoietic closure “… is the condition for autonomy in autopoietic systems in general, and that it “… is realized through a continuous structural change under conditions of continuous material interchange with the medium.” With regard to the thermodynamic constraints relevant to the physical space, “… autopoietic closure in living systems does not imply the violation of these constraints, but constitutes a particular mode of realization of autopoiesis in a space in which thermodynamic constraints are valid.”
This term is used only within one paragraph in this paper, and as such it’s somewhat difficult to discern whether it is being used as (a) a summary term for the ‘mode of closure’ evidenced in autonomous / autopoietic systems generally, or (b) a specific analogue to more clearly delineated constructs such as operational closure or organizational closure. Because the term is invoked specifically to discuss autonomy , one might make a case that it connotes organizational closure. However, there is no evidence beyond this to suggest such a linkage between the two constructs.
A machine / system which is a member of the class of autonomous systems and which meets the requirement of being organized (defined as a unity ) as a network of processes of production, transformation and destruction of components that produces the components which:
(i) through their interactions and transformations regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it as a concrete unity in the space in which they exist by specifying the topological domain of its realization as such a network. (Maturana & Varela, 1980, p. 135, Cf. : Varela, 1979, p. 13)
Any unity meeting these specifications is an autopoietic machine / system, and any such autopoietic system realized in the physical space is a living system. The particular substantiation of a given unity — its structure — is not a sufficient factor for making the system “living”. The key feature of a living system is maintenance of its organization, i.e, preservation of the relational network which defines it as a systemic unity. Phrased another way, ‘…autopoietic systems operate as homeostatic systems that have their own organization as the critical fundamental variable that they actively maintain constant.’ (Maturana, 1975, p. 318)
Varela, Maturana & Uribe (1974) provide a concise set of criteria for autopoietic machine, arranged as a 6-point key by which one may proceed step-by-step in evaluating autopoiesis for a given unity. This key is illustrated in Table AutoKey below.
TABLE AUTOKEY:
A Six-Step Key for Determining Whether a Given Unity is Autopoietic
(Varela, Maturana & Uribe, 1974, pp. 192-193)
1. Determine if:
The unity has identifiable boundaries (via interactions)
If so:Proceed to 2.
If not:“The unity is indescribable and we can say nothing.” (p. 192)
2. Determine if:
“…there are constitutive elements of the unity, that is, components of the unity.” (p. 192)
If so:Proceed to 3.
If not:“…the unity is an unanalyzable whole and therefore not an autopoietic system.” (p. 192)
3. Determine if:
…the unity is a mechanistic system, that is, the components properties are capable of satisfying certain relations that determine in the unity the interactions and transformations of these components.” (p. 192)
If so:Proceed to 4.
If not:“…the unity is not an autopoietic system.” (p. 193)
4. Determine if:
“…the components that constitute the boundaries of the unity constitute these boundaries through preferential neighborhood relations and interactions between themselves, as determined by their properties in the space of their interactions.” (p. 193)
If so:Proceed to 5.
If not:“…you do not have an autopoietic unity because you are determining its boundaries, not the unity itself.” (p. 193)
5. Determine if:
“…the components of the boundaries of the unity are produced by the interactions of the components of the unity, either transformation of previously produced components, or by transformations and/or coupling of non-component elements that enter the unity through its boundaries.” (p. 193)
If so:Proceed to 6.
If not:“…you do not have an autopoietic unity.” (p. 193)
6. Determine if:
“…all the other components of the unity are also produced by the interactions of its components as in 5.
If so:“…you have an autopoietic unity in the space in which its components exist.” (p. 193, emphasis in the original)
If not:“…and there are components in the unity not produced by components of the unity as in 5., or if there are components of the unity which do not participate in the production of other components, you do not have an autopoietic unity.” (p. 193)
Autopoietic machines are the opposite of allopoietic machines, which are defined in terms of a purpose other than maintenance of their own organization. However, an observer can ascribe allopoietic ( allo-referred) status to an autopoietic machine within a subsuming context. Autopoietic machines may be described or manipulated as components of “…a larger system that defines the independent events which perturb them … [and] can in fact be integrated into a larger system as a component allopoietic machine, without any alteration in its autopoietic organization.” (Varela, 1979, p. 16) (See Also: higher-order, second-order, third-order) Conversely, an observer may analytically decompose an autopoietic machine, treating each of its “…partial homeostatic and regulatory mechanisms as allopoietic machines (submachines) by defining their input and output surfaces.” (Varela, 1979, p. 17) Such a decomposition does not sum up (as a collection of allopoietic submachines) to an appropriate description of autopoietic machines, because it “…does not reveal the nature of the domain of interactions that … [autopoietic machines] … define as concrete entities operating in the physical universe.” (Varela, 1979, p. 17)
A term used by Varela (1979, p. 13) to denote that “…particular network of processes (relations) of production of components …” which characterizes an autopoietic machine / system.
autopoietic organization
The generic term denoting the organization characterizing autopoietic machines / systems. The term “…simply means processes interlaced in the specific form of a network of productions of components which realizing the network that produced them constitute it as a unity.” (Maturana & Varela, 1980, p. 80)
“An autopoietic organization constitutes a closed domain of relations specified only with respect to the autopoietic organization that these relations constitute, and thus it defines a space in which it can be realized as a concrete system, a space whose dimensions are the relations of production of the components that realize it.”(Maturana & Varela, 1980, p. 135)
Note that this “autopoietic space” is not isomorphic with the general physical space which is the context for realization of the composite unity . Perhaps the best interpretation is to consider an autopoietic space to be analogous to a state space (a depictive construct for a system’s attributes). Maturana and Varela (1980, pp. 90 ff.) ascribe three dimensions to the autopoietic space, corresponding to the three classes of relations of production.
Christina N. Lessov-Schlaggar
Joshua B. Rubin
Bradley L. Schlaggar
Sergi Valverde
About Artem Kaznatcheev
Hiding behind chaos and error in the double pendulum
JUNE 15, 2019 BY ARTEM KAZNATCHEEV LEAVE A COMMENT
If you want a visual intuition for just how unpredictable chaotic dynamics can be then the go-to toy model is the double pendulum. There are lots of great simulations (and some physical implementations) of the double pendulum online. Recently, /u/abraxasknister posted such a simulation on the /r/physics subreddit and quickly attracted a lot of attention.
The resulting dynamics are at right.
It is certainly unpredictable and complicated. Chaotic? Most importantly, it is obviously wrong.
But because the double pendulum is a famous chaotic system, some people did not want to acknowledge that there is an obvious mistake. They wanted to hide behind chaos: they claimed that for a complex system, we cannot possibly have intuitions about how the system should behave.
In this post, I want to discuss the error of hiding behind chaos, and how the distinction between microdynamics and global properties lets us catch /u/abraxasknister’s mistake.
A number of people on Reddit noticed the error with /u/abraxasknister’s simulation right away. But the interesting part for me was how other people then jumped in to argue that the correctors could not possibly know what they were talking about.
For example, /u/Rickietee10 wrote:
Or /u/chiweweman’s dismissing a correct diagnosis of the mistake with:
These detractors were trying to hide behind complexity. They thought that unpredictable microdynamics meant that nothing about the system is knowable. Of course, they were wrong.
But their error is an interesting one. This seems like an unfortunately common misuse of chaos in some corners of complexology. We say that a some system (say the economy) is complex. Thus it is unknowable. Thus, people offering liner theories (say economists) cannot possibly know what they are talking about. They cannot possibly be right.
Have you encountered variants of this argument, dear reader?
This kind of argument is wrong. And in the case of the double pendulum, /u/GreatBigBagOfNope responded best:
So let us use the conservation of energy to explain why the simulation is wrong.
From the initial conditions, we can get an estimate of the system’s energy. This is particularly easy in this case since the masses start at rest at some height — thus all energy is potential energy. From this — due to the time-invariance of the Hamiltonian specifying the double pendulum — we know by Noether’s theorem that this initial energy will be conserved. In this particular case, this means that we cannot ever have both of the masses above their initial position at the same time. If that happened then (just) the potential energy of this configuration will be strictly higher than the total initial energy. Since we see both of the masses simultaneously above their initial position in the gif, we can conclude that there is an error in /u/abraxasknister simulation.
I enjoy this kind of use of global abstract argument to reason without knowing the details of microdynamics. For me, this is the heart of theoretical computer science.
Based on this violation of energy conservation, many theories were discussed for what the error in the simulation might have been. And the possibility of energy-pumping from finite step size was a particularly exciting candidate. A ‘bug’ (that can be a ‘feature’) that I’ll discuss another day in the context of replicator dynamics.
The actual main mistake turned out to be much less exciting: a typo in the code. A psy instead of phi in one equation.
I’m sure that all of us that have coded simulations can relate. If only we always had something as nice as the conservation of energy to help us debug.
Of course, this doesn’t mean that there aren’t interesting discussions to be had on chaos and prediction. I’ve written before on computer science on prediction and the edge of chaos and on how stochasticity, chaos and computations can limit prediction. But we shouldn’t use chaos or complexity to stop ourselves from asking questions or making predictions. Instead, we should use apparent complexity as motivation to find the limits of more linear theories. And whatever system we work with, we should look for overarching global principles like the conservation of energy that we can use to abstract over the chaotic microdynamics.
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