Snakes all the Way Down: Varela’s Calculus for Self-Reference and the
Praxis of Paradise
André Reichel*
European Center for Sustainability Research, Zeppelin University, Friedrichshafen, Germany
This contribution seeks to commemorate Francisco Varela’s formal conceptions of self-reference, providing an overview of his writings while shedding some light in the praxis of self-reference, from where a future research agenda can be derived. The architecture of Varela’s thinking, determined by the interrelated notions of autopoiesis, autonomy, closure and self-reference, was examined. The emphasis was on the development and expansions of his calculus for self-reference from George Spencer Brown’s Laws of Form. After dealing with some of the criticism launched at both works, an appraisal of the praxis of self-reference of Varela’s thinking in action was given. The outlook rounds up this contribution, shedding some light on a possible future research agenda for the formalization of theory and praxis of self-reference.
Keywords Francisco Varela; self-reference; Laws of Form; closure; autonomy
“If everybody would agree that their current
reality is a reality, and that what we essentially
share is our capacity for constructing a reality,
then perhaps we could agree on a metaagreement for computing a reality that would
mean survival and dignity for everybody on
the planet, rather than each group being sold
on a particular way of doing things. Thus,
self-reference is, for me, the nerve of this logic
of paradise . . .”
Humans communicate using systems of interconnected stimuli or concepts — from language and music to literature and science — yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system’s network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with…
The Basis for the Viable System Model / Stafford Beer // Javier Livas
Stafford Beer explains the basic thinking behind the creation of the Viable System Model. The stuff of management is complexity and variety is the scientific name used to quantify complexity. Amplifiers and filters allow managers to exercise control over the process and then over the environment. Beer evokes Hegel’s thinking and the cybernetic paradigm which were mentioned in the first part of the conference also on YOUTUBE.
It is said that the Macy Conferences were the most important meetings of minds for the purpose of understanding control of human behavior. They are also considered as the breeding ground for Cybernetics and breakthroughs in Systems Theory. In essence, they brought “systems thinking” to the awareness of a cross-disciplinary group of intellectuals.
The Macy Conferences were ten meetings of scholars from different academic disciplines held in New York between 1946 and 1953. They were initiated and organised by Warren McCullochand the Josiah Macy, Jr. Foundation. The main purpose of these meetings was to set the foundations for a general science of the workings of the human mind.
The first conference, which was entitled “Feedback Mechanisms and Circular Causal Systems in Biological and Social Systems” was attended by an unprecedented network of great minds at the time:
William Ross Ashby; psychiatrist and a pioneer in cybernetics
Gregory Bateson; anthropologist, social scientist, linguist, visual anthropologist, semiotician and cyberneticist
Julian Bigelow; pioneering computer engineer
Heinz von Foerster; biophysicist, scientist combining physics and philosophy and architect of cybernetics
Lawrence K. Frank; social scientist
Ralph W. Gerard; neurophysiologist and behavioral scientist known for his work on the nervous system, nerve metabolism, psychopharmacology, and biological basis of schizophrenia
Molly Harrower; pioneering clinical psychologist
Lawrence Kubie; psychatrist
Paul Lazarsfeld; sociologist and founder of Columbia University’s Bureau for Applied Social Research
Kurt Lewin; psychologist, often regarded as the founder of social psychology
Warren McCulloch (chair); psychatrist, neurophysiologist and cybernetician
Margaret Mead; cultural anthropologist
John von Neumann; one of the foremost mathematicians of the 20th century
Walter Pitts; logician and co-author of the paper that founded neural networks
Arturo Rosenblueth; researcher, physician, physiologist and a pioneer of cybernetics
Leonard J. Savage; mathematician and statistician
Norbert Wiener; mathematician and founder of cybernetics
An incredible collection of guests attended the Cybernetics Group sessions during their seven years of existence. Among them were Max Horkheimer, the head of the Frankfurt School, and Claude Shannon, “the father of information theory”.
The foundation for the conferences was laid in May 1942, when the key participants met to exchange ideas, which created the enthusiasm and motivation to hold the Macy Conferences a few years later after the war. Attendance for the initial small meeting was by invitation only, and the two topics on the agenda were hypnotism and conditioned reflex. As soon as the war ended, Bateson contacted Fremont-Smith, pushing for some sort of conference to follow up on the concepts from the 1942 meeting.
Unfortunately, there is a lack of comprehensive documentation on the Macy Conferences. Part of this derives from the fact that the first five conferences were never formally documented with published proceedings.
Follow the links below to find out more in-depth information about the Macy Conferences (which also served as sources for this blogpost):
The purpose of this research is to explore the principles and concepts of systems theory in pursuit of a collection of complex systems archetypes that can be used for system exploration and diagnostics. The study begins with an examination of the archetypes and classification systems that already exist in the domain of systems theory. This review includes a critique of their purpose, structure, and general applicability. The research then develops and employs a new approach to grounded theory, using a visual coding model to explore the origins, relationships, and meanings of the principles of systems theory. The goal of the visual grounded theory approach is to identity underlying, recurrent imagery in the systems literature that will form the basis for the archetypes.
Using coding models derived from the literature, the study then examines the interrelationships between system principles. These relationships are used to clearly define the environment where the archetypes are found in terms of energy, entropy and time. A collection of complex system archetypes is then derived which are firmly rooted in the literature, as well as being demonstrably manifested in the real world. The definitions of the emerging complex systems archetypes are consistent with the environmental definition and are governed by the system’s behavior related to energy collection, entropy displacement, and the pursuit of viability.
Once the archetypes have been identified, this study examines the similarities and differences that distinguish them. The individual system principles that either define or differentiate each of the archetypes are described, and real-world manifestations of the archetypes are discussed. The collection of archetypes is then examined as a continuum, where they are related to one another in terms of energy use, entropy accumulation, self-modification and external-modification.
To illustrate the applicability of these archetypes, a case study is undertaken which examines a medium-sized organization with multiple departments in an industrial setting. The individual departments are discussed in detail, and their archetypical forms are identified and described. Finally, the study examines future applications for the archetypes and other research that might enhance their utility for complex systems governance.
DOI 10.25777/6xmx-r674
Recommended Citation
Akers, Walter L.. “An Approach for the Development of Complex Systems Archetypes” (2015). Doctor of Philosophy (PhD), dissertation, Engineering Management, Old Dominion University, DOI: 10.25777/6xmx-r674
https://digitalcommons.odu.edu/emse_etds/18
The motivation for this multi-part series is solely my observation that much of the writing on complexity theory seems to have arbitrarily ignored the vast systems theory literature. I don’t know whether this omission is deliberate (i.e., motivated by the political need to differentiate and promote one set of topical boundaries from another; a situation unfortunately driven by a reductionist funding process) or simply the result of ignorance. Indeed, Phelan (1999) readily admits that he was “both surprised and embarrassed to find such an extensive body of literature [referring to systems theory] virtually unacknowledged in the complexity literature.” I am going to assume the best of the complexity community and suggest that the reason systems theory seems to have been forgotten is ignorance, and I hope this, and subsequent, articles will familiarize complexity thinkers with some aspects of systems theory; enough to demonstrate a legitimate need to pay more attention to this particular community and its associated body of literature. The upcoming 11th Annual ANZSYS Conference/Managing the Complex V Systems Thinking and Complexity Science: Insights for Action (a calling notice for which can be found in the “Event Notices” section of this issue) is a deliberate attempt to forge a more open and collaborative relationship between systems and complexity theorists.
There are undoubtedly differences between the two communities, some of which are analyzed by Phelan (1999). Six years on from Phelan’s article, I find that some of the differences he discusses have dissolved somewhat, if not entirely. For example, he proposes that systems theory is preoccupied with “problem solving” or confirmatory analysis and has a critical interpretivist bent to it, whereas complexity theory is exploratory and positivist. Given the explosion in the management science literature concerning the application of complexity to organizational management I would argue that the complexity community as a whole is rather more inclined to confirmatory analysis than it might have been in 1999. I think that part of Phelan’s assessment that complexity theory is positivist comes from his characterization of complexity as a preoccupation with agent-based modelling. Again, this may well have been an accurate assessment in 1999, but I find the assessment a little too forced for 2005. See for example Cilliers (1998) and Richardson (2004a) for views of complexity that explore the limitations of a positivist-only view of complexity. Also, refer to Goldstein’s introduction “Why complexity and epistemology?” in this issue for reasons as to why a purely positivistic characterization of complexity theory is no longer appropriate. I think it is still valid to suggest that there are philosophical and methodological differences between the systems and complexity communities, although, if one looks hard enough there is sufficient diversity within the communities themselves to undermine such simplistic characterizations in the first place.
Of course there are differences between systems theory and complexity theory, but there are also many similarities. For example, most, if not all, the principles/laws of systems theory are valid for complex systems. Given the seeming lack of communication between complexity and systems theorists this series of articles will focus on the overlaps. The first few articles will review some general systems laws and principles in terms of complexity. The source of the laws and principles of general systems theory are taken solely from Skyttner’s General Systems Theory: Ideas and Applications, which was recently republished (Skyttner, 2001).
The second law of thermodynamics
The second law of thermodynamics is probably one of the most important scientific laws of modern times. The ‘2nd Law’ was formulated after nineteenth century engineers noticed that heat cannot pass from a colder body to a warmer body by itself. According to philosopher of science Thomas Kuhn (1978: 13), the 2nd Law was first put into words by two scientists, Rudolph Clausius and William Thomson (Lord Kelvin), using different examples, in 1850-51. Physicist Richard P. Feynman (Feynman, et al., 1963: section 44-3), however, argued that the French physicist Sadi Carnot discovered the 2nd Law 25 years earlier – which would have been before the 1st Law (conservation of energy) was discovered!
Simply stated the 2nd Law says that in any closed system the amount of order can never increase, only decrease over time. Another way of saying this is that entropy always increases. The reason this law is an important one to discuss in terms of complexity theory is that it is often suggested that life itself contradicts this law. In more familiar terms – to complexologists at least – the phenomena of self-organization, in which order supposedly emerges from disorder, completely goes against the 2nd Law.
There are several reasons why this assertion is incorrect. Firstly, the 2nd Law is concerned with closed systems and nearly all the systems of interest to complexity thinkers are open; so why would we expect the 2nd Law to apply? What if we consider the only completely closed system that we know of: the Universe? Even if it is True that the entropy of the Universe always increases this still does not deny the possibility of local entropy decrease. To understand why this is the case we need to understand the nature of the 2nd Law itself (and all scientific laws for that matter). The 2nd Law is a statistical law. This means it should really be read as on average, or on the whole, the entropy of closed systems will always increase. The measure of disorder, or entropy, is a macroscopic measure and so is the average over the whole system. As such there can be localized regions within the system itself in which order is created, or entropy decreases, even while the overall average is increasing. Another way to say this is that microlevel contradictions to macrolevel laws do not necessarily invalidate macrolevel laws. The 2nd Law and the self-organizing systems principle (which will be covered in a later installment) operate in different contexts and have different jurisdictions.
Despite the shortcomings of applying the 2nd Law to complex systems, there are situations in which it is still perfectly valid. Sub-domains, or subsystems, can emerge locally within a complex system that are so stable that, for a period, they behave as if they were closed. Such domains are critically organized, and as such that they could qualitatively evolve rather rapidly. However, during their stable periods it is quite possible that the 2nd Law is valid, even if only temporarily and locally.
The complementary law
The complementary law (Weinberg, 1975) suggests that any two different perspectives (or models) about a system will reveal truths regarding that system that are neither entirely independent nor entirely compatible. More recently, this has been stated as: a complex system is a system that has two or more non-overlapping descriptions (Cohen, 2002). I would go as far as to include “potentially contradictory” suggesting that for complex systems (by which I really mean any part of reality I care to examine) there exists an infinitude of equally valid, non-overlapping, potentially contradictory descriptions. Maxwell (2000) in his analysis of a new conception of science asserts that:
“Any scientific theory, however well it has been verified empirically, will always have infinitely many rival theories that fit the available evidence just as well but that make different predictions, in an arbitrary way, for yet unobserved phenomena.” (Maxwell, 2000).
In Richardson (2003) I explore exactly this line of thinking in my critique of bottom-up computer simulations.
The complementary law also underpins calls in some complexity literature for philosophical/epistemological/methodological/theoretical pluralism in complexity thinking. What is interesting here is how the same (or at least very similar) laws/principles have been found despite the quite different routes that have been taken – a process systems theorists call equifinality. This is true for many of the systems laws I will discuss here and in future installments.
System holism principle
The system holism principle is probably the most well known principle in both systems and complexity communities, and is likely the only one widely known by ‘outsiders’. It has it roots in the time of Aristotle and simply stated it says “the whole is greater than the sum of its parts”. More formally: “a system has holistic properties not manifested by any of its parts and their interactions. The parts have properties not manifested by the system as a whole” (Skyttner, 2001: 92). This is one of most interesting aspects of complex systems: that microlevel behavior can lead to macrolevel behavior that cannot be easily (if at all) derived from the microlevel from which it emerged. In terms of complexity language we might re-label the system holism principle as the principle of vertical emergence. (N.B. Sulis, in this issue, differentiates between vertical and horizontal emergence).
The wording: “the whole is greater than the sum of its parts” is problematic to say the least. To begin with the use of the term “greater than” would suggest that there is some common measure to compare the whole and its parts and that by this measure the whole is greater than the sum of those parts. I think this wrong. An important property of emergent wholes is that they cannot be reduced to their parts (a reversal of the system holism principle), i.e., wholes are qualitatively different from their parts (other than they can be recognized as coherent objects) – they require a different language to discuss them. So, in this sense, wholes and their component parts are incommensurable – they cannot be meaningfully compared – they are different! Of course, if mathematicians do find a way to bootstrap (without approximation) from micro to macro – a step that is currently regarded by many as intractable – then maybe a common (commensurable) measure can be applied. What we really should say is “the whole is different from the sum of its parts and their interactions”.
The issue of commensurability is an interesting one and crops up time and time again in complexity thinking. Indeed, it was the focus of a recent ISCE conference held in Washington (September 18-19, 2004). One way to make the incommensurable commensurable is to abstract/transform the incommensurable entities in a way that allow comparison. As long as we remember that it is the transformed entities that are being compared and not the entities themselves (as abstraction/transformation is rarely a conservative process) then useful comparisons can indeed be made.
One last issue with the system holism principle: the expression “the whole is greater than the sum of its parts” implies that, although the problem of intractability prevents us from deriving wholes from parts, in principle the whole does emerge from those parts (and their interactions) only, i.e., the parts are enough to account for the whole even if we aren’t able to do the bootstrapping itself. In Richardson (2004b) the problematic nature of recognizing emergent products is given as a reason to undermine this possibility. In that paper, it is argued that the recognition of wholes as wholes is the result of applying a particular filter. Filters remove information and so the resulting wholes are what is left after much of reality has been filtered out – oddly enough, what remains after the filtering process is what is often referred to as ‘reality’. So, molecules do not emerge from atoms, as atoms are only an idealized representation of that level of reality (a level which is determined by the filter applied) and as idealizations are not sufficient in themselves to account for the properties of molecules (which represent another idealization). An implication of this is that there exists chemistry that cannot be explained in terms of physics – this upsets of whole unification-of-the-sciences programme. Only in idealized abstractions can we assume that the parts sufficiently account for the whole.
Darkness principle
In complexity thinking the darkness principle is covered by the concept of incompressibility. The darkness principle says that “no system can be known completely” (Skyttner, 2001: 93). The concept of incompressibility suggests that the best representation of a complex system is the system itself and that any representation other than the system itself will necessarily misrepresent certain aspects of the original system. This is a direct consequence of the nonlinearity inherent in complex systems. Except in very rare circumstances nonlinearity is irreducible (although localized linearization techniques, i.e., assuming linearity locally, do prove useful).
There is another source of ‘darkness’ in complexity theory as reported by Cilliers (1998: 4-5):
“Each element in the system is ignorant of the behavior of the system as a whole, it responds only to information that is available to it locally. This point is vitally important. If each element ‘knew’ what was happening to the system as a whole, all of the complexity would have to be present in that element.” (original emphasis).
So, there is no way a member of a complex system can ever know it completely – we will always be in the shadow of the whole.
Lastly there is the obvious point that all complex systems are by definition open and so it is nigh on impossible to know how the system’s environment will affect the system itself – we simply cannot model the world, the Universe and everything.
Eighty-twenty principle
The eighty-twenty principle has been used in the past to justify the removal of a large chunk of an organization’s resources, principally its workforce. According to this principle, in any large, complex system, eighty per cent of the output will be produced by only twenty per cent of the system. Recent studies in Boolean networks, a particularly simple form of complex system, have shown that not all members of the network contribute to the function of the network as a whole. The function of a particular Boolean network is related to the structure of its phase space, particularly the number of attractors in phase space. For example, if a Boolean network is used to represent a particular genetic regulatory network (as in the work of Kauffman, 1993) then each attractor in phase space supposedly represents a particular cell type that is coded into that particular genetic network. It has been noticed that the key to the stability of these networks is the emergence of stable nodes, i.e., nodes whose state quickly freezes. These nodes, as well as others called leaf nodes (nodes that do not input into any other nodes), contribute nothing to the asymptotic (long-term) behavior of these networks. What this means is that only a proportion of a dynamical network’s nodes contribute to the long-term behavior of the network. We can actually remove these stable/frozen nodes (and leaf nodes) from the description of the network without changing the number and period of attractors in phase space, i.e., the network’s function. Figure 1 illustrates this. The network in Figure 1b is the reduced version of the network depicted in Figure 1a. I won’t go into technical detail here about the construction of Boolean networks, please feel free to contact me directly for further details. Suffice to say, by the way I have defined functionality, the two networks shown in Figure 1 are functionally equivalent. It seems that not all nodes are relevant. But how many nodes are irrelevant?
Figure 2 shows the frequency of different sizes of reduced network. The experiment performed was to construct a large number (100,000) of random Boolean networks containing only ten nodes, each having a random rule (or transition function) associated with it, and two randomly selected inputs. Each network is then reduced so that the resulting network only contains relevant nodes. Networks of different sizes resulted and their proportion to the total number of networks tested was plotted. If we take an average of all the networks we find that typically only 60% of all nodes are relevant. This would suggest a one hundred – sixty principle (as 100% of functionality is provided by 60% of the network’s nodes), but it should be noted that this ratio is not fixed for networks of all types – it is not universal. This is clearly quantitatively different from the eighty-twenty ratio but still implies that a good proportion of nodes are irrelevant on average. What do these so-called irrelevant nodes contribute? Can we really just remove them with no detrimental consequences? A recent study by Bilke and Sjunnesson (2001) showed that these supposedly irrelevant nodes do indeed play an important role.
One of the important features of Boolean networks is their intrinsic stability, i.e., if the state of one node is changed/perturbed it is unlikely that the network trajectory will be pushed into a different attractor basin. Bilke and Sjunnesson (2001) showed that the reason for this is the existence of the, what we have called thus far, irrelevant nodes. These ‘frozen’ nodes form a stable core through which the perturbed signal is dissipated, and therefore has no long term impact on the network’s dynamical behavior. In networks for which all the frozen nodes have been removed, and only relevant nodes remain, it was found that they
Fig. 1: An example of (a) a Boolean network, and (b) its reduced form
The nodes which are made up of two discs feedback onto themselves. The connectivity and transition function lists at the side of each network representation are included for those readers familiar with Boolean networks. The graphics below each network representation show the attractor basins for each network. The phase space of both networks contain two period-4 attractors, although it is clear that the basin sizes (i.e., the number of states they each contain) are quite different.
were very unstable indeed – the slightest perturbation would nudge them into a different basin of attraction, i.e., a small nudge was sufficient to qualitatively change the network’s behavior. As an example, the stability (or robustness) of the network in Figure 1a is 0.689 whereas the stability of its reduced, although functionally equivalent network, is 0.619 (N.B. The robustness measure is an average probability measure indicating the chances that the system will move into a different attractor basin if a single bit of one node, selected at random, is perturbed. The measure ranges from 0 to 1 where 1 represents the most stable. The difference in robustness for the example given is not that great, but the difference does tend to grow with network size – we have considered networks containing only ten nodes here).
Prigogine said that self-organization requires a container (self-contained-organization). The stable nodes function as the environmental equivalent of a container, and indeed one of the complex systems notions not found in systems theory is that environmental embodiments of weak signals might matter.
Fig. 2: The relative frequency distribution of reduced network sizes
So it seems that, although many nodes do not contribute to the long term behavior of a particular network, these same nodes play a central role as far as network stability is concerned. Any management team tempted to remove 80% of their organization in the hope of still achieving 80% of their yearly profits, would find that they had created an organization that had no protection whatsoever to even the smallest perturbation from its environment – it would literally be impossible to have a stable business.
Summing up
As mentioned in the opening paragraphs of this article, my aim in writing this series is to encourage a degree of awareness with general systems ideas that is currently not exhibited in the ‘official’ complexity literature. In each installment I will explore a selection of general systems laws and principles in terms of complexity science. When this task has been completed we might begin to develop a clearer understanding of the deep connections between systems theory and complexity theory and then make a concerted effort to build more bridges between the two supporting communities – there are differences but not as many as we might think.
Notes
The website for the recent ISCE Event Inquiries, Indices and Incommensurabilities, held in Washington DC last September (2004) is: http://isce.edu/ISCE_ Group_Site/web-content/ISCE%2oEvents/Washington_2004.html. Selected papers from this event will soon appear in a future issue of E:CO.
References
Bilke, S. and Sjunnesson, F. (2001). “Stability of the Kauffman model,” Physical Review E, 65: 016129-1.
Cilliers, P. (1998). Complexity andpostmodernism: Understand complex systems, NY: Routledge.
Cohen, J. (2002). Posting to the Complex-M listserv, 2nd September.
Feynman, R. P, Leighton, R. B. and Sands, M. (1963), The Feynman lectures on physics: Volume 1, Reading, MA: Addison-Wesley Publishing Company.
Kauffman, S. (1993). The origins of order: Self-organization and selection in evolution, New York, NY: Oxford University Press.
Kuhn, T. (1978), Black-body theory and the quantum discontinuity, 1894-1912, The University of Chicago Press.
Maxwell, N. (2000). “A new conception of science,” Physics World, August: 17-18.
Phelan, S. E. (1999). “A note on the correspondence between complexity and systems theory,” Systemic Practice and Action Research, 12(3): 237-246.
Richardson, K. A. (2003). “On the limits of bottom-up computer simulation: Towards a nonlinear modeling culture,” Proceedings of the 36th Hawaiian international conference on system sciences, Jan 7th-10th, IEEE: California.
Richardson, K. A. (2004a). “The problematization of existence: Towards a philosophy of complexity,” Nonlinear Dynamics, Psychology, and Life Science, 8(1): 17-40.
Richardson, K. A. (2004b). “On the relativity of recognizing the products of emergence and the nature of physical hierarchy,” Proceedings of the 2nd Biennial International Seminar on the Philosophical, Epistemological and Methodological Implications of Complexity Theory, January 7th-10th, Havana International Conference Center, Cuba.
Skyttner, L. (2001). General systems theory: Ideas and applications, NJ: World Scientific.
Weinberg, G. (1975). An Introduction to general systems thinking, New York, NY: John Wiley.
Over the last three decades, the Santa Fe Institute and its network of researchers have been pursuing a revolution in science.
Ignoring the boundaries of disciplines and schools and searching for novel fundamental ideas, theories, and practices, this international community integrates the full range of scientific inquiries that will help us to understand and survive on a complex planet.
This volume collects essays from the past thirty years of research, in which contributors explain in clear and accessible language many of the deepest challenges and insights of complexity science.
Explore the evolution of complex systems science with chapters from Nobel Laureates Murray Gell-Mann and Kenneth Arrow, as well as numerous pioneering complexity researchers, including John Holland, Brian Arthur, Robert May, Richard Lewontin, Jennifer Dunne, and Geoffrey West.
The Ackoff Center Weblog provides an opportunity to keep up with the latest research in Systems Thinking. It is also a forum where you can interact with others in the field and share your own experiences.
A conversation between Russell Ackoff and Edward Deming
This is the unedited transcript of the only conversation between Ackoff and Deming, as moderated by Clare Crawford Mason. This transcript reveals the views of two pre-eminent thinkers in systems thinking. They discuss the relevancy and the application of a systems worldview to intractable problems and societal ills.
The conversation took place in l992 and was edited and released as Volume 21 of The Deming Library series in l993. It is called “A Theory of a System for Educators and Managers” It is available from CC-M Productions and includes a second DVD with discussion/teaching guides for it and the rest of the Deming Library at The CC-M website @ www.managementwisdom.com
Drs. Deming and Ackoff explain why systems theory is essential knowledge for managing an organization in a world of change and uncertainty. Dr. Ackoff discusses synthesis as a necessary logic for understanding why a system behaves the way it does. He contrasts synthesis with analysis, which is useful for understanding how an organization and its units operate. Analysis is synonymous with thinking in the traditions of Western cultures.
Dr. Ackoff was fond of saying the East is learning scientific thinking more rapidly than the West is learning systems thinking. The combination of the two is the next leap forward in ability to manage and predict change and complexity.
To read this transcript download the attached PDF file.
via Stuart Umbpleby and the CYBCOM google group discussion list (https://groups.google.com/forum/#!forum/cybcom):
Michael Lissack attended a conf. on Scientonomy in Toronto last month. Below are links from a google search. This effort sounds very similar to “second order science” or “expanding the conception of science” which people in ASC and IASCYS have been working on in recent years.
Stuart
Scientonomy – Encyclopedia of Scientonomy
https://www.scientowiki.com/Scientonomy
Mar 15, 2018 – In the scientonomic context, this question was first formulated by Hakob … Scientonomy (Barseghyan-2015) is currently accepted by …
Mar 3, 2019 – The purpose of this encyclopedia is to trace, document, and promote the development of scientonomy, the newly emerging empirical science of …
Oct 7, 2018 – In the scientonomic context, this question was first formulated by Hakob Barseghyan in 2018. The question is currently accepted as a legitimate …
Scientonomy 2019 – CFP
https://easychair.org/cfp/Scientonomy2019
Scientonomy 2019: The Challenges of Constructing a Theory of Scientific Change. University of Toronto. Toronto, Canada, May 23-24, 2019 …
Submission deadline: February 28, 2019
Scientonomy: Journal for the Science of Science
https://scientojournal.com/index.php/scientonomy
Jan 1, 2018 – The field-defining Journal of scientonomy offers research that deepens our understanding of the process of scientific change. The underlying …
In economics, the Jevons paradox (/ˈdʒɛvənz/; sometimes Jevons effect) occurs when technological progress or government policy increases the efficiency with which a resourceis used (reducing the amount necessary for any one use), but the rate of consumption of that resource rises due to increasing demand.[1] The Jevons paradox is perhaps the most widely known paradox in environmental economics.[2] However, governments and environmentalists generally assume that efficiency gains will lower resource consumption, ignoring the possibility of the paradox arising.[3]
In 1865, the English economist William Stanley Jevons observed that technological improvements that increased the efficiency of coal-use led to the increased consumption of coal in a wide range of industries. He argued that, contrary to common intuition, technological progress could not be relied upon to reduce fuel consumption.[4][5]
Efficiency, the Jevons Paradox, and the limits to economic growth
I’ve been thinking about efficiency. Efficiency talk is everywhere. Car buyers can purchase ever more fuel-efficient cars. LED lightbulbs achieve unprecedented efficiencies in turning electricity into visible light. Solar panels are more efficient each year. Farmers are urged toward fertilizer-use efficiency. And our Energy Star appliances are the most efficient ever, as are the furnaces and air conditioners in many homes.
I’m following a set of David Ing-originated rabbit-holes this afternoon, though I’m running out of time for now – this is from the archive of James Kay’s site.
I must say, this and the other relevant diagrams are strikingly similar to my own thinking about using the viable systems model in #systemschange – which I think just shows some irreducible principle, which were a lot more obvious in 2018 than before Jame Kay passed away in 2004, partly thanks to his efforts.
Tribute to James Kay here: http://www.postnormaltimes.net/wpblog/a-tribute-to-james-kay/
Also, I can’t find a full text open copy of An ecosystem approach for sustainability: Addressing the challenge of complexity
The dynamics of ecosystems and human systems need to be addressed in the context of post-normal science grounded in complex systems thinking. We portray these systems as Self-Organizing Holarchic Open (SOHO) systems and interpret their behaviours and structures with reference to non-equilibrium thermodynamics: holons, propensities and canons; and information and attractors. Given the phenomena exhibited by SOHO systems, conventional science approaches to modelling and forecasting are inappropriate, as are prevailing explanations in terms of linear causality and stochastic properties. Instead, narratives in the form of scenarios to depict morphogenetic causal loops, autocatalysis, and multiple possible pathways for development need to be considered. Short examples are given. We also link SOHO system descriptions to issues of human preferences and choices concerning the preferred attributes of particular SOHO systems, and to the implications for achieving them through adaptive management, monitoring and appropriate structures for governance. A heuristic framework to guide reasoning for this is presented, and reiterative steps for applying it are identified. In this way we provide a coherent conceptual basis, in the workings of both natural systems and decision systems, for the practice of post-normal science.
The diagram illustrates the necessity to integrate the biophysical sciences and the social sciences to generate an ecosystem description of the biophysical and socio-economic-political situation. This is used to formulate feasible and desirable futures, one of which is chosen to promote. It is then necessary to design a collaborative learning process for the ongoing adaptive process of governance, management, and monitoring for sustainability.
•In Rosennean complexity there is emergence above and beyond constituent models.
•Complexity may have material ramifications without being itself a straightforward material distinction.
•Complication may be correlated with complexity, or a prerequisite for it, but it is not the same thing.
Abstract
This paper assesses the conceptualizations and analytical uses of complexity. Throughout the paper, we carefully eschew ontological issues, and sort out the epistemology of complexity. We try to explain why the ontology of complexity makes no sense to us, much like significance is neither material nor ontological. Our tool of choice is levels of analysis. First, we analyze the conceptualization of complexity. Much discussion of complexity is confused because complexity is mistaken as a material issue. Complexity arises from the way the situation is addressed, and is not material in itself. Even so, complexity does seem to have material ramifications without being itself a straightforward material distinction. We use an illustrative parallel example where genetic dominance is shown not to be material while having material consequences, but only after a gene is asserted to be dominant on normative criteria. Secondly, the paper compares two analytical approaches based on complexity, namely Robert Rosen’s work and Joseph Tainter’s work. In Rosennean complexity a system is complex if not all its constituent models are simulable, if certainty is denied. In that sense, complexity cannot be defined. Rosen’s distinction is between simple and complex systems makes complexity an all or nothing proposition. Complexification is seen by Tainter as a device used by societies to solve their problems. This leads to complexity being a matter of degree in successive societal complexifications, perhaps from Neolithic hunter-gatherers to industrial societies.
Sadly I missed putting this up in time for the free 23 May webinar from the excellent Co-Creative in association with the Ashoka Foundation – this training well worth looking into though, if relevant to you – I believe it will be top class.
We also want you to know that we have three opportunities this fall for friends, colleagues or your network partners to be exposed to our methodology, tools and hands-on learning environment during our Collaborative Innovation Essentials course.
This course will support you to:
Design and lead multi-stakeholder collaborations fueled by real alignment, engagement, and momentum
Lead more confidently through the fear and uncertainty of leading complex change across ideological and cultural boundaries
Help groups navigate the confusion and polarization that shows up when engaging diverse constituents
You will leave this course with increased effectiveness and skill in:
Establishing the conditions for powerful collaboration
Aligning diverse interests around a powerful shared goal
Mapping a shared understanding of system dynamics
Helping stakeholders develop real empathy for everyone affected by the work
Identifying the critical shifts that need to happen in order to realize your goal
Developing a powerful set of ideas, build them into working prototypes and test them in the real world
Scaling up the work and the impact
Building a shared learning environment
Do you have someone to recommend or to whom you’d be willing to pass along the registration link?
For more information about joining the no-cost webinar, registering for one of our open training sessions, or scheduling a time to speak with us about designing an opportunity just for your team, please contact Melissa Darnell at melissa@cocreativeconsulting.com.
In collaboration,
Russ
—
Russ Gaskin
Managing Director
CoCreative Consulting
Cell: +1-202-253-8846
What others have said about our training
(from anonymous session feedback):
“Simply the best session I’ve ever attended, hands down.”
Session Attendee,
U.S. Food and Drug Administration
“I’ve been doing professional development workshops for 20-plus years. This one stands out. It’s not too much to say that it’s already changed my perspective forever.”
Session Attendee,
UN Development Program
“I should have learnt this 5 years ago. I feel like we wasted a lot of time and energy without this.”
Session Attendee,
New Zealand Institute of Management
What others have said about our
collaborative innovation approach:
“CoCreative helped our network of stakeholders create a powerful framework for shared action in our community. Your team’s facilitation, wealth of knowledge, and creative approaches helped our coalition of the willing find common vision across our diverse ideas and interests. It was exciting to see a shared understanding of system gaps and stakeholder needs emerge and inform the design of Stone Soup Makers–our new Collective Impact capacity building program for the LeHigh Valley.”
Marci Ronald, Executive Vice President,
United Way of the Greater Lehigh Valley
“I’ve participated in a number of multi-stakeholder meetings on these issues, and a lot of them were led by well-known institutions, but we’ve accomplished more the last day and a half than we did in six months of those meetings.”
Kellee James, CEO, Mercaris
“This innovation network approach is really powerful and we’re getting a lot done, but honestly the most valuable part of it for me is that I’ve learned so much about how to run my own business better. Your approaches actually work to get people focused on big goals and moving fast.”
Greg Likteig, Senior Director,
The Scoular Companies
“Russ Gaskin and his team at CoCreative Consulting are a dream to work with. They help you figure out, and accomplish, what seems impossibly tangled and unmanageable. I’ve been in the fields of corporate responsibility, progressive economic development, and social investing for a quarter century now, and I’ve worked with and met a lot of consultants. CoCreative is, hands down, the absolute best at creating large-scale system change.”
This and the last couple of pieces via the excellent Systems Studio newsletter – bit late on acting on it so I’m only picking up the links that are still relevant
In September 2018, a group of global educators, innovators, and funders convened at Yale School of Management to discuss systems change education in an innovation context. The premise of the convening was that current paradigms in social innovation and entrepreneurship education overlooked many core competencies and perspectives needed for systems change. This site provides a place to explore the content generated in preparation for and as a result of that convening shared in the hopes of sparking further innovations and expansions in systems change education.
GALLERY WALK
During the September 2018 convening, a gallery walk was created of framed “art” from participants including frameworks, educational models, and courses. Visit the online Gallery Walk here.
The Report contains a section on innovations related to systems change education in an innovation context. We’ve pulled that section out here to make it easier to explore.
You must be logged in to post a comment.