Thinking in maps: from the Lascaux caves to knowledge graphs – Anne-Laure le Cunff, Ness Labs


Thinking in maps: from the Lascaux caves to knowledge graphs

Thinking in maps: from the Lascaux caves to modern knowledge graphs

Anne-Laure Le Cunff • Reading time: 18 minutes

What do hieroglyphs, flowcharts, road signs, and knowledge graphs have in common? They’re all thinking maps. Humans have been thinking in maps since the very first symbolic communication systems.

While thinking in maps may first bring to mind the idea of cartography, a map does not need to be geographic—it can be any symbolic depiction of the relationship between elements of some physical or mental space, such as themes, objects, or areas.

In the December 2007 edition of Philosophy of Mind, Professor Elisabeth Camp, whose research has focused on forms of thoughts that do not fit standard models, wrote: “Thinking in maps is substantively different from thinking in sentences.”

continues in source:

Thinking in maps: from the Lascaux caves to knowledge graphs

Span. Dig Deep. Solve complex problems. | Symbolic Systems Program


Span. Dig Deep. Solve complex problems. | Symbolic Systems Program

Symbolic Systems ProgramSchool of Humanities & SciencesSearch

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Span. Dig Deep. Solve complex problems.

How does human cognition work?
How do people interact with machines?
How human-like can a machine be?

SymSys will help you answer these questions and more. Students take an array of courses in computer science, linguistics, mathematics, philosophy, psychology, and statistics while also pursuing  an area of concentration. Created in 1986 by faculty members at the Center for the Study of Language and Information, the program has become one of the top five undergraduate majors at Stanford.

Silos are for farms. Not for learning.

SymSys breaks down traditional academic boundaries to train your mind and expand your thinking. But don’t mistake it for “light” or “less than”, this is one rigorous, intensive, rock-your-mind kind of education.

Interdisciplinary Breadth

The program is designed to help students see connections, consider diverse  perspectives, and explore new frontiers of knowledge across varying disciplines. Not only are there courses in different fields but there are overlaps in course content that complement each other.

Concentrated Depth

Each student chooses an area of concentration, allowing you to dig deeper and focus on what interests you most.  Concentrations can include: cognitive science, artificial intelligence, human-computer interaction, learning, computer music, neuroscience, and decision making and rationality.

Multi-Faceted Problem Solving

SymSys brings together multiple disciplines and methodologies to help you see complex problems from multiple angles and perspectives. We believe this is essential for 21st century learning, not to mention better problem solving. 

Limitless Possibilities

What can you do with a SymSys major? Practically anything.  Invent. Research. Teach. Lead. With hands-on technical training and a deep understanding of how people think and communicate, your SymSys degree will help you stand out.  Our alumni are academics, business pioneers, journalists, lawyers, and more.

Still wondering why we call it Symbolic Systems?

You’re not alone. Learn why


Span. Dig Deep. Solve complex problems. | Symbolic Systems Program

About – The Kihbernetics Institute


About – The Kihbernetics Institute

A New Kind of Cybernetics

I became interest in Cybernetics in the mid 70’s when our Automation class teacher introduced us to A.Y. Lerner’s book “Fundamentals of Cybernetics”. What fascinated me most in this book was the description of “Analogous systems” in chapter 3.4 and the realization that very different structural patterns (mechanical, hydraulic, electrical) have similar behaviour and can be described with identical mathematical patterns:

I’m still enjoying skimming through this easy to read book and finding out how, even after half a century, most of it still holds true, some things have changed with technology advancements, and many of the questions still remain unanswered.

However, there was one thing in this book (and Cybernetics as a whole) I was never comfortable with: the distinction between the Control and Controlled (sub)systems.

continues in source:

About – The Kihbernetics Institute

A Mathematical Theory of Communication – Shannon, 1948

Reprinted with corrections from The Bell System Technical Journal,
Vol. 27, pp. 379–423, 623–656, July, October, 1948.
A Mathematical Theory of Communication

pdf –

What is Submerging? Nora Bateson, Jan/Feb 2021

What is Submerging? Nora Bateson Feb 18·20 min read Nora Bateson, Jan 2021

What is Submerging?. Nora Bateson, Jan 2021 | by Nora Bateson | Feb, 2021 | Medium

Discontinuous Improvement: Five Catapulting Ideas – Russell Ackoff (2005)

Discontinuous Improvement: Five Catapulting Ideas

Over a number of years I have worked on the development of at least five catapulting ideas. They are the product of an approach to organizational problems that I first formulated in 1974 under the name “idealized redesign of the corporation.” Rather than merely solving problems, this approach dissolves them. To solve a problem is to change the effects of one or more undesirable causes; to dissolve a problem is to eliminate the causes and thus also eliminate the effects. Implementation of these catapulting ideas requires radical transformation of corporations, not mild reform. They are: interactive planning, the internal market economy, the circular organization, the multidimensional organization, and decision support (learning) systems.

pdf –

Interactive planning – Wikipedia

Interactive planning is a concept developed by Russell L. Ackoff, an American theorist, early proponent of the field of operations research and recognized as the pioneer in systems thinking. Interactive planning forwards the idea that in order to arrive at a desirable future, one has to create a desirable present and create ways and means to resemble it. One of its unique features is that development should be ideal-oriented.[1] Interactive planning is unlike other types of planning such as reactive planning, inactive planning, and preactive planning.

Interactive planning – Wikipedia

Small-world networks


introductory presentation:

Analyzing Kleinberg’s (and other) Small-world Model – Martel and Nguyen (2005) –

Models of the Small World: A Review – Newman (2000) –

(NB see also ‘small world experiment/phenomenon’):

A Critical Systems Thinking overview of the ‘GAPPS’​ and the ‘EU Science Hub/Cynefin Centre’​ guides to leadership in times of complexity | Professor Mike Jackson on LinkedIn

please comment at source:

A Critical Systems Thinking overview of the ‘GAPPS’​ and the ‘EU Science Hub/Cynefin Centre’​ guides to leadership in times of complexity | LinkedIn

A Critical Systems Thinking overview of the ‘GAPPS’​ and the ‘EU Science Hub/Cynefin Centre’​ guides to leadership in times of complexity

  • Published on February 28, 2021

Status is reachableDr Mike C Jackson OBECentre for Systems Studies7 articles Following

Two reports have recently appeared (February 2021) aimed at improving the capacity of decision-makers to lead and manage in the face of complexity and crisis. The GAPPS (Global Alliance for the Project Professions) document offers ‘A Guiding Framework for Leadership in Complexity’. The EU Science Hub/Cynefin Centre report is a field guide to ‘Managing complexity (and chaos) in times of crisis’. The purpose of this article is to compare and contrast the two documents and to subject them to an initial critique using critical systems thinking (CST). Both are worthy of closer attention and I’m sure they will receive this in the future.

The GAPPS framework argues that governments, organisations, and individuals are increasingly perceiving themselves as confronted by VUCA (volatile, uncertain, complex, ambiguous) environments. These environments arise from dynamic interdependencies, within and between systems, and the existence of multiple stakeholders with differing perspectives. The framework seeks to set out the competencies that leaders require to navigate in VUCA environments. The competencies identified are ‘performance based’ and describe the minimum acceptable performance a leader should exhibit in the workplace in “trying to get things done in the face of complexity”.

Lists of competencies are common in the ‘project professions’. The GAPPS framework draws upon previous work of this kind and a lengthy period of consultation and workshops involving significant numbers of experienced project professionals. Although not explicitly a ‘systems thinking’ document, it is reasonable to regard its underlying world-view as being that ‘complexity is the issue and systems thinking the way forward’. I should declare that my own systems thinking work was an input into the original ‘International Centre for Complex Project Management’ standards, upon which the framework draws, and my most recent book is included in the references of the GAPPS document.

At the heart of the framework are 5 ‘Units of Competency’ in the workplace, incorporating 22 elements of competency and 81 criteria of threshold performance. The first unit, ‘Think Holistically’, is about applying appropriate systems approaches in the face of dynamic interrelationships and multiple perspectives, and emerging threats and opportunities. The second, ‘Exercise Personal Mastery’, deals with the qualities a leader, confronted by complexity, should demonstrate in their personal behaviour, in building trust, and in leading sensitively. The third, ‘Provide Conditions to Enable Decisions and Action’, concerns maintaining strategic direction, setting the minimal rules necessary to enable action (providing scope for autonomy and self-organisation), supplying data needs, and establishing control systems that contribute to learning. This unit also requires leaders to ‘act sustainably’, taking into account the UN’s ‘Sustainable Development Goals’. In particular, attention must be given to the impact of decisions on individuals and teams, the community, diversity, society, and the environment. Unit four, ‘Respond to the Environment’, demands that leaders establish flexible structures and processes, and continually review their assumptions in the light of new learning. The final unit, ‘Engage Collaboratively’, requires close engagement with stakeholders, working across boundaries to ensure open communication, and collaborative teamwork which respects diverse perspectives.

To many working in the ‘project professions’, used to mandates on how to manage the project life-cycle, integrate ‘systems of systems’, etc., this may all sound a bit ‘airy-fairy’. But the framework makes it clear that it is concerned to set out ‘what’ needs to be considered in dealing with complexity, not ‘how’ things should be done. For this, it is to be commended. Experienced project professionals may feel that they have imbibed most of the lessons the framework seeks to deliver through their practice. But it is still useful for them, and even more so for those learning the ropes, to have them clearly articulated in a manner which acts as a reminder and enables them to be enhanced.

The report is comprehensive, benefiting no doubt from the combined knowledge of the large number of contributors. It addresses the wide range of issues that CST sees as essential in managing complexity – strategic direction, responsiveness to the environment, stakeholder involvement, mutual understanding, sustainability, diversity, etc. That said, it also suffers from being produced ‘by committee’. It sets out a long list of competencies which, despite the structuring around five ‘Units’, lacks overall coherence. For example, once the need to ‘Think Holistically’ has been established, the next unit, ‘Exercise Personal Mastery’, might have been more clearly related to this requirement. Further, from the point of view of CST, it fails to capitalise on the opportunity to show how the competence of fitting selected systems thinking approaches to the problem context can be realised. Although various systems approaches are mentioned, there is no explicit recognition that they have quite different strengths and weaknesses. The competencies around ‘Provide Conditions to Enable Decisions and Action’ and ‘Respond to the Environment’ might usefully have been linked to the strengths of the ‘viable system model’ and ‘socio-technical systems thinking’; those around ‘Engage Collaboratively’ with soft systems approaches; and those concerned with ‘act sustainably’ to ‘critical systems heuristics’. Questions about how such a differentiated range of competencies can be exhibited together, might have been answered with some CST insight into how to work with different perspectives and manage a pluralism of systems approaches. Finally, from the CST perspective, and the point has also been made from a complexity theory viewpoint by Dave Snowden (on LinkedIn), the list gives the impression that competencies are static, and that homogeneity is desirable. In the midst of a crisis, brought on by complexity, the appropriate leadership qualities are likely to be emergent rather than fixed and those dealing with the crisis better served if they display a diversity of competencies.

The EU Science Hub/Cynefin Centre field guide advises decision-makers how they can best make sense of the world during crises and respond effectively. It therefore differs from the GAPPS framework in being praxis oriented. Its world-view is that complexity is the issue and decision-makers are better placed to navigate complexity if they employ a sensibility and methods derived from complexity theory. The field guide was written by Dave Snowden and Alessandro Rancati, and inspired by Snowden’s Cynefin framework. This ensures a certain coherence but means that other complexity and systems perspectives receive little attention. These include natural science variants of complexity (such as developed at the Santa Fe Institute or derived from Prigogine); social science variants (interactionist, radical change, postmodern, critical realist, etc.) developed during complexity theory’s promiscuous crawl through social theory; and cybernetic, soft systems, and critical systems approaches.

The Cynefin version of complexity theory is a ‘naturalising’ approach which seeks to be relevant to social- or anthro-complexity. It wants to bring ‘good science’ to bear to understand how humans interact with each other and engage with the world. Snowden is critical of existing science-based variants of complexity theory when they reduce the complexity exhibited by humans. Humans are not the same as ants, birds or crystals, he insists. Any complexity theory worth the name, and seeking to address anthro-complexity, must take account of human identities, values, intentions, and cultural practices. It is a difficult feat, I will argue, to remain ‘scientific’ while embracing those features of human systems that have been subject to multiple interpretations in the social sciences, leading to the paradigm wars with which other forms of complexity theory have had to become engaged.

Cynefin is about multi-ontology sense making but, as the field guide is concerned with ‘times of crisis’, its emphasis is very much on the ‘un-order’ domains of ‘complexity’ and ‘chaos’. Decision-makers are advised to navigate through crises by adopting a 4-stage approach – ‘Assess’, ‘Adapt’, ‘Exapt’, and ‘Transcend’. ‘Assess’ starts with a state of confusion which involves deciding whether an apparent crisis can be managed using existing protocols or will demand radical change. If the latter, it is important to gain some initial control by adjusting the ‘constraints’ that are operating. In the case of Covid-19 (and this example is used to good effect throughout the document), this would translate into tightening them by closing borders, insisting on confinement, and encouraging social distancing and remote working. At the same time, it is crucial to start to move away from bureaucracy and conservative practices by delegating decision-making, creating more flexible boundaries to improve communication, and empowering informal networks. Decision-makers should start ‘journaling’ – capturing in notebooks, using sketches as much as possible, the principles they are applying and the new relationships that develop. ‘Adapt’ is about managing ’emerging evolutionary possibilities’ (definitely not about designing some ideal future). This will require loosening organisational constraints and any narrative constraints which stand in the way of a wider variety of ‘stories’ coming forward. Overall co-ordination must be maintained but the organisation needs to become a distributed ‘human sensor network’ in which informal teams and various specialised ‘crews’ seek to reframe the problem space from diverse perspectives, react to weak signals, and seek out new opportunities. Prototyping of innovative solutions can begin but the overarching mantra is to keep options open. Journaling is essential as a means of recording lessons learnt and sharing insights. Decision-makers may still be uncertain what to do but a sense of urgency builds. At some point there is an ‘aporetic turn’, confusion begins to dissipate, and it becomes possible to produce a ‘map’ showing possible changes and how their impact can be monitored. Potential solutions are evaluated, and resources allocated to the most promising. The third stage, ‘Exapt’, sees action begin in earnest. ‘Exapting’ is a process of “radical repurposing of roles, processes, paradigms, values”. On the basis of a thorough knowledge of the present, intervention strategies are designed which will create new processes and structures, and the new ‘conceptual scaffoldings’ necessary for the organisation to transform itself. To ensure that the questioning of existing practices and conceptual boundaries is radical enough, it may be necessary to temporarily enter the domain of ‘chaos’. Multiple contributions should be encouraged and orchestrated so that agreement is reached on actionable ideas which can be carried forward and tested. By the time the ‘Transcend’ stage is attained, the organisation is likely to have changed dramatically. It is necessary to consolidate and establish greater stability. The ‘new normal’ must build on the freshly developed activities, the shared learning that has been obtained, and the narratives and stories that correspond to and give coherence to the new present. People will be acting more in concert but must still maintain the ‘requisite diversity’ necessary to respond to the next crisis. They will be stronger for having learnt from past failures.

I am conscious that in summarizing, tidying up even, the field guide’s account, I have lost some of its dynamism and much of the technical vocabulary. In the original, the stages overlap, and myriads of concepts and methods compete for the reader’s attention, sowing a degree of confusion. Being generous, I imagine that this is meant to convey the urgency and creativity that must accompany an appropriate response to crises. But it’s now time to stand back and put on CST glasses.

In broad outline, we have an account of an organisation responding to changes in environmental circumstances by shifting from a mechanistic management system to an organic and then back again (Burns and Stalker, ‘The Management of Innovation’, 1961). The description is enriched with complexity theory concepts. The field guide also provides the 4-stage methodology, and many accompanying methods, for achieving such transitions. This is a significant advance for complexity theory. While systems thinkers have usually been willing to accept that complexity theory has introduced many novel ideas that help improve understanding of the VUCA world, they have been quick to point to the lack of overt methodologies for putting the ideas into practice. In fact, the 4-stage approach closely resembles the methodologies developed by systems thinkers for translating systems ideas into practice. John Mingers (‘Systems Thinking, Critical Realism and Philosophy’, 2014) provides a generic version of such methodologies (‘appreciation’, ‘analysis’, ‘assessment’, and ‘action’) into which the field guide’s stages could be fitted without too much distortion. In this respect, it is interesting to speculate whether Ralph Stacey, for example, would regard what the field guide presents as complexity theory at all. From his interactionist perspective (‘Complexity and Management’, 2000, with Griffin and Shaw), he would likely see it as too influenced by systems thinking and as falling into the contradiction of regarding decision-makers as acting on the basis of ‘rationalist teleology’, trying to manage complexity, while treating the organisation as subject to ‘formative teleology’, evolving according to a pattern set by some hidden order.

Returning to the main critique, the Cynefin approach echoes CST by insisting that there are no context-free solutions, that use of a variety of methods is necessary, and that no automatic assignment of particular tools and techniques to the different stages of a methodology is sensible. For example, attention to narratives and stories is essential throughout, as is continuous learning supported by journaling, and the maintenance of ‘requisite diversity’. The inevitable question arises, therefore, of why well-established systems approaches are absent from the toolkit offered by the field guide, even when they seem to offer the most obvious and proven resource for helping decision-makers with ‘managing complexity (and chaos) in times of crisis’. The primary ‘constraints’ the field guide concentrates on managing, throughout the 4-stage process, can be classified into the organisational and the conceptual. The organisational issues of ‘coherent heterogeneity’, central co-ordination of delegated decision-making, balancing adaptability and stability, reallocation of resources, etc., are exactly those which Stafford Beer’s ‘viable system model’ (‘Heart of Enterprise’, 1979) can offer advice on and structure discussions around. Conceptual matters, such as encouraging diverse perspectives, explicating existing narratives and challenging them, reframing the problem space, developing new archetypal stories, etc., fall into the arena of soft systems approaches such as Peter Checkland’s ‘soft systems methodology’ (‘Systems Thinking, Systems Practice’, 1981) and of ‘strategic assumption surfacing and testing’ (see Mason and Mitroff, ‘Challenging Strategic Planning Assumptions’, 1981). The idea of ‘journaling’ as a means of promoting continuous learning would benefit from Checkland’s concept of ‘rich pictures’ and the method of continuously up-dating ‘Analyses 1, 2 and 3’ during a project.

There are, it seems to me, two plausible explanations for why the contributions systems approaches can offer are ignored. First, there is a tendency for complexity theorists, when extending their ideas to the social domain, to want to claim that complexity theory is something new, different, and a step beyond systems thinking. Ralph Stacey describes his version of complexity theory as a radical alternative to the systems approach; a “decisive move away from systems thinking”. Dave Snowden, the originator of the Cynefin framework, has similarly sought to position complexity theory as a “new and emerging body of theory and practice”, based upon more up-to-date science, that is leaving systems thinking and cybernetics behind (on LinkedIn). In original formulations of Cynefin, systems thinking was identified with system dynamics and pinned to the ‘complicated’ domain. This allowed complexity theory to present itself as a new answer to the challenges posed by the ‘un-ordered’ domains. That said, Snowden has readily acknowledged the influence of Ackoff, Beer, and Checkland upon his thinking and so it is strange that the field guide fails to make any use of their tried-and-tested approaches to managing complexity. Here, I think, the second reason comes into play. Snowden insists that Cynefin is a ‘naturalising approach’ – bringing good science to the understanding of how humans interact with each other and engage with the world. In other words, although he rightly insists that anthro-complexity is different, that humans aren’t the same as ants, birds, and crystals, he doesn’t see that this requires a radical shift in epistemology. By contrast, the soft systems tradition of work has abandoned natural science as a model for gaining understanding of and seeking to intervene in human systems. Geoffrey Vickers, for example, argued that the components of human systems, active individuals using ‘appreciative systems’ to attribute meaning to their situation, makes it impossible to study them using the natural scientific approach. Following Vickers’ insights, and drawing upon hermeneutics and phenomenology, Checkland rejected any attempt to understand problematic social situations in scientific terms, and developed ‘soft systems methodology’ as an approach that works with different perceptions of reality and facilitates a systemic process of learning that can lead to purposeful action in pursuit of improvement. Snowden talks a lot about narratives, micro-narratives, and stories, and sees them as crucial constraints and enablers but, from his naturalising perspective, understands them and responds to them completely differently to soft systems thinkers (or second-order cyberneticians for that matter). [And, although it does not make an appearance in the field guide, the same argument holds for Snowden’s SenseMaker]. His approach is to invent a whole new technical language of concepts, derived from complexity science, which he hopes decision-makers will learn and come to understand the world through, thus responding to it more effectively. Soft systems thinkers proceed, by contrast, by enabling decision-makers, and other stakeholders, to express themselves better (more openly and systemically) in their own language in a way that addresses the problems as they see them. The rationale is that change will come when they understand each other better and reach mutual understanding about what they decide it is feasible and desirable to do. For soft systems thinkers, providing decision-makers with better science will not get you anywhere because there is no ‘science’ of human systems. To take an example, the field guide pictures narratives as acting as ‘strange attractors’ which bring human beings into coherent interaction and lead to co-ordinated action. I suppose this is a nice metaphor, but it is shared appreciations, values, and intentions, at the level of meaning, that actually leads human beings to act in consonance, not some weird compulsion. The attempt to understand anthro-complexity with concepts and tools drawn from the natural sciences acts as a significant constraint on the argument of the field guide. As other complexity theorists have found, the very different epistemologies found in social theory are essential to relate complexity thinking appropriately to human systems. I have made the case for the ‘interpretive’ sociological paradigm underpinning soft systems approaches. An even stronger case can be made for ‘radical’ sociological paradigms which point in the direction of conflicts of interest, the exercise of power, systemic discrimination and disadvantage, etc., none of which make an appearance as issues in the field guide. Systems thinking has a methodology, ‘critical systems heuristics’ (Ulrich ‘Critical Heuristics of Social Planning’, 1983) which can help draw these matters to the attention of decision-makers and other stakeholders and suggest how they might be addressed. Dave Snowden needs social theory to really get to grips with social complexity and the easiest way he can improve the field guide is to recommend systems approaches which have already translated the insights of the different epistemologies offered in social theory into practical methodologies. His naturalising approach to anthro-complexity is currently preventing him from seeing their value.

Readers of this article may want to know more about the CST which underpins this critique of the two reports. Details can be found in my 2019 book ‘Critical Systems Thinking and the Management of Complexity’ (use code ENG21 for a discount if ordering directly from Wiley). The main element of CST employed here is second-order critique – revealing the blind spots of particular systems and complexity approaches by comparing them to other systems approaches (accepting that the other approaches will also provide limited perspectives).Report this

Published by

Dr Mike C Jackson OBE Centre for Systems Studies

full article in source:

A Critical Systems Thinking overview of the ‘GAPPS’​ and the ‘EU Science Hub/Cynefin Centre’​ guides to leadership in times of complexity | LinkedIn

The Cybernetic “General Model Theory”: Unifying Science or Epistemic Change?

he Cybernetic “General Model Theory”: Unifying Science or Epistemic Change?

  • February 2018 Perspectives on Science 26(1):76-96

September 2020


With the marginalization of cybernetics, efforts to develop a universal epistemological method ceased as well. But the question remains open as to whether cybernetics contributed to the reconceptualization of the model and the popularity of scientific modeling since the mid-twentieth century. The present study approaches this question using the example of the general model theory of the German cyberneticist Herbert Stachowiak. Although this theory failed to produce a unifying and common model concept, its characteristics point toward a change in epistemological positions that is important for today’s scientific practice and also anticipated recent developments in the philosophy of science.

pdf –

The recent history of model theory | Casanovas (2000)

The recent history of model theory

  • January 2000


The influence of Alfred Tarski was decisive in this early stage and in the successive years. This is due not only to his discovery of unquestionable definitions of the notions of truth and definability in a structure, but also to his founding of the basic notions of the theory, such as elementary equivalence and elementary extension. In the fifties and six- ties Jerry Los introduced the ultraproducts, Ronald Fra¨ isse developed the back-and-forth methods and investigated amalgamation properties, and Abraham Robinson started his voluminous contribution to Model Theory, including his celebrated non-standard analysis. Robinson’s non-standard analysis attracted the attention of mathematicians and philoso- phers. But at that time a feeling of exhaustion started pervading the whole theory. Daniel Lascar describes the situation as “un temps d’arret, comme si la machinerie, prete ` a tourner, ne savait quelle direction prendre.” At this point Michael Morley appeared in the scene, causing what can be called a second birth of Model Theory. A theory is said to be categorical at if it has only one model of cardinality up to isomorphism. In 1954 J. Los had asked whether, for every (countable) theory, categoricity at one uncountable cardinal implies categoricity at every other uncountable cardinal. In 1965 M. Morley publishes Categoricity in power (Transactions of the American Math. Society 114, 514-538) where he solves the problem in the armative. He introduces the (topological) spaces of types and defines a rank on types and formulas, now called Mor- ley rank. A theory is called totally trascendental if all types have ordinal Morley rank. M. Morley shows that for countable theories, this is just !-stability: over a countable set there are at most countably many complete types. He also proves that any theory categorical in an uncountable cardinality is !-stable. In the proofs he uses heavily con- structions with indiscernible sequences which had been studied a few years ago by Andrezj Ehrenfeucht and Andrzej Mostowski. The methods were partly combinatorial, based on Ramsey’s theorem, and partly topological. The importance of the results, methods and notions of M. Morley was recognized very soon. M. Morley investigated also the structure of countable models of uncountably

pdf –

General Model Theory by Stachowiak

Overview on modelpractice:

Thirteen years of SysML: A systematic mapping study – Wolny, 2019


Thirteen years of SysML: A systematic mapping study

Thirteen years of SysML: A systematic mapping study

by Sabine Wolny | Oct 22, 2019 | (meta)modelingarticlestandard | 3 commentsTaxonomy of SysML Diagrams

The Christian Doppler Laboratory for Model-Integrated Smart Production investigates the usage of modelling for engineering intelligent production facilities as well as the development of modelling languages which are able to deal with operational production information collected at runtime. In this context, we also explore the state of the art, current developments, and application of Systems Modeling Language (SysML). This OMG standard has been on the market for about thirteen years.

SysML is an extended subset of UML providing a graphical modeling language for designing complex systems by considering software as well as hardware. By conducting a systematic mapping study about SysML we aimed for

  • getting an overview of existing research topics and groups
  • identifying whether there are any publication trends
  • uncovering possible missing links

Our analysis revealed the following main findings:

  • There is a growing scientific interest in SysML in the last years particularly in the research field of Software Engineering.
  • SysML is mostly used in the design and validation phases, rather than in the implementation phase.
  • The most commonly used diagram types are the SysML specific requirement diagram, parametric diagram and block diagram, together with the activity diagram and state machine diagram reused from UML.
  • SysML is a specific UML profile mostly used in systems engineering, however, the language has been customized to accommodate domain-specific aspects.
  • Related to collaborations for SysML research over the world, there are more individual research groups than large international networks.

continues in source:

Thirteen years of SysML: A systematic mapping study

Untangling Complexity Theory – The Kihbernetics Institute


Untangling Complexity Theory – The Kihbernetics Institute


Untangling Complexity Theory

An excellent introductory article for anyone interested in Complexity Theory (CT) is An Introduction to Complexity Theory by Jun Park. Among other things, it provides an insight about its origins which are, I think, as diverse and complex as the theory itself (highlights are mine):

Complexity Theory and its related concepts emerged in the mid-late 20th century across multiple disciplines, including the work of Prigogine and his study on dissipative structures in non-equilibrium thermodynamics, Lorenz in his study of weather systems and non-linear causal pathways (i.e. the butterfly effect), Chaos theory and its new branch of mathematics, as well as evolutionary thinking informed by Lamarck’s perspectives on learning and adaptation (Schneider and Somers, 2006).

Prigogine’s dissipative structures can for sure explain some of the complexity of life, while Chaos theory as a branch of mathematics, can shed some light on underlying patterns of deterministic laws in recurrent (autopoietic) processes in dynamical systems undergoing apparently random states of disorder.

Not sure, though, about the usefulness of the other two “non-linear causal pathways (i.e. the butterfly effect)” and “evolutionary thinking informed by Lamarck’s perspectives on learning and adaptation“. Let’s explore those two a little bit closer:

continues in source:

Untangling Complexity Theory – The Kihbernetics Institute

An Introduction to Complexity Theory | by Jun Park | Medium


An Introduction to Complexity Theory | by Jun Park | Medium

An Introduction to Complexity Theory

What it is, what it replaces, and why it’s important.

Jun Park

Jun ParkOct 8, 2017·7 min read

Complexity Theory allows us to better understand systems as diverse as cells, human beings, forest ecosystems, and organizations, that are only partially understood by traditional scientific methods (Zimmerman et al. 2001). While it represents a relatively nascent field of study, it spans across a wide variety of disciplines in the physical, biological, and social sciences, and has profound implications for the way we think about and act within the world (Schneider & Somers, 2006).

This is especially important in the study of organizations, organizational change, and leadership, where complexity theory can offer insights into how organizations become more sustainable, adaptive, and innovative (Uhl-Bien et al., 2007). In the following sections, we will examine the origins of the mechanical, bureaucratic paradigms of organizations and leadership, the development of complexity science, and the implications that a paradigm shift from the former to the latter has on the study and leadership of organizations.

continues in source:

An Introduction to Complexity Theory | by Jun Park | Medium