Art of Long View: Future, Uncertainty and Scenario Planning

Art of Long View: Future, Uncertainty and Scenario Planning


Key Concepts:

  • Fundamental Uncertainty
  • Knightian Uncertainty
  • Long term Thinking
  • Possibility Space
  • Probabilistic Space
  • Plausibility
  • Anticipation
  • Strategic Conversations
  • Strategic Narratives
  • Strategic Scenarios
  • Normative Scenarios
  • Causal Layered Analysis
  • Strategic Learning
  • Integral Futures
  • Multiple Futures
  • Multiple Horizons


Key People:

  • Peter Schwartz
  • Stewart Brand
  • Jay Ogilvy
  • Kees Van Der Heijden
  • Michel Godet
  • Pierre Wack
  • Herman Kahn
  • P J H Schoemaker
  • Arie De Gues
  • Napier Collyns
  • Eric Best
  • Art Kleiner
  • Thomas J Chermack
  • Gill Ringland
  • Angela Wilkinson
  • Adam Kahane
  • Ged Davis
  • Russell Ackoff
  • Jay Forrester
  • Peter Senge
  • Andy Hines
  • Peter Bishop
  • R Slaughter
  • Sohail Inayatullah
  • Rafael Ramirez
  • Roberto Poli
  • Riel Miller
  • George Wright
  • Eamonn Kelly
  • Katherine Fulton


From How plausibility-based scenario practices are grappling with complexity to appreciate and address 21st century challenges

The tighter interconnections of natural, social and economic systems lead to increased uncertainty and greater complexity. The growing list of today’s significant concerns, whether focused on fixing the financial crisis or progressing socio-ecological sustainability highlights the urgency to look forward and manage large scale, system transformations [1] and challenges the conventional western economic wisdom of continuous, linear or exponential growth. Failure to engage with irreducible uncertainty is more widely appreciated and attempts to tame uncertainty can make matters worse [2].

Scenarios were introduced over 50 years ago as a means to overcome the limits of linear, reductionist and deterministic thinking that underpinned the then dominant practices of forecast-based planning. Scenario builders reject the notion of wholly predictable futures and instead seek to construct alternative futures which explore not only the paths to each, but do so in a way that emphasizes the need to attend to disruptive change as normal. Scenarios work is conducted in different sectors – public, private, civil and academia – and for a wide range of purposes, such as learning [7], strategy [8], or conflict avoidance [9].

Scenario practices have evolved from a “hypothetical sequencing of events constructed with the purpose of focusing attention on causal structures and decision points” [10] to attendance to the dynamic interactions that create disruptive and turbulent change as organizations co-evolve with their wider contexts [11]. At the same time, continuous innovation and diversity of scenario practices result in methodological confusions and misunderstandings [12]. To avoid contributing to further confusion we first define and then justify our interest in one particular tradition of practice.

Bradfield et al. [13] highlight three different scenario ‘schools’. In this paper we focus on what those authors refer to as Intuitive Logics, with its emphasis on plausible alternative futures, in contrast with the normative French School and the probabilistic USA School. Our choice to focus on the intuitive logics school is justified by evidence of its growing dominance in non-probabilistic scenario work [14].

Schoemaker [15] describes how plausibility-based scenarios are useful approaches in situations characterized by increasing uncertainty and complexity. He notes the effectiveness of scenarios as a psychological basis for addressing biases due to cognitive limits and overcoming ‘group think’ resulting from consensus building processes in social organizations.

In the intuitive logics tradition, the future is a fiction. Scenarios are ‘open stories’ [16] and stories and storytelling are deployed as a means to engage intuition, expose deeply held assumptions and forge new and shared interpretative frames. The assumption is that the emerging future cannot be forecasted but can be imagined and “lived in” and offers a different perspective to learning about the present than history alone provides. In effect, plausibility-based scenarios offer reframing devices rather than forecasting tools [17,18]. Scenarios are not populated with facts but with perceptions, assumptions and expectations.

Quality of a good scenario is not determined by its predictive accuracy but by its impact which can be evaluated in different ways — cognitive shift, enhancing judgment, leading to more and better strategic options and/or motivating change [19].

Despite the extensive and continued use of intuitive logics scenarios in the public and private sectors, the diversity of methods can lead to a wholesale dismissal of these practices by empiricist traditions of inquiry and evidence-based decision making cultures [20,21]. At the same time organizations, such as Shell, which have sustained the practice of plausibility-based, intuitive logics scenarios for over 50 years, appreciate the added value in terms of enabling decision makers to engage with uncertainty, enabling systemic insights and contributing to the adaptive capacity of the firm [21].

In contrast with the objectivist and positivist ontologies of probabilistic scenario practices, constructivism, nominalism and post-normal science are the mainstays of the plausibility-based, intuitive logics tradition [10,12,48,49]. As Burrell and Morgan [50] noted, a realist sees the nature of reality as ‘out there’, hard and concrete, while the nominalist sees the social world as the result of individual cognition and made up of names, labels and concepts. Wilkinson and Eidinow [12] note the objectivist– constructivist dichotomy between probable and plausible scenario traditions. Scenarios are pragmatic rather than positivistic: events and behaviors are explained from the perspective of the individuals involved and thus reflect equally valid understandings from multiple points in a system. A central challenge is thus to navigate plurality [51] (Table 1).

For many complexity practitioners, the science of multi- level interconnected systems is extending the boundary of uncertainty where quantitative analysis is applicable. Agent- based modeling is one of the new techniques being used to undertake quantitative assessment of the probability of the collapse of system resilience [52], enabling a statistical forecast of the transition between various regimes of the system. Such approach proved relevant in addressing in- stabilities in financial markets and the role of contagion of norms as proposed by Axelrod [53], or Gintis [54] in the reframing obesity as an epidemic [55] rather than induced by the marketing of dubious foods.

Paul Cilliers [56] reflects on the ontology of complexity as follows: “The argument from complexity thus wants to move beyond the objective/subjective dichotomy”. He goes on to say that complexity science is in some ways an extension of the traditional scientific approach, but the ontological issues are shifted to the problem of boundaries. Since complex systems are open systems that interact with other systems, the choice of boundary is arbitrary. He quotes the notion of ‘operational closure’ as a useful approach, rooted in pragmatism. The uncertainty on the state of the system in the future is therefore objectively bound by formal mathematical modeling, but at the same time subjectively framed through the (explicit or implicit) choices concerning critical systems heuristics e.g. definition of the system boundaries.



Key Sources of Research:


Scenario Planning and Strategic Forecasting

Jay Ogilvy


Living in the futures

Angela Wilkinson


Scenarios: Uncharted Waters Ahead

Pierre Wack


Scenarios: Shooting the Rapids

Pierre Wack


Planning As Learning


The Living Company


The Use and Misuse of Scenarios


Scenario Planning


WHAT IF? The Art of Scenario Thinking for Nonprofits


Click to access What_If.pdf


Shell Scenarios


A Review of Scenario Planning Literature

Click to access Scenario%20PlanningA%20Review%20of%20the%20Literature.PDF


The origins and evolution of scenario techniques in long range business planning

Ron Bradfielda, George Wrightb, George Burt, George Cairns, Kees Van Der Heijden


Directions in Scenario Planning Literature – A Review of the Past Decades

Celeste Amorim Varuma, Carla Melo


Click to access 0a85e53c946a22d99c000000.pdf


A review of scenario planning


Muhammad Amer, Tugrul U. Daim *, Antonie Jetter



Click to access 53dbe98c0cf2a76fb667b0b3.pdf


The current state of scenario development: an overview of techniques

Peter Bishop, Andy Hines and Terry Collins


Click to access Bishop_et_al_2007.pdf


Integrating organizational networks, weak signals, strategic radars and scenario planning

Paul J.H. Schoemaker ⁎, George S. Day, Scott A. Snyder

Click to access 0a85e5352fd617b0f6000000.pdf


Advantages and disadvantages of scenario approaches for strategic foresight

Dana Mietzner and Guido Reger




Rafael Ramirez  & Cynthia Selin


Click to access ACCEPTED__Plausibility_and_Probability_in_Scenario_Planning_March_24_2013.pdf


Scenario building: Uses and abuses

Philippe Durance, Michel Godet


The Role of System Theory in Scenario Planning


Thomas Chermack


The Art of Scenarios and Strategic Planning: Tools and Pitfalls


Click to access art_of_scenarios.pdf


A Scenario-based Approach to Strategic Planning – Integrating Planning and Process Perspective of Strategy

Torsten Wulf, Philip Meißner, Stephan Stubner


Click to access 553b7c780cf2c415bb093eb0.pdf


An Introduction to the Ontology of Anticipation

Roberto Poli


Click to access read_Poli-An-Introduction-to-the-Ontology-of-Anticipation.pdf


Being Without Existing: The Futures Community at a Turning Point? A Comment on Jay Ogilvy’s “Facing the Fold”

By Riel Miller

Click to access 53f70d4d0cf22be01c452fae.pdf


Riel Miller, Roberto Poli and Pierre Rossel

The Discipline of Anticipation: Exploring Key Issues



Towards an ontology of the present moment


Anthony Hodgson


Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation

James Derbyshire , George Wright


Plotting Your Scenarios

Jay Ogilvy and Peter Schwartz

Click to access plotting_your_scenarios.pdf


When and How to Use Scenario Planning: A Heuristic Approach with Illustration

Paul J.H. Schoemaker

Click to access 0c9605325c140d52e9000000.pdf


Futures literacy: A hybrid strategic scenario method

Riel Miller

Click to access 54783ef50cf293e2da287b54.pdf


From Forecasting and Scenarios to Social Construction: Changing Methodological Paradigms in Futures Studies

Richard A. Slaughter


Developing and Applying Strategic Foresight

Richard A. Slaughter

Click to access 2002slaughter_Strategic_Foresight.pdf



What difference does ‘integral’ make?

Richard A. Slaughter

Click to access What_Diff_Integral.pdf


Framework foresight: Exploring futures the Houston way

Andy Hines , Peter C. Bishop

Click to access 93-Framework-Foresight.pdf



Anthony Hodgson and Gerald Midgley


Seeing in Multiple Horizons: Connecting Futures to Strategy

Andrew Curry

Anthony Hodgson


Click to access Curry-three-time-horizons.pdf


Introduction to Strategic Foresight : A Resource Bibliography

Dr. Peter Bishop


40 Years of Shell Scenarios

Shell International

Click to access shell-scenarios-40yearsbook080213.pdf


Scenarios as a Tool for the 21st Century

Ged Davis

Shell International


Click to access davis_how_does_shell_do_scenarios.pdf


The Evolution of Integral Futures: A Status Update

Terry Collins & Andy Hines


Click to access Collins_Hines_Evo_of_Integral_Futs_2011.pdf


integral futures

by Richard A. Slaughter

Click to access Integral_Futures_APF_Overview_2012.pdf


Six pillars: futures thinking for transforming

Sohail Inayatullah


How plausibility-based scenario practices are grappling with complexity to appreciate and address 21st century challenges

Angela Wilkinson, Roland Kupers , Diana Mangalagiu

Click to access Link-16.pdf


Scenario Method: Current developments in theory and practice

Technological Forecasting and Social Change

Volume 80, Issue 4, Pages 561-838 (May 2013)

Edited by George Wright, George Cairns and Ron Bradfield

Reflexivity, Recursion, and Self Reference

 Reflexivity, Recursion, and Self Reference


From Reflexivity and Eigenform The Shape of Process


“Reflexive” is a term that refers to the presence of a relationship between an entity and itself. One can be aware of one’s own thoughts. An organism produces itself through its own action and its own productions. A market or a system of finance is composed of actions and individuals, and the actions of those individuals influence the market just as the global information from the market influences the actions of the individuals. Here it is the self-relations of the market through its own structure and the structure of its individuals that moves its evolution forward. Nowhere is there a way to cut an individual participant from the market effectively and make him into an objective observer. His action in the market is concomitant to his being reflexively linked with that market. It is just so for theorists of the market, for their theories, if communicated, become part of the action and decisionmaking of the market. Social systems partake of this same reflexivity, and so does apparently objective science and mathematics. In order to see the reflexivity of the practice of physical science or mathematics, one must leave the idea of an objective domain of investigation in brackets and see the enterprise as a wide ranging conversation among a group of investigators. Then, at once, the process is seen to be a reflexive interaction among the members of this group. Mathematical results, like all technical inventions, have a certain stability over time that gives them an air of permanence, but the process that produces these novelties is every bit as fraught with circularity and mutual influence as any other conversation or social interaction.


In such a context, every object is inherently a process, and the structure of the domain as a whole comes from the relationships whose exploration constitutes the domain. There is no place to hide in a reflexive domain, no fundamental particle, no irreducible object or building block. Any given entity acquires its properties through its relationships with everything else. The sense of such a domain is not at all like the set theoretic notion of collections or unrelated things, or things related by an identifiable property. It is more like a conversation or an improvisation, held up and moving in its own momentum, creating and lifting sound and meaning in the process of its own exchange. Conversations create spaces and events, and these events create further conversations. The worlds appearing from reflexivity are worlds nevertheless, with those properties of partial longevity, emergence of patterns, and emergence of laws that we have come to associate with seemingly objective reality.


From The march of self-reference


One of the main characteristics of social systems as well as individual systems, distinguishing them from many other systems, is indeed their potential for self- referentiality in the latter sense. Concretely, this means not only that the self- knowledge accumulated by the individual in turn affects both his structure and modus operandi, but it also implies – as especially stressed by constructivism – that in self-referential systems like individuals and social systems, feedback loops exist between parts of external reality on the one hand, and models and theories about these parts of reality on the other hand. To a large extent, both individuals and collectivities indeed produce their own world.

While constructivism, as an explanatory paradigm, is focused on individuals, it is certainly valid for social systems as well. Concretely, whenever social scientists systematically accumulate new knowledge about the structure and functions of their society, or about subgroups within that society, and when they subsequently make that knowledge known, through their publications or sometimes even through the mass media – in principle also to those to whom that knowledge pertains – the consequence often is that such knowledge will be invalidated, because the research subjects may react to this knowledge in such a way that the analyses or forecasts made by the social scientists are falsified. In this respect, social systems are different from many other systems, including (most?) biological ones. There is a clearly two-sided relationship between self-knowledge of the system on the one hand, and the behavior and structure of that system on the other hand.

Biological systems, like social systems, admittedly do show goal-oriented behavior of actors, self-organization, self-reproduction, adaptation and learning. But it is only psychological and social systems that arrive systematically, by means of experiment and reflection, at knowledge about their own structure and operating procedures, with the obvious aim to improve these. This holds true on the micro-level of the individual, as in psychoanalysis or other self-referential activities, and on the macro-level of world society, as in planning international trade or optimal distribution of available resources.

For social scientists, the consequences of self-referentiality are interesting not only for gaining an insight in the functioning of social systems, but also for the methodology and epistemology used to study them. There is a paradox here: as stated above, the accumulation of knowledge often leads to a utilization of that knowledge – both by the social scientists and the objects of their research – which may change or even invalidate the validity of that knowledge (Geyer and van der Zouwen, 1988; Henshel, 1990). It is maintained here that this paradox is interesting as well for psychologists, and exists also at the individual level where the individual not only constructs his world, but also continually reconstructs it. This can be seen in a normal life, but is especially visible in several forms of individual therapy, where old self- knowledge is invalidated by new – though not necessarily always better – self-knowledge.

The usual examples of self-referential behavior in social science consist of self-fulfilling and self-defeating prophecies. Henshel (1990) for example, has studied serial self-fulfilling prophecies, where the accuracy of earlier predictions, themselves influenced by the self-fulfilling mechanism, impacts upon the accuracy of the subsequent predictions. In much of empirical social science research. However, self-referential behavior does not loom large – which is rather amazing in view of its supposedly being an essential characteristic of individual human functioning. In this case the research methodology used may be an issue: survey research, where people are asked what they think or feel, offers little opportunity to bring out self-referential behavior, while depth interviews, which concentrate more on the awhyo than the awhato of people’s opinions have a better chance to elicit self-referential remarks in this respect.


Key People:

  • Francisco Varela
  • Lois Kauffman
  • Steven J Bartlett
  • Howard Pattee
  • Niklas Luhmann
  • Felix Geyer
  • George Soros
  • Eric D. Beinhocker
  • Stuart A. Umpleby
  • Heinz Von Foerster


Key Sources of Research:


Reflexivity. A Source-Book in Self-Reference

Steven Bartlett


Self-reference: Reflections on Reflexivity

Steven J. Bartlett & Peter Suber (Eds.)

Dordrecht: Martinus Nijhoff, 1987. Now published by Springer Science.


Self Reference and Recursive Forms

Lois Kauffman

Click to access SelfRefRecurForm.pdf


The Complexity of Self-reference
A Critical Evaluation of Luhmann’s Theory of Social Systems

Roberto Poli


Click to access quad50.pdf


Reflexivity and Eigenform The Shape of Process

Louis H. Kauffman


Click to access ReflexPublished.pdf


The march of self-reference

Felix Geyer


Essays on Self-reference

By Niklas Luhmann


Evolving Self Reference

Howard Pattee

Click to access 09e4150577eb05a2cd000000.pdf



Steven James Bartlett

Click to access Bartlett_Self-reference,%20Phenomenology,%20and%20Philosophy%20of%20Science.pdf



Recursivity and Self-Referentiality of Economic Theories and Their Implications for Bounded Rational Actors


Marco Lehmann-Waffenschmidt Serena Sandri

Click to access DDPE200703.pdf


A Calculus for Self Reference

F Varela

Click to access VarelaCSR.pdf


Ouroboros avatars:
A mathematical exploration of Self-reference and Metabolic Closure

Jorge Soto-Andrade, Sebastia ́n Jaramillo ,Claudio Gutie ́rrez and Juan-Carlos Letelier

Click to access 0262297140chap115.pdf


Self Reference and Autopoiesis

A Locker

Click to access metatheor-presupp-autopoiesis.pdf


Snakes all the Way Down: Varela’s Calculus for Self-Reference and the Praxis of Paradise

André Reichel


Click to access 09e4150d0287eebed2000000.pdf


Self-Reference and Time According to G. Spencer-Brown 

Andreas Kull


Reflexivity in Economics: An Experimental Examination on the Self-Referentiality of Economic Theories

By Serena Sandri 2009



Operational Closure and Seif-Reference: On the Logic of Organizational Change

Markus Schwaninger1and Stefan N. Groesser

Click to access 235_Operational%20Closure%20and%20Self-Reference_SRBS%202012.pdf


Fallibility, reflexivity, and the human uncertainty principle

George Soros


Reflexivity, complexity, and the nature of social science

Eric D. Beinhocker


Reflexivity and Economics: George Soros’s Theory of Reflexivity and the Methodology of Economic Science



Stuart Umpleby

Click to access 0c9605187eecf06dff000000.pdf


Reflexivity, Expectations Feedback and Almost Self-fulfilling Equilibria: Economic Theory, Empirical Evidence and Laboratory Experiments

Cars Hommes

August 14, 2013


 Reflexivity, path dependence, and disequilibrium dynamics

Anwar Shaikh

Click to access 2-Reflexivity,%20path%20dependence,%20and%20disequilibrium%20dynamics.pdf


Second-Order Economics as an Example of Second-Order Cybernetics

Stuart A. Umpleby


Click to access 890.pdf


Reflexivity and Equilibria

Francesco Guala


Click to access DEMM-2013_16wp.pdf


Umpleby, Stuart A.

“Fundamentals and history of Cybernetics.”

World Multi-Conference on Systemics, Cybernetics, and Informatics. 2006.

Click to access videoSUfh-slides.pdf


Complexity to Reflexivity: Underlying Logics Used in Science

Stuart A. Umpleby

Click to access 55edc5ad08ae199d47be4b99.pdf



Louis H. Kauffman


Click to access Eigen.pdf


Laws of Form and the Logic of Non-Duality

Louis H. Kauffman

Click to access KauffSAND.pdf



Louis H. Kauffman


Click to access Eigenform.pdf


Truth, Beauty, and Goodness: Integral Theory of Ken Wilber

Truth, Beauty, and Goodness: Integral Theory of Ken Wilber



I, we, It/Its



Self, Culture, and Nature




The Eight Zones




Levels in each Quadrant




“The word integral means comprehensive, inclusive, non-marginalizing, embracing. Integral approaches to any field attempt to be exactly that: to include as many perspectives, styles, and methodologies as possible within a coherent view of the topic. In a certain sense, integral approaches are “meta-paradigms,” or ways to draw together an already existing number of separate paradigms into an interrelated network of approaches that are mutually enriching.” – Ken Wilber


The world has never been so complex as it is right now—it is mind boggling and at times emotionally overwhelming. Not to mention, the world only seems to get more complex and cacophonous as we confront the major problems of our day: extreme religious fundamentalism, environmental degradation, failing education systems, existential alienation, and volatile financial markets. Never have there been so many disciplines and worldviews to consider and consult in addressing these issues: a cornucopia of perspectives. But without a way of linking, leveraging, correlating, and aligning these perspectives, their contribution to the problems we face are largely lost or compromised. We are now part of a global community and we need a framework—global in vision yet also anchored in the minutiae of our daily lives—that can hold the variety of valid perspectives that have something to offer our individual efforts and collective solution building.

In 1977 American philosopher Ken Wilber published his first book, The Spectrum of Consciousness. This groundbreaking book integrated the major schools of psychology along a continuum of increasing complexity, with different schools focused on various levels within that spectrum. Over the next 30 years he continued with this integrative impulse, writing books in areas such as cultural anthropology, philosophy, sociology of religion, physics, healthcare, environmental studies, science and religion, and postmodernism. To date, Wilber has published over two dozen books and in the process has created integral theory.2 Wilber’s books have been translated into more than 24 languages, which gives you an idea as to the global reach and utility of integral theory.3 Since its inception by Wilber, integral theory has become one of the foremost approaches within the larger fields of integral studies and meta-theory.4 This prominent role is in large part the result of the wide range of applications that integral theory has proven itself efficacious in as well as the work of many scholar-practitioners who have and are contributing to the further development of integral theory.

Integral theory weaves together the significant insights from all the major human disciplines of knowledge, including the natural and social sciences as well as the arts and humanities. As a result of its comprehensive nature, integral theory is being used in over 35 distinct academic and professional fields such as art, healthcare, organizational management, ecology, congregational ministry, economics, psychotherapy, law, and feminism.5 In addition, integral theory has been used to develop an approach to personal transformation and integration called Integral Life Practice (ILP). The ILP framework allows individuals to systematically explore and develop multiple aspects of themselves such as their physical body, emotional intelligence, cognitive awareness, interpersonal relationships, and spiritual wisdom. Because integral theory systematically includes more of reality and interrelates it more thoroughly than any other current approach to assessment and solution building, it has the potential to be more successful in dealing with the complex problems we face in the 21st century.

Integral theory provides individuals and organizations with a powerful framework that is suitable to virtually any context and can be used at any scale. Why? Because it organizes all existing approaches to and disciplines of analysis and action, and it allows a practitioner to select the most relevant and important tools, techniques, and insights. Consequently, integral theory is being used successfully in a wide range of contexts such as the intimate setting of one-on-one psychotherapy as well as in the United Nations “Leadership for Results” program, which is a global response to HIV/AIDS used in over 30 countries. Towards the end of this article I provide additional examples of integral theory in action to illustrate the variety of contexts in which people are finding the integral framework useful.

Wilber first began to use the word “integral” to refer to his approach after the publication of his seminal book Sex, Ecology, Spirituality in 1995. It was in this book that he introduced the quadrant model, which has since become iconic of his work in general and integral theory in particular. Wilber’s quadrant model is often referred to as the AQAL model, with AQAL (pronounced ah-qwal) standing for all quadrants, all levels, all lines, all states, and all types. These five elements signify some of the most basic repeating patterns of reality. Thus, by including all of these patterns you “cover the bases” well, ensuring that no major part of any solution is left out or neglected. Each of these five elements can be used to “look at” reality and at the same time they represent the basic aspects of your own awareness in this and every moment. In this overview I will walk you through the essential features of each of these elements and provide examples of how they are used in various contexts, why they are useful for an integral practitioner, and how to identify these elements in your own awareness right now. By the end of this tour, you will have a solid grasp of one of the most versatile and dynamic approaches to integrating insights from multiple disciplines. So let us begin with the foundation of the AQAL model: the quadrants.



According to integral theory, there are at least four irreducible perspectives (subjective, intersubjective, objective, and interobjective) that must be consulted when attempting to fully understand any issue or aspect of reality. Thus, the quadrants express the simple recognition that everything can be viewed from two fundamental distinctions: 1) an inside and an outside perspective and 2) from a singular and plural perspective. A quick example can help illustrate this: imagine trying to understand the components of a successful meeting at work. You would want draw on psychological insights and cultural beliefs (the insides of individuals and groups) as well as behavioral observations and organizational dynamics (the outsides of individuals and groups) to fully appreciate what is involved in conducting worthwhile meetings.

These four quadrants also represent dimensions of reality. These dimensions are actual aspects of the world that are always present in each moment. For instance, all individuals (including animals) have some form of subjective experience and intentionality, or interiors, as well as various observable behaviors and physiological components, or exteriors. In addition, individuals are never just alone but are members of groups or collectives. The interiors of collectives are known generally as intersubjective cultural realities whereas their exteriors are known as ecological and social systems, which are characterized by interobjective dynamics. These four dimensions are represented by four basic pronouns: “I”, “we”, “it”, and “its.” Each pronoun represents one of the domains in the quadrant model: “I” represents the Upper Left (UL), “We” represents the Lower Left (LL), “It” represents the Upper Right (UR), and “Its” represents the Lower Right (LR).


As both of the Right-Hand quadrants (UR and LR) are characterized by objectivity, the four quadrants are also referred to as the three value spheres of subjectivity (UL), intersubjectivity (LL), and objectivity (UR and LR). These three domains of reality are discernable in all major languages through pronouns that represent first-, second-, and third-person perspectives and are referred to by Wilber as “the Big Three:” I, We, and It/s. These three spheres can also be characterized as aesthetics, morals, and science or consciousness, culture, and nature.


Integral theory insists that you cannot understand one of these realities (any of the quadrants or the Big Three) through the lens of any of the others. For example, viewing subjective psychological realities primarily through an objective empirical lens distorts much of what is valuable about those psychological dynamics. In fact, the irreducibility of these three spheres has been recognized throughout the history of Western philosophy, from Plato’s True, Good, and Beautiful to Immanuel Kant’s famous three critiques of pure reason, judgment, and practical reason to Jürgen Habermas’ validity claims of truth, rightness, and truthfulness (Fig. 2). Wilber is a staunch advocate of avoiding reducing one of these spheres into the others. In particular, he cautions against what he calls flatland: the attempt to reduce interiors to their exterior correlates (i.e., collapsing subjective and intersubjective realities into their objective aspects). This is often seen in systems approaches to the natural world, which represent consciousness through diagrams of feedback loops and in the process leave out the texture and felt-sense of first- and second-person experience.


One of the reasons integral theory is so illuminating and useful is it embraces the complexity of reality in ways few other frameworks or models do. In contrast to approaches that explicitly or inadvertently reduce one quadrant to another, integral theory understands each quadrant as simultaneously arising. In order to illustrate the simultaneity of all quadrants I will provide a simple example with Figure 1 in mind. Let us say I decide I need to buy some flowers for the garden and I have the thought, “I want to go to the nursery.” The integral framework demonstrates that this thought and its associated action (e.g., driving to the garden store and purchasing roses) has at least four dimensions, none of which can be separated because they co-arise (or tetra-mesh) and inform each other. First, there is the individual thought and how I experience it (e.g., mentally calculating travel time, the experience of joy in shopping, or the financial anxiety over how I will pay for my purchase). These experiences are informed by psychological structures and somatic feelings associated with the UL quadrant. At the same time, there is the unique combination of neuronal activity, brain chemistry, and bodily states that accompany this thought, as well as any behavior that occurs (e.g., putting on a coat, getting in the car). These behaviors are associated with various activities of our brain and physiological activity of the body, which are associated with the UR quadrant. Likewise, there are ecological, economic, political, and social systems that supply the nursery with items to sell, determine the price of flowers, and so on. These systems are interconnected through global markets, national laws, and the ecologies associated with the LR quadrant. There is also a cultural context that determines whether I associate “nursery” with an open-air market, a big shopping mall, or a small stall in an alley, as well as determining the various meanings and culturally appropriate interactions that occur between people at the nursery. These cultural aspects are associated with worldviews in the LL quadrant.


Thus to have a full understanding of and appreciation for the occurrence of the thought, “I’m going to the nursery,” one cannot explain it fully through just the terms of either psychology (UL), or neurobiology and physiology (UR), or social and economic dynamics (LR), or cultural meaning (LL). For the most complete view, as we will see, one should take into consideration all of these domains (and their respective levels of complexity). Why is this practical? Well if we tried to summarize this simple situation by leaving out one or more perspectives, a fundamental aspect of the integral whole would be lost and our ability to understand it and address it would be compromised. Thus, integral practitioners often use the quadrants as their first move to scan a situation or issue and bring multiple perspectives to bear on the inquiry or exploration at hand.



Key Sources of Research:


Introduction to the Integral Approach (and the AQAL Map)

Ken Wilber


Click to access IntroductiontotheIntegralApproach_GENERAL_2005_NN.pdf



Ken Wilber

Journal of Consciousness Studies, 4 (1), February 1997, pp. 71­92 Copyright, 1997, Imprint Academic


Click to access www.imprint.co_.uk_Wilber.pdf


Integral Theories of Everything: Ervin Laszlo and Ken Wilber


Click to access LASZLOWILBERCOMPAR.pdf



Sean Esbjörn-Hargens


Click to access Integral%20Ecology_Intermediate.pdf


Integral Spirituality

The Role of Spirituality in the Modern and Postmodern World

Ken Wilber
Summer 2005


Click to access Integral%20Spirituality.pdf




A Comprehensive Approach to Today’s Complex Planetary Issues

Sean Esbjörn-Hargens Michael E. Zimmerman


Click to access IntegralEcology_031809.pdf




An All-Inclusive Framework for the 21st Century

Sean Esbjörn-Hargens


Click to access IT_3-2-2009.pdf



Integral Theory in Action

Sean Esbjörn-Hargens


Click to access 62114.pdf



In defense of Integral Theory

In response to Critical Realism

Ken Wilber


Click to access JITP_7(4)_Wilber.pdf



Social Work as an Integral Profession

Heather Larkin


Click to access social_work_as_an_integral_profession.pdf



The Evolution of Integral Futures

A Status Update

By Terry Collins and Andy Hines


Click to access 79-Evolution-of-integral-futures-WFR-JunJul2010.pdf



Ken Wilber – Excerpt G:

Toward A Comprehensive Theory of Subtle Energies


Click to access KKCExcerptG.pdf



Integral Resources


Click to access Integral-Resources-15.pdf




A Synoptic Overview and Resource Guide for Integral Scholars

Nicholas H. Hedlund-de Witt

Click to access Critical%20Realism_REVISED.pdf




The Full-Spectrum Project

Christian Arnsperger




Opportunities and Challenges

Christian Arnsperger



Full-Spectrum Economics: Toward an Inclusive and Emancipatory Social Science

By Christian Arnsperger


Published by Routledge




Sean Esbjörn-Hargens



Click to access IU_Ecology_Intro.pdf


Metatheory for the 21st Century: Critical Realism and Integral Theory in Dialogue
Roy Bhaskar, Sean Esbjö-Hargens, Mervyn Hartwig, Nicholas Hedlund-de Witt

Routledge, Jul 22, 2015 – Social Science – 358 pages

Semiotics, Bio-Semiotics and Cyber Semiotics

Semiotics, Bio-Semiotics, and Cyber Semiotics


From The Biosemiotic Approach in Biology: Theoretical Bases and Applied Models

Biosemiotics is a growing field that investigates semiotic processes in the living realm in an attempt to combine the findings of the biological sciences and semiotics. Semiotic processes are more or less what biologists have typically referred to as “signals,” “codes,” and “information pro- cessing” in biosystems, but these processes are here understood under the more general notion of semiosis, that is, the production, action, and interpretation of signs. Thus, biosemiotics can be seen as biology interpreted as a study of living sign systems—which also means that semiosis or sign process can be seen as the very nature of life itself. In other words, biosemiotics is a field of research investigating semiotic processes (meaning, signification, communication, and habit formation in living systems) and the physicochemical preconditions for sign action and interpretation.

To treat biosemiotics as biology interpreted as sign systems study is to emphasize an important intertheoretical relation between biology as we know it (as a field of inquiry) and semiotics (the study of signs). Biosemiotics offers a way of understanding life in which it is considered not just from the perspectives of physics and chemistry, but also from a view of living systems that stresses the role of signs conveyed and inter- preted by other signs in a variety of ways, including by means of molecules. In this sense, biosemiotics takes for granted and preserves the complexity of living processes as revealed by the existing fields of biology, from molecular biology to brain science and behavioral studies. However, biosemiotics attempts to bring together separate findings of the various disciplines of biology (including evolutionary biology) into a sign- theoretical perspective concerning the central phenomena of the living world, from the ribosome to the ecosystem and from the beginnings of life to its ultimate meanings. From this perspective, no positivist (i.e., theory-reductionist) form of unification is implied, but simply a broader approach to life processes in general, paying attention to the location of biology between the psychological (the humanities) and the physical (natural) sciences.

Furthermore, by incorporating new concepts, models, and theories from biology into the study of signs, biosemiotics attempts to shed new light on some of the unsolved questions within the general study of sign processes (semiotics), such as the question about the origins of signification in the universe (e.g., Hoffmeyer 1996), and the major thresholds in the levels and evolution of semiosis (Sebeok 1997; Deacon 1997; Kull 2000; Nöth 2000). Here, signification (and sign action) is understood in a broad sense, that is, not simply as the transfer of information, but also as the generation of the very content and meaning of that information in all living sign producers and sign receivers.

Sign processes are thus taken as real: they are governed by regularities (habits, or natural rules) that can be discovered and explained. They are intrinsic in living nature, but we can access them—not directly, but indirectly through other sign processes (e.g., scientific measurements and qualitative distinction methods)—even though the human representation and understanding of these processes in the construction of explanations is built up as a separate scientific sign system distinct from the organisms’ own sign processes.

One of the central characteristics of living systems is the highly organized character of their physical and chemical processes, partly based upon informational and molecular properties of what has been described in the 1960s as the genetic code (or, more precisely, organic codes). Distinguished biologists, such as Ernst Mayr (1982), have seen these informational aspects as one of the emergent features of life, namely, as a set of processes that distinguishes life from everything else in the physical world, except perhaps human-made computers. However, while the informational teleology of computer programs are derived, qua being designed by humans to achieve specific goals, the teleology and informational characteristics of organisms are intrinsic, qua having evolved naturally, through adaptational and evolutionary processes. The reductionist and mechanistic tradition in biology (and philosophy of biology) has seen such processes as being purely physical and having to do with only efficient causation. Biosemiotics is an attempt to use the concepts of semiotics in the sense employed by Charles Sanders Peirce to answer questions about the biological emergence of meaning, intentionality, and a psychological world (CP 5:484).  Indeed, these are questions that are hard to answer within a purely mechanistic and reductionist framework.


From The Biosemiotic Approach in Biology: Theoretical Bases and Applied Models

The term “biosemiotic” was first used by F. S. Rothschild in 1962, but Thomas Sebeok has done much to popularize the term and the field.  Apart from Charles Peirce (1939–1914) and Charles Morris (1901– 1979), early pioneers of biosemiotics were Jakob von Uexküll (1864– 1944), Heini Hediger (1908–1992), and Giorgio Prodi (1928–1987), and the founding fathers were Thomas Sebeok (1920–2001) and Thure von Uexküll (1908–2004). After 2000, an institutionalization of biosemiotics can be noticed: since 2001, annual international meetings of biosemioticians have been taking place (initially organized by the Copenhagen and Tartu groups); in 2004, the International Society for Biosemiotic Studies was established (with Jesper Hoffmeyer as its first president; see Favareau 2005); the specialized publications Journal of Biosemiotics (Nova Science) and Biosemiotics (Springer) have appeared; several collections of papers have characterized the scope and recent projects in biosemiotics, such as a special issue of Semiotica 127 (1/4) (1999), Sign Systems Studies 30 (1) (2002), Sebeok and Umiker-Sebeok 1992, Witzany 2007, and Barbieri 2007.

Also, from the 1960s to the 1990s, the semiotic approach in biology was developed in various branches:

a. Zoosemiotics, the semiotics of animal behavior and communication

b. Cellular and molecular semiotics, the study of organic codes and protolinguistic features of cellular processes

c. Phytosemiotics, or sign processes in plant life

d. Endosemiotics, or sign processes in the organism’s body

e. Semiotics in neurobiology

f. Origins of semiosis and semiotic thresholds


From Cybersemiotics: A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaningful Communication and the Interaction Between Nature and Culture


Cybersemiotics constructs a non-reductionist framework in order to integrate third person knowledge from the exact sciences and the life sciences with first person knowledge described as the qualities of feeling in humanities and second person intersubjective knowledge of the partly linguistic communicative interactions, on which the social and cultural aspects of reality are based. The modern view of the universe as made through evolution in irreversible time, forces us to view man as a product of evolution and therefore an observer from inside the universe. This changes the way we conceptualize the problem and the role of consciousness in nature and culture. The theory of evolution forces us to conceive the natural and social sciences as well as the humanities together in one theoretical framework of unrestricted or absolute naturalism, where consciousness as well as culture is part of nature. But the theories of the phenomenological life world and the hermeneutics of the meaning of communication seem to defy classical scientific explanations. The humanities therefore send another insight the opposite way down the evolutionary ladder, with questions like: What is the role of consciousness, signs and meaning in the development of our knowledge about evolution? Phenomenology and hermeneutics show the sciences that their prerequisites are embodied living conscious beings imbued with meaningful language and with a culture. One can see the world view that emerges from the work of the sciences as a reconstruction back into time of our present ecological and evolutionary self- understanding as semiotic intersubjective conscious cultural and historical creatures, but unable to handle the aspects of meaning and conscious awareness and therefore leaving it out of the story. Cybersemiotics proposes to solve the dualistic paradox by starting in the middle with semiotic cognition and communication as a basic sort of reality in which all our knowledge is created and then suggests that knowledge develops into four aspects of human reality: Our surrounding nature described by the physical and chemical natural sciences, our corporality described by the life sciences such as biology and medicine, our inner world of subjective experience described by phenomenologically based investigations and our social world described by the social sciences. I call this alternative model to the positivistic hierarchy the cybersemiotic star. The article explains the new understanding of Wissenschaft that emerges from Peirce’s and Luhmann’s conceptions.


Key People:

  • Thomas Sebeok
  • L M Rocha
  • Jesper Hoffmeyer
  • Charles Sanders  Pierce
  • Soren Brier
  • Marcello Barbieri
  • Howard Pattee
  • Jakob von Uexküll
  • Stanley Salthe
  • Claus Emmeche
  • M. Florkin
  • Kalevi Kull
  • Donald Favareau
  • Umberto Eco
  • Koichiro Matsuno
  • Thure von Uexküll
  • Gregory Bateson


Key Sources of Research:


A Short History of Biosemiotics

Marcello Barbieri

Click to access Marcello%20Barbieri%20(2009)%20A%20Short%20History%20of%20Biosemiotics.pdf



The Biosemiotic Approach in Biology : Theoretical Bases and Applied Models

Jo ã o Queiroz, Claus Emmeche, Kalevi Kull, and Charbel El-Hani



Irreducible and complementary semiotic forms

Howard Pattee


Click to access irreducible_and_complementary_semiotic_howard_pattee.pdf



Howard Pattee;jsessionid=0E1C125F151B5165F839E8FAC5411A00?doi=


Essential Readings in Biosemiotics: Anthology and Commentary

D. Favareau,

Essential Readings in Biosemiotics, Biosemiotics 3,

Springer Science+Business Media B.V. 2010



Introduction: An Evolutionary History of Biosemiotics

Donald Favareau

Essential Readings in Biosemiotics, Biosemiotics 3


Click to access Lesson_13_Favareau_History_biosemiotics.pdf



Introduction to Biosemiotics: The New Biological Synthesis

edited by Marcello Barbieri


A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaningful Communication and the Interaction Between Nature and Culture

Søren Brier


Click to access Brier,%20Cybersemiotics,%20Vol.%209,%20No.%202.pdf



Levels of Cybersemiotics: Possible ontologies of signification

Søren Brier


Click to access 2_Brier_v1_2.pdf


Design and Information in Biology: From Molecules to Systems

By J. A. Bryant


Cognitive Biology: Dealing with Information from Bacteria to Minds

By Gennaro Auletta


The cell as the smallest DNA-based molecular computer

Sungchul Ji


Click to access The_cell_as_the_smallest_DNA_based_molecular_computer.pdf


Semiotics Web page of Umberto Eco


Biosemiotics in the twentieth century: A view from biology



Click to access semi.1999.127.385.pdf


Biosemiotics: a new understanding of life

Marcello Barbieri


Click to access Bar08.pdf


What Does it Take to Produce Interpretation? Informational, Peircean and Code-Semiotic Views on Biosemiotics

Søren Brier & Cliff Joslyn

Click to access 02e7e529745b2b7e66000000.pdf


Spencer-Brown, G. (1972).

Laws of Form

New York: Crown Publishers


The Paradigm of Peircean Biosemiotics

Søren Brier

Click to access Brier_2008_peircean_biosemiotics.pdf




Click to access BiosemBiophys.pdf


Biosemiotic Questions

Kalevi Kull & Claus Emmeche & Donald Favareau

Click to access a4414fbb4bdca11561d08cb4de0a0d6c.pdf


Autocatalysis, Autopoiesis and Relational Biology

AutoCatalysis, Autopoiesis, and Relational Biology



The term autopoiesis is often encountered in the systems literature and is generally interpreted loosely as concerned with self-organizing systems and life. While this is partially true, the concept is actually very detailed and particular, and its implications are very far-reaching. This is not always fully appreciated, not least because of the difficulty of the original papers. Auto­poiesis was coined by Humberto Maturana and Francisco Varela to describe the nature of living as opposed to nonliving systems – it is thus an explanation of the nature of life. This, in itself, is an important enough subject and their theory has far-reaching implications for biology. They went further, however, and also developed fundamental ideas about the nervous system, perception, language, and cognition in general. These, too, have very significant impli­cations, not least for methodologies concerned with taking action within human activity systems, the design of systems in general and computer systems in particular, and for cognitive science and artificial intelligence.


Autopoiesis is a concept developed by Humberto Maturana and Francisco Varela in order to analyze the nature of living systems. It takes into account the circular organization of metabolism and it redefines the concepts of structure and organization.

Any system can be decomposed into processes and components, which interact through processes to generate other components. The definition of an Autopoietic system considers that “it is organized as a bounded network of processes of production, transformation and destruction of components that produces the components which: a)through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them and b)constitute it (the machine) as a concrete entity in the space in which they (the components) exist by specifying the topological domain of its realization as such a network”.


From Autopoietic and (M,R) systems



In 1972, in the middle of a cataclysmic political turmoil, two Chilean biologists introduced the concept of Autopoietic systems6 (‘‘auto’’=self and ‘‘poiesis’’= generating or producing) as a theoretical construction on the nature of living systems centering on two main notions: the circular organization of metabolism and a redefinition of the systemic concepts of structure and organization. Maturana and Varela’s starting point was that any system can be decomposed into processes and components. Components interact through processes to generate other components.

The notion of circular organization is given in Autopoiesis, and it is immediately clarified in the theory by the very definition of an Autopoietic system:

‘‘an Autopoietic system is organized as a bounded network of processes of production, transformation and destruction of components which:

  1. (i)  through their interactions and transformations continuously regenerate and realize the network of processes that produced them
  2. (ii)  constitute the system as a concrete entity in the space in which the components exist by specifying the topological realization of the system as such a network’’ (Varela et al., 1974; Maturana and Varela, 1975, 1980).

In an Autopoietic system, the result of any given process is the production of components that eventually would be transformed by other processes in the network into the components of the first process. This property, termed operational closure, is an organizational property that perfectly coexists with the fact that living systems are, from a physical point of view, energetically and materially open systems. The molecules that enter the system determine the system’s organization, which generates pathways whose operation produces molecular structures that determine the physical system and the system’s organization (Fig. 7) (Fleischaker, 1990). Thus an Autopoietic system does not have inputs or outputs, instead it creates a web of molecular processes that result in the maintenance of the autopoietic organization. Because an Autopoietic system’s internal dynamics are self-determined, there is no need to refer any operational (or organizational) aspect to the outside. Thus the environment does not inform, instruct or otherwise define the internal dynamics, it only perturbs the system’s dynamics. This does not mean that an Autopoietic system is completely independent from its medium. Instead it means that the system specifies its own internal states and the domain of its changes. In this context, external events act as perturbations that only trigger internal changes. But the magnitude and direction of these changes are defined by the internal dynamics of the system and not by the external perturbations (Maturana and Mpodozis, 2000).


The second clause demands that an Autopoietic system has ‘‘sufficiently complex’’ dynamics to self- produce the boundaries that separate the systems from the ‘‘non-system’’. This apparently trivial clause has profound implications as it touches upon the problem of autonomy and also serves to weed out from the Autopoietic forest some pure formal systems. Thus Autopoietic systems are not simple relational devices that connect components with components via complex graphs. Autopoietic systems must conform to an important topological property: their boundary (in the space where their components exist) is actively produced by the network of processes that define the system’s identity. This property of Autopoietic systems couples a purely relational property (operational closure) with a topological property and it demands that an Autopoietic system must be an autonomous unity, topographically and functionally segregated from its medium, but yet dependent from this medium (Weber, 2001). In the realm of molecules, the coupling of these two conditions necessarily implies that the minimal metabolism must be rather more complex than the spatial coupling of a direct chemical reaction with its reverse reaction.


From Autopoietic and (M,R) systems


Relational Biology

In the 1930s, Nicolas Rashevsky, a physicist by training, championed the biophysical approach to understanding living systems. Rashevsky and his stu- dents created a systematic theoretical effort that consisted of applying theories from physics to explain biological phenomena like cell division and neural processing (Rashevsky, 1938). Around 1950, Rashevsky became convinced that his intense and novel ‘‘bio- physical’’ approach was fundamentally limited for understanding living systems as a whole. He realized that his previous work had dealt only with bit parts of the phenomena of living systems, without considering their peculiar organization. Thus, Rashevsky coined the term Metric Biology to refer to all aspects where a reductionist approach to biology was valid and the term Relational Biology to aspects that depended on the organization of living systems rather than the matter found inside them (Rashevsky, 1954).

In 1958–1959, as a graduate student of Nicolas Rashevsky, Robert Rosen published three papers (Rosen, 1958a, b, 1959) that were a rigorous attempt to formalize the intuitive notions of relational biology. His formalism (known as (M,R) systems) used mathematical language based on a modern and abstract branch of mathematics (Theory of Categories (Eilenberg and MacLane, 1945)). Since not many biologists are well-enough versed in algebraic theory to evaluate its utility, (M,R) systems has not had the wide impact it may deserve. Despite the limited audience Rosen could capture with his ideas, Rosen continued to develop the theory of (M,R) systems and the use of the theory of categories in Biology for 40 years until his death in 1998.

(M R) Systems 

Metabolism-repair systems ((M,R)) were introduced by Robert Rosen as an abstract representation of cell metabolic activity. The representation was obtained in the context of Relational Biology, which means that organization prevails over the physico- chemical structure of the components involved. This fact was determinant for algebraically formalizing (M,R) systems using the theory of categories.

Two elements are considered in the construction of (M,R) systems: the metabolic activity (M) and the repair functions (R) acting on the unities of the metabolic process.

The metabolic system M is considered as an input-output system. In the categorical representation, inputs and outputs are the objects of the category and the processes connecting these elements are represented by the arrows of the category.


From Autocatalytic Sets: From the Origin of Life to the Economy


Autocatalytic Sets

The framework was originally developed in the context of a chemical reaction system, which can be described formally as a set (collection) of molecules; possible chemical reactions between these molecules; and, additionally, catalysts. A catalyst is a molecule that significantly increases the rate at which a chemical reaction happens, without being consumed in that reaction. In this context, catalysts can be viewed as providing functionality, because they determine which reactions happen at high enough rates to be relevant. In fact, without catalysts, life would most likely not be possible at all, because the chemical reactions vital for life would not happen fast enough, and they would not be synchronized with one another. Finally, we assume that there are small numbers of molecules, called the food set, that are assumed to be freely available from the environment. This reflects the notion that at least certain types of molecules (e.g., water, hydrogen, nitrogen, carbon dioxide, iron) would have been around on the early Earth, before the origin of life, and could be used freely as chemical building blocks.


Given such a chemical reaction system, a subset of its chemical reactions, together with the molecules involved in them, is called an autocatalytic set if (a) every reaction in the subset is catalyzed by at least one molecule from this subset and (b) every molecule in the subset can be produced from the food set by a series of reactions from this subset only. This two-part definition formally captures the idea of a functionally closed (part a) and self-sustaining (part b) system. The molecules mutually help (through catalysis) in each others’ production, and the set as a whole can be built up and maintained (through these mutually catalyzed reactions) from a steady supply of food molecules.


Stuart Kauffman (1971) was one of the first scientists to introduce this notion of autocatalytic sets. He subsequently constructed a simple mathematical model of chemical reaction systems to argue that such autocatalytic sets will arise spontaneously (Kauffman 1986, 1993). In his model (known as the binary polymer model), molecules are represented by simple bit strings (sequences of zeros and ones) of maximum length n. The chemical reactions consist of either gluing two bit strings together into a larger one (e.g., 000 + 11 → 00011), or cutting one bit string into two smaller ones (e.g., 010101 → 01 + 0101). The molecules (bit strings) are then assigned randomly, with a given probability, p, as catalysts for the possible reactions. In other words, there is a probability, p, that an arbitrary molecule will catalyze an arbitrary reaction. By changing the values of the parameters n and p and randomly generating the catalysis assignments, different instances of the model can be created.


Kauffman then developed a mathematical argument to show that, in his binary polymer model, given a fixed value for the probability of catalysis, p, and a large enough value for the maximum molecule length, n, the existence of autocatalytic sets is basically inevitable. However, this argument was later criticized (Lifson 1997) because it implies an exponential increase in the (average) level of catalysis. In other words, every time the maximum length n of the molecules (bit strings) in the model is increased by one, each molecule will end up catalyzing about twice as many reactions as before. This will indeed eventually lead to the existence of autocatalytic sets (for large enough n), but at a chemically unrealistically high level of catalysis. Furthermore, this notion of autocatalytic sets was also criticized for lacking evolvability (Vasas et al. 2010). In Kauffman’s argument, an autocatalytic set will appear as one “giant connected component” in the chemical reaction network. This, however, implies that there is no room for change, growth, or adaptation— in other words, no possibility for the autocatalytic set to evolve.


Key Ideas and Concepts:

  • Relational Biology
  • System Biology
  • Biosemiotics
  • Anticipation
  • Autopoiesis
  • Social Autopoiesis
  • MR systems
  • Self Reference
  • Mathematical Biology
  • Theoretical Biology
  • Socio-Cybernetics
  • Cyber Semiotics
  • Autocatalysis
  • Hyper Recursive/Incursive Automata


Each of these Idea needs a separate post.  Can not do justice to them all here.  Will try to write future posts expanding these ideas.

Autopoiesis, Autocatalysis, and Relational biology have been extended into other areas of inquiry.  Autopoiesis has been extended into Social systems theory through work of Niklas Luhmann.  Other researchers have extended it into organizational theory for firms.  Relational Biology has also extended into Futures research using concept of biology of Anticipation.


Key People:

  • Robert Rosen
  • Niklas Luhmann
  • Humburto Maturana
  • F Varela
  • Roberto Poli
  • Nicholas Rashevsky
  • John Kineman
  • M Nadin
  • A H Louie
  • Dirk Baecker
  • Soren Brier
  • Stuart Kaufman
  • Daniel Dubois
  • Donald C. Mikulecky
  • Milan Zeleny
  • Tibor Ganti


Key Sources of Research:


A relational theory of biological systems

Robert Rosen


Anticipatory Systems

Robert Rosen


A relational theory of biological systems II

Robert Rosen


The representation of biological systems from the standpoint of the theory of categories

Robert Rosen


Robert Rosen’s anticipatory systems

A.H. Louie


Click to access 09e4150cdd961e4a87000000.pdf


A Critical Evaluation of Luhmann’s Theory of Social Systems



Click to access quad50.pdf


Systems biology: The reincarnation of systems theory applied in biology?

Olaf Wolkenhauer

Date received (in revised form): 5th June 2001

Click to access 258.full.pdf





Click to access Zeleny%201977%20Formal.pdf



Dr. John Jay Kineman, Ph.D


The Dawn of Mathematical Biology


Daniel Sander Hoffmann


Click to access 1511.01455.pdf


Modeling Living Systems

Peter Andras


Click to access PAmodlivECAL2009.pdf


theory of organismic sets and mathematical relations


Click to access 41184.pdf


Tracing organizing principles:

Learning from the history of systems biology


Click to access Green%26Wolkenhauer.pdf



Eliseo Fernández

Click to access biosemiotics_and_the_relational_turn_in_biology.pdf


Autopoietic and (M,R) systems

Juan Carlos Letelier, Gonzalo Mar!ın, Jorge Mpodozis

Click to access Autop.Rosen.pdf






Click to access louie-mr-2006.pdf


Some Thoughts on A. H. Louie’s ‘‘More Than Life Itself: A Reflection on Formal Systems and Biology’’

Claudio Gutie ́rrez • Sebastia ́n Jaramillo • Jorge Soto-Andrade


Click to access thoughts-Louie.pdf


Relational Models of Social Systems


Click to access seidman.PDF


A Unified Approach to Biological and Social Organisms

N. Rashevsky


Essays on More Than Life Itself

A. H. Louie

Click to access essaysOnMoreThanLifeItself.pdf



Rosen’s (M,R) system in process algebra

Derek Gatherer1,3* and Vashti Galpin2

Click to access 1752-0509-7-128.pdf



The reflection of life: functional entailment and imminence in relational biology,

by A. H. Louie,

Springer, New York, NY, 2013, xxxii + 243 pp., ISBN 978-1-4614-6927-8

Click to access quo_vadis_relational_biology.pdf


Even more than life itself: beyond complexity

Donald C. Mikulecky


Click to access beyondcomplexityrev8310.pdf


Rosen R (1991)

 Life itself: a comprehensive inquiry into the nature, origin, and fabrication of life.

Columbia University Press, New York


Rosen R (2000)

Essays on life itself.

Columbia University Press, New York


Prolegomena: What Speaks in Favorof an Inquiry into Anticipatory Processes?

Mihai Nadin

Click to access edit_prolegomena.pdf



Eliseo Fernández

Click to access PRfinal.pdf


An Introduction to the Ontology of Anticipation

Roberto Poli

Click to access read_Poli-An-Introduction-to-the-Ontology-of-Anticipation.pdf


Autopoiesis 40 years Later. A Review and a Reformulation

Pablo Razeto-Barry

Click to access Autopoiesis_40_years_later_2012%20-%20Razeto-Barry_1.pdf


The mathematical biophysics of Nicolas Rashevsky

Paul Cull

Click to access rashevsky.pdf


The spread of hierarchical cycles

A.H. Louiea* and Roberto Poli


Louie, A.H.,


More than life itself: a synthetic continuation in relational biology.


Catalysis at the Origin of Life Viewed in the Light of the (M,R)-Systems of Robert Rosen

Athel Cornish-Bowden* and María Luz Cμrdenas



Click to access Bolzano09.pdf


Luhmann, Niklas.

“Insistence on systems theory: Perspectives from Germany-An essay.”

Social Forces (1983): 987-998.

Click to access LuhmannSystems.pdf


Luhmann N. (1986)

The autopoiesis of social systems.

In: Geyer F. & van der Zouwen J. (eds.) Sociocybernetic paradoxes. Sage, London: 172–192.



Gotthard Bechmann and Nico Stehr


Click to access luhmann_02.pdf


Luhmann, N.

“Essays on Self Reference.




Mingers J. (2002)

Can social systems be autopoietic? Assessing Luhmann’s social theory.

Sociological Review 50(2): 278–299.


Mingers J. (1989)

An Introduction to Autopoiesis – Implications and Applications.

Systems Practice 2(2): 159–180.



Maturana H. R. (1980)

Autopoiesis: Reproduction, heredity and evolution.

In: Zeleny M. (ed.) Autopoiesis, dissipative structures and spontaneous social orders, AAAS Selected Symposium 55 (AAAS National Annual Meeting, Houston TX, 3–8 January 1979). Westview Press, Boulder CO: 45–79







Click to access 1207.pdf


Varela F. J. (1980)

Describing the logic of the living. The adequacy and limitations of the idea of autopoiesis.

In: Zeleny M. (ed.) Autopoiesis: A theory of living organization. North-Holland, New York: 36–48



What Is Autopoiesis?

Milan Zeleny

Click to access 1194.pdf


Autopoiesis, a Theory of Living Organizations

Milan Zeleny


Maturana H. R. (1980)

Man and society.

In: Benseler F., Hejl P. M. & Köck W. K. (eds.) Autopoiesis, communication, and society: The theory of autopoietic systems in the social sciences


Order through fluctuation: Self-organization and social system

Ilya Prigogine

In Erich Jantsch (ed.), Evolution and Consciousness: Human Systems in Transition. Reading Ma: Addison-Wesley 93–130 (1976)


Maturana H. R. (1981)


In: Zeleny M. (ed.) Autopoiesis: A theory of the living organization. Westview Press, Boulder CO: 21–33.


Maturana H. R. (2002)
Autopoiesis, structural coupling and cognition: A history of these and other notions in the biology of cognition.
Cybernetics & Human Knowing 9(3–4): 5–34.



Pier Luigi Luisi

Autopoiesis: a review and a reappraisal

Click to access luisi_autopoiesis.pdf


From autopoiesis to neurophenomenology:
Francisco Varela’s exploration of the biophysics of being


Click to access art05.pdf


Life and mind: From autopoiesis to neurophenomenology. A tribute to Francisco Varela


Click to access Thompson,%20Evan%20-%20Life%20and%20Mind%20From%20autopoiesis%20to%20neurophenomenology.pdf


Autopoiesis, Communication, and Society: The Theory of Autopoietic Systems in the Social Sciences

Frank Benseler, Peter M. Hejl & Wolfram K. Köck


Boden M. (2000)

Autopoiesis and life.

Cognitive Science Quarterly 1: 117–145.


Systems Typologies in the Light of Autopoiesis: A Reconceptualization of Boulding’s Hierarchy, and a Typology of Self-Referential Systems

John Mingers


Click to access 550181e60cf24cee39f79f7c.pdf


The Problems of Social Autopoiesis

John Mingers

Click to access 004635379d4ef9f5b7000000.pdf


Varela F. J. (1996)

The early days of autopoiesis: Heinz and Chile.

Systems Research 13(3): 407–417


Uribe R. B. (1981)

Modeling autopoiesis.

In: Zeleny M. (ed.) Autopoiesis: A theory of living organization. Elsevier North Holland, New York: 49–62.


Some Remarks on Autocatalysis and Autopoiesis

Barry McMullin



Category Theoretical Distinction between Autopoiesis and (M,R) Systems

Tatsuya Nomura

Click to access 00b7d518a427671ea9000000.pdf


Smith J. D. (2014)

Self-concept: Autopoiesis as the Basis for a Conceptual Framework.

Systems Research and Behavioral Science 31(1): 32–46.


Fleischaker G. R. (1992)

Questions concerning the ontology of autopoiesis and the limits of its utility.

International Journal of General Systems, 21(2): 131–141.


Villalobos M. & Ward D. (2015)

Living systems: Autopoiesis, autonomy and enaction.

Philosophy & Technology 28(2): 225–239.


A Calculus for Autopoiesis

Dirk Baecker

June 1, 2012


The Sciences of Complexity and “Origins of Order”

Stuart A. Kauffman



Approaches to the Origin of Life on Earth

Stuart A. Kauffman



The phase transition in random catalytic sets


Rudolf Hanel, Stuart A. Kauffman, and Stefan Thurner



Click to access 0504776.pdf


Origins of Order in Dynamical Models



Click to access Origins_of-Order_review.pdf


On emergence, agency, and organization


Click to access 557065ab08ae7d0f5f900e19.pdf


 Autocatalytic Sets: From the Origin of Life to the Economy

Wim Hordijk


Autocatalysis, Information and Coding

Peter R. Willis


Click to access 00-01-003.pdf


Autocatalytic sets and boundaries

Wim Hordijk and Mike Steel*~hmac=6760deec426b5c9098efc365d7e9f047b20e06f02e1216aca77226e764abda13


Catalysis at the Origin of Life Viewed in the Light of the (M,R)-Systems of Robert Rosen

Athel Cornish-Bowden and María Luz Cμrdenas


Click to access Bolzano09.pdf


Closure to efficient causation, computability and artificial life

Mar ́ıa Luz Ca ́rdenasa,∗ Juan-Carlos Letelierb, Claudio Gutie ́rrezc, Athel Cornish-Bowdena and Jorge Soto-Andrade


Autopoietic and (M,R) systems

Juan Carlos Letelier*, Gonzalo Mar!ın, Jorge Mpodozis


Click to access autopoietic_mr.pdf



J. C. Letelier(1) and A. N. Zaretzky


Click to access AutopoiesisandMRsystems.pdf



Economics And The Collectively Autocatalytic Structure Of The Real Economy

November 21, 201112:28 PM ET





Click to access autocatalyticreplication.pdf


Systems and Organizational Cybernetics

Systems and Organizational Cybernetics


From  System Dynamics and the Evolution of Systems Movement A Historical Perspective


The systems movement has many roots and facets, with some of its concepts going back as far as ancient Greece. What we call ”the systems approach” today materialized in the first half of the twentieth century. At least, two important components should be mentioned, those proposed by von Bertalanffy and by Wiener.

Ludwig von Bertalanffy, an American biologist of Austrian origin, developed the idea that organized wholes of any kind should be describable, and to a certain extent explainable, by means of the same categories, and ultimately by one and the same formal apparatus. His General Systems Theory triggered a whole movement, which has tried to identify invariant structures and mechanisms across different kinds of organized wholes (for example, hierarchy, teleology, purposefulness, differentiation, morphogenesis, stability, ultrastability, emergence, and evolution). 

Norbert Wiener, an American mathematician at Massachusetts Institute of Technology, building on interdisciplinary work, accomplished in cooperation with Bigelow, an IBM engineer, and Rosenblueth, a physiologist, published his seminal book on Cybernetics. His work became the trans-disciplinary foundation for a new science of capturing, as well as designing control and communication mechanisms in all kinds of dynamical systems. Cyberneticians have been interested in concepts such as information, communication, complexity, autonomy, interdependence, cooperation and conflict, self-production (”autopoiesis”), self-organization, (self-) control, self-reference, and (self-) transformation of complex dynamical systems.

From System Dynamics and the Evolution of Systems Movement A Historical Perspective


Along the tradition which led to the evolution of General Systems Theory (Bertalannfy, Boulding, Gerard, Miller, Rapoport) and Cybernetics (Wiener, McCulloch, Ashby, Powers, Pask, Beer), a number of roots can be identified, in particular:

  • Mathematics (for example, Newton, Poincaré, Lyapunov, Lotka, Volterra, Rashevsky);
  • Logic (for example, Epimenides, Leibniz, Boole, Russell and Whitehead, Goedel, Spencer-Brown);
  • Biology, including general physiology and neurophysiology (for example, Hippocrates, Cannon, Rosenblueth, McCulloch, Rosen);
  • Engineering, including its physical and mathematical foundations (for example, Heron, Kepler, Watt, Euler, Fourier, Maxwell, Hertz, Turing, Shannon and Weaver, von Neumann, Walsh); and
  • Social and human sciences, including economics (for example, Hume, Adam Smith, Adam Ferguson, John Stuart Mill, Dewey, Bateson, Merton, Simon, Piaget).


From System Dynamics and the Evolution of Systems Movement A Historical Perspective

Levels of Organizations 

In this strand of the systems movement, one focus of inquiry is on the role of feedback in communication and control in (and between) organizations and society, as well as in technical systems. The other focal interest is on the multidimensional nature and the multilevel structures of complex systems. Specific theory building, methodological developments and pertinent applications have occurred at the following levels:

  • Individual and family levels (for example, systemic psychotherapy, family therapy, holistic medicine, cognitivist therapy, reality therapy);
  • Organizational and societal levels (for example, managerial cybernetics, organizational cybernetics, sociocybernetics, social systems design, social ecology, learning organizations); and
  • The level of complex technical systems (systems engineering).


From System Dynamics and the Evolution of Systems Movement A Historical Perspective

Mathematical/Quantitative Strand


As can be noted from these preliminaries, different kinds of system theory and methodology have evolved over time. One of these is Jay W. Forrester’s theory of dynamical systems, which is a basis for the methodology of System Dynamics. In SD, the main emphasis is on the role of structure, and its relationship with the dynamic behavior of systems, modeled as networks of informationally closed feedback loops between stock and flow variables. Several other mathematical systems theories, for example, mathematical general systems theory (Klir, Pestel, Mesarovic & Takahara), as well as a whole stream of theoretical developments, which can be subsumed under the terms ”dynamical systems theory” or ”theories of non-linear dynamics,” for example, catastrophe theory, chaos theory, complexity theory have been elaborated. Under the latter, branches such as the theory of fractals (Mandelbrot), geometry of behavior (Abraham) and self- organized criticality (Bak) are subsumed. In this context, the term ”sciences of complexity” has also been used. In addition, a number of essentially mathematical theories, which can be called ”system theories,” have emerged in different application contexts, examples of which are discernible in such fields as:

  • Engineering, namely information and communication theory and technology (for example, Kalman filters, Walsh functions, hypercube architectures, automata, cellular automata, artificial intelligence, cybernetic machines, neural nets);
  • Operations research (for example, modeling theory and simulation methodologies, Markov chains, genetic algorithms, fuzzy control, orthogonal sets, rough sets);
  • Social sciences, economics in particular (for example, game theory, decision theory); and
  • Ecology (for example, H. Odum’s systems ecology).

Qualitative System Theories

Examples of essentially non-mathematical system theories can be found in many different areas of study, for example:

  • Economics, namely its institutional/evolutionist strand (Veblen, Myrdal, Boulding);
  • Sociology (for example, Parsons’ and Luhmann’s social system theories, Hall’s cultural systems theory);
  • Political sciences (for example, Easton, Deutsch, Wallerstein);
  • Anthropology (for example, Levi Strauss’s structuralist-functionalist anthropology);
  • Semiotics (for example, general semantics (Korzybski, Hayakawa, Rapoport)); and
  • Psychology and psychotherapy (for example, systemic intervention (Bateson, Watzlawick, F. Simon), fractal affect logic (Ciompi)).

Quantitative and Qualitative

Several system-theoretic contributions have merged the quantitative and the qualitative in new ways. This is the case for example in Rapoport’s works in game theory as well as General Systems Theory, Pask’s Conversation Theory, von Foerster’s Cybernetics of Cybernetics (second order cybernetics), and Stafford Beer’s opus in Managerial Cybernetics. In all four cases, mathematical expression is virtuously connected to ethical, philosophical, and epistemological reflection. Further examples are Prigogine’s theory of dissipative structures, Mandelbrot’s theory of fractals, Kauffman’s complexity theory, and Haken’s Synergetics, all of which combine mathematical analysis and a strong component of qualitative interpretation.

System Dynamics vs Managerial Cybernetics

At this point, it is worth elaborating on the specific differences between two major threads of the systems movement: the cybernetic thread, from which Managerial Cybernetics has emanated, and the servomechanic thread in which SD is grounded [Richardson 1999]. As Richardson’s detailed study shows, the strongest influence on cybernetics came from biologists and physiologists, while the thinking of economists and engineers essentially shaped the servomechanic thread. Consequently, the concepts of the former are more focused on the adaptation and control of complex systems for the purpose of maintaining stability under exogenous disturbances. Servomechanics, on the other hand, and SD in particular, take an endogenous view, being mainly interested in understanding circular causality as a source of a system’s behavior. Cybernetics is more connected with communication theory, the general concern of which can be summarized as how to deal with randomly varying input. SD, on the other hand, shows a stronger link with engineering control theory, which is primarily concerned with behavior generated by the control system itself, and the role of nonlinearities. Managerial cybernetics and SD both share the concern of contributing to management science, but with different emphases and with instruments that are, in principle, complementary. Finally, the quantitative foundations are generally more evident in the basic literature on SD, than in the writings on Managerial Cybernetics, in which the mathematical apparatus underlying model formulation is confined to a small number of publications [e.g., Beer 1962, 1981], which are less known than the qualitative treatises.

Positivistic Tradition

A positivistic methodological position is tendentially objectivistic, conceptual–instrumental, quantitative, and structuralist–functionalist in its approach. An interpretive position, on the other hand, tendentially emphasizes the subjectivist, communicational, cultural, political, ethical, and esthetic: the qualitative, and the discursive aspects. It would be too simplistic to classify a specific methodology in itself as ”positivistic” or as ”interpretative.” Despite the traditions they have grown out of, several methodologies have evolved and been reinterpreted or opened to new aspects (see below).

In the following, a sample of systems methodologies will be characterized and positioned in relation to these two traditions:

  • ”Hard” OR methods. Operations research (OR) uses a wide variety of mathematical and statistical methods and techniques––for example of optimization, queuing, dynamic programming, graph theory, time series analysis––to provide solutions for organizational problems, mainly in the domains of operations, such as production and logistics, and finance.
  • Living Systems Theory. In his LST, James Grier Miller [1978], identifies a set of 20 necessary components that can be discerned in living systems of any kind. These structural features are specified on the basis of a huge empirical study and proposed as the ”critical subsystems” that ”make up a living system.” LST has been used as a device for diagnosis and design in the domains of engineering and the social sciences.
  • Viable System Model. Stafford Beer’s VSM specifies a set of control functions and their interrelationships as the sufficient conditions for the viability of any human or social system [cf. Beer, 1981]. These are applicable in a recursive mode, for example, to the different levels of an organization. The VSM has been widely applied in the diagnostic mode, but also to support the design of all kinds of social systems. Specific methodologies for these purposes have been developed, for instance, for use in consultancy. The term viable system diagnosis (VSD) is also widely used.

Interpretative Tradition

The methodologies addressed up to this point have by and large been created in the positivistic tradition of science. However, they have not altogether been excluded from fertile contacts with the interpretivist strand of inquiry. In principle, all of them can be considered as instruments to support discourses about different interpretations of an organizational reality or alternative futures studied in concrete cases.

  • Interactive Planning. IP is a methodology, designed by Russell Ackoff [1981], and developed further by Jamshid Gharajedaghi, for the purpose of dealing with ”messes” and enabling actors to design their desired futures, as well as bring them about. It is grounded in theoretical work on purposeful systems, reverts to the principles of continuous, participative, and holistic planning, and centers on the idea of an ”idealized design.”
  • Soft Systems Methodology. SSM is a heuristic designed by Peter Checkland [1981] for dealing with complex situations. Checkland suggests a process of inquiry constituted by two aspects: a conceptual one, which is logic based, and a sociopolitical one, which is concerned with the cultural feasibility, desirability, and the implementation of change.
  • Critical Systems Heuristics. CSH is a methodology, which Werner Ulrich [1996] proposed for the purpose of scientifically informing planning and design in order to lead to an improvement in the human condition. The process aims to uncover the interests that the system under study serves. The legitimacy and expertise of actors, and particularly the impacts of decisions and behaviors of the system on others – the ”affected” – are elicited by means of a set of boundary questions.

All of these three methodologies (IP, SSM, and CSH) are positioned in the interpretive tradition. They were designed to deal with the qualitative aspects in the analysis and design of complex systems, emphasizing the communicational, social, political, and ethical dimensions of problem solving. Several of them mention explicitly that they do not preclude the use of quantitative techniques.


Key People:

  • Markus Schwaninger
  • Stafford Beer
  • Werner Ulrich
  • Raul Espejo
  • Peter Checkland
  • John Mingers
  • M C Jackson 
  • Peter Senge
  • Russell Ackoff
  • C. West Churchman
  • R L Flood
  • J Rosenhead
  • Gregory Bateson
  • Fritjof Capra
  • D C Lane 
  • Ralph Stacey
  • James Grier Miller
  • Hans Ulrich


Key Sources of Research:


System theory and cybernetics

A solid basis for transdisciplinarity in management education and research

Markus Schwaninger


Click to access System%20Theory%20and%20Cybernetics_%20A%20Solid%20Basis.pdf


Intelligent Organizations: An Integrative Framework

Markus Schwaninger

Click to access Intelligent%20Organizations_An%20Integrative%20Framework.pdf


System Dynamics and the Evolution of the Systems Movement

Markus Schwaninger

Click to access System%20Dynamics%20and%20the%20Evolution%20of%20the%20Systems%20Movement_SysResBehSc%2023.pdf


Methodologies in Conflict: Achieving Synergies Between System Dynamics and Organizational Cybernetics

Markus Schwaninger


Click to access Integrative%20Systems%20Methodology%20-%20Methodologies%20in%20Conflict%202004_.pdf


System dynamics and cybernetics: a synergetic pair


Markus Schwaningera and José Pérez Ríos

Click to access System%20Dynamics%20and%20Cybernetics_SDR_2008.pdf


Managing Complexity—The Path Toward Intelligent Organizations

Markus Schwaninger


Click to access Managing%20Complexity%20-%20The%20Path%20Toward%20Intelligent%20Organizations.pdf


Design for viable organizations: The diagnostic power of the viable system model


Markus Schwaninger


Click to access Design%20for%20Viable%20Organizations_06.pdf


Contributions to model validation: hierarchy, process, and cessation

Stefan N. Groesser and Markus Schwaninger

Click to access 233_Contributions%20to%20Model%20Validation_SDR%2028-2,%202012.pdf




Click to access A%20Cybernetic%20Model%20to%20Enhance%20Organizational%20Intelligence-Systems%20Analysis%20Modeling%20Simulation_2003.pdf


System Dynamics and Cybernetics: A Necessary Synergy

Schwaninger, Markus; Ambroz, Kristjan & Ríos, José Pérez

Click to access System%20Dynamics%20and%20Cybernetics%20-%20A%20Necessary%20Synergy%20072004_IntSDConf%20Oxford.pdf


System Dynamics and the Evolution of Systems Movement

A Historical Perspective

Markus Schwaninger

Click to access DB52_Schwaninger_historical.pdf.pdf


System Dynamics in the evolution of Systems Approach

Markus Schwaninger


Click to access 214_System%20Dynamics%20in%20the%20Evolution%20of%20the%20Systems%20Approach_Encycl.%20SySciences_2009.pdf


The Evolution of Organizational Cybernetics

Markus Schwinger

Click to access The%20Evolution%20of%20Organizational%20Cybernetics.pdf


Operational Closure and Self-Reference: On the Logic of Organizational Change

Markus Schwaninger and Stefan N. Groesser

Click to access 235_Operational%20Closure%20and%20Self-Reference_SRBS%202012.pdf



Model-based Management: A Cybernetic Concept

Markus Schwaninger



Click to access 254_Model-Based%20Management_A%20Cybernetic%20Concept-SRBS-2015.pdf




Raul Espejo 2003


Click to access INTRODUCTION%20TO%20THE%20VIABLE%20SYSTEM%20MODEL3.pdf



A complexity approach to sustainability – Stafford Beer revisited


A. Espinosa *, R. Harnden, J. Walker


Click to access 57043bc708ae74a08e2461d9.pdf




Allenna Leonard with Stafford Beer


Stafford Beer

The Viable System Model:

its provenance, development, methodology and pathology



Cybernetics and the Mangle: Ashby, Beer and Pask

Andrew Pickering

Click to access 544529760cf2f14fb80ef419.pdf


What Can Cybernetics Contribute to the Conscious Evolution of Organizations and Society?

Markus Schwaninger

Click to access What%20can%20Cybernetics%20Contribute%20to%20the%20Conscious%20Evolution….pdf


Fifty years of systems thinking for management

MC Jackson



Introducing Systems Approaches

Martin Reynolds and Sue Holwell


Click to access systems-approaches_ch1.pdf


A review of the recent contribution of systems thinking to operational research and management science

John Mingers
Leroy White

Click to access EJOR-Systems_version_1_sent_Web.pdf


Managing Complexity by Recursion

by Bernd Schiemenz


Hard OR, Soft OR, Problem Structuring Methods, Critical Systems Thinking: A Primer

Hans G. Daellenbach

Click to access Daellenbach.pdf


Anticipatory Viable Systems

Maurice Yolles

Daniel Dubois


Second-order cybernetics: an historical introduction

Bernard Scott

Click to access 1798.pdf


Glanville R. (2003)

Second-Order Cybernetics.


Systems Theory, Systems Thinking

S White

Click to access Systems%20Theory%20-%20Systems%20Thinking%20Baltimore%20talk%2010022012.pdf


Theoretical approaches to managing complexity in organizations: A comparative analysis

Estudios Gerenciales
Volume 31, Issue 134, January–March 2015, Pages 20–29


Helping business schools engage with real problems: The contribution of critical realism and systems thinking

John Mingers

Click to access Tackling%20Real%20Problems%20EJOR%20Rev1%20sent.pdf


Only Connect! An Annotated

Bibliography Reflecting the Breadth and Diversitv of Svstem.sThinking

David C. Lane

Mike C. Jackson

Click to access 548f08000cf2d1800d861f3f.pdf


The greater whole: Towards a synthesis of system dynamics and soft systems methodology
David C. Lane  Rogelio Oliva

Click to access 54d9e2e20cf2970e4e7d06ae.pdf


Feedback Thought in Economics and Finance

Feedback Thought in Economics and Finance

  • Negative Feedbacks
  • Positive Feedbacks
  • Stocks and Flows
  • Limiting Factors


Key People:

  • Jay Forrester
  • George Richardson
  • John Sterman
  • Michael Radzicki
  • Mikhail Oet
  • Oleg Pavlov
  • Eric D. Beinhocker
  • Stuart A. Umpleby
  • Khalid Saeed
  • Kaoru Yamaguchi


Reflexivity and Second order economics are closely related concepts.


From System Dynamics and Its Contribution to Economics and Economic Modeling


System dynamics is a computer simulation modeling methodology that is used to analyze complex nonlinear dynamic feedback systems for the purposes of generating insight and designing policies that will improve system performance. It was originally created in 1957 by Jay W. Forrester of the Massachusetts Institute of Technology as a methodology for building computer simulation models of problematic behavior within corporations. The models were used to design and test policies aimed at altering a corporation’s structure so that its behavior would improve and become more robust.

Today, system dynamics is applied to a large variety of problems in a multitude of academic disciplines, including economics. System dynamics models are created by identifying and linking the relevant pieces of a system’s structure and simulating the behavior generated by that structure. Through an iterative process of structure identification, mapping, and simulation a model emerges that can explain (mimic) a system’s problematic behavior and serve as a vehicle for policy design and testing. From a system dynamics perspective a system’s structure consists of stocks, flows, feedback loops, and limiting factors.

Stocks can be thought of as bathtubs that accumulate/de-cumulate a system’s flows over time. Flow can be thought of as pipe and faucet assemblies that fill or drain the stocks. Mathematically, the process of flows accumulating/de-cumulating in stocks is called integration. The integration process creates all dynamic behavior in the world be it in a physical system, a biological system, or a socioeconomic system. Examples of stocks and flows in economic systems include a stock of inventory and its inflow of production and its outflow of sales, a stock of the book value of a firm’s capital and its inflow of investment  spending and its outflow of depreciation, and a stock of employed labor and its inflow of hiring and its outflow of labor separations.

Feedback is the transmission and return of information about the amount of information or material that has accumulated in a system’s stocks. Information travels from a stock back to its flow(s) either directly or indirectly, and this movement of information causes the system’s faucets to open more, close a bit, close all the way, or stay in the same place. Every feedback loop has to contain at least one stock so that a simultaneous equation situation can be avoided and a model’s behavior can be revealed recursively. Loops with a single stock are termed minor, while loops containing more than one stock are termed major. 

Two types of feedback loops exist in system dynamics modeling: positive loops and negative loops. Generally speaking, positive loops generate self-reinforcing behavior and are responsible for the growth or decline of a system. Any relationship that can be termed a virtuous or vicious circle is thus a positive feedback loop. Examples of positive loops in economic systems include path dependent processes, increasing returns, speculative bubbles, learning by-doing, and many of the relationships found in macroeconomic growth theory. Forrester [12], Radzicki and Sterman [46],Moxnes [32], Sterman (Chap. 10 in [55]), Radzicki [44], Ryzhenkov [49], and Weber [58] describe system dynamics models of economic systems that possess dominant positive feedback processes.

Negative feedback loops generate goal-seeking behavior and are responsible for both stabilizing systems and causing them to oscillate. When a negative loop detects a gap between a stock and its goal it initiates corrective action aimed at closing the gap. When this is accomplished without a significant time delay, a system will adjust smoothly to its goal. On the other hand, if there are significant time lags in the corrective actions of a negative loop, it can overshoot or undershoot its goal and cause the system to oscillate. Examples of negative feedback processes in economic systems include equilibrating mechanisms (“auto-pilots”) such as simple supply and demand relationships, stock adjustment models for invetory control, any purposeful behavior, and many of the relationships found in macroeconomic business cycle theory. Meadows [27], Mass [26], Low [23], Forrester [12], and Sterman [54] provide examples of system dynamics models that generate cyclical behavior at the macro-economic and micro-economic levels.

From a system dynamics point of view, positive and negative feedback loops fight for control of a system’s behavior. The loops that are dominant at any given time determine a system’s time path and, if the system is nonlinear, the dominance of the loops can change over time as the system’s stocks fill and drain. From this perspective, the dynamic behavior of any economy that is, the interactions between the trend and the cycle in an economy over time can be explained as a fight for dominance between the economy’s most significant positive and negative feedback loops.


Key Sources of Research:


Systemic Financial Feedbacks – Conceptual Framework and Modeling Implications

Dieter Gramlich1 and Mikhail V. Oet

Click to access 54992d5c0cf2519f5a1df20b.pdf




Mikhail V. Oet

Oleg V. Pavlov

Click to access P1441.pdf



Mr. Hamilton, Mr. Forrester, and a Foundation for Evolutionary Economics

Michael J. Radzicki


Click to access 0a85e52e41951a468c000000.pdf


European Contributions to Evolutionary Institutional Economics: The Cases of ‘Cumulative Circular Causation’ (CCC) and ‘Open Systems Approach’ (OSA).
Some Methodological and Policy Implications


Sebastian Berger and Wolfram Elsner


System Dynamicsand Its Contribution to Economics and Economic Modeling



Click to access 02e7e53331fe5f394b000000.pdf



Institutional Economics, Post Keynesian Economics, and System Dynamics: Three Strands of a Heterodox Economics Braid

Michael J. Radzicki, Ph.D.

Click to access 02e7e53331eeea388c000000.pdf



Was Alfred Eichner a System Dynamicist?


Michael J. Radzicki

Click to access 0f317536d3f41a13fb000000.pdf


Second-Order Economics as an Example of Second-Order Cybernetics

Stuart A. Umpleby


Click to access 890.pdf


Reflexivity, complexity, and the nature of social science

Eric D. Beinhocker


Click to access Beinhocker%20(JEM%202013).pdf


Path dependence, its critics and the quest for ‘historical economics’


Paul A. David

Click to access 0deec53b482217c114000000.pdf


Endogenous Feedback Perspective on Money in a Stock-Flow Consistent Model

I. David Wheat
University of Bergen


Click to access Wheat%20Endogenous%20Feedback%20Perspective%20on%20Money%20WP.pdf


Classical Economics on Limits to Growth

Khalid Saeed


Click to access Classical%20Economics%20on%20Limits%20to%20Growth.pdf



Misperceptions of Feedback in Dynamic Decisionmaking

John D. Sterman


Click to access 54359e4e0cf2bf1f1f2b3520.pdf


Learning in and about complex systems

John D. Sterman


Click to access sterman-learning-in-and-about-complex-systems.pdf


Micro-worlds and Evolutionary Economics

Michael J. Radzicki

Click to access radzi533.pdf


Feedback Thought in Social Science and Systems Theory

George Richardson

Pegasus Communications, Inc. ©1999


The Feedback concept in American Social Sciences 

George Richardson


Click to access richa001.pdf


Evolutionary Economics and System Dynamics

Radzicki and Sterman


Effects of Feedback Complexity on Dynamic Decision Making
Ernst Diehl, John D. Sterman

Organizational Behavior and Human Decision Processes

Volume 62, Issue 2, May 1995, Pages 198-215


Old Wine in a New Bottle:
Towards a Common Language for Post-Keynesian Macroeconomics Model

Ginanjar Utama


Click to access P1307.pdf


On Component Based Modeling Approach using System Dynamics for The Financial System (With a Case Study of Keen-Minsky Model)

Ginanjar Utama


Click to access P1209.pdf


On the Monetary and Financial Stability under A Public Money System

– Modeling the American Monetary Act Simplified –

Kaoru Yamaguchi


Click to access P1065.pdf


Integration of Real and Monetary Sectors with Labor Market
– SD Macroeconomic Modeling (3) –

Kaoru Yamaguchi


Balance of Payments and Foreign Exchange Dynamics

– SD Macroeconomic Modeling (4) –

Kaoru Yamaguchi, Ph.D


Click to access YAMAG211.pdf



Money and Macroeconomic Dynamics

Accounting System Dynamics Approach

Kaoru Yamaguchi, Ph.D


Click to access Macro%20Dynamics.pdf


Does Money Matter on the Formation of Business Cycles and Economic Recessions ?
– SD Simulations of A Monetary Goodwin Model –


Kaoru Yamaguchi

Click to access DBS12-01.pdf


Head and Tail of Money Creation and its System Design Failures

– Toward the Alternative System Design –

JFRC Working Paper No. 01-2016

Kaoru Yamaguchi, Ph.D.

Yokei Yamaguchi

Click to access Head-and-Tail-2016_WP__-_Japan_Futures_Research_Center.pdf


Modelling the Great Transition


Emanuele Campiglio

New Economics Foundation

Click to access Emanuel-SD-conference-9-2-12.pdf


The role of System Dynamics modelling to understand food chain complexity and address challenges for sustainability policies

Irene Monasterolo1, Roberto Pasqualino, Edoardo Mollona


Click to access CFP3-06_Full_Paper.pdf


Dynamic regional economic modeling: a systems approach

I. David Wheat



Click to access 1.17_wheat_pawluczuk.pdf


Expectation Formation and Parameter Estimation in Uncertain Dynamical Systems: The System Dynamics Approach to Post Keynesian-Institutional Economics



Michael J. Radzicki


Click to access 0deec536d3da974962000000.pdf


The Circular and Cumulative Structure of Administered Pricing

Mark Nichols, Oleg Pavlov, and Michael J. Radzicki


Click to access 02e7e5282d33c933df000000.pdf


A System Dynamics Approach to the Bhaduri‐Marglin Model

Klaus D. John

Click to access P1306.pdf


An Institutional Dynamics Model of the Euro zone crisis: Greece as an Illustrative Example

Domen Zavrl

Miroljub Kljajić

Click to access P1144.pdf


Is system dynamics modelling of relevance to neoclassical economists? 

Douglas J. Crookes Martin P. De Wit

Click to access 00b7d53861d6b14d9f000000.pdf


System dynamics modelling and simulating the effects of intellectual capital on economic growth

Ivona Milić Beran



Repo Chains and Financial Instability

Repo Chains and Financial Instability

There are three issues with Repos.

  • Repo chains as source of instability
  • Impact of Repo on Money Supply
  • Re-hypothecation: Reuse of Repo and Leverage


From Collateral Shortages and Intermediation Networks


In the pre-crisis period, financial markets witnessed a growing reliance on short-term funds raised in wholesale markets. In particular, there was a dramatic rise in the use of sales and repurchase (repo) agreements to fund longer-term investment opportunities or to finance inventories of securities held for market-making purposes. Given that such funding opportunities were secured by collateral, they were mostly considered to be safe. Since the crisis of 2007–8, however, the repo and the asset-backed commercial paper (ABCP) markets have been viewed as one of the potential sources of fragility in the financial system, with conventional wisdom (partially) attributing the collapse of Bear Stearns, Lehman Brothers, and Northern Rock to their reliance on wholesale funding.


From The Economics of Collateral Chains


The ‘supply’ of pledged collateral is typically received by the central collateral desk of banks that re-use the collateral to meet the ‘demand’ from the financial system. The key providers of primary (or source) collateral to the ‘street’ (or large banks) are: hedge funds; securities lending (via custodians) on behalf of pension funds, insurers, official sector accounts, etc. and commercial banks that liaise with large banks. The securities they hold are continuously re-invested to maximize returns over their maturity tenor. Source collateral is collateral that can be re-pledged, creating dynamic collateral chains. The term re-pledged is a legal term and means that the dealer receiving the collateral has the right to reuse in its own name.  Since a single piece of source collateral can be re-used several times by several different intermediaries, the aggregate volume of repledged collateral reflects both the availability of collateral (that is collateral from the source) as well as the velocity (or reuse rate) of source collateral.


From The Economics of Collateral Chains


The ratio of the total collateral received by the large banks divided by the ‘source’ collateral is the velo- city of collateral due to the intermediation by the street. For end-2007, the numerator of $10 trillion is what the large banks received in pledged collateral. We then compare it to the denominator or the primary sources of collateral via the hedge funds and security lenders acting on behalf of pension, insurers, official accounts, etc. − this was about $3.4 trillion. Empirical evidence suggests that the chains were longer pre-Lehman and around 3 as of end-2007; they have decreased to about 2.4 as of end-2010. Intuitively, this means that collateral from a primary source now takes ‘fewer steps’ to reach the ultimate client. This is due to the concern of source collateral providers about counterparty risk of the large banks, and also from the demand for higher quality collateral by the ultimate clients. Lower quality collateral is difficult to move in the present time.


From The Economics of Collateral Chains


This decline in the re-use of collateral may be viewed positively from a financial stability perspective. However, from a monetary policy perspective, the lubrication in the global financial markets is now lower as the velocity of money type instruments has declined. The shorter “chains” − from constraining the collateral moves lowers global financial lubrication will increase overall cost of capital to the real economy.

Overall, global liquidity remains below pre-Lehman levels. When we consider collateral use/re-use in addition to M2 or the monetary base in U.S., U.K. and Eurozone, financial lubrication was over $30 trillion before Lehman (and one-third came via pledged collateral); now it is lower by about $4-5 trillion. Since cross-border funding is important for large banks, allowing for the efficient arbitrage of their funding operations, (e.g., consider the recent surge in the demand for U.S. dollar funding by European banks), the state of the pledged collateral market needs to be considered when setting monetary policy.


Key Sources of Research:


Velocity of Pledged Collateral: Analysis and Implications

Manmohan Singh

November 2011


Sizing Up Repo 

Arvind Krishnamurthy   Stefan Nagel

Dmitry Orlov

June 2011


Click to access Sizing-up-repo_June29.pdf



Jason Roderick Donaldson Eva Micheler

January 2, 2016


Click to access


Infante, S. (2015).

Liquidity windfalls: The consequences of repo rehypothecation.

Technical report, Federal Reserve Board of Governors Finance and Economics Dis- cussion Series 2015-22.


Kahn, C. M. and H. J. Park


Collateral, rehypothecation, and efficiency.

UIUC Working paper.


Lee, J. (2015).

Collateral circulation and repo spreads.

Technical report


Singh, M. (2010).

The velocity of pledged collateral.

Technical report, IMF.


Singh, M. and J. Aitken (2010).

The (sizable) role of rehypothecation in the shadow banking system.

Technical report, IMF.


Gorton, G. and A. Metrick (2010).


Review (Nov), 507–520.


Gorton, G. and A. Metrick (2012).

Securitized banking and the run on repo.

Journal of Financial Economics 104(3), 425–451.


Copeland, A., A. Martin, and M. Walker (2014).

Repo runs: Evidence from the tri- party repo market.

The Journal of Finance 69(6), 2343–2380.


Antinolfi, G., F. Carapella, C. Kahn, A. Martin, D. Mills, and E. Nosal (2014).

Repos, Fire Sales, and Bankruptcy Policy.

Review of Economic Dynamics, (forthcoming).


Financial Intermediation Networks

Marco Di Maggio† Alireza Tahbaz-Salehi

March 2015

Click to access Intermeidation-March2015.pdf


Collateral Shortages and Intermediation Networks

Marco Di Maggio  Alireza Tahbaz-Salehi

October 1, 2015


The political economy of repo markets

Daniela Gabor

Click to access gabor_political_economy_of_repo_markets.pdf


Collateral Risk, Repo Rollover and Shadow Banking

Shengxing Zhangú

August 28, 2014


Click to access Zhang%20paper.pdf


Shadow Interconnectedness: The Political Economy of (European) Shadow Banking

Daniela Gabor

September 16, 2013


Systemic Risk, Contagion, and Financial Networks: A Survey

Matteo Chinazzi Giorgio Fagiolo

June 3, 2015


A Map of Collateral Uses and Flows


Andrea Aguiar Richard Bookstaber Dror Y. Kenett Thomas Wipf

Click to access OFRwp-2016-06_Map-of-Collateral-Uses.pdf


Aguiar, A., R. Bookstaber, and T. Wipf.

“A Map of Funding Durability and Risk.”

Office of Financial Research Working Paper no. 14-03, 2014.


Baklanova, V.,

“Repo and Securities Lending: Improving Transparency with Better Data.”

Office of Financial Research Brief no. 15-03, 2015.


Baklanova, V., A. Copeland, and R. McCaughrin.

“Reference Guide to U.S. Repo and Securities Lending Markets.”

Office of Financial Research Working Paper no. 15-17, 2015.


Shadow Banks and Systemic Risks

Rui Gong Frank H. Page Jr.

July 23, 2015


Financial Contagion with Collateralized Transactions: A Multiplex Network Approach

Gustavo Peralta  Ricardo Crisóstomo

July 2016


Non-bank financial institutions: Assessment of their impact
on the stability of the financial system


Click to access ecp472_en.pdf


Taxonomy of Studies on Interconnectedness

Gazi Kara  Mary H. Tian Margaret Yellen

December 15, 2015



Financial Plumbing and Monetary Policy

Manmohan Singh

June 2014


Click to access wp14111.pdf


Haircuts and Repo Chains

Tri Vi Dang

Gary Gorton

Bengt Holmström

Click to access Paper_Repo.pdf


Nonbank Financial Intermediation, Financial Stability, and the Road Forward

Stanley Fischer


Click to access fischer20150330a.pdf


Financial Intermediation Chains in an OTC Market

Ji Shen  Bin Wei  Hongjun Yan

December 15, 2015


Dealer Networks

Dan Li Norman Schürhoff

October 22, 2014


Systemic Risks in Repo Markets


Somnath Chatterjee


8, November 2013

Click to access 2013-operacionalizacion-estabilidad-financiera-t-07.pdf


Systemic risk in the repo market.


Alexander Shkolnik

Click to access fmws1_12590.pdf


The Economics of Collateral-Chains

Manmohan Singh



Click to access 010112.pdf


Collateral Reuse as a Direct Funding Mechanism in Repo Markets

George Issa Elvis Jarnecic

Click to access EFMA2016_0566_fullpaper.pdf


Repo Runs

Antoine Martin David Skeie Ernst-Ludwig von Thadden

Click to access sr444.pdf


Securitized Banking and the Run on Repo

Gary Gorton

Andrew Metrick

First version: January 22, 2009 This version: November 13, 2009

Click to access gorton_run_on_repo_nov.pdf


Who Ran on Repo?

Gary Gorton, Andrew Metrick

October 4, 2012

Click to access whorancompleteoctober4.pdf


Repo Runs: Evidence from the Tri-Party Repo Market

Adam Copeland Antoine Martin Michael Walker

Click to access sr506.pdf


Matching Collateral Supply and Financing Demands in Dealer Banks


Adam Kirk, James McAndrews, Parinitha Sastry, and Phillip Weed


Click to access 1412kirk.pdf


Collateral Reuse in Shadow Banking and Monetary Policy

Ameya Muley∗

7th January 2016


Money for Nothing: The Consequences of Repo Rehypothecation

Sebastian Infante

Federal Reserve Board September 19th, 2014 Abstract


Intermediary Funding Liquidity and Rehypothecation as Determinants of Repo Haircuts and Interest Rates

Egemen Eren

Stanford University July 23, 2014

Click to access eren2014.pdf


Re-use of collateral in the repo market

Lucas Marc Fuhrer, Basil Guggenheim and Silvio Schumacher


Click to access working_paper_2015_02.n.pdf


Collateral Reuse as a Direct Funding Mechanism in Repo Markets

George Issa Elvis Jarnecic


Click to access EFMA2016_0566_fullpaper.pdf


Rehypothecation and Liquidity

David Andolfatto Fernando M. Martin  Shengxing Zhang



Matching Prime Brokers and Hedge Funds∗

Egemen Eren†


December 23, 2015

Click to access eren_jmp.pdf


Collateral, Rehypothecation, and Efficiency

Charles M. Kahn† Hye Jin Park‡

Last updated: April 15, 2015

Click to access Collateral_Rehypothecation_and_Efficiency.pdf


The market for Collateral: the Potential impact of Financial Regulation

Jorge Cruz Lopez, Royce Mendes and Harri Vikstedt

Click to access fsr-0613-lopez.pdf



David Andolfatto Fernando Martin Shengxing Zhang

February 26, 2014

Click to access andolfatto_spring2014.pdf


Collateralized Security Markets

John Geanakoplos William R. Zame

Click to access refs4661465000000000040.pdf




Click to access brq411_Rehypothecation.pdf


Collateral and Monetary Policy

Manmohan Singh


Click to access wp13186.pdf


Financial Plumbing and Monetary Policy

Prepared by Manmohan Singh

June 2014

Click to access wp14111.pdf


Understanding the role of collateral in financial markets

M Singh


Click to access 20150223-singh-slides.pdf


Systemic Risk and Stability in Financial Networks

Daron Acemoglu Asuman Ozdaglar Alireza Tahbaz-Salehi
This version: January 15, 2013



Systemic Risk in Endogenous Financial Networks

Daron Acemoglu Asuman E. Ozdaglar Alireza Tahbaz-Salehi

January 22, 2015


Towards a theory of shadow money

Daniela Gabor and Jakob Vestergaard

Click to access Towards_Theory_Shadow_Money_GV_INET.pdf


Do Shadow Banks Create Money?

Jo Michell

Click to access 1602.pdf


How Does Monetary Policy A􏰝ffect Shadow Bank Money Creation? 

Kairong Xiao†

June 17, 2016

Click to access paper_296.pdf


The Economic Consequences of ‘Market-Based’ Lending

Carolyn Sissoko

May 24, 2016


Key Mechanics of the U.S. Tri-Party Repo Market

Adam Copeland, Darrell Duffie, Antoine Martin, and Susan McLaughlin


Click to access 1210cope.pdf


The Failure Mechanics of Dealer Banks

Darrell Duffie


Click to access DuffieFailureMechanicsDealerBanks2010.pdf


The Euro Interbank Repo Market

Loriano Mancini Angelo Ranaldo Jan Wrampelmeyer

March 26, 2014

Click to access angeloranaldopaper_4.pdf




Multiplex Financial Networks


Multiplex Financial Networks


These are multilayer networks but not Hierarchical (multi-scale) networks.  This is one of latest research area in financial networks. Several research papers published in 2015 and current year are using multiplex networks to analyze multiple credit relationships in Interbank market.

  • Short term debt chains
  • Overnight debt chains
  • Long Term Debt chains
  • Collateral chains


From The multiplex structure of interbank networks


Network analysis has contributed to characterize, understand and model complex systems of interconnected financial institutions and markets (Gai and Kapadia, 2010; Battiston et al., 2012). These tools are gaining popu- larity also among policymakers. Most contributions focus on the interbank market1, the plumbing of modern financial systems, especially in the Euro area. In the network perspective, the interbank market is commonly rep- resented as a standard directed and weighted graph. Each link represents a credit relation between two counterparties. Directionality identifies the borrower and the lender; the weight of the link represents the loan amount. In general, the interbank market is much richer and complex than a simple weighted graph. In this paper we explore the differences in credit relations due to maturity of the contract or the presence of collateral. Due to lack of data availability, existing empirical literature either (i) disregards the hetero- geneity of credit relations or (ii) focuses only on one type, implicitly assuming that the network of the selected type of credit relations is a good proxy for the networks of other types. In the latter case, the vast majority of contributions focus on the overnight unsecured market. These two approaches are parsimo- nious but may provide biased results if the underlying “representativeness” assumptions fail.


From  Interbank markets and multiplex networks: centrality measures and statistical null models


Interlinkages between any two financial institutions is more complex than the information that can be summarized in a single number (the weight of the link) and a direction, such as in a directed and weighted network. This is due to the fact that between two institutions there exists a multiplicity of linkages, each of them related to one class of claims/obligations. The interplay between different types of relation could be relevant for systemic risk analysis. In the network jargon, such a situation is best modeled with a multiplex network or simply multiplex. A multiplex is made up with several ”layers”, each of them composed by all relations of the same type and modeled with a simple (possibly weighted and directed) network. Since the nodes in each layer are the same, the multiplex can be visualized as a stack of networks or equivalently by a network where several different types of links can coexist between two nodes, each type corresponding to a layer.


Key Sources of Research:


The multi-layer network nature of systemic risk and its implications for the costs of financial crises

Sebastian Poledna1, Jos ́e Luis Molina-Borboa4, Seraf ́ın Mart ́ınez-Jaramillo4, Marco van der Leij5,6,7, and Stefan Thurner


Click to access 1505.04276.pdf


Using multiplex networks for banking systems dynamics modelling

Valentina Y. Guleva, Maria V. Skvorcova, and Alexander V. Boukhanovsky


Systemic risk in multiplex networks with asymmetric coupling and threshold feedback

Rebekka Burkholz, Matt V. Leduc, Antonios Garas & Frank Schweitzer

Click to access 1506.06664.pdf


Networks of Networks: The Last Frontier of Complexity-A Book Review

Manuel Alberto M. Ferreira


Interconnected Networks

Editors: Garas, Antonios (Ed.)


Multilayer networks

Mikko Kivelä Alex Arenas Marc Barthelemy James P. Gleeson Yamir Moreno

and Mason A. Porter


Multi-layered Interbank Model for Assessing Systemic Risk

Mattia Montagna and Christoffer Kok

Click to access 1873_KWP.pdf


Nonlinear Dynamics on Interconnected Networks

Alex Arenas, Manlio De Domenico


The multiplex dependency structure of financial markets


Nicol ́o Musmeci,1 Vincenzo Nicosia,2 Tomaso Aste,3, 4 Tiziana Di Matteo,1, 3 and Vito Latora

Click to access 1606.04872.pdf


Process Systems Engineering as a Modeling Paradigm for Analyzing Systemic Risk in Financial Networks


Click to access OFRwp-2015-02-11-Process-Systems-Engineering-as-a-Modeling-Paradigm.pdf


Financial Contagion with Collateralized Transactions: A Multiplex Network Approach
Gustavo Peralta  Ricardo Crisóstomo

July 2016


The multiplex structure of interbank networks

L. Bargigli,a G. di Iasio,b L. Infante,c F. Lillo,d F. Pierobone

November 20, 2013

Click to access 1311.4798.pdf


Interbank markets and multiplex networks: centrality measures and statistical null models

Leonardo Bargigli, Giovanni di Iasio, Luigi Infante, Fabrizio Lillo and Federico Pierobon

Click to access 1501.05751.pdf


Strength of weak layers in cascading failures on multiplex networks: case of the international trade network

Kyu-Min Lee & K.-I. Goh


Click to access srep26346.pdf



 Cascades in multiplex financial networks with debts of different seniority

Charles D. Brummitt

Teruyoshi Kobayashi


Multiplex interbank networks and systemic importance An application to European data

In ̃aki Aldasoro† Iva ́n Alves‡

May 2015

Click to access AldasoroAlves.pdf


Control of Multilayer Networks

Giulia Menichetti1, Luca Dall’Asta2,3 & Ginestra Bianconi

Click to access srep20706.pdf


Centrality Measurement of the Mexican Large Value Payments System from the Perspective of Multiplex Networks

Bernardo Bravo-Benitez, Biliana Alexandrova-Kabadjova, Serafin Martinez-Jaramillo

Click to access 00b7d53c415e038c7d000000.pdf



Recent advances on failure and recovery in networks of networks

Louis M. Shekhtman∗, Michael M. Danziger, Shlomo Havlin

Click to access Recent%20advances%20on%20failure%20and%20recovery%20in%20networks%20of%20networks.pdf


Financial Stability and Interacting Networks of Financial Institutions and Market Infrastructures

Carlos León  Ron J. Berndsen   Luc Renneboog


The structure and dynamics of multilayer networks

S. Boccaletti,∗, G. Bianconic, R. Criadod,, C.I. del Geniof,, J. Gómez-Gardeñes, M. Romanced, Sendiña-Nadal, Z. Wang. Zaninm,


Click to access 1407.0742.pdf


A multi-layer network of the sovereign securities market

Carlos León  Jhonatan Pérez  Luc Renneboog

Click to access be_840.pdf


Growing Multiplex Networks with Arbitrary Number of Layers

Babak Fotouhi1 and Naghmeh Momeni

Click to access 1506.06278.pdf



Quantitative Finance

Special Issue on Interlinkages & Systemic Risk