Theoretical framework - living systems

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First published in June 23, 2011 (Open the original doc.), edited by Tiberius Brastaviceanu, last modified on Oct 28, 2012 before content was moved here. If you contribute to this text please make sure you respect Content rules.

Open Value Networks can be modeled as living systems, a position advocated early on by Tiberius Brastaviceanu. Between 2013 and 2014 Yasir Siddiqui has built a different theoretical framework based on business considerations. See Open Value Network: A framework for many-to-many innovation

See Education / Dynamical systems theory on Sensorica.

Organizations as living organisms

We are observing a movement to capture and surface intangibles, which leads to the rising of truly living organizations.

Are we moving towards an economy of intangibles? Could this be the waking up of organizations as truly living systems?

People have conceptualized organizations as living systems, but in reality organizations have existed more as mechanical systems, as automata. The new tools that are developed are able to not only capture data, but also to get into the understanding thinking and behavior, through language processing, AI and neural networks.

Social intelligence

About ability to solve world problems


Introduced by Chad during a RWI deep dive. People are developing tools that analyze discussions and characterize a group in terms of cohesion, wholeness.


Introduced by Paula during a RWI (re-imagining wealth initiative), coined by Vini.

Active inference

About adaptive capacity

Regenerative / sustainable

It is about organization's ability to sustain their activities, to see themselves as part of an ecosystem, accessing its support structures and at the same time adding to these support structures.

General considerations

We recognize the fact that the IT infrastructure of OVNs can affect their nature, as the behavior of agents are determined by formal / established relations and processes. The design of the IT infrastructure must remain flexible enough and integrate feedback to allow the OVN to evolve as a living system, a system that has properties of emergence, self-replication, adaptation and evolution, and proliferation.

The totality of the IT infrastructure makes up the digital environment of the organization as a living system. To use a biological metaphor, it makes up the anatomy of the living system, which determines how cells (an other microorganisms, biom) behave, which leads to some level of coherence and the emergence of the organism.


Ontology builder - we are using WebProtege. See also SUMO. Contact Tibi to participate.

Short introduction to living systems and systems theory

Inspired from Wikipedia "Living Systems"

Living systems are open self-organizing living things that interact with their environment. These systems are maintained by flows of information, energy and matter.

Some scientists have proposed in the last few decades that a general living systems theory is required to explain the nature of life. Such general theory, arising out of the ecological and biological sciences, attempts to map general principles for how all living systems work. Instead of examining phenomena by attempting to break things down into component parts (which is the traditional scientific method of analysis), a general living systems theory explores phenomena in terms of dynamic patterns of the relationships of organisms with their environment. It is a synthesis process, moving from portions of the system upwards. It is also a holistic approach.

Living systems theory is a general theory about the existence of all living systems, their structure, interaction, behavior and development. James Grier Miller, wanted to formalize the concept of life. According to Miller's original conception as spelled out in his magnum opus Living Systems, a "living system" must contain each of twenty "critical subsystems", which are defined by their functions and visible in numerous systems, from simple cells to organisms, countries, and societies. In Living Systems Miller provides a detailed look at a number of systems in order of increasing size, and identifies his subsystems in each. Miller considers living systems as a subset of all systems. Below the level of living systems, he defines space and time, matter and energy, information and entropy, levels of organization, and physical and conceptual factors, and above living systems ecological, planetary and solar systems, galaxies, etc.

Living systems according to Parent (1996) are by definition "open self-organizing systems that have the special characteristics of life and interact with their environment. This takes place by means of information and material-energy exchanges. Living systems can be as simple as a single cell or as complex as a supranational organization such as the European Union. Regardless of their complexity, they each depend upon the same essential twenty subsystems (or processes) in order to survive and to continue the propagation of their species or types beyond a single generation".

Miller said that systems exist at eight "nested" hierarchical levels: cell, organ, organism, group, organization, community, society, and supranational system. At each level, a system invariably comprises twenty critical subsystems, which process matter–energy or information except for the first two, which process both matter–energy and information: reproducer and boundary.

The processors of matter–energy are: ingestor, distributor, converter, producer, storage, extruder, motor, supporter

The processors of information are: input transducer, internal transducer, channel and net, timer (added later), decoder, associator, memory, decider, encoder, output transducer.

Twenty critical subsystems that process inputs, throughputs, and outputs of various forms of matter–energy and information.

Miller says the concepts of space, time, matter, energy, and information are essential to his theory because the living systems exist in space and are made of matter and energy organized by information. Miller's theory of living systems employs two sorts of spaces: physical or geographical space, and conceptual or abstracted spaces. Time is the fundamental "fourth dimension" of the physical space–time continuum/spiral. Matter is anything that has mass and occupies physical space. Mass and energy are equivalent as one can be converted into the other. Information refers to the degrees of freedom that exist in a given situation to choose among signals, symbols, messages, or patterns to be transmitted.

Other relevant concepts are system, structure, process, type, level, echelon, suprasystem, subsystem, transmissions, and steady state. A system can be conceptual, concrete or abstracted. The structure of a system is the arrangement of the subsystems and their components in three-dimensional space at any point of time. Process, which can be reversible or irreversible, refers to change over time of matter–energy or information in a system. Type defines living systems with similar characteristics. Level is the position in a hierarchy of systems. Many complex living systems, at various levels, are organized into two or more echelons. The suprasystem of any living system is the next higher system in which it is a subsystem or component. The totality of all the structures in a system which carry out a particular process is a subsystem. Transmissions are inputs and outputs in concrete systems. Because living systems are open systems, with continually altering fluxes of matter–energy and information, many of their equilibria are dynamic—situations identified as steady states or flux equilibria.

Other concepts

  1. distinction between concrete and abstracted systems
  2. totipotential systems, and partipotential systems
  3. concept of joint subsystem—a subsystem that belongs to two systems simultaneously
  4. inclusion—inclusion of something from the environment that is not part of the system
  5. adjustment process, which combats stress in a system;
  6. critical subsystems, which carry out processes that all living systems need to survive
  7. social systems are linked to biological systems
  8. irregularities or "organizational pathologies" of systems functioning (e.g., system stress and strain, feedback irregularities, information–input overload).
  9. role of entropy in social research while it equates negentropy with information and order.
  10. emphasizes both structure and process, as well as their interrelations

See also Systems Theory

A system is composed of regularly interacting or interrelating groups of activities. For example, in noting the influence in organizational psychology as the field evolved from "an individually oriented industrial psychology to a systems and developmentally oriented organizational psychology," it was recognized that organizations are complex social systems; reducing the parts from the whole reduces the overall effectiveness of organizations. This is different from conventional models that center on individuals, structures, departments and units separate in part from the whole instead of recognizing the interdependence between groups of individuals, structures and processes that enable an organization to function. Laszlo explains that the new systems view of organized complexity went "one step beyond the Newtonian view of organized simplicity" in reducing the parts from the whole, or in understanding the whole without relation to the parts. The relationship between organizations and their environments became recognized as the foremost source of complexity and interdependence. In most cases the whole has properties that cannot be known from analysis of the constituent elements in isolation.

The systems view is a world-view that is based on the discipline of SYSTEM INQUIRY. Central to systems inquiry is the concept of SYSTEM. In the most general sense, system means a configuration of parts connected and joined together by a web of relationships. The Primer group defines system as a family of relationships among the members acting as a whole. Von Bertalanffy defined system as "elements in standing relationship.

The emphasis with systems theory shifts from parts to the organization of parts, recognizing interactions of the parts are not "static" and constant but "dynamic" processes. Conventional closed systems were questioned with the development of open systems perspectives. The shift was from absolute and universal authoritative principles and knowledge to relative and generalconceptual and perceptual knowledge, still in the tradition of theorists that sought to provide means in organizing human life. Meaning, the history of ideas that preceded were rethought not lost. Mechanistic thinking was particularly critiqued, especially the industrial-age mechanistic metaphor of the mind from interpretations of Newtonian mechanics by Enlightenment philosophers and later psychologists that laid the foundations of modern organizational theory and management by the late 19th century. Classical science had not been overthrown, but questions arose over core assumptions that historically influenced organized systems, within both social and technical sciences.

Subjects like complexity, self-organization, connectionism and adaptive systems had already been studied in the 1940s and 1950s. In fields like cybernetics, researchers like Norbert Wiener, William Ross Ashby,John von Neumann and Heinz von Foerster examined complex systems using mathematics. John von Neumann discovered cellular automata and self-reproducing systems, again with only pencil and paper.Aleksandr Lyapunov and Jules Henri Poincaré worked on the foundations of chaos theory without any computer at all.

The systems view was based on several fundamental ideas. First, all phenomena can be viewed as a web of relationships among elements, or a system. Second, all systems, whether electrical, biological, or social, have common patterns, behaviors, and properties that can be understood and used to develop greater insight into the behavior of complex phenomena. The economist Kenneth Boulding, an early researcher in systems theory, had concerns over the manipulation of systems concepts.

Cybernetics, catastrophe theory, chaos theory and complexity theory have the common goal to explain complex systems that consist of a large number of mutually interacting and interrelated parts in terms of those interactions. Cellular automata (CA), neural networks (NN), artificial intelligence (AI), and artificial life (ALife) are related fields, but they do not try to describe general (universal) complex (singular) systems.

Complex adaptive systems are special cases of complex systems. They are complex in that they are diverse and made up of multiple interconnected elements and adaptive in that they have the capacity to change and learn from experience. The term complex adaptive systems was coined at the interdisciplinary Santa Fe Institute (SFI), by John H. Holland, Murray Gell-Mann and others. However, the approach of the complex adaptive systems does not take into account the adoption of information which enables people to use it.

CAS ideas and models are essentially evolutionary. Accordingly, the theory of complex adaptive systems bridges developments of the system theory with the ideas of 'generalized Darwinism', which suggests that Darwinian principles of evolution help explain a wide range of phenomena.

The systems framework is also fundamental to organizational theory as organizations are complex dynamic goal-oriented processes. One of the early thinkers in the field was Alexander Bogdanov, who developed his Tectology, a theory widely considered a precursor of von Bertalanffy's GST, aiming to model and design human organizations (see Mattessich 1978, Capra 1996). Kurt Lewin was particularly influential in developing the systems perspective within organizational theory and coined the term "systems of ideology", from his frustration with behavioral psychologies that became an obstacle to sustainable work in psychology. Jay Forrester with his work in dynamics and management alongside numerous theorists including Edgar Schein that followed in their tradition since the Civil Rights Era have also been influential.

The systems to organizations relies heavily upon achieving negative entropy through openness and feedback. A systemic view on organizations is transdisciplinary and integrative. In other words, it transcends the perspectives of individual disciplines, integrating them on the basis of a common "code", or more exactly, on the basis of the formal apparatus provided by systems theory. The systems approach gives primacy to the interrelationships, not to the elements of the system. It is from these dynamic interrelationships that new properties of the system emerge. In recent years, systems thinking has been developed to provide techniques for studying systems in holistic ways to supplement traditional reductionistic methods. In this more recent tradition, systems theory in organizational studies is considered by some as a humanistic extension of the natural sciences.

Systems theory has also been developed within sociology. An important figure in the sociological systems perspective as developed from GST is Walter Buckley (who from Bertalanffy's theory). Niklas Luhmann (see Luhmann 1994) is also predominant in the literatures for sociology and systems theory. Miller's living systems theory was particularly influential in sociology from the time of the early systems movement. Models for dynamic equilibrium in systems analysis that contrasted classical views from Talcott Parsons and George Homans were influential in integrating concepts with the general movement. With the renewed interest in systems theory on the rise since the 1990s, Bailey (1994) notes the concept of systems in sociology dates back to Auguste Comte in the 19th century, Herbert Spencer and Vilfredo Pareto, and that sociology was readying into its centennial as the new systems theory was emerging following the World Wars. To explore the current inroads of systems theory into sociology (primarily in the form of complexity science) see sociology and complexity science.

In sociology, members of Research Committee 51 of the International Sociological Association (which focuses on sociocybernetics, have sought to identify the sociocybernetic feedback loops which, it is argued, primarily control the operation of society. On the basis of research largely conducted in the area of education, Raven (1995) has, for example, argued that it is these sociocybernetic processes which consistently undermine well intentioned public action and are currently heading our species, at an exponentially increasing rate, toward extinction. See sustainability. He suggests that an understanding of these systems processes will allow us to generate the kind of (non "common-sense") targeted interventions that are required for things to be otherwise - i.e. to halt the destruction of the planet.

Industrial designer, and founder of The Venus Project, Jacque Fresco advocates the utilization of sociocybernetics for the benefits it could bring to society. A major theme of Fresco's is the concept of a resource-based economy that replaces the need for the current monetary economy, which is "scarcity-oriented" or "scarcity-based". Fresco argues that the world is rich in natural resources and energy and that — with modern technology and judicious efficiency — the needs of the global population can be met with abundance, while at the same time removing the current limitations of what is deemed possible due to notions of economic viability.

System Dynamics was founded in the late 1950s by Jay W. Forrester of the MIT Sloan School of Management with the establishment of the MIT System Dynamics Group. At that time, he began applying what he had learned about systems during his work in electrical engineering to everyday kinds of systems. Determining the exact date of the founding of the field of system dynamics is difficult and involves a certain degree of arbitrariness. Jay W. Forrester joined the faculty of the Sloan School at MIT in 1956, where he then developed what is now System Dynamics. The first published article by Jay W. Forrester in the Harvard Business Review on "Industrial Dynamics", was published in 1958. The members of the System Dynamics Society have chosen 1957 to mark the occasion as it is the year in which the work leading to that article, which described the dynamics of a manufacturing supply chain, was done.

As an aspect of systems theory, system dynamics is a method for understanding the dynamic behavior of complex systems. The basis of the method is the recognition that the structure of any system — the many circular, interlocking, sometimes time-delayed relationships among its components — is often just as important in determining its behavior as the individual components themselves. Examples are chaos theory and social dynamics. It is also claimed that, because there are often properties-of-the-whole which cannot be found among the properties-of-the-elements, in some cases the behavior of the whole cannot be explained in terms of the behavior of the parts. An example is the properties of these letters which when considered together can give rise to meaning which does not exist in the letters by themselves. This further explains the integration of tools, like language, as a more parsimoniousprocess in the human application of easiest path adaptability through interconnected systems.

Systems engineering is an interdisciplinary approach and means for enabling the realization and deployment of successful systems. It can be viewed as the application of engineering techniques to the engineering of systems, as well as the application of a systems approach to engineering efforts. Systems engineering integrates other disciplines and specialty groups into a team effort, forming a structured development process that proceeds from concept to production to operation and disposal. Systems engineering considers both the business and the technical needs of all customers, with the goal of providing a quality product that meets the user needs.

Important concepts in context

This section is inspired from

See this page Diigo annotated.

Space and time

Physical spaces

Related to production, distribution and service

Some OVNs are designed to produce and distribute material goods, which implies that materials need to be transported in space (flow) to be assembled into products in some location (composition requires sharing the same location), and products need to be transported to the customer’s location (flow). This imposes important constraints to OVNs. Some activities, especially the ones which rely on data, information, and knowledge processes, like design, analysis, planning, some logistics, etc. can be delocalised. Physical space is less relevant in these cases. Anyone connected to the Internet can participate. But manufacturing/assembling, distribution and service cannot be dissociated from space.

We need to implement and optimize the logistics for materials handling in order to reduce costs, environmental impact, lead-time, to have timely and quality service... For example, it is important to strategically place assembly/manufacturing sites and service providers in order to match the geographical distribution of the addressed market. In other words, all sub-systems processing matter need to be strategically located to map the geographical distribution of end-points of material products. We are talking here about an optimized system of flow of materials, which needs to be emergent, adaptive, in order to minimize the amount of resources spent on moving matter in space, and to improve the response of the network to needs signaled from the market side. Feedback must be provided in order to allow the community to constantly optimize the this flow system.

Suppliers, which are the origin of materials, are fixed locations in the system. The same can be said for the points of consumption, the destination of products or customers.

New tools for materials management need to be created for OVNs, as part of the infrastructure. See a flow chart prototype based on data from Sensorica. We'll discuss more about materials flow, but here we need to stress the spacial dimension of these sub-systems.

The advent of CNC and 3D printing relaxes some spatial constraints imposed by the materiality of products. It becomes possible to transfer the manufacturing of some parts to the consumer, using a virtual representation of the part (a CAD file for example), some requirements for materials, assembling instructions, and the personal/local means of fabrication (3D printer for example).

See Sensorica's telemanufacturing page.

Related to development and design

Research and development are mostly information and knowledge processes, and are less dependent on space. But sometimes collaborators need to share experiences and to share/exchange materials (equipment, samples, etc.).

In order to reduce spatial constraints, Sensorica is implementing the lab-online concept, which enables direct remote access to an experiment through the Internet. If the experiment necessitates a fast iteration based on direct feedback synchronous participation is required. In other cases, asynchronous participation can suffice.

Other considerations

Sensorica emerged as a glocal network, a rhisomic structure. Some processes are highly dependent on local resources, local culture, local institutions... Local practices are developed in order to fit better within the local ecosystem.

Physical proximity between members plays an important role in the dynamics of the network. The CCC infrastructure allows strong connections between these local networks into a global network.

Virtual spaces

An OVN is a glocal collaborative network. Some of its processes are technology-mediated, i.e. through the Internet. Thus, we can identify different types of virtual spaces:

(virtual) Space for storage: a database to contain data and information, a server, a cloud... - Memory

(virtual) Space for co-creation: this is a coherent assembly of internet-based collaboration tools, see digital environment. These are for example tools used to co-produce content, synchronously and/or asynchronously. We can also mention the labs-online, virtual spaces to conduct remote synchronous experiments (with no spatial constraints). - Collective intelligence

(virtual) Social space: tools to convene webmeetings, to share content, to discuss,... Examples are mailing lists, forums, G+ and Facebook groups, Tweeter hash tags, etc.

(virtual) Presence on the www: Social networks are virtual spaces defined by a set of interests in which we must establish a presence. There is also a notion of contextual space that emerges after a web search - the content of the website and the tags associated with it, the behavior of internauts while interacting with the OVN's IT infrastructure determines the context in which the OVN, or aspects of it, appears on web searches.

OVN affiliates need to have access to a general layout of the entire virtual infrastructure and presence, which represents all the virtual spaces and the way they are interconnected and used. This helps agents to navigate these virtual spaces and to utilize them to their best (collective) advantage.

Conceptual and abstract spaces

An OVN can also be understood seen as an abstract space.

We can define different abstract entities within an OVN or within its environment, with different metrics. For example, the role system defines different functional groups within an OVN: designers, engineers, business people, etc. We can define a “distance/proximity” between these organically formed groups, in terms of their interaction parameters. The reputation system is also a space, a multidimensional one. We can also define a “distance” between the market and the OVN. For example, the service system is transparent and inclusive, which means that the “distance” between the consumer/prosumer/customer and the OVN is greatly reduced. The OVN's internal currency, or the internal flow of value, defines its own abstract space. The value accounting system with its valuation metrics defines another abstract space.


An OVN is a global network, which means that it never sleeps.

Using online tools for cooperation/collaboration we can perform synchronous and asynchronous activities. This reduces time constraints. For example, many individuals with different skills, distributed across the planet, can co-edit a complex document without the need to convene a meeting, physical or virtual. Everyone works whenever he/she can.

The service system needs to take time constraints into consideration and also exploit the fact that an OVN is functioning around the clock. For example, anyone awake can take a service call, assess the needs, and after dispatch it to another person better suited to solve the problem, who matches with the spatio-temporal constraints (someone physically near the customer).

Timing and duration is also an important aspect. Develop on the duration of different internal processes, timing between interdependent processes.

Also, develop on feedback loops. In order to get effective adaptations or responses feedback loops and response mechanisms heed to fit within the required order of time.

Pathologies can develop from non-adequate temporal parameters.

Matter and Energy

The processors of matter–energy are: ingestor, distributor, converter, producer, storage, extruder, motor, supporter. They are investigated one-by-one below.

Matter is anything which has mass (m) and occupies physical space. Energy (E) is defined in physics as the ability to do work. ''Action'' is any change of state of matter-energy or its movement over space, from one point to another. It is one form of process. see of this source

As mentioned in the Physical spaces section, an OVN can produce material artifacts, which implies that matter needs to be transported in space to be assembled in one location (different parts can be assembled at different locations and assembled into a deliverable again at another location). Moreover, the material artifact (can be a product) or parts of it need to be transported in space to the beneficiary (user, customer, etc.) site and be assembled and installed.


The processors of information are: input transducer, internal transducer, channel and net, timer (added later), decoder, associator, memory, decider, encoder, output transducer. They are investigated one-by-one below.

Questions: What limits an OVN's ability to process information? How can we improve information processing? Is the information well stored/archived? Is it easily found by agents? Is it easily processed? Is is circulating freely throughout the network? What should be the level of transparency of the network? Is there information that should not be shared?

Within an OVN information flows many-to-many (different from one-to-many like during a lecture). Special tools are needed to allow large numbers of individuals to participate in a discussion. We believe that there is a link between effective p2p communication and relations of power. See Power relations and information.

An OVN extracts value from the long tail of its distribution, utilizing small inputs/contributions from a very large base of members (scattered around the planet). The challenge is to filter and to structure these inputs. Corporations operate with a small number of contributors that contribute a lot, they use the normal curve. Thus, there is a major difference between how corporations and OVNs deal with information.

An OVN is transparent, which means that whoever wants to extract information from it can do it without the need to join it (some info is only accessible to members and this level of transparency needs to be carefully adjusted). An OVN is open, which means that external agents can easily join it and participate. These two characteristics have a dramatic effect on internal dynamics (we talk about hyperinnovation, exponential growth), on size (we talk about large scale affects), on its ability to adapt (fast feedback from the environment), etc. Openness and transparency also expose the network to danger - intruders, parasites.

An OVN needs a locus of analysis and forecasting. No need for a coordination center; coordination happens at the agent level, it is distributed.

The Internet enables real-time communication across the planet. OVN agents/affiliates can engage one-on-one (Skype, email, etc), one-to-many (netmeetings, forms, social media) and many-to-many (forums) interactions. Agents can also broadcast information beyond the network's fuzzy frontiers using social media. They can also address different networks at the same time (posting to different mailing lists at the same time), linking them through a common topic, and allowing transfer of data, information, knowledge and know how between networks.

A very important information module is the feedback system. The OVN is a self-organizing structure; We also call these modules tools for self-awareness. We are already building visualization tools associated with the value, roles and reputation systems.

Can we build a map showing the information flow within and around an OVN? This would provide real-time information to the network about how and who the network affects. The information flow distribution should map the structure of the OVN, but it would also provide information about the debit of information in different parts of the network. This is important feedback, which can have an impact of the self-organization of the network.

We can also envision diagnostic tools, which can reveal, in real time, information-related "pathologies" of the network.

Information is also important in in distributed manufacturing or telemanufacturing. First, if the product is shipped in parts from different locations to be assembles by the customer (IKEA style), the customer needs information to assemble the product. Moreover, some parts of the product can be 3D printed or CNC-ed by the customer from a CAD file provided with the product.

Taking a piageen approach to development, in order to let the OVN develop into a resilient living system it needs information/feedback from the environment. It is important to establish a good flow of information from the environment into the network, and a good receiver and processor of that information in order to translate it into internal configurational changes. For that we could establish events, meetings, where some members present information and analysis from the environment and decisions are made, or initiatives are brought up to trigger adaptational changes. These meetings/events have this dual function, to present information (some analysis in the middle) and to initiate conformational changes.

Information and entropy

...the concept of Prigogine that in an open system (that is one in which both matter and energy can be exchanged with the environment) the rate of entropy production within the system, which is always positive, is minimized when the system is in a steady state. This appears to be a straightforward generalization of the second law, but after studying certain electrical circuits they conclude that this theorem does not have complete generality, and that in systems with internal feedbacks, internal entropy production is not always minimized when the system is in a stationary state. In other words, feedback couplings between the system parameters may cause marked changes in the rate of development of entropy. Thus it may be concluded that the "information flow" which is essential for this feedback markedly alters energy utilization and the rate of development of entropy, at least in some such special cases which involve feedback control. While the explanation of this is not clear, it suggests an important relationship between information and entropy. Go to source


A system is a set of interacting units with relationships among them [Ludwig von Bertalanffy]. The word "set" implies that the units have some common properties. These common properties are essential if the units are to interact or have relationships. The state of each unit is constrained by, conditioned by, or dependent on the state of other units. The units are coupled. Moreover, there is at least one measure of the sum of its units which is larger than the sum of that measure of its units. Go to source

Conceptual system


Concrete system


Abstracted system


Abstracted vs concrete systems


Level: An OVN is an organization

Organizations are complex dynamic goal-oriented processes.

The systems to organizations relies heavily upon achieving negative entropy through openness and feedback. A systemic view on organizations is transdisciplinary and integrative. In other words, it transcends the perspectives of individual disciplines, integrating them on the basis of a common "code", or more exactly, on the basis of the formal apparatus provided by systems theory. The systems approach gives primacy to the interrelationships, not to the elements of the system. It is from these dynamic interrelationships that new properties of the system emerge. In recent years, systems thinking has been developed to provide techniques for studying systems in holistic ways to supplement traditional reductionistic methods. In this more recent tradition, systems theory in organizational studies is considered by some as a humanistic extension of the natural sciences.

The twenty "critical subsystems" of an OVN, seen as a living system

The processors of matter–energy

Am OVN needs a Materials management system. It contains different subsystems.

Shareables inventory and management system

Distinction between commons and the pool of shareables

ToDo: build the shareables management system, as a module of the larger assets management system.

Materials inventory and management for production and distribution


Different things are “ingested” by an OVN. Related to capacity building. When it comes to new participants, see onboarding. For resources see sourcing or crowdsourcing.

In essence, the ingestor in OVNs depends on a crowd-based process, everything is crowdsourced. The ingestor is the process and the means of intaking new stuff and orienting it towards internal proper processes. In other words, the ingestion process is designed to be compatible with the other downstream processes. In terms of architecture, we need for example to link outreach to facilitation in a larger onboarding process.

Praxis: implementations of the ingestor are forms for example, to join ventures, or process gateways, the way someone takes a task in a process for example.


Materials processed by an OVN such as tools, instruments, parts and components... used for R&D, administration, production...

New affiliates/members.

Currencies/funding - used to fund operations.

See more on resources and resource types.


Data, information and knowledge


Internal distributors

The value system is an internal distributor of surplus value. It takes into consideration all contributions and produces fluid equity pie charts for every project, in real time (fluid equity concept).

Tools for resource sharing can also be seen as part of internal distributors, between sherables and affiliates. These tools are part of the Sharebles inventory and management system.

Project management systems can be seen as internal distributors of tasks.

External distribution

The service system

The product distribution system

Social media channels can be seen as distributors of information about the OVN. These same channels are used to signal current and future needs.

We need to see social media as a system of channels, targeting specific pools of individuals. In that sense, the social media system of channels is a selective and targeted distribution channel.




The commons and the pool of sharebles are storage.

Every affiliate can also be seen as storage of value and reputation. The quality of affiliated determine the quality of the OVN.


The normative system may contain rules of exclusion (of affiliates), which can be seen as part of the extruder.

The outreach/marketing system is intended to push products on the market, together with sales, can be seen as an extruder of products.


Everything related to creation, production and growth.

The Incentive system can be seen as pulling forces.

All the infrastructure used for cooperation, co-creation of value, is part of the motor.


The processors of information

The Feedback system is part of the processor of information.

The Dashboard (see example), pulls data from contributions logged into the value accounting system, used economic models and allows inferences to be made on data, generating information that can be used in planning.

The processor relies on metrics. See OVN metrics.

Input transducer

For immaterial/intangible

Tor data, information and knowledge: the social media channel system.

The Service system has feedback features to inform the network about the product and quality of services.

Recruiting is an activity through which new affiliates are drawn into the OVN. There must be a process in place through which new affiliates get accommodated within the network, absorb the local culture, get used with the virtual and physical environment, finds a niche etc.

Internal transducer

Channel and net


Coordination tools are part of the timer: different agendas, time sharing systems for tools/instruments, spaces, Gantt charts, etc.

Project management tools are also part of this subsystem, as well as the path to market (see example for the Mosquito project).




Database - containing all types of files.

Data, information and knowledge can also be scattered across other platforms like

  1. shareable links and web pages annotation services (Diigo),
  2. video sharing (Youtube),
  3. documents (Google Drive),
  4. social media (Facebook),
  5. picture sharing (Picasa)
  6. wikies
  7. website


The Decision making system

OVNs are horizontal, which gives affiliates a lot of decision making power. Most decisions regarding development of new products are taken at the individual level. Therefore, individuals constitute the greatest part of the Decider. It is assumed that these individuals make informed and rational decisions, if the other modules of the infrastructure responsible for building awareness and understanding are properly functioning.


Output transducer

Building an ontology

See the project on WebProtege (if you want to contribute, contact Tibi to get edit access)

A few words about building an ontology - annotated Diigo link

Determine the domain and scope of the ontology

We suggest starting the development of an ontology by defining its domain and scope. That is, answer several basic questions:

  1. What is the domain that the ontology will cover? A representation of the living systems model adapted to Sensorica. The text above informs about this adaptation.
  2. For what we are going to use the ontology? This ontology will be the backbone of the OVN infrastructure, modeled as a living system. It will affect the structure of this infrastructure, and how different models will interact with each other. Primarily, it will be used as a guide by developers. This ontology is important for applications/agents to search and analyse data, and to render information. It will allow interoperability between different OVNs.
  3. For what types of questions the information in the ontology should provide answers? How is value created and how it flows within the OVN, as well as between the network and its immediate environment? How is information flowing within the OVN, as well as between the network and its immediate environment? Who is the best/worst contributor to the OVN? How can we modify the value system in order to improve creativity and productivity. Is there a problem with the decision making system? Is the commons growing? Is the OVN sustainable?
  4. Who will use and maintain the ontology? The ontology will be maintained by the OVN’s affiliates that take on the role of maintaining and developing its infrastructure. All affiliates will use the ontology directly or indirectly, by interacting with the infrastructure, using applications/tools in their activities.

Competency questions

One of the ways to determine the scope of the ontology is to sketch a list of questions that a knowledge base based on the ontology should be able to answer, competency questions

add questions here...


From Wikipedia - Autopoesis

Though others have often used the term as a synonym for self-organization, Maturana himself stated he would "[n]ever use the notion of self-organization ... Operationally it is impossible. That is, if the organization of a thing changes, the thing changes".[4] Moreover, an autopoietic system is autonomous and operationally closed, in the sense that there are sufficient processes within it to maintain the whole. Autopoietic systems are "structurally coupled" with their medium, embedded in a dynamic of changes that can be recalled as sensory-motor coupling.This continuous dynamic is considered as a rudimentary form of knowledge or cognition and can be observed throughout life-forms.
An application of the concept of autopoiesis to sociology can be found in Niklas Luhmann's Systems Theory, which was subsequently adapted by Bob Jessop in his studies of the capitalist state system. Marjatta Maula adapted the concept of autopoiesis in a business context. The theory of autopoiesis has also been applied in the context of legal systems by not only Niklas Luhmann, but also Gunther Teubner.
"In the context of textual studies, Jerome McGann argues that texts are "autopoietic mechanisms operating as self-generating feedback systems that cannot be separated from those who manipulate and use them". Citing Maturana and Varela, he defines an autopoietic system as "a closed topological space that 'continuously generates and specifies its own organization through its operation as a system of production of its own components, and does this in an endless turnover of components'", concluding that "Autopoietic systems are thus distinguished from allopoietic systems, which are Cartesian and which 'have as the product of their functioning something different from themselves'". Coding and markup appear allopoietic", McGann argues, but are generative parts of the system they serve to maintain, and thus language and print or electronic technology are autopoietic systems.
In his discussion of Hegel, the philosopher Slavoj Žižek argues, "Hegel is – to use today's terms – the ultimate thinker of autopoiesis, of the process of the emergence of necessary features out of chaotic contingency, the thinker of contingency's gradual self-organisation, of the gradual rise of order out of chaos."

Question: what are the basic conditions for self-organisation to occur (in an organisation)?

by Kees Berg (ref. Research Gate)

Interesting question, it is my thesis research question actually. I focus my research on human systems, in which I found the following principles necessary so far to achieve self-organizing behavior;

  • 1. Distribution of control/authority.
Distribution of authority makes the system highly complex, and, therefore, self-organizing, because the freedom of each agent is increased, which creates a chaotic context to work with. The chaos and tensions between all agents because of the distributed control in an organisation creates the fuel for novelty. New pathways are continuously explored and tested, first using mental models, then testing it out in the real to see which path to take is the best 'fit' based on external tensions the system has with other systems.The chaos caused by the competition and cooperation of the agents in the organization is, therefore, necessary to put the system far from equilibrium, on the edge of chaos, so that the whole organization can continuously evolve according to its purpose. Which brings me to the following principle;
  • 2. Evolutionary purpose/goal.
All agents in the organization are constantly (re)defining the purpose of the organization based upon the information they have of their surroundings. The existence of the organization is rooted in evolutionary principles, so the organization has to make itself relevant to other systems in their environment in order to survive and thrive. This purpose is formed bottom up by the agents, and creates a structure/a higher order within which the agents can execute their freedoms. This structure influences the behavior of the agents by top-down causality through positive and negative feedback loops. Decisions by individual agents that add to this purpose are enforced through positive feedback loops, deviant behavior that deflects this purpose are certainly allowed to a certain level in order to experiment, but probably corrected or cancelled because of the negative feedback loops if it turns out that the behavior is not contributing or damaging the purpose of the organization.
  • 3. Qualities of the people/agents in the organization.
In order for self-organization to work, the people in the organization need to be willing to take responsibility, initiative and be willing to make mistakes. They have to differentiate themselves from others in their professions and expertise in order to create diversity in the system, plus they have to be able to communicate and exchange clearly with other agents so that there is a high level of connectivity between all agents. Basically said, there has to be diversity and connectivity between the agents to create a complex enough context out of which novelty can occur.
  • 4. Self-organizing systems need constant energy to sustain its processes based on the principles of thermodynamics.
More obvious, following evolutionary principles, an organization of people are just like biological systems, semi-autonomous and requires external help/inputs to function. Organizations are co-dependent upon relevant systems in their environments. No organization is a one-man island on the longer term. So they require energy, matter or information (raw materials of a low level of entropy) to put products and services back into their environment to create a level of reciprocity. Waste is dissipated into 'buffer zones' into neighboring systems with a higher level of entropy. This dissipative structure of self-organizing systems is key in creating efficiency and prolonging the system from maximum entropy, until eventually, it ceases to exist, like pretty much everything.

See also

  • CDE model, mixing patterns. complex systems dynamics, self-organisation

Other important documents

Value Network converging mechanisms

OVN Infrastructure

See also

Geoff West from Santa Fe Institute


James Grier Miller, Living Systems The Basic Concepts (1978)

See other papers in folder