October 30, 2009

The basic idea of Autopoietic Systems


In this post, I shall explain the autopoietic systems theory. Note that the following explanation based on my interpretation of the formulation by Niklas Luhmann, who generalized the concept from biological systems theory to general systems theory for building a new social system theory.

Autopoiesis means self-production, and autopoietic system means the system that produce itself. The concept of "autopoiesis" was originally proposed by biologists Humberto Maturana and Francisco Varela, and the term "autopoiesis" is invented from Greek words: "auto" for self- and "poiesis" for creation or production (Maturana & Varela 1972, Varela et. al. 1974, Maturana & Varela 1980; 1987).

"An autopoietic machine is a machine organized (defined as a unity) as a network of process of production (transformation and destruction) of components that produces the components which: (i) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produced them; and (ii) constitute it (the machine) as a concrete unity in the space in which they (the components) exist by specifying the topological domain of its realization as such a network. It follows that an autopoietic machine 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 under conditions of continuous perturbations and compensation of perturbations." (Maturana & Varela 1980; p.79)

In short, an autopoietic system is a unity whose organization is defined by a particular network of production processes of elements, not by the components themselves or their static relations. Summarizing the concept of autopoiesis, it turns out that the system has three fundamental features; (1) element as momentary event, (2) boundary reproduction of the system, (3) element constitution based on the system.



The crucial point of autopoiesis in systems theory is the shift of viewpoint of element from substances to momentary events. Element of the system conventionally considered to keep existing, for example cell in living system or actor in social system. In the autopoietic system theory, however, the elements are the momentary event that has no duration. It means that elements disappear as soon as they are realized. Consequently, system must produce the elements in order to keep itself existing. Thus, the boundary of system is determined circularly by the production of elements, and it is called autopoietic system.

In this sense, autopoietic system is not emerged from so-called "bottom-up", just because the concept of bottom-up is assumed to given elements before emerging the whole. Autopoietic system intrinsically implies circular relation between the system and its elements. Luhmann pointed out as follows:

"Whether the unity of an element should be explained as emergence `from below' or as constitution `from above' seems to be a matter of theoretical dispute. We opt decisively for the latter. Elements are elements only for the system that employs them as units and they are such only through this system. This is formulated in the concept of autopoiesis."(Luhmann 1984; p.22)


In the next post, I will explain more detail about element constitution in autopoietic systems.


References

Luhmann, N. (1984). Soziale Systeme: GrundriƟ einer allgemeinen Theorie, Suhrkamp. (English translation: Social Systems, John Bednarz Jr., Dirk Baecker (translator), Stanford University Press, 1995)

Maturana, H. R. & Varela, F. J. (1972). De Maquinas y Seres Vivos. Editorial Universitaria S.A.

Maturana, H. R. & Varela, F. J. (1980). Autopoiesis and Cognition: The realization of The Living, D. Reidel Publishing Company.

Maturana, H. R. & Varela, F. J. (1987). The tree of knowledge: The biological roots of human understanding. Shambhala Publications.

Varela, F.J., Maturana, H.R. & Uribe, R. (1974). "Autopoiesis: the organization of living systems, its characterization and a model", Biosystems, Vol.5, No.4, pp.187-196.


October 24, 2009

A Brief History of Systems Theory


In the current academic context, there are several theories under the name of "systems theory". In this post, I shall overview a history of the systems theory. We adopt, here, a categorization suggested by Hideo Kawamoto (1995), where the development of the systems theory is divided into three generation (See the Table below).




First generation is summarized as the theories for dynamic equilibrium systems, and their key concept is "homeostatis". They focused on the mechanism how a system maintains itself under the fluctuation from the environment. Leading scholars in this generation are Walter Bradford Cannon of "homeostasis" (Cannon 1932), Ludwig von Bertalanffy of "general systems theory" (Bertalanffy 1968), Norbert Wiener and W. Ross Ashby of "cybernetics" (Wiener 1948; Ashby 1956). The sociologist who applies this generation theory is Talcott Parsons as "social systems theory" (Parsons 1951).

Second generation is the theories for dynamic nonequilibrium systems, and their key concept is "self-organization". They focused on the mechanism how a structure of system is crystallized from disorders. Leading scholars in this generation are Ilya Prigogine of "dissipative structure" (Prigogine & Nicolis 1977), Manfred Eigen of "hypercycle" (Eigen & Schuster 1979), and Hermann Haken of "synergetics" (Haken 1977).

Third generation is the theories for self-production system, and their key concept is "autopoiesis". They focused on the mechanism how a system itself is realized over time. Autopoietic system means a unity whose organization is defined by a particular network of production processes of elements. Leading scholars in this generation are Humberto Maturana and Francisco Varela of "autopoiesis" (Maturana & Varela 1972, 1980; Varela & Maturana, 1974). The sociologist who applies this generation theory is Niklas Luhmann as "social systems theory" (Luhmann 1984).

Note that there is a clear distinction between "self-organization" and "autopoiesis" after the revolution caused by third generation. In this context, self-organization is focused on structural formation, but autopoiesis is focused on system formation. Luhmann emphasizes this distinction as follows:

"Autopoietic systems, then, are not only self-organizing systems, they not only produce and eventually change their own structures; their self-reference applies to the production of other components as well. This is the decisive conceptual innovation. […] Thus, everything that is used as a unit by the system is produced as a unit by the system itself. This applies to elements, processes, boundaries, and other structures and, last but not least, to the unity of the system itself." (Luhmann 1990: p.3)
"In order to clarify how much this concept of basal self-reference differs from an earlier discussion of "self-organization", Maturana and Varela have proposed the designation `autopoiesis’ for it." (Luhmann 1984: p.34).

As just quoted, the difference between "self-organization" and "autopoiesis" is of decisive importance for understanding the conceptual innovation of the systems theory.


References
Ashby W. R. (1956). Introduction to Cybernetics, Methuen.
Bertalanffy, L. v. (1968). General System Theory: Foundations, Development, Applications, George Braziller
Cannon, W. B. (1932). The Wisdom of the Body, W. W. Norton.
Eigen M. & Schuster P.(1979) The Hypercycle: A principle of natural self-organization, Springer
Haken, H. (1977). Synagetics, An Introduction. Nonequilibrium Phase-Transitions and Self-Organization in Physics, Chemistry and Biology, Springer.
Kawamoto, H. (1995) Autopoiesis: The Third Generation System (in Japanese), Seido-sha Publishers.
Luhmann, N. (1984). Soziale Systeme: GrundriƟ einer allgemeinen Theorie, Suhrkamp. (English translation: Social Systems, John Bednarz Jr., Dirk Baecker (translator), Stanford University Press, 1995)
Luhmann, N. (1990). Essays on Self-Reference, Columbia University Press.
Maturana, H. R. & Varela, F. J. (1972). De Maquinas y Seres Vivos, Editorial Universitaria S.A.
Maturana, H. R. & Varela, F. J. (1980). Autopoiesis and Cognition: The realization of The Living, D. Reidel Publishing Company.
Parsons, T. (1951). The Social System, Free Press.
Prigogine, I. & Nicolis, G. (1977). Self-Organization in Non-Equilibrium Systems, Wiley.
Varela, F.J., Maturana, H.R. & Uribe, R. (1974). "Autopoiesis: the organization of living systems, its characterization and a model", Biosystems, Vol.5, No.4, pp.187-196.
Wiener, N. (1948; 1965). Cybernetics: Or Control and Communication in the Animal and the Machine, 2nd edition, MIT Press.

October 14, 2009

My Interests with "C"-words


I thought of a new way of my self-introduction, when preparing presentation slides for a conference last week. It is the self-introduction with using only words beginning with "C". Reflecting my research for past 15 years, I found that there are several C-words related to my research.

Okay, now let me show.


First keyword is "Computation". I have engaged in computational sciences, for example, combinational optimization with artificial neural networks, econometrics, computer simulation, and network analysis. Beyond just using computers as an end-user, I have developed several software programs including scientific tools, community ware, and games. The associated words are "Component" as building block for modeling and thinking, "Constructivism" for scientific understanding and learning, and "Connected" to others via cyberspace.

Second keyword is "Complexity". My first book (1998) was about complexity and complex systems, which is written when I was master-course student. The words associated to the concept of complexity are "Chaos", "Contingency", and "Collective". Chaos is quite fascinating phenomena which the unexpected irregular patterns are emerged from simple deterministic laws. Contingency means that things or events do not occurred necessarily nor at random. Then, collectiveness implies there are interactions among elements.

Third keyword is "Creativity". I'm interested in what goes on in creative process and also how one can enhance their creativity. I sometimes invent totally new ideas and think in a different way from others, so I seem be considered as "creative" person. While I don't know whether I am really creative, I'm interested in my thinking process when I create something new. Based on my experience, abstraction to "Concept" is quite important. As Daniel Pink called an emerging creative age "conceptual age", conceptualization is getting to be more important. At the same time, "Cultivation" through prototyping, experiment and improvement of idea is important. Furthermore, in today's society, creative process by more than one person, namely "Collaboration", is important.

Thus, my major keywords are "Computation", "Complexity", and "Creativity". My studies have been done based on these keywords. This is spiral process rather than linear process. You might find a big "C" in the spiral process.

Finally, as an aside, my favorites in daily life can be also shown as C-words: "Cafe", "Camera", and "Cute Characters" :-)

October 12, 2009

My New Blog "Creative Systems Lab"

I've just started my new blog "Creative Systems Lab". In the blog, I will explore the nature of creativity with using systems theory.

I decided to keep writing this blog "Concept Walk", therefore follow both of them, please.

"Creative Systems Lab"
http://creativesystemslab.blogspot.com/


September 14, 2009

Re-reading "Mindstorms" (S.Papert)



Seymour Papert's book "Mindstorms" has been one of my favorite books ever since I read this book almost 10 years ago. I’ve been fascinated with the philosophy of Papert about learning and education, including key concepts of “object-to-think-with” and “debugging”.

Today, however, I would like to take up another minor parts of this book, but which seems to be important to consider, rather than the explicit key concept as above.

"The idea of programming is introduced through the metaphor of teaching the Turtle a new word." (p.12)

This statement implies quite important thought about the relationship between children and computer agent such as "Turtle" in LOGO. Providing this kind of tools, children can obtain not only tools to manipulate but also "buddy" whom they need to teach. Like a children’s play with dolls, robots, and stuffed toys, children can replace the standpoint between themselves and their computer agent, and empathize with the agent. They can be, so to speak, a big sister or a big brother of the agent.

This situation is very interesting, because children play both roles of "learner" and "teacher" at the same time. Borrowing the phrase of this book, it looks like the situation as follows.

"Novice is not separated from expert, and the experts are also learning" (p.179)

I think that the possibility of this nesting structure of learning and teaching should be considered more seriously.

The thought just described seems be true in the light of my own experience. When I was junior and high school student, I really enjoyed to making programs like games, tools for developing it, and some kind of artificial intelligence. My start point was to make games such as action games to go to a goal, puzzle games including competition with a computer player, and conversation game with computer agent. For me, thinking about the algorithm for thinking of characters in games was really attractive, and I felt that I needed to teach a lot of things for the characters to think and act in their world, which is an artificial world in games. I tried to make them more intelligent to overcome the human players, including me, in order to make the games more interesting and difficult. At that time, I thought of myself as a robot designer like the future-type doctor in cartoon, and was getting to empathize each of computer agents.

With these experiences, I became to read books about Artificial Intelligence and also tips for smart thinking, and implemented it to my computer agents. This was my exploration with "object-to-think-with". Then, as an aside, I studied Artificial Intelligence, especially neural network and distributed AI, when I was university student, and wrote my doctor’s thesis about modeling tools for multi-agent social simulation. Furthermore, I have been teaching complex systems with multi-agent modeling and simulation in university. So I could say that my current profession is based on these experiences. I thus learned a lot from my learning experience with "object-to-think-with".

- Seymour Papert, "Mindstorms: Children, Computers, and Powerful Ideas", Second Edition, Basic books, 1993