The notion of autopoiesis as the defining quality of living systems has not been taken up by the biological community as sufficient criterion for establishing the difference between living and non-living. Although interesting, this issue is incidental to my discussion, so I will not consider any details here. See Fleishaker (1992) and Mingers (1995) for some discussion.
I consider sympoietic systems to be living systems, although disagreements would be found here also. Ecosystems, which I believe represent a prime example of sympoietic systems, are often argued as being composed of living systems, but not living themselves.
In neither case is the reverse true: all autopoietic systems are not necessarily living systems. Some authors, however, do argue for this point. (See Fleishaker 1992.)
For other discussions regarding the definition of living systems that incorporate some of the systems concepts discussed in this thesis, although not necessarily the concept of autopoiesis, see Kay (1984), Csanyi (1986, 1989), Cramer (1993), and Gunther and Folke (1993).
Six point key for identifying an autopoietic system (from Varela et al. 1974):
- Determine, through interaction, if the unity has identifiable boundaries. If the boundaries can be determined, proceed to 2. If not, the entity is indescribable and we can say nothing.
- Determine if there are constitutive elements of the unity, that is, components of the unity. If these components can be described, proceed to 3. If not, the unity is an unanalyzable whole and therefore not an autopoietic system.
- Determine if the unity is a mechanistic system, that is, the component properties are capable of satisfying certain relations that determine in the unity the interactions and transformations of these components. If this is the case, proceed to 4. If not, the unity is not an autopoietic system.
- Determine if the components that constitute the boundaries of the unity constitute these boundaries through preferential neighborhood interactions and relations between themselves, as determined by their properties in the space of their interactions. If this in not the case, you do not have an autopoietic unity because you are determining its boundaries, not the unity itself. If 4 is the case, however, proceed to 5.
- Determine if the components of the boundaries of the unity are produced by the interactions of the components of the unity, either by transformation of previously produced components, or by transformations and/or coupling of non-component elements that enter the unity through its boundaries. If not, you do not have an autopoietic unity; if yes proceed to 6.
- If all the other components of the unity are also produced by the interactions of its components as in 5, and if those which are not produced by the interactions of other components in the production of other components, you have an autopoietic unity in the space in which its components exist. If this is not the case, and there are components in the unity not produced by components of the unity as in 5, or if there are components of the unity which do not participate in the production of other components, you do not have an autopoietic unity (Varela et al. 1974, p 192-3).
Information is a word which has never been easy to pin down. (Campbell 1982)
The first point regarding information is to recognize that it is neither a thing, nor a referent, it is a means. Information is most commonly defined as a means of reducing uncertainty and, therefore, of increasing organization and order.
In a precise sense, information is measured in bits, where a bit is defined as the amount of information required to reduce an alternative or choice by half. This points to the context dependent quality of information. A signal or message is not really information until it has been received.
Repeating structures increase expectations by making patterns. The very essence of information is pattern (Resnikoff 1989). As such, it can exist in all domains at all levels of complexity from molecular structure to scientific knowledge. Any pattern, from the ordered arrangement of atoms to letters forming words on a page, has the potential to reduce uncertainty. Discrepancies in the definition of information relate to this range of possibilities.
To be clear, discussion of information must include reference to who/what is receiving the information and the scale and type of information that is being considered.
Trajectory is another term that has disciplinary and common meanings with potentially different connotations. In a strict sense, trajectory refers to the curve described by a projectile such as an arrow or rocket. It is precise and predictable. In common language, trajectory is used to refer to "any path or course" (Random House 1991). I use it in the latter sense, referring to path of a system, primarily with respect to the temporal dimension.
In a strict sense, unpredictable trajectory is an oxymoron, however, I apply the term to the temporal trajectories of both autopoietic and sympoietic systems. The latter, however, are unpredictable. Despite contradiction with the strict definition, I prefer this term to path or course because of its connotation. Used in the common sense it suggests a certain momentum - a certain degree of inevitability - to the path or course of the system. Although sympoietic systems are unpredictable, their direction is a result, at least to some extent, of their history. Due to this, and the lack of potential for controlling them, the systems carry the degree of inevitability suggested by use of trajectory. It is important, however, to understand that I do not intend to imply that these trajectories are predictable.
For an alternate, see Kay (1991) who uses the term thermodynamic branch.
A common dictionary definition of equilibrium is "a state of rest or balance due to the equal action of opposing forces; equal balance between any powers, influences, etc." (Random House 1991). When discussing systems, it is important to consider what parameters the "state of rest or balance" refers to. For example, as Kay (1991, p491) notes, "equilibrium in a stability sense is different from equilibrium in a thermodynamic sense. The former refers to a balance of the forces acting on a system and has its origin in classical mechanics. Thermodynamic equilibrium refers to a system state that is uniform throughout and undistinguishable from its surroundings. For a biological system this represents death." Discussion regarding the importance of non-equilibrium thermodynamic conditions arises because many interesting phenomena - life as a key example - arise under these conditions. (See Nicolis and Prigogine 1977, 1989, Kay 1984, 1991, and other authors listed in Box 2.7.)
Beyond these definitional issues there are further implications relevant to discussion of natural system stability that are worthy of the minor digression detailed in Box 2.6.
A standard approach for understanding and communicating stability in natural systems is to use ball-in-bowl diagrams with shapes relating to the different degrees or aspects of stability (see Kimmins 1987 p 421 for examples). Our traditional natural resource management approaches are permeated by the comfortable representation of a ball resting in the bottom of the bowl: systems in equilibrium. We "know" the ball will roll back to the bottom: security for the species from a graphic representation and the unconscious influence of gravity. For poietic systems I suggest a gestalt: we need to invert the bowl. The gestalt causes a double take, positing the immediate question: what keeps the ball there? A ball on an inverted bowl necessitates recognition of the fine balancing act systems are in - a notion that presents a different view of "stability."
One might argue that this is nothing more than a graphic representation - everybody (or at least any good scientist) "knows" that this diagram does not reflect "reality." Admittedly true, however, the most common question I have been asked when showing the illustration is: "but what keeps it there?" I am surprised at the different reaction generated by such a subtle difference. The actual natural system interactions are no different, yet the question is. One would certainly not say that even a naturally disturbed forest "slides back down" into a new vigorous one, yet we are more comfortable representing ecosystems with the bowl. How often do we ask what keeps the ball in the bottom?
I believe that there is a fundamental, yet unrecognized belief at work here. The notion of stability represented by the ball in the bowl is symbolic of a deeply held belief in the "balance of nature." Since our actions are grounded in our beliefs, this equilibrium-centered view has allowed exploitation according to maximum sustainable yields and a lack of concern for the regeneration of natural resources. Forest ecosystems and fish stocks, for example, are perceived to have almost limitless resilience. Push them up the side of the bowl - clear-cut the forest or harvest quantities of fish - and they just slide back down - naturally regenerate. More resilient systems or management interventions - silvicultural treatments or hatcheries - make them slide back down even faster.
The gestalt poses a subtle, but significant alteration: the system has to work to get back to the original position. (See Figure 2.5 for extensions on this representation.) The change in perception does not, however, make all previous understanding of systems invalid. It does not change the shape of the bowl, just the mind sets: standard ecology from a different angle. The gestalt simply provides a different and, I argue, more accurate, representation. Schrader-Freschette (1994) points out that those who frame the questions often control the answers. I suggest the gestalt in order to generate new questions.
We must not think: Equilibrium. We must think: Balancing?
Classic Works Related to Self-Organization
Self-Organization in Non-equilibrium Systems (Nicolis and Prigogine 1977)
The Self-Organizing Universe: Scientific and Human Implications of the Emerging Paradigm of Evolution (Jantsch 1979)
The Hypercycle: A Principle of Natural Self-Organization (Eigen and Schuster 1979)
Synergetics: An Introduction (Haken 1983)
Order Out of Chaos: Man's New Dialogue with Nature (Prigogine and Stengers 1984)
Structure, Context, Complexity, Organization (Eriksson, Lindgren, and Mansson 1987)
Chaos (Gleick 1987)
Exploring Complexity (Nicolis and Prigogine 1989)
Recent Publications Written for Laypersons
Complexity: Life at the Edge of Chaos (Lewin 1992)
Complexity: The Emerging Science at the Edge of Order and Chaos (Waldrop 1992)
Chaos and Order: The Complex Structure of Living Systems (Cramer 1993)
Complexification: Explaining a Paradoxical World through the Science of Surprise (Casti 1994)
Thinking In Complexity: The Complex Dynamics of Matter, Mind, and Mankind (Mainzer 1994)
The Web of Life (Capra 1996)
Other Works Including Edited Compilations
Self-Organizing Systems: An Interdisciplinary Approach (Roth and Schwegler, eds. 1981)
Self-Organization and the Thermodynamics of Living Systems: A Paradigm (Kay 1984)
Self-Organizing Systems: The Emergence of Order (Yates, ed. 1987)
The Paradigm of Self-Organization: Current Trends in Self-Organization (Dalenoort, ed. 1989)
Systems Science: Addressing Global Issues (Stowell, West and Howell, eds. 1993)
Complexity and Thermodynamics: Towards a new ecology (Schneider and Kay 1994)
Complex Ecology: The Part-Whole Relation in Ecosystems (Patten, Jorgensen, Auerbach, eds. 1996)
Jantsch (1980) makes a basic distinction between conservative and dissipative self-organization. The former "reflects only the interplay of static attracting and repelling forces" (Jantsch 1980, p 81). The second is "based on entropy production ("work") and thus involving particular thermodynamics" (Jantsch 1980, p81).
Along similar lines Dalenoort (1989a) notes that non-cybernetic self-organization, referring to chemical reactions or physical approaches to equilibrium (e.g. a fluid going horizontal), differs from cybernetic self-organization which involves feedback/forward mechanisms such as in organic systems. Cybernetic self-organization can in turn be classified as homogenous (reactions that are all of the same type) or non-homogenous, such as the self-organization present in a system of cells (Dalenoort 1989).
Pattee (1987) differentiates between statistical and information dependent self-organization. The former refers to statistically unstable systems which are typically physical, the latter to biological systems.
Holland (1992) discusses complex adaptive systems - systems that "change and reorganize their component parts to adapt themselves to the problems posed by their surroundings" (p. 18). As an example, Holland begins with a brief description of the immune system, pointing out that it, and other complex adaptive systems "all seem to share three characteristics: evolution, aggregate behaviour, and anticipation." In particular, the "ability of the parts to adapt or learn is the pivotal characteristic of complex adaptive systems" (p. 19). One of the most complex aspects of this ability is the ability to anticipate. "This emergent ability to anticipate in one of the features we least understand about complex adaptive systems, yet it is one of the most important" (p. 20).
His descriptions of complex adaptive systems holds many similarities to my descriptions of sympoietic systems. These systems have "no single governing equation, or rule, that controls the system. Instead, it has many distributed, interacting parts, with little or nothing in the way of a central control. Each of the parts is governed by its own rules. Each of these rules may participate in influencing an outcome, and each may influence the actions of other parts. The resulting rule-based structure becomes grist for the evolutionary procedures that enable the systems to adapt to its surroundings" (p. 21-22). I believe that sympoietic systems are a particular type of complex adaptive system which, as he applies the term, covers a more general type of system.
Many other authors also use the term complex adaptive systems (e.g. Kauffman 1993, Gell-Mann 1994). Funtowicz and Ravetz (1994) discuss emergent complex systems - providing slight variations on Holland's definition.
Bella (1997) describes CANL systems - complex adaptive non-linear systems - also including aspects similar to Holland's recognition of emergent, anticipatory behaviour - behaviour that makes the system non-linear. He notes that "CANL systems display the properties of self-organization. They are self-renewing, drawing in behaviours from a sea of dis-order toward coherent patterns on vast scales" (ibid. p 620). He describes attractors that act like gravitational fields, pulling system behaviours toward organized complexity. His work and examples stress the interplay between order and disorder and the tension between attractors as constitutive factors in generation of CANL systems.
Kelly (1994) refers to swarm systems and vivisystems, noting that they are also called complex adaptive systems, and networks. His work emphasizes the interplay between bio-logic and machine-logic, drawing parallels, and noting differences.
Regier and Kay (1996), following Koestler, use the term SOHO systems - self-organizing holarchic open systems. They follow Koestler in order to emphasize recognition that a "holon" must be defined contextually - they are 'parts' as well as 'wholes.' SOHOs are self-organizing in that "they will look after themselves" (Kay 1994), and are dissipative systems. Using an example of benthic-pelagic transformations in the Great Lakes, they also describe the role of attractors to express the propensities of SOHOs to be 'attracted to' particular system states.
Hollick (1993) discusses self-organizing systems listing five essential characteristics of such a system:
Hollick also lists properties of these systems, noting that each is a "whole greater than the sum of the parts" that they "are self-controlled within larger-scale constraints," and that they evolve (ibid., p 622).