Okay, I think we’ve got the confusion part…

Notes and Reflections on Selected Self-Organization and the Semiotics of Evolutionary Systems

by Luis Mateus Rocha

“You have to be confused before you can reach a new level of understanding anything” – Dudley Herschbach – Nobel Prize winner (Chemisty).

This quotation can be found at the end of George Siemens’ article, Connectivism: Learning as Network Creation. I came across it last week, right about the time that Siemens also mentioned, I think in the first UStream weekly discussion for the Connectivism and Connective Knowledge course, that he would be concerned if participants in the course were not experiencing confusion.

Looking through the bibliography for the article mentioned above, I noted an article by L.M Rocha called Selected Self-Organization and the Semiotics of Evolutionary Systems. The following quotation is especially relevant to my interests, and I think that it also intersects with suggestions that are being brought forward by Stephen Downes in Learning Networks and Connective Knowledge, namely, that knowledge is subsymbolic, and that knowledge is distributed:

…Varela, Thompson, and Rosch [1991] have proposed an embodied, inclusive, approach to cognition which acknowledges the different levels of description necessary to effectively deal with emergent representation…

In this case I believe that the different levels of description to which the author is referring are namely the biological and the conceptual. To continue:

Cognitive science used to be traditionally concerned solely with those aspects of cognitive representation which can be described as symbolic. In other words, it was concerned with semantic relation between cognitive categories and their environmental counterparts through some direct representational relation (intentionality), without taking into account any sort of material or internal organizational constraints: real-world categories directly represented by discrete symbols which could be freely manipulated. The connectionist, emergent, or self-organizing paradigm has changed this focus to the lower level of attractor behavior. That is, cognitive systems are defined as those systems capable of self-organizing their components into discrete basins of attraction used to discriminate the environment they are able to construct. Classifications become subsymbolic and reside in some stable pattern of activation of the dynamic system’s components, instead of based on some higher level symbols (emergent representation) (3).

Here is Rocha’s definition of self-organization:

…the spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions. The systems used to study this phenomenon are referred to as dynamical systems: state-determined systems. They possess a large number of elements or variables, and thus very large state spaces. However, when started with some initial conditions they tend to converge to small areas of this space (attractor basins) which can be interpreted as a form of self-organization (3).

What I find interesting in terms of this formulation is that when we discuss self-organization, we are talking about its emerging from an initially chaotic state. When “some initial conditions” are present, there is a movement towards self-organization. From what I can ascertain, “very large state spaces” according to this model may be suggestive of a pre-network “container,” and “attractor basins” may be understood as the starting point for early nodes in a network.

Consider the following:

This process of self-organization is also often interpreted as the evolution of order from a disordered start. Self-organizing approaches to life (biological or cognitive), in particular second-order cybernetics [see Pask, 1992], take chaotic attractors as the mechanism which will be able to increase the variety (physiological or conceptual) of organizationally closed systems. External random perturbations will lead to internal chaotic state changes; the richness of strange attractors is converted to a wide variety of discriminative power (3).

Let’s try to apply this description back to the framework being used to realize this course. There are “some initial conditions,” i.e. there is a syllabus, there are readings, there are daily updates and weekly discussions, there are individual blogs being maintained by course participants, there is a course blog and there are Moodle fora for discussions. There is certainly chaos. And there are certainly also “random perturbations” impacting individual learners, inducing “internal chaotic state changes”, each in (presumably) very different ways.

Learning is described by Rocha in terms of its relation to memory:

The dynamical approach of von Foerster [1965] to cognition emphasized the concept of memory without a record…Today, we name this kind of memory distributed, and the kind of models of memory so attained as connectionist. As previously discussed, for a self-organizing system to be informationally open, that is, for it to be able to classify its own interaction with an environment, it must be able to change its structure, and subsequently its attractor basins, explicitly or implicitly. Explicit control of its structure would amount to a choice of a particular dynamics for a certain task (the functional would be under direct control of the self-organizing system) and can be referred to as learning (4).

Creativity is described in terms of the ability of a dynamical system to experience structural perturbation:

…Self-organization alone cannot escape its own attractor behavior. A given dynamic system is always bound to the complexity its attractor landscape allows. For a dynamic system to observe genuine emergence of variety can only be attained by structural perturbation of a dynamical system. One way or another, this structural change leading to efficient classification (not just random change), has only been achieved through some external influence on the self-organizing system (4).

In other words, chaos is a predecessor for introducing learning and adaptation into an organism.

Kauffman [1993, page 232] further hypothesizes that “living systems exist in the [ordered] regime near the edge of chaos, and natural selection achieves and sustains such a poised state”. This hypothesis is based on Packard’s [1988] work showing that when natural selection algorithms are applied to dynamic systems, with the goal of achieving higher discriminative power, the parameters are changed generally to lead these systems into this transitional area between order and chaos. This idea is very intuitive, since chaotic dynamical systems are too sensitive to parameter changes, that is, a singlemutation leads the system into another completely different behavior (sensitive to damage). By contrast, ordered systems are more resilient to damage, and a small parameter change will usually result in a small behavior change which is ideal for smooth adaptation (hill-climbing) in correlated fitness landscapes (4).

The Connectivism & Connective Knowledge course could be characterized as a system precariously balanced on the edge of chaos, teetering on the edge of coherence.

It is here that systems at the edge of chaos enter the scene, they are not as sensitive to damage as chaotic systems, and thus, some mutations will accumulate (by causing minor changes) and some others will cause major changes in the dynamics allowing more distant searches in fitness spaces. These characteristics of simultaneous mutation buffering (to small changes) and dramatic alteration of behavior (in response to larger changes) is ideal for evolvability [Conrad, 1983, 1990].

Is the course intentionally being framed in these terms towards the end of fostering the greatest evolvability of its participants? The answer is entirely possibly in the affirmative.

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One Response to “Okay, I think we’ve got the confusion part…”

  1. Blair Krishnan Says:

    I will not debate with your decisions because I think you’re right on the money! You have put together a consistent case for your sentiments and now I know more about this unusual topic. Gives Thanks for this amazing post and i will come back for more.

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