Adaptive and generative learning: implications from complexity theories. icon

Adaptive and generative learning: implications from complexity theories.


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Complex adaptive systems are able to develop three types of behavior: stable, unstable or chaotic, and limited instability or tension between various forces that place them at the edge of chaos. The edge of chaos is regarded as a phase change. According to Gell-Mann (1994), this stage represents the ‘highest effective complexity’. If effective complexity is defined in terms of the length of the model, then it is low when there is a high level of chaos and the environment is random, although the algorithmic information complexity is very high (Stacey, 1996: 96). Effective complexity is also low when a system operates in an environment that is highly stable, in the sense that its component systems behave in a perfectly regular manner. In this situation very little happens and little learning or evolution is needed (Stacey, 1996: 96). A complex adaptive system can learn only when effective complexity is sizeable, that is, in conditions that are intermediate between chaos and stability (Gell-Mann, 1994).


Complex adaptive systems evolve over time through the entry, exit and transformation of agents that interact and scan their environment and develop schemas. The adaptation of a complex adaptive system to its environment emerges from the adaptive efforts of individual agents that attempt to improve their own payoffs (Anderson, 1999). Complex adaptive systems continuously co-evolve (Anderson, 1999; Axelrod and Cohen, 1999; Boisot and Child, 1999), which means that organizations have a mutually adaptive relationship with their environment, such that they are not simply trying to adapt to a static environment, but rather the organization is learning to adapt to an environment that is itself adapting to the market (other organizations and industries). McKelvey (1997) has argued that evolution of organizations cannot be understood in isolation from the simultaneous evolution of the environment. One characteristic of a complex adaptive system that is closely related to connectivity is the tendency of several systems, or several subsystems within one main system, to move together toward new forms of existence or new stages of development (Luoma, 2006). This is known as co-evolution. Co-evolution is the mutual influence among systems or agents that become dependent on each other. Each party in a co-evolutionary relationship exerts selective pressures on the other, thereby affecting each other's evolution. Few perfectly isolated examples of evolution can be identified: essentially all evolution is co-evolution. Jantsch (1980), who attributed the entire evolution of the cosmos to co-evolution, regards co-evolution as an essential aspect of the dynamics of self-organization.

Co-evolution also happens among entities within a system, and the rate of their co-evolution (Jantsch, 1980; McKelvey, 1999) is worth considering. Co-evolution can take place within an organization, the actors being any units with the ability to interact (Luoma, 2006). As this author maintains, environment is not just everything that is not us; it is a rich collection of other players. We do not adapt to some overall environmental forces; rather we constantly co-evolve with other players.

In sum, complex adaptive systems self-organize when they are at the edge of chaos. This implies the evolution of a system into an organized form in the absence of external constraints. Adaptability is one of the characteristics of complex adaptive systems that implies the system’s capacity to adjust to changes in the environment without endangering its essential organization. Adaptive learning is essential in these systems.

However, existing schemas can undergo first order change or single loop learning and second order change or double loop learning (Stacey, 1996; Dooley, 1997). The former occurs when a system employs its schema without change, adapting its behavior to the stimuli presented to it so that this behavior becomes more beneficial. Second order change or double loop learning occurs when a system adapts its behavior to the stimuli presented to it in a beneficial way as a result of changing its schema. Schema change generally has the effect of making the agent more robust (it can perform in the light of increasing variation or variety), more reliable (it can perform more predictably), or making it grow in requisite variety (it can adapt to a wider range of conditions).


In similar terms, Jantsch (1980) explains that as the system reaches beyond the boundaries of its identity, it becomes creative. This author points out the importance of self-transcendence: the creative reaching out of a human system beyond its boundaries. Creation is the core of evolution, which is the result of self-transcendence at all levels. Jantsch (1980) highlights that social systems are re-creative systems because they can create new reality; socio-cultural human beings have the ability to create the conditions for their further evolution all by themselves. Creativity means the ability to create something new that seems desirable and helps to achieve defined goals. By anticipating the future and creating new reality, social systems transcend themselves (self-transcendence). Human beings can create images of the future and actively strive to make these images become social reality. Individuals can anticipate possible future states of the world, society as it could be or as one would like it to become; and they can act according to these anticipations. By all this, Jantsch (1975, 1980) appeared to explain the difference between simply adapting to an environment (adaptive learning) and creating a new reality or transcending (generative learning).



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Implicate order


Einstein’s disciple Bohm (1980) used the theory of the implicate order to present a new model of reality that contains a holistic view. It connects everything with everything else. In principle, any individual element could reveal information about every other element in the universe.

Bohm (1980) developed his theory of the implicate order to explain the strange behavior of subatomic particles, which he believed might be caused by unobserved forces that may be reflective of a deeper dimension of reality. He calls this reality the implicate order. Bohm (1980) uses the metaphor of the hologram (Pibram, 1991) to explain the implicate order. He notes that the hologram illustrates how information about the entire holographed scene is enfolded into every part of the film. It resembles the implicate order in the sense that every point on the film is completely determined by the overall configuration of the interference patterns. Within the implicate order everything is connected and enfolded into everything else. This contrasts with the explicate order or manifest world where things are unfolded. The explicate order derives from the implicate order. This concept is very much related to Plato’s theory of forms. Plato suggested that the world as it seems to us is not the real world, but only a shadow of the real world, that the world of appearances (explicate order) is the shadow of a more profound world of forms or ideas (implicate order). Within the implicate order, there is a totality of forms, which enfold everything.

Bohm (1980) describes the implicate order as a kind of generative order, which is primarily concerned with a deep and inward order out of which the manifest form of things can emerge creatively. In fact he believes there may be an infinite hierarchy of implicate orders. Bohm (2004a) maintains that everybody has many experiences of the implicate order. The most obvious one is ordinary consciousness, in which consciousness enfolds everything that you know or see.

According to Bohm (1980) and Bohm and Peat (2000), to approach the implicate or generative order requires (creative) intelligence, which is an unconditioned act of perception (intuition) that must lie beyond any factors that can be included in any knowable law. Bohm (1980) considers thought is essentially mechanical and limits perception and intuition. He suggests that the perception of whether or not any particular thoughts are relevant or fitting requires the operation of an energy that is not mechanical, energy that we shall call intelligence. He gives an example; one may be working on a puzzling problem for a long time. Suddenly, in a flash of understanding, one may see the irrelevance of one’s whole way of thinking about the problem, along with a different approach; such a flash is essentially an act of perception. Similarly, Krishnamurti (1994) understands that real learning brings order and when learning ceases, it becomes the mere accumulation of knowledge (knowing) then disorder and conflict begin. He believes that knowledge prevents learning.

Bohm (1980) considers that the movement from the explicate order to the implicate order and back again, if repeated enough, could give rise to a fixed disposition. The point is that, via this process, past forms would tend to be repeated or replicated in the present, which implies the existence of certain patterns of vibration that “create” the visible forms we see in reality; that implicate orders influence the external forms through a process of “tuning in”, or morphic resonance (Sheldrake, 1981, 1994; Sheldrake et al., 2001). Morphic signifies form, and resonance implies the “tuning in” of two or more parts into a pattern of the same frequency. Therefore, it means “tuning in” the form (Plato). Through morphic resonance, the patterns of activity in complex systems are influenced by similar past patterns, giving each species and each kind of system a kind of collective memory (Sheldrake, 1981). It should be noted here that Sheldrake’s concept of morphic resonance blends with that of Jung’s theory of synchronicity (1972). Synchronous events or meaningful coincidence reveal an underlying pattern, a conceptual framework that encompasses, but is larger than, any of the systems which display the synchronicity (Peat, 1987).

Bohm (1980) considers that humanity, together with the whole of the biosphere, is a holistic system. All beings are part of one consciousness known as implicate order. All parts are connected with each other by frequencies and are in resonance. Frequencies, information and energies are all connected with each other in continuous cycle; they all are part of the whole. If a new impulse enters into a holistic system then it is effective in all its parts. If the impulse contains new core information then a field-like change occurs that makes itself noticed either as a mutation, evolutionary leap or as transformation (generative learning). Such transformations occur in the lives of individuals as well as in the lives of entire populations.

The idea is that there is a kind of internal memory in nature. Each kind of thing has a collective memory. Sheldrake (1981) affirms that systems are shaped by morphic fields, a very similar concept to implicate order, which organize atoms, molecules, crystals, cells, organs, organisms, societies, organizations, ecosystems, planetary systems, solar systems, galaxies. In other words, they organize systems at all levels of complexity, and are the basis for the wholeness that we observe in nature, which is more than the sum of the parts. Morphic fields also contain an inherent memory given by the process of morphic resonance, whereby each kind of thing has a collective memory. As we have stated, in the human realm this is similar to Jung’s theory of the collective unconscious (1972). And how that influence moves across time is given by the internal process Sheldrake (1981, 1994) calls morphic resonance. Morphic resonance suggests that it becomes easier to learn what other people have already learned; we all benefit from what other people have previously learned through a kind of collective memory, morphic field or implicate order.




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