Georg Ivanovas From Autism to Humanism - systems theory in medicine

4. Systemic Basics

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4.10 Emergence

A term often used in the context of systems sciences and in the study of the living is emergence. It describes the fact that elements which are structurally interconnected produce a new behaviour not inherent in their parts. It is another way to formulate that the whole is something different than the sum of its parts, that the resulting function or behaviour is not determined by the parts. Emergent behaviour is achieved autonomously through interaction of the elements with one another. Such systems have been described as self-organizing or as teleological. The elements interact in order to achieve dynamically a global function or behaviour (Gershenson 2007: 32). Actually the terms emergent behaviour and pattern are to a large extend used synonymously..

Nearly everything encountered in medicine is emergent. Osteoporosis is an emergent phenomenon as it develops out of the interplay of two opposite principles, the osteoclasts and the osteoblasts, integrated into a larger regulating network. Asthma is an emergent condition of the breathing. Depression has been found to be a stress-induced overdrive of the hypothalamic-pituitary-adrenal axis creating a stable neurophysiological recursion, a typical emergent state, sometimes triggered by external factors like an abuse in early childhood (Holden 2003), financial or erotic stressors (Capsi et al 2003) and maintained by environmental factors.


Common models to investigate emergent phenomena (that is, the interrelation between parts leading to a certain the behaviour of a whole system) are cellular automata in which individual cells stand in interrelation.

The ‘Game of Life’ cellular automaton functions at each step according to the following laws:

  • any cell with exactly three live neighbours will stay alive or become alive;
  • any live cell with exactly two live neighbours will stay alive;
  • all other cells die.

Certain Game of Life configurations create patterns. The most famous is the glider, a pattern of on and off cells that moves diagonally across the grid. It is possible to implement an arbitrary Turing machine by arranging Game of Life patterns. Computability theory applies to such Turing machines. Thus while not eluding the Game of Life rules, new laws (computability theory) that are independent of the Game of Life rules apply at the Turing machine level of abstraction (Abbott 2007)

Such models makes show visually and mathematically that an emergent pattern obeys a different logic than the behaviour of its parts. This had been already Russell’s point (chap. 3.2). Although it is always possible to reduce emergent phenomena to the level of their parts, such an analysis provides no or only a minor information about the function as a whole (Abbott 2007). Such a situation is, for example, found in the interaction of genes. The step from the behaviour of the parts to the behaviour of the human, that is, the step from genotype to phenotype cannot be understood by the function of the parts. This is not a question of knowledge. It is a fundamental law of organisation (appendix III).

Another cellular automaton is ‘Bittorio’. It is a circular ring made out of cells which might have the state 0 or 1. Bittorio is then dropped into a milieu of random soup of 0s and 1s. The rule is that whenever a cell encounters one of the two alternatives, the state of the cell is replaced by the state it encounters. This model has not only an inner structure, but also an environment. It is able to demonstrate how perturbations lead to certain reactions of the ‘system ring’. When Bittorio’s inner rule consists of a simple or a chaotic attractor, then the consequences of the perturbation is simply invisible. Bittorio either goes back to its previous homogenous state, or it remains in a random like state. In the case of a more complex inner rule, a series of changing sates is seen until a new inner balance is achieved (Varela et al: 151).

A perturbation (or in other words: an intervention into a system) might lead to a change if the system is not chaotic or rigid. The reaction is according to the inner structure and expresses itself as a pattern, just as seen with the ‘Game of Life’ automaton. The pattern is a result of the communication between the parts and cannot be understood or described by their individual reactions.

We have here in a nutshell two further principles necessary to understand the course of events in therapies.

First, when confronted with a perturbation, the system will normally not jump to a different state immediately. The perturbation will induce a series of intermediate patterns until a new balance is found (Varela et al 1993: 151-157). Therefore a therapeutic intervention will normally not lead to a new state at once. It will show a changing pattern first. An example is that months after a surgery the health risks like cardiovascular complications are still increased (Meiler 2006)

Second, if the system is rigid it will not react to perturbations and no reactive pattern will be observed. That is, the system has lost its ability to adapt. It reveals a learning zero situation. According to the principles of autopoiesis such a system is more prone to disintegrate.


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