Causality in a network is kind of distributed.
Each element is a causal agent in its own right. It is however constrained in its behavior by the inputs it receives from the other elements. And in turn it will constraint the behavior of those elements which will receive its outputs.
Moreover, not all elements will receive or emit directly to each others. Some may even never receive some types of inputs. In such cases, specific localizations may appear in the network. This is possibly a model in the case of the progressive organization of the neuronal network in the brain (see "neurode"). On the other hand, no transmission is instantaneous and the resulting time lags will insure different conditions in different places in the network, as a result of differential spatial and temporal progagation of effects. In this way, the general determinism of the network can be somewhat disrupted locally.
A result is that any network will readapt permanently, somehow in the way of ASHBY's homeostat.
However the general behavior of the network would remain within the global – but lax – constraints imposed to the network either by the context, or by its constructor (in this last case, for ex. by a back propagation algorithm).
This model of network causality could possibly be also useful for research on complex social interactions in human groups.
- 1) General information
- 2) Methodology or model
- 3) Epistemology, ontology and semantics
- 4) Human sciences
- 5) Discipline oriented
To cite this page, please use the following information:
Bertalanffy Center for the Study of Systems Science (2020). Title of the entry. In Charles François (Ed.), International Encyclopedia of Systems and Cybernetics (2). Retrieved from www.systemspedia.org/[full/url]
We thank the following partners for making the open access of this volume possible: