Both Scientists and System Analytics share common experience that complex interfaces (for example "human - machine" interface within the complex hybrid system, or synapse in the human brain) susceptibly react both to the dimension of the task (i.e.: the number / type of interface parameters / markers) and to the degree of uncertainty.
In order to quantitatively evaluate this effect, the model of interface is presented first. Then the problem is analyzed. The results of the study indicate:
Even a low degree of uncertainty, "acting" homogeneously on all parameters of the respective interface, has significantly adverse effect on the interface regularity (consequently the reliability of systems processes as well) if the number of parameters (i.e. dimension of the pertinent task) is sufficiently high.
Even a significant uncertainty in one or in a small number (typically 1 or 2) of interface parameters has a limited or negligible impact on the interface regularity if this interface is sufficiently robust.
There are three basic attempts how to increase the regularity of complex interfaces: (a) smart simplification (b) utilizing redundancy or contextuality (c) interface conjugation.