A Cybernetic Paradigm for Organizational Assessment

Espejo, R. (1976). A Cybernetic Paradigm for Organizational Assessment. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-76-028

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Abstract

The main purpose of this paper is to focus attention in organizations on a cybernetic viewpoint. From this point of view organization and planning are homologous. What a system does does not depend on what it would like to do. A system does what its organization allows it to do, no more, no less. The organization of a system in one way or another represents a measure of the level of environmental situations that it is capable of controlling.

The criterion of effectiveness is viability in the long run. To make this criterion of assessment operational, I shall elucidate my concept of organization as opposed to an institution, and provide a cybernetic language to refer to complexity and control.

The basic elements of the analysis are variety, or the number of possible states of a system, Ashby's law of requisite variety or the fact that variety can only be absorbed by variety and Beer's organizational model of any viable system. Under this conceptual framework three steps are developed to analyze organizational effectiveness, and they are presented in order of generality.

The first is the organizational consistency. It is in general a metasystemic analysis of relevant institutions and their subsystems. Is it possible or not for them, considering their metasystemic relationships, to fulfill their "established purposes"?

The second step after testing the consistency is the structural effectiveness. It is concerned with the distribution of variety along the organizational structure. Some structures are more effective than others in matching environmental variety. This step is concerned with the traditional dichotomy -- centralization versus decentralization.

The third step is the organizational epistemology or the particular way in which systems acquire knowledge about their relevant environment. The necessary filtering of complexity suggests that systems select a set of variables or quantities which define the system-environment area of stability.

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