Explanation, Prediction, Problem‐Solving and Decision‐Making in Social Systems

Abstract

Inferences concerning analysis, explanation, prediction, problem‐solving, policy formulation and decision‐making are systematically derived by means of a social cybernetic methodology. The basis of the methodology lies in the construction of a multi‐feedback loop model of the social phenomenon/situation investigated. Salient variables of the system are then identified as those located at the intersection of several feedback cycles. They represent analogs of Ashby's “essential variables”. Their measurement in terms of a scale of “regulatedness” or viability (λ), permits an estimation of time‐varying states of the system's “health” and defines the specific goals of a problem solution. The λ ‐ equivalence of salient variables in terms of Wiener's Principle of Entrainment of Frequencies, yields verifiable predictive inferences. Their hierarchic disaggregation generates a “morphological map” of the problem/phenomenon. This map shows the micro‐level requirements of policies and/or problem‐solving. Maximisation of the viability of decision alternatives provides a logically simple approach to multi‐criteria/attribute decision‐making.

Citation

Rastogi, P.N. (1988), "Explanation, Prediction, Problem‐Solving and Decision‐Making in Social Systems", Kybernetes, Vol. 17 No. 1, pp. 33-45. https://doi.org/10.1108/eb005779