The Production of Probabilistic Entropy in Structure/Action Contingency Relations

TL;DR

This paper develops a probabilistic framework linking societal communication systems and human actions, integrating theories from sociology, AI, and information theory to analyze their dynamic interactions and stability.

Contribution

It introduces a general algorithm for probabilistic structure/action contingency by combining sociological and AI theories, with an empirical example.

Findings

Probabilistic entropy production in social systems

Stabilization mechanisms in communication-action relations

Application of Shannon's information theory to social dynamics

Abstract

Luhmann (1984) defined society as a communication system which is structurally coupled to, but not an aggregate of, human action systems. The communication system is then considered as self-organizing ("autopoietic"), as are human actors. Communication systems can be studied by using Shannon's (1948) mathematical theory of communication. The update of a network by action at one of the local nodes is then a well-known problem in artificial intelligence (Pearl 1988). By combining these various theories, a general algorithm for probabilistic structure/action contingency can be derived. The consequences of this contingency for each system, its consequences for their further histories, and the stabilization on each side by counterbalancing mechanisms are discussed, in both mathematical and theoretical terms. An empirical example is elaborated.