Organizational Information Flow Analysis

Origin. Shannon's information theory (1948) integrated into Soviet organizational cybernetics by Glushkov, Kharkevich, and the OGAS project team (1960s-1980s). The Soviet contribution was applying channel capacity theory to organizational and economic systems.

Mechanism. Information flow has measurable capacity, and control requires information flow from the controlled system to the controller and back. Bottlenecks in the information path manifest as control lag, outdated decisions, and oscillation. Channels have capacity C = B log₂(1 + S/N) where B is bandwidth and S/N is signal-to-noise ratio; the channel can carry at most C bits per second reliably. Soviet cyberneticians recognized that organizations have information pathways with measurable capacity, and that centralized decision-making is fundamentally limited by those channel capacities.

Procedure. Map the information pathways: what signals flow where, at what rate, with what delay. Identify decision points and the information they require. Compute the required channel capacity: decision rate times bits per decision. Compare to available capacity. If required exceeds available, either reduce the decision rate (slower response), reduce bits per decision (coarser categories), increase bandwidth (more frequent reporting), or increase signal-to-noise (better measurement). Do not increase decision rate beyond channel capacity; the result is thrashing, not control.

Applies to. Organization design, monitoring system design, and any system where decisions depend on distributed observation.

Limitations. Treats information as context-independent bits, but semantic information content is not the same as Shannon entropy. A message can have high Shannon information (surprising, low-probability) and low semantic content (irrelevant to the decision). The capacity bound governs symbol transmission, not meaning transmission. Use to diagnose bottlenecks and overload, not to optimize semantic content.