The Rigor of Systems Thinking

Knowledge, as opposed to “just facts”, is something you can use in different situations, something you can take with you from one project to another. Facts, on the other hand, can characterize specific projects and the objects in them. Knowledge of meters as units of measure is common to all projects. In some projects, a track length is 14 meters — that can’t be applied to other projects, so it’s not “knowledge”, it’s just a “fact”.

The logic (rules of reasoning leading to plausible judgments from plausible premises) of science and engineering is not necessarily Boolean/discrete with “true” and “false” values. Modern logic is probabilistic, and we are talking about Bayesian probability, not frequency probability. Modern logic uses probabilistic reasoning, and experiments do not prove or disprove something but only shift probabilities. Life is not made up of “truths” and “lies”, it is judged by statements about probabilities!

Is systems thinking necessarily formal (expressed in symbolic form, accessible to rigorous logical inference through the meanings of “true” and “false”), or is it entirely informal, i.e., intuitive?

The main thing to discuss here is the presence and importance of completely informal, intuitive, and inexpressible knowledge, including knowledge of systems. Especially since today, such knowledge can be possessed by humans and computers programmed to operate within the connectionist paradigm. Modern achievements of artificial intelligence are related to the development of “computer savvy” (rather than the development of logical programming languages) within the framework of machine learning in general and deep learning in particular.