System Dynamics 101

  Ockham and his razor Frequently, scientists cite loosely a principle known as Ockham’s razor : models with fewer assumptions are to be preferred.  This a principle some call Parsimony . Another view is that the canon of parsimony, forbids the empirical scientist from affirming what, as an empirical scientist, the scientist does not know. Here is Richard McElreath, Statistical Rethinking (2020, p. 191), to help us with our thoughts and understanding (wisdom might come later). "Mikołaj Kopernik (also known as Nicolaus Copernicus, 1473–1543): Polish astronomer ... Famous for his heliocentric model of the solar system, Kopernik argued for replacing the geocentric model, because the heliocentric model was more “harmonious.”  But Kopernik’s justification looks poor to us now ...  "There are two problems: The model was neither particularly harmonious nor more accurate than the geocentric model. The Copernican model was very complicated. In fact, it had similar epicycle clutter as

 ... That is the question. Cause and effect Is this different from antecedent and consequent? In statistics causation usually means the so-called correct prediction of the impact of some intervention. Is this reality though? We have been using System Dynamics for two weeks now to set up our cause and effect model.  Then we used the model at the end of this week to generate rather shocky data by vibrating key parameters. We only drew ONE sample, ONE possible realization of the causal model! What we generated is very typical of so-called observational models. Does every realization simply come from the cause and effect? Or is there something else at work?  Then we performed some light exploratory analysis with a 2 dimensional density contour map of a scatter plot -- now we are in the realm of cause and effect? or is this just antecedent and consequence? The question before the house: What does causation mean in business analytics? to the consumer of our analytical product?  McElreath p.

Be sure to introduce yourselves to one another by replying to this post.  Form teams of 2-3.  For this week's sharing please answer these questions: What might I expect to learn from this course? Describe your experience of downloading VensimPLE, R and R Studio and building your first model? Who are my team mate(s)?

  What are some of ethical concerns of building group participative models?  What if a “member” of the group is a generative AI?

What horizons, system boundaries, do we include to validly, robustly, examine our assumptions, and interpret our results?

Whence does  vulnerability   arise?  Find a definition of workplace or organizational vulnerability.  Using this definition describe your observations and/or experiences of vulnerability in the workplace, especially management response.