First Blog

Ah, yes.   Causality.   We love it and hate it.   We seek it for resolution, but sometimes we don’t want to find it. ·         Advertising causes sales. ·         Fear causes flight or fight. ·         She dumped me because I flirt. ·         Vaccinations prevent disease. To my understanding, causality is fundamentally difficult or impossible to prove. It is a truth. Causality seems to be a consensus of experts, claimed by countless experiments and observations - sometimes by an authority. In centuries past, causality was the domain of religion, philosophy, and God. Permanent. Yet, today’s cause may become tomorrow’s fantasy. Now, causality is mostly an aspect of science. Rushing to causality is a modern consequence of ubiquitous models, each establishing, in part, a correlation or correspondence. Personal causality is always a risk, always subject to emotion. Think of causality as a working solution to a problem, a pathway to finding a cure, or leading to deeper unde

Predictions of elections reside in a world of experts, the poll dancers.   Let’s check ‘em out.   Most predicted a Clinton win by about 3% of the vote.   Nate Silver, the guru of statistics and outcome predictions, predicted chances for Clinton win at 70%. Other outlets predicted a Clinton win at 85%.   Even the odds-makers (bookies) gave her at least 2-1 odds for a win*. Some more so. Indeed, so confident was her team that Hillary Clinton left early from home for her hotel room near their NYC HQ anticipating the prospect of making an early winning announcement.    All were wrong.   Only a very few predicted a Trump win, and they were all discounted.   OK. Analysts will be analyzing away for years, among them the so very many who were wrong. They will conjecture, debate, write articles, write papers, write books, and appear on television, ad nauseam . The point here is not to give our own description of why.   Our point is about the “who.”    The “who” were acknowled

Models .   The deep skinny on model building based on the application of analysis is this:   Make it as simple as possible, but make it as complex as necessary .     Tall order and a laudable goal, though achieving both simplicity and complexity is a challenge.   We have a min-max problem, difficult to solve.   Nonetheless, it is attempted by all. Each suffers at the expense of the other.   The question I pose here is whether this is possible?   Both goals are vague and meeting either is virtually impossible to measure.     So, depending on the interpreter, the model will be seriously affected.   This is the case even with the most scientific of problems.   When the solution goal is unclear, the situation vague, the problem is wicked, or the course of action is fuzzy, there arise conflicts on what model to build.   Deformed models are produced.     Once a model is in place, it has a systemic existence.   It has invested adherents.   It doesn’t die easily no matter how wrong it