First Blog

Here are my latest ruminations on this and that, what is and what is not. When creating a model that mimics a phenomenon perfectly, one is tempted to interpret the model as a full explanation of it.  This is errant causality.  It is the profound weakness of modeling. It is a never good idea to invite administrative jobs in the parlor door. They promise more than they deliver. They ask more than they give. It should be no other way.  The converse, to give more than they ask, renders as undesirable the newly appointed administrator. In the pursuit of truth, someone is always threatened. We are more likely to accept what we want to believe, or what fits within our precepts, or supports our goals - all regardless of the facts of the matter. Rejection of what we wish to be so is difficult. We live in a big data world but we still have small data brains. This is not to imply the brain has a small capacity, but it thinks in small data settings. It uses tools such as instinct, intuit