Stronger Physical and Biological Measurement Strategy for Biomedical and Wellbeing Application by CICT

摘要

Traditional good data and an extensive factual knowledge base still do not guarantee a biomedical or clinical good decision; good problem understanding and problem-solving skills are equally important. Decision theory, based on a "fixed universe" or a model of possible outcomes, ignores and minimizes the effect of events that are "outside model". In fact, deep epistemic limitations reside in some parts of the areas covered in decision making. Mankind's best conceivable worldview is at most a partial picture of the real world, a picture, a representation centered on man. Clearly, the observer, having incomplete information about the real underlying generating process, and no reliable theory about what the data correspond to, will always make mistakes, but these mistakes have a certain pattern. Unfortunately, the "probabilistic veil" can be very opaque computationally, and misplaced precision leads to confusion. Paradoxically if you don't know the generating process for the folded information, you can't tell the difference between an information-rich message and a random jumble of letters. This is "the information double-bind" (IDB) problem in contemporary classic information and algorithmic theory. The first attempt to identify basic principles to get stronger physical and biological measurement and correlates from experiment has been developing at Politecnico di Milano since the end of last century. In 2013, the basic principles on computational information conservation theory (CICT), from discrete system parameter and generator, appeared in literature. An operative example is presented. Specifically, high reliability organization (HRO), mission critical project (MCP) system, very low technological risk (VLTR) and crisis management (CM) system will be highly benefited mostly by these new techniques.