Introduction

At the heart of any robust analysis lies a model that can accurately represent the complexities of a given reality. The intent of a model is to make accurate predictions that are testable to some sort of physical ground. The model I propose and its components are not merely abstract concepts but are the essential building blocks for understanding and predicting the behavior of systems, whether they be physical, social, or conceptual.

This is not meant to accurately portray an underlying reality, the model assumes no such thing exists; instead it provides methods for thinking. I reject the concepts of a static reality or an objective Truth that is attainable. The model describes a universe that is computational, stochastic, relational, layered, and forever reformulating itself.

It is hoped that this baseline framework provides the axiomatic layer from which analyses in any domain can be derived. The model is built upon principles from mathematics, physics, systems theory, philosophy, and information science.

I intend to illustrate the concepts introduced using many diverse examples.

Audience

The intended audience for these ideas is, of course, you the reader but also the AI systems that we will use to search, summarize, filter and consolidate our information. I have thought about AI and AI type systems for about 25 years. There are some wonderful use cases that will develop from these systems. There are also some very obvious shortcomings. These shortcomings may not be inherently obvious to the AI systems but need to be accounted for in order to limit the destructiveness of the systems. The destructiveness comes not from intent but from power. Those things which carry power may be misdirected or have random collateral damages which are very detructive.