Foreword by Lotfi Zadeh

Professor Mihai Nadin’s scholarly treatise, Anticipation—The End is Where We Start From, or Anticipation for short, addresses an issue that does not have high visibility; and yet, as Professor Nadin convincingly argues, it is an issue that is of fundamental importance.

What is anticipation? Putting aside a dictionary definition, Professor Nadin guides us, with insight and high expository skill, through a sequence of twelve nuanced definitions. The first definition reads: An anticipatory system is a system whose current state is determined by a future state. As stated, the definition raises a question in my mind. However, my question can easily be resolved by qualifying “future state” with “a perception of future state,” leading to the amended definition, “An anticipating system is a system whose current state is determined by a perception of a future state.” I will have more to say about this suggestion at a later point.

The leitmotif of Anticipation is that everything humans do involves anticipation and, more specifically, that anticipation, as a characteristic of the living, can be seen as a realization in the domain of possibilities. In developing this theme, Professor Nadin examines the concept of anticipation in twelve different contexts, starting with system theory, moving through prediction, correlation and quantum theory, and ending with possibility theory, feedback and power laws. Professor Nadin’s guided tour throws much light on the concept of anticipation and underscores its basic role in science and human cognition.

Returning to the point which I made earlier, my suggested modification of Professor Nadin’s definition of anticipation leads to the concept of what may be called perception-based anticipation. The marriage of anticipation and perception has important implications. First, it highlights that all living organisms, including humans, employ perception-based anticipative control to guide decision-making on goal-oriented stage decision processes. More specifically, if at a stage of a decision process, I have n alternatives, a, …, a, to choose from, then using a perception-based model of the underlying system, I form a perception of the next state and next output, and choose that a which brings me closer to the goal. As a simple example, this is what we do when we drive a car or balance a pile. More generally, perception-based anticipation is what makes it possible for humans to perform a wide variety of physical and mental tasks without any measurements and any computations. It is this remarkable capability that machines do not have.

In my recent writings, I mentioned a theory, referred to as the computational theory of perceptions (CTP). In this theory, perceptions are dealt with through their descriptions in a natural language, e.g., traffic is heavy, Robert is very honest, speed is high, etc. The use of CTP opens the door to adding to machines the capability to operate on perception-based information expressed in a natural language. In particular, it makes it possible to train a neural network to produce perceptions in response to measurements. Such networks may be said to be neuroperceptive. Neuroperceptive networks may find important applications in automation of processes in which the output is a human assessment of, say, food or, more generally, of sensory perceptions.

Professor Nadin’s treatise makes an important contribution to a better understanding of some of the most fundamental aspects of human cognition. He and the publisher deserve our thanks and congratulations.

Lotfi A. Zadeh
July 23, 2002