Higher Principles

One of the central tasks of physics is the identification of higher organising principles in nature. These manifest as emergent laws and phenomena that have triggered major shifts in the evolution of life and the universe since its genesis almost 14 billion years ago. An overarching Universal Principle is required to integrate and unify the many disparate theories, models and laws upon which our scientific and cultural paradigms are based.

It is postulated that evolution is possibly the paramount principle as defined at a much broader and deeper level than previously realised. It provides not only a framework for the biological processes based on the Darwinian model, but a major overarching paradigm to rationalise all processes and phenomena comprising the universe at large.
The author’s hypothesis proposes that life, the universe and everything is driven at the core by a far deeper, more complex and enigmatic manifestation of the evolutionary process based on the principles that follow.

It is further proposed that these principles form a comprehensive basis for a formal proof of a complete information based unified theory of evolution. The theory defined in this book is the Decision-Network Evolutionary Theory or D-Net.

Principle 1- Evolution is an adaptive decision/information-based convergent process that occurs at all levels and for all forms of process complexity- physical, biological and social. Adaptation is a common language for both living and non-living systems.
At the core, it involves reducing the information differential between a system and its environment.

Principle 2- Life as we know it is an outcome of the evolutionary process because it continually reinforces the capacity for more complex information processing.
As information is continually generated by life and its support systems, it inevitably leads to more complex life and therefore more efficient information processing systems. Life therefore, as an outcome of this complexity gain, represents a continually evolving class of efficient information processors.

Principle 3- Evolution is an adaptive process that seeks to minimise the functional differential between a system and its environment by increasing its information processing capacity. This hypothesis is supported by the recently discovered genetic process of ‘bet hedging’ in bacteria, as well as adaptive learning processes in all living systems. In addition, the Principle of Least Action of physics as adapted by Roy Frieden to extract the laws of nature from information measurement, provides a powerful supporting mathematical framework for this theory.

Principle 4- Evolutionary based system transformations can be expressed in terms of information, network and decision theory and can be modelled by causal information/decision networks. In this model the network’s edges represent channels for information flows and network nodes the decision processes for selection of the most efficient information transformations governing system/environment interactions. Additionally, decision states or outcomes from each node may be modelled as quantum states in a Hilbert space.

Principle 5- Decision/network processes are an integral part of evolution, involving the selection of information that can manage the adaptation or information differential minimisation process. This in turn leads to the accrual of more complex decision capacity and value-based outcomes for the system. In other words, it represents a positive feedback loop that is increasingly beneficial to the system over time. It facilitates selection of the most appropriate information via decision probability outcomes. Information selection and injection using decision networks therefore reduces entropy within the system and increases complexity. In turn this increases the capacity for survival of life by eventually allowing an increasing amount of information to be processed in subjective time.

Principle 6- The decision network theory is based on a highly abstract model of quantum information processing, including the laws governing matter and forces. It therefore has much in common with the latest quantum gravity model of spacetime- Loop Quantum Gravity. As described in the previous chapter, this model maps spacetime and physical processes via the medium of networks representing quantised volumes, areas and in the latest model-qubits. Fundamental particles such as quarks and electrons are not primary in the LQG model but secondary outcomes of causal information flows generated and guided by such braided networks of quantized spacetime.

Principle 7- Evolution is therefore the basis of adaptation and increase in complexity, which is achieved through a process of directed information and energy flows managed by the system’s self-organising processes. Self-organization acts by continuously transforming the system’s structure and topology, minimising energy and resource usage to create the most efficient and effective evolutionary outcomes.
The system seeks to maximise its information processing and energy efficiency and minimise its energy costs in relation to its environment. This is represented in physical systems by the Principle of Least Action, as previously outlined. However, unlike competing theories of evolution, energy processing is secondary to the key requirement of optimal information management.

Principle 8 – Applied to living systems, evolution therefore results in an increase in organisational and computational complexity over time, which then results in an increase in the system’s environmental complexity through a process of reciprocal adaptation. Complex systems tend to be more stable as well as more adaptive than simpler ones. This provides improved selective advantage and ensures the system transforms over time to one of greater complexity.

Principle 9- the inevitable outcome of this positive reinforcement in life’s complexity and optimisation as a sophisticated knowledge processor, is the emergence of a seamlessly cooperative network or entity that eventually takes on the more abstract form of a global/universal consciousness- Omega, as described in the final chapter.


Life’s capacity to continually expand and improve its abilities as an extremely efficient processor of information, using the evolutionary process, is now provable as a scientific hypothesis. Since life emerged on this planet almost 4 billion years ago it has continued to increase its processing capability through the development of more flexible and adaptable cellular and neural structures as well as adaptive body forms. In addition, all life from multi-cellular organisms to human societies has learnt the value of cooperation. Humans, the most advanced information processors on earth, now utilise a variety of sophisticated mathematical and computational techniques including artificial intelligence, cellular automata, quantum computing, neural networks, evolutionary algorithms and biological computing, as well as vastly amplifying its problem solving capability through the internet’s massive computational intelligence.
This trajectory will continue at an exponential rate.

Systems, network, information and quantum theory therefore provides the theoretical basis for the unified evolutionary theory as postulated in this book. Some of the most critical aspects are now discussed.

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