Ron Cottam

Autocreative hierarchy II: dynamics self-organization, emergence and level-changing

Natural systems are characterized more by the way they change than by their appearance at any one moment in time. There is, however, no self-consistent theory capable of ascribing the development of living hierarchical organisms to conventional scientific rationality. We have derived a generic model for the dynamics and evolution of natural hierarchical systems. We present the resultant birational dynamics which may be attributed to a real hierarchy. We describe the nature of self-organization and of emergence in hierarchies, and the rationality which may be employed to move between scalar levels. We propose the use of diffusely-rational recursive Dempster-Shafer-probability to model inter-hierarchical-level complex regions, and consider its implications. The evolution of living from nonliving systems is attributed to a change in the style of emergence which characterizes the appearance of new scalar levels.

Towards Distributed Statistical Processing — Aquarium: A Query and Reflection Interaction Using Magic: Mathematical Algorithms Generating Interdependent Confidences

The examination of inherent defects in classical computing structures leads to the proposition of an intuitive computational machine based on distributed statistical processing. The implications of distributed processing and inter-model statistics are considered, and the usual fundamental requirement for computational inversion is discounted. A possible form of primary relational database is proposed, and the possibilities of differential model-fit mapping and the auto-generation of model rules are suggested. The desirable decomposition of computation into interrelational and decision-making processes presupposes an intermediate structure capable of linking the two in a bi-directional communicative manner. We propose a query-reflection architecture to achieve this and describe its required characteristics. The pseudo-implementation of such a structure demands a statistical treatment of the combination of counter-propagating data and knowledge, which suggests a new approach to the design of fast optical computers.

A Framework for Computing Like Nature

We address the context within which ‘Natural’ computation can be carried out, and conclude that a birational ecosystemic hierarchical framework would provide for computation which is closer to Nature. This presages a major philosophical change in the way Science can be carried out. A consequence is that all system properties appear as intermediates between unattainable dimensional extremes; even existence itself. We note that Classical and Quantum mechanical paradigms make up a complementary pair. What we wish to do is to bring all of Science under a generalized umbrella of entity and its ecosystem, and then characterize different types of entity by their relationships with their relevant ecosystems. The most general way to do this is to move the ecosystemic paradigm up to the level of its encompassing logic, creating a complementary pair of conceivably different logics – one for the entity we are focusing on; one for the ecosystem within which it exists – and providing for their quasi-autonomous birational interaction.

Localisation and nonlocality in computation

It is becoming necessary to carefully re-evaluate the meaning we attach to the term computation. This paper considers the conditions prerequisite to the implementation of a general description of computation which is formulated in terms of reactions to environmental stimuli, and which can be used to model natural processes and consequently information processing in cells and tissues.

Hierarchy and the Nature of Information

We address the nature of information from a systemic structural point of view. Starting from the Natural hierarchy of living systems, we elucidate its decomposition into two partial hierarchies associated with its extant levels and inter-level regions, respectively. External observation of a hierarchical system involves the generation of approximate hyperscalar representations of these two partials, which then reintegrate to give a singular metascalar result. We relate Havel’s categories of reality and Peirce’s categories of experience to this result, and indicate that the ultimate result of the reintegration of hyperscalar data and context is a sign which is information.

Back to the Future: Anatomy of a System

System design and implementation targets operation in the future. Success depends on anticipation and timely response. Artificial systems are designed to emulate living organisms, but do they really do that? Does our existing image of a system reflect life? We have dissected widely held organizational concepts and misconceptions to try and establish the essential “anatomy” of a system. This paper reports our conclusions. “A system” implies unity: quantum‐mechanical “systems” are unified by entanglement; Newtonian ones are inescapably fragmented. A Newtonian system is not directly unified: we are inevitably a part of the system: the necessary entanglement is provided by our brains! We conclude that system unification is always through quantum‐mechanical entanglement. Artificial systems can never be both Newtonian and autonomous. Anticipation of future events requires multiply‐scaled models of the environment, created in the past for use in the future. These, must be united through entanglement into a syste...

Bi-Sapient Structures for Intelligent Control

The control of autonomous systems requires provision of at least a synthetic form of intelligence or sapience. While descriptions of these are common, there is no current model which relates their definitions to the physical structure of an information-processing system. Sapience is a direct result of hierarchical structure. In this chapter we describe the self-consistent general model of a birational hierarchy, and associate data, information, understanding, sapience and wisdom with aspects of its constitution. In a birational hierarchy there are two sapiences, one associated with each hyperscalar correlation, and their interactions support the most general information-processing relationship – wisdom. One and the same general model applies both to material structure and information-processing structure: the brain is the unique example of material-structural and information-processingstructural correspondence.We attribute the stabilization of dynamic self-observation to anticipative stasis neglect, and propose that neuron mirroring provides a useful metaphor for all of the brain’s information-processing, including the bi-sapient interactionswhich generate auto-empathy.We conclude that hyperscalar bi-sapience is responsible for Metzinger’s ‘illusory self’, for Theory of Self, presence transfer, and Theory of Mind, and indicate how multiscalar access from within hyperscale provides a massive advantage in promoting survival.

Bridging the Gap between Life and Physics

ion The foundation of all Natural computation is abstraction. Merriam-Webster defines abstraction as the act of obtaining or removing something from a source: the act of abstracting something. More precisely, in our context, abstraction refers to the generation of a reduced model of a situation or a systemic description. As such it supports the temporal computation of consequences by reducing computational quantity and/or complexity. There can be many levels of degree of abstraction of a system—these may constitute the hierarchy of levels we have already seen in previous chapters. The ultimate abstraction of a system is its identity as existent or not, and lesser levels of abstraction constitute the systemic models which are the target of our considerations. But first, before considering abstraction itself, we must be clear what kind of logic we are applying to the situation or system. Nature uses natural logic, which has unavoidable consequences. Science and thought in general use abstract logic, which is inconsequential. Digital technology uses real implementations of abstract logic which exhibit limited consequences, local to a computer’s organization. In constructing abstractions of a situation or system we must take account of the purpose of the abstraction, and the scheme into which they will be embedded. Our central concern here is for abstraction in one of two scenarios. The first is the abstraction of constructional aspects of an organism. Here we must have recourse to natural logic and its rational consequences. The second is the abstraction of the content or processes of neural activity. Here the situation is much more complex, as the consequences may be either absent—related to abstract logic, as in the case of pure imagination—or fundamental—related to natural logic, as in the case of neural motor activation. Multiple abstractions of neural activity, particularly in the latter case, can lead to a progressive hierarchical change from parallel processing to the serial mode required to stimulate possibly multiple specific muscular stimulations. This adds further complexity to the processes of abstraction. 198 10 Under the Hood

And Yet It Moves

While nominally addressing here models of living systems, we consider that in doing so we automatically address models of Nature itself, in both its organic and inorganic appearances. We distinguish between the conceptual existence of life and its current instantiation, and address static and dynamic aspects of life. The limited number of previous models of life here considered are Robert Rosen’s (M,R) Systems (this extensively), Maturana and Varelas’ Autopoietic Systems, James Grier Miller’s book Living Systems, Gerard Jagers op Akkerhuis’s Operator Hierarchy, Ehresmann & Vanbremeerschs’ Memory Evolutive Neural Systems, Thomas Sebeok and Thure von Uexkulls’ Approach to Biosemiotics, and Chris Langton’s Life at the Edge of Chaos. We conclude with a comment on the inappropriate nature of restricting modelling mappings to one-to-one and many-to-one, thus excluding one-to-many, which may itself provide the basis for system evolution.

Seeing the Wood

We introduce perceptional scale in the context of a multi-scaled system. This is followed by a description of Ivan Havel’s concept of scale, and a short criticism of illustration using tree structures. Hierarchy now appears for the first time, in its social or organizational context, and this is refined in terms of Stanley Salthe’s work. However, the hierarchical structure which will concern us is that of a model hierarchy, which appears to be the logical parent of Salthe’s hierarchies. This provides a suitable medium within which a multi-scaled biological system can be represented. We consider briefly ‘upward’ emergence and ‘downward’ slaving, and describe the nature of the inter-scalar complex regions. Inter-scale transit cannot take place using the logic of the scales themselves, but appears to be possible using a generic form of quantum error correction. We conclude with general comments on the properties of a model hierarchy.