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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet Lifescape

C. The Information Computation Turn

Witzany, Gunther. Can Mathematics Explain the Evolution of Human Language? Communicative & Integrative Biology. 4/5, 2011. As another example of what our second decade of the 21st century can reveal as not before, the German geneticist, linguist, and philosopher cites three prime informational agencies – molecular nucleotide codes, literal communication, and their innate source in mathematical materialities. After years of their separate study and elucidation, a common affinity can now be affirmed between them, as if a single, manifest, ramifying program.

Investigation into the sequence structure of the genetic code by means of an informatic approach is a real success story. The features of human language are also the object of investigation within the realm of formal language theories. They focus on the common rules of a universal grammar that lies behind all languages and determine generation of syntactic structures. This universal grammar is a depiction of material reality, i.e., the hidden logical order of things and its relations determined by natural laws. Therefore mathematics is viewed not only as an appropriate tool to investigate human language and genetic code structures through computer science based formal language theory but is itself a depiction of material reality. (516)

Both Manfred Eigen and Martin Nowak assumed that the evolution of self-reproducing and self-organizing organisms represents the realisation of the universal grammar underlying the logical order of the world. This universal grammar, as a representation of mathematically expressible reality, is also the formal basis for the evolution of these organisms. (518)

Wolfram, Stephen. Is the Universe Like π or Ω? Dinneen, Michael, et al, eds. Computation, Physics, and Beyond. Berlin: Springer, 2012. In these proceedings of an International Workshop on Theoretical Computer Science held in Auckland, New Zealand, February, 2012, the wizardly founder of cellular automata, Mathematica and Wolfram Alpha suites, and author of A New Kind of Science, provides a succinct cosmic reality check. To be or not to be is posed by the Pi ratio for a comprehensible nature, versus Gregory Chaitin’s Omega number of an “uncomputable” universe. After many years now, it is said that standard particle physics is inadequate, does not contain gravity, string theories are no help. But in our computer age, if extant existence could be imagined as the result of a programmatic source, a software/hardware model, then a plausible explanation may be at hand. Indeed, in this capsule Wolfram well expresses the informational – computational turn. Moving on, could this be a bridge from the old Ptolemaic physics, through this abstract, machine doubleness, and cross over to an organic, procreative universe, to the witness and admission of an essential genotype and phenotype?

I often wonder what it will be like if we actually do find that one of these simple programs can reproduce our universe. In a sense it will be a very anti-Copernican moment. For ever since Copernicus, we have repeatedly been confronted with ways in which we are not special. Our planet is not at the center of the universe. And so on. But if our universe is a simple one in the space of all possible universes, then it would seem that in that way we are in fact special. (317-318)

To find out that there is a simple computational rule for the universe – as there is for Pi – would however be a remarkable achievement for human intellect. For it would show us that our brains can successfully capture the underlying rules for our whole universe. (319) And I myself hope very much to be able to pursue this goal, and to see whether in fact all the remarkable richness and complexity of our universe can be reduced to something as simple as Pi> (319)

Wolfram, Stephen. Talking about the Computational Future at SXSW 2013. blog.stephenwolfram.com/2013/03/talking-about-the-computational-future-at-sxsw-2013. A presentation given at the South by Southwest Conferences & Festivals, March 8-17, in Austin, Texas, available at SW’s Blog site in both video and illustrated text. For a public audience, a ten year retrospective of the polymath’s treatise A New Kind of Science, and a good introduction into these fertile imaginations of cellular automata. Stephen recounts how in the 1980s he became dissatisfied with the limitations of mechanical physics, and wondered if a realm of natural programs might actually be operating that serve to generate an evolutionary complexity and we peoples to figure it out.

After 300 years of being dominated by Newton-style equations and math, the frontiers are definitely now going to simple programs and the new kind of science. But there’s still one ultimate app out there to be done: to figure out the fundamental theory of physics — to figure out how our whole universe works. It’s kind of tantalizing. We see these very simple programs, with very complex behavior. (An image of a lacey, network spacetime is next.) A giant network of nodes, that make up space a bit like molecules make up the air in this room. Well, you can start just trying possible programs that create such things. Each one is in a sense a candidate universe.

It makes one think that maybe there’s a simple program for our whole universe. And that even though physics seems to involve more and more complicated equations, that somewhere underneath it all there might just be a tiny little program. We don’t know if things work that way. But if out there in the computational universe of possible programs, the program for our universe is just sitting there waiting to be found, it seems embarrassing not to be looking for it. Now if there is indeed a simple program for our universe, it’s sort of inevitable that it has to operate kind of underneath our standard notions like space and time and so on. Maybe it’s a little like this.

And when you do this, you can pretty quickly say most of them can’t be our universe. Time stops after an instant. There are an infinite number of dimensions. There can’t be particles or matter. But what surprised me is that you don’t have to go very far in this universe of possible universes before you start finding ones that are very plausible. And that for example seem like they’ll show the standard laws of gravity, and even some features of quantum mechanics. At some level it turns out to be irreducibly hard to work out what some of these candidate universes will do. But it’s quite possible that already caught in our net is the actual program for our universe. The whole thing. All of reality.

Yang, Xin-She. Nature-Inspired Optimization Algorithms. Amsterdam: Elsevier, 2014. In our context of a worldwide learning project, the adoption of iterative methods such as Bayesian statistics, computational evolution, Markov processes, genetic algorithms, decision or game theory, and universal Darwinism seems to imply a dynamic cosmic development as some manner of self-selective maximization. This latest volume by the Middlesex University, London computer scientist and scholar provides as an extensive review of these mathematical programs at their generative work. After describing algorithmic programs, it goes on to biomimetic firefly, cuckoo search, bat, flower pollination, ant, bee, and particle swarm versions in use today. Their procedural operations are then perceived as a natural self-organization whence many agents interact by common rules to achieve a better fitness. To reflect, might we focus our own efforts to achieve a universe to humanity optimum? And could it all be a natural genetic code that emerges with evolution and meant to pass to our reception and continuance? Might one say therefore choose Earth?

In essence, an algorithm is a step-by-step procedure of providing calculations or instructions. Many algorithms are iterative. The actual steps and procedures depend on the algorithm used and the context of interest. However, in this book, we mainly concern ourselves with the algorithms for optimization, and thus we place more emphasis on iterative procedures for constructing algorithms. (1) In essence, a genetic algorithm (GA) is a search method based on the abstraction of Darwinian evolution and natural selection of biological systems and representing them in the mathematical operators: crossover or recombination, mutation, fitness, and selection of the fittest. (17)

A General Formula for Algorithms. Whatever the perspective, the aim of such an iterative process is to let the system evolve and converge into some stable optimality. In this case, it has strong similarity to a self-organizing system. Such an iterative, self-organizing system can evolve according to a set of rules or mathematical equations. As a result, such a complex system can interact and self-organize into certain converged states, showing some emergent characteristics of self-organization. In this sense, the proper design of an efficient optimization algorithm is equivalent to finding efficient ways to mimic the evolution of a self-organizing system. (176)

Heuristic: (Greek: "Εὑρίσκω", "find" or "discover") refers to experience-based techniques for problem solving, learning, and discovery that find a solution which is not guaranteed to be optimal, but good enough for a given set of goals. In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a lower-level procedure or heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. (Wikipedia)

Zenil, Hector, ed. A Computable Universe: Understanding and Exploring Nature as Computation. Singapore: World Scientific, 2012. Due by June, a contribution to Alan Turing Centenary celebrations edited by the University of Sheffield computer scientist. The result is a compendium upon the welling integration of physics and information. A foreword by Roger Penrose is followed by four sections: Foundations, Universality & Early Models; Physics, Computation & and the Computation of Physics; Computation in Nature & the World; The Quantum & Computation. Salient chapters may be “What is Ultimately Possible in Physics” by Stephen Wolfram, “The Universe as a Quantum Computer,” Seth Lloyd, and Matthew Szudzik’s “The Computable Universe Hypothesis.” The editor's opening chapter, noted in Anthropic Principle, is available at arXiv:1206.0376. The Penrose preface is also on this site. From Zenil’s own page http://www.mathrix.org/zenil can be accessed recent papers indicative of this theory and movement.

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