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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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III. Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet Lifescape

5. Universal Evolution: A Celestial Expanse

Kuchling, Franz, et al. Morphogenesis as Bayesian Inference: A Variational Approach to Pattern Formation and Control in Complex Biological Systems. Physics of Life Reviews. Online June, 2019. FK and Michael Levin, Tufts University, Georgi Georgiev, Assumption College, MA and Karl Friston, Wellcome Trust Centre, UK continue their collegial efforts to envision and articulate a self-organizing, composing, vivifying, learning, evolution of life’s phenomenal complexity and consciousness. A common iterative, computational process thus seems in procreative effect as a natural selective optimization from somatic creatures and to sensory cerebral form and dynamics.

Recent advances in molecular biology such as gene editing, bioelectric recording and live cell microscopy can now measure molecular signaling pathways with spatiotemporal precision. However, an overarching concept that can predict the emergent form of complex anatomy is largely missing. In this (neurobiology) setting, a variational free energy principle has emerged based upon self-organization via active Bayesian inference. For biological processes such as development or regeneration, this model treats cells as information processing agents. The free energy principle applied to pattern formation promises a quantitative formalism for cellular decision-making in the context of embryogenesis, regeneration, and cancer suppression. We derive the mathematics behind Bayesian inference and use simulations to show how it can better explain complex morphogenesis. (Abstract excerpt)

Mayfield, John. The Engine of Complexity: Evolution as Computation. New York: Columbia University Press, 2013. Since computers are the icon of our present cyberage, the physical cosmos and life’s emergence are methodically being reinterpreted in kind. The Iowa State University emeritus biologist achieves a conceptual survey and articulation in this regard. With Seth Lloyd (search) and others, a digital universe springs from and develops by mathematical laws as they may run and iterate. A software-like information and processing then becomes a primary source and agency. A companion approach is the theory of evolutionary or genetic algorithms, from Richard Dawkins and Daniel Dennett, along with John Holland’s complex adaptive systems. By this theme, fitter organisms with apparent purposes are the result of their relatively successful instructions.

To consider, while winnowing selection goes on, the model does enters a deeper, prior program in operation that impels life's procession. To illustrate, Mayfield cites Iowa State colleague Dan Ashlock’s work from his Evolutionary Computation for Modeling and Optimization, to distill this five step sequence: Generate a population of informational structures, Calculate relative quality, Select best structures to copy, Replace the worst with them, Generate variations, and Output the best result. And just now we optimum (or good enough) peoples pop out as the universe’s way of consciously learning this procedure and, as alluded in closing, so that might we continue such encoding and begin a new creation.

There are at least three things that make the subject of information interesting to me, a biologist, who happens also to be fascinated by larger issues. First, it is obvious to any modern biologist that a proper understanding of life is not possible without a detailed understanding of how the information stored in DNA is utilized to make new living organisms. Second, the process of evolution is very easily understood and illustrated when presented in computational terms. In this mode of thinking, evolution occurs by following a particular strategy for information manipulation and accumulation. In this book I call that strategy “the engine of complexity.” Third, complexity of any significant kind, living or not, is only possible to achieve through processes that can be broadly described as computing. (3)

Mirkin, Nicolas and Diego Wisniacki. Many-Body Localization and the Emergence of Quantum Darwinism. Entropy. 23/11, 2021. A paper by University of Buenos Aires physicists for a Quantum Darwinism and Friends, edited by Sebastian Deffner, et al, for the 70th birthday of its theoretical founder, Wojciech Zurek (LANL) contributes a further explanatory credence.

Quantum Darwinism shows how the perception of objective classical reality arises via selective amplification and the spreading of information in our fundamentally quantum universe. Quantum Darwinism goes beyond decoherence, as it recognizes that the many copies of the system’s pointer states are imprinted on the environment: agents acquire data indirectly, by intercepting environment fragments (rather than directly measuring systems of interest). The data disseminated through the environment provide us with shared information about stable, effectively classical pointer states. Humans rely primarily on the photon environment, eavesdropping on “objects of interest” by intercepting tiny fractions of photons that contributed to decoherence. (Editors)

Newman, Stuart. Universal EvoDevo? Biological Theory. 13/67, 2018. The biologist editor has been a leading proponent (search) for this view of intrinsic rational forms which serve to guide life’s evolutionary developmental gestation. It is thus a view more akin to D’ Arcy Thompson than the modern synthesis. The note opens a special Astrobiology issue whose papers broach how such natural lineaments might be in similar effect for exoplanet entities. Some examples are Astrobiology as a Hybrid Science by Linnda Caporael, From Earth to the Universe: Life, Intelligence, and Evolution by Linda Billings, and Why We Should Care about Universal Biology by Carlos Mariscal and Leonore Fleming.

Okasha, Samir and Ken Binmore, eds. Evolution and Rationality: Decisions, Cooperation and Strategic Behavior. Cambridge: Cambridge University Press, 2012. A Bristol University philosopher and a University College London economist edit select papers from workshops held at Bristol from 2008 to 2011 on this novel confluence. Life’s developmental course via iterations of populate and select is seen akin to decision making processes, Bayesian inference, economic game theory, and rational choice methods. Each involves a series of refinements to reach a good enough fitness. Some chapters are Towards a Darwinian Theory of Decision Making: Games and the Biological Roots of Behavior by Peter Hammerstein, and An Evolutionary Perspective on the Unification of the Behavioral Sciences by Herbert Gintis.

This volume explores from multiple perspectives the subtle and interesting relationship between the theory of rational choice and Darwinian evolution. In rational choice theory, agents are assumed to make choices that maximize their utility; in evolution, natural selection 'chooses' between phenotypes according to the criterion of fitness maximization. So there is a parallel between utility in rational choice theory and fitness in Darwinian theory. This conceptual link between fitness and utility is mirrored by the interesting parallels between formal models of evolution and rational choice. The essays in this volume, by leading philosophers, economists, biologists and psychologists, explore the connection between evolution and rational choice in a number of different contexts, including choice under uncertainty, strategic decision making and pro-social behaviour. (Publisher)

There exist deep and interesting connections, both thematic and formal, between evolutionary theory and the theory of rational choice, despite their apparently different subject matters. These connections arise because a notion of optimization or maximization is central to both areas. In rational choice theory, agents are assumed to make choices that maximize their utility, while in evolutionary theory, natural selection ‘chooses’ between alternative phenotypes, or genes, according to the criterion of fitness maximization. (1)

Panov, Alexander. Prebiological Panspermia and the Hypothesis of the Self-Consistent Galaxy Origin of Life. Grinin, Leonid and Andrey Krotayev, eds. Evolution: From Big Bang to Nanobots. Volgograd: Uchitel Publishing, 2015. Within the “megaparadigm” (Grinin) of this mainly Russian endeavor which advances an innately animate, fertile, universe to human evolutionary cosmos, a Moscow State University physicist contends that a natural prebiological chemistry detected in fertile galaxies could be the likely source of Earthly life. See also Post-Singular Evolution and Post-Singular Civilizations by Panov in Green Galaxy.

We argue that panspermia can mean not only the other place of the origin of life but also another mechanism of the origin of life that increases the probability of the origin of life to many orders of magnitude compared to a single-planet prebiological evolution. The prebiological evolution can be an all-Galaxy coherent process due to the fact that prebiological panspermia and the origin of life are similar to Galaxy-scale second-order phase transition. This mechanism predicts life to have the same chemical base and the same chirality everywhere in the Galaxy. (Abstract)

Saladino, Raffaele, et al. Chemomimesis and Molecular Darwinism in Action: From Abiotic Generation of Nucleobases to Nucleosides and RNA. Life. 8/2, 2018. University of Tuscia, CNR. Rome,and Czech Academy of Sciences, Brno biologists including Jiri Sponer experimentally produce and quantify early abiotic conditions akin to a warm pond wherein better adaptive candidates are selected from many precursor biochemical variations.

Molecular Darwinian evolution is an intrinsic property of reacting pools of molecules resulting in the adaptation of the system to changing conditions. It has no a priori aim. From the point of view of the origin of life, Darwinian selection behavior, when spontaneously emerging in the ensembles of molecules composing prebiotic pools, initiates subsequent evolution of increasingly complex and innovative chemical information. On the conservation side, it is a posteriori observed that numerous biological processes are based on prebiotically promptly made compounds, as proposed by the concept of Chemomimesis. Molecular Darwinian evolution and Chemomimesis are principles acting in balanced cooperation in the frame of Systems Chemistry. (Abstract)

Siddique, Nazmul and Hojjat Adeli. Physics-Based Search and Optimization: Inspirations from Nature. Expert Systems. 33/6, 2016. In this Wiley journal, Ulster University, Londonderry and Ohio State University computer scientists gather for the first time a broad survey of dynamic iterative phenomena which occur even across cosmic and material realms, akin to many biological and behavioral examples cited herein (Yang). The novel insight is that nature’s universal (Darwinian) evolutionary process of myriad candidates from which a relative optimum result or condition is winnowed and selected can also be seen in universal effect. See, for example, Black Hole: A New Heuristic Optimization for Data Clustering by Abdolreza Hatamlou in Information Sciences (222/175, 2013).

This paper presents a review of recently developed physics‐based search and optimization algorithms that have been inspired by natural phenomena. They include Big Bang–Big Crunch, black hole search, galaxy‐based search, artificial physics optimization, electromagnetism optimization, charged system search, colliding bodies optimization, and particle collision algorithm. (Abstract)

Szilagyi, Andras, et al. Breeding Novel Solutions in the Brain: A Model of Darwinian Neurodynamics. F1000Research. September, 2016. A posting on this referred site for frontier studies by Eotvos University, Budapest theorists AS, Istvan Zachar, Anna Fedor, Harold de Vladar, and Eors Szathmary. We enter here because it situates itself as a 2010s continuation of the Neural Darwinism theme of Gerald Edelman, Jean Paul Changeux, Antoine Danchin and others from 1980s and 1990s, as its 76 references cite. Search these names, also Intelligent Evolution, for more. Some manner of candidate and optimization process, a winnowing of probabilities, seems to be going on as an essential evolution and trajectory for both universe and human. Well now, whatever does this mean, what grand salutary insight might we glean and avail to save us in time?

Background: The fact that surplus connections and neurons are pruned during development is well established. We complement this selectionist picture by a proof-of-principle model of evolutionary search in the brain, that accounts for new variations in theory space. We present a model for Darwinian evolutionary search for candidate solutions in the brain. Conclusions: Attractor dynamics of recurrent neural networks can be used to model Darwinian search. The proposed architecture can be used for fast search among stored solutions (by selection) and for evolutionary search when novel candidate solutions are generated in successive iterations. Since all the suggested components are present in advanced nervous systems, we hypothesize that the brain could implement a truly evolutionary combinatorial search system, capable of generating novel variants.

This is a thought provoking simulation study of Darwinian neurodynamics. It uses populations of attractor networks to illustrate the distinction between purely selectionist and evolutionary optimisation. This demonstration rests upon the dynamical instability of the neuronal networks considered – and the explicit introduction of variation or mutations in graduating from a selectionist to an evolutionary scheme. The paper is rather dense and I do not pretend to follow all the subtleties and nuances; however, the basic ideas are compelling and are described with sufficient clarity and detail for the interested reader to understand. (Karl Friston comment)

Touil, Akram, et al. Branching States as the Emergent Structure of a Quantum Universe. arXiv:2208.05497. AT, Sebastian Deffner, University of Maryland, Fabio Anza, University of Washington and James Crutchfield, UC Davis seek to build on and advance the Wojciech Zurek’s collegial Darwinian version as it gains a depth, breadth and credence since the 1980s.. See a concurrent posting by Zurek as Quantum Theory of the Classical: Einselection, Envariance, Quantum Darwinism (2208.09019) where the LANL physicist continues to perceive, articulate, define, clarify and express a physical environ with its own broadly selective essence.

Quantum Darwinism builds on decoherence effects to explain the emergence of classical behavior within a quantum universe. Here we describe how the differential geometric underpinnings of quantum mechanics provide a unique informative window into the correlations which can validate these theories. We show that the branching structure of the joint system state is compatible with zero discord, find that the global structure of the pure state branches as expected. (Flavor excerpt)

Geometric Quantum Mechanics is a differential-geometric approach to quantum mechanics by way adding new tools and concepts. In this work, we exploited its ability to describe open quantum systems via classical probability measures. Part of the strength of using GQM is its ability to visualize the structure of the globally pure wave-function. We leveraged this to investigate the structures of quantum states compatible with the emergence of a classical phenomenology, as prescribed by Quantum Darwinism. By way of both geometric quantum mechanics and quantum information theory we established that classical phenomenology can emerge if the global wave-function is close to a branching form. The emergence of classicality is therefore understood as a self-organizing process occurring in the space of quantum states. (6)

Valiant, Leslie. Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World. New York: Basic Books, 2013. A Harvard University mathematician broaches an innovative view of life’s advance as due to interactions between creatures and their environment. This novel evolution is a learning process that proceeds by way of algorithmic computations, which in this sense are dubbed “ecorithms” as they try to reach a sufficient optimum. In so doing, Valiant joins current perceptions from Bayesian, rationality (Okasha), Markov, genetic, approaches that view not only Darwinian evolution as a computational learning mechanism, but seem to imply the entire universe is some optimization or maximization process.

Other LV works are a presentation in the Computation, Computational Efficiency, and Cognitive Science unit at the 2013 AAAS meeting entitled “Biological Evolution as a Form of Learning.” (http://aaas.confex.com/aaas/2013/webprogram/Paper9186.html), and an earlier paper “Evolvability” in the Journal of the Association for Computing Machinery (56/1, 2009). Both the talk and paper Abstracts are below. And we might add that the author notes the modern word algorithm is attributed to the Persian mathematician and scholar Al-Khwarizmi (780-850, see Wikipedia) who worked at the House of Wisdom in Baghdad as a founder of algebraic equations. Once again here is the true heart of Islam that must be remembered and recovered anew today.

Algorithms are the step-by-step instructions used in computing for achieving desired results, much like recipes in cooking. In both cases the recipe designer has a certain controlled environment in mind for realizing the recipe, and foresees how the desired outcome will be achieved. The algorithms I discuss in this book are special. Unlike most algorithms, they can be run in environments unknown to the designer, and they learn by interacting with the environment how to act effectively in it. After sufficient interaction they will have expertise not provided by the designer but extracted from the environment. I call these algorithms ecorithms. The model of learning they follow, known as the probably approximately correct model, provides a quantitative framework in which designers can evaluate the expertise achieved and the cost of achieving it. These ecorithms are not merely a feature of computers. I argue in this book that such learning mechanisms impose and determine the character of life on Earth. The course of evolution is shaped entirely by organisms interacting with and adapting to their environments. This biological inheritance, as well as further learning from the environment after conception and birth, have a determining influence on the course of an individual’s life. The focus here will be the unified study of the mechanisms of evolution, learning, and intelligence using the methods of computer science. (Book Summary)

Living organisms function as protein circuits. We suggest that computational learning theory offers the framework for investigating the question of how such circuits can come into being adaptively from experience without a designer. We formulate Darwinian evolution as a form of learning from examples. The targets of the learning process are the functions of highest fitness. The examples are the experiences. The learning process is constrained so that the feedback from the experiences is Darwinian. We formulate a notion of evolvability that distinguishes function classes that are evolvable with polynomially bounded resources from those that are not. We suggest that the close technical connection this establishes between, on the one hand, learning by individuals, and on the other, biological evolution, has important ramifications for the fundamental nature of cognition. (Talk Abstract)

Living organisms function according to complex mechanisms that operate in different ways depending on conditions. Evolutionary theory suggests that such mechanisms sevolved through random variation guided by selection. However, there has existed no theory that would explain quantitatively which mechanisms can so evolve in realistic population sizes within realistic time periods, and which are too complex. In this paper we suggest such a theory. Evolution is treated as a form of computational learning from examples in which the course of learning is influenced only by the fitness of the hypotheses on the examples, and not otherwise by the specific examples. We formulate a notion of evolvability that quantifies the evolvability of different classes of functions. It is shown that in any one phase of evolution where selection is for one beneficial behavior, monotone Boolean conjunctions and disjunctions are demonstrably evolvable over the uniform distribution, while Boolean parity functions are demonstrably not. The framework also allows a wider range of issues in evolution to be quantified. We suggest that the overall mechanism that underlies biological evolution is evolvable target pursuit, which consists of a series of evolutionary stages, each one pursuing an evolvable target in our technical sense, each target being rendered evolvable by the serendipitous combination of the environment and the outcome of previous evolutionary stages. (Evolvability Abstract)

Vukotic, Branislav and Milan Cirkovic. Astrobiological Complexity with Probabilistic Cellular Automata.. Origins of Life and Evolution of Biospheres. Online July, 2012. In an extensive paper, also arXiv:1206.3467, Astronomical Observatory Belgrade astrophysicists consider how a nonlinear “digital perspective” analysis might help quantify the appearance and proliferation of life, intelligence and civilizations across the Milky Way. See also their 2010 “Cellular Automation of Galactic Habitable Zone” at arXiv:1001.4624, and from Russia, “Where is Everybody: New Approach to the Fermi Paradox” arXiv:1007.2774 by I. V. Bezsudnov and A. A. Snarskii. And noted above, Cambridge University Press has published a major 2012 work by Cirkovic The Astrobiological Landscape.

In an extensive paper, also arXiv:1206.3467, Astronomical Observatory Belgrade astrophysicists consider how a nonlinear “digital perspective” analysis might help quantify the appearance and proliferation of life, intelligence and civilizations across the Milky Way. See also their 2010 “Cellular Automation of Galactic Habitable Zone” at arXiv:1001.4624, and from Russia, “Where is Everybody: New Approach to the Fermi Paradox” arXiv:1007.2774 by I. V. Bezsudnov and A. A. Snarskii. And noted above, Cambridge University Press has published a major 2012 work by Cirkovic The Astrobiological Landscape.

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