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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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III. An Organic, Conducive, Habitable MultiUniVerse

3. Universal Evolutionism: An Astrobiological Milieu

This is a 2014 section to gather viewpoints which make note of a Darwinian modus operandi of many candidate entities within a vicarious environment from which an optimum or good enough result can be selected. As some entries herein (John Mayfield, Leslie Valiant, etc) infer this operation can appear as a computational method. A statistical physics earlier version would be many local bodies which yet give rise to a global predictability. Here we wish to report expansive applications of this evolutionary process both across a temporal cosmic development and to its physical composition. The celestial domain has been dubbed a Universal Darwinism, see John O. Campbell, Lee Smolin, Milan Cirkovic and other sources.

And in its deepest materiality, Wojciech Zurek has advanced a Quantum Darwinism which grows in veracity and usage. This Western view is lately complemented by a Russian school via publications in a substantial Evolution almanac from Volgograd, see Leonid Grinin, et al. But this alternative Eastern vista does not hold to a Darwinian contingency. Rather, an innately organic cosmos is seen to develop as a teleological “Universal Evolutionism.” Life’s emergent advance is due to and springs from an independent, informative self-organization in prior effect before winnowing selection. This is a distinction and difference which quite aligns with our Natural Genesis persuasion. Could it be, as Earthropic Principle alludes, that it even applies to a quintillion bioplanets?

Baladron, Carlos and Andrei Khrennikov. Outline of a Unified Darwinian Evolutionary Theory for Physical and Biological Systems. Progress in Biophysics and Molecular Biology. Online May, 2017. Universidad de Valladolid, Spain, and Linnaeus University, Sweden researchers continue to conceive ways that life and mind can be theoretically joined with the resident, favorable cosmos from which they naturally arise. A prime, unifying feature ought to be an informational quality, as it traces a continuum from deep quantum domains to our retrospective witness, akin to J. A. Wheeler’s “it form bit” version. Three main principles of an optimizing complex structure, dynamic information flow, and system interactions are seen to distinguish.

The scheme of a unified Darwinian evolutionary theory for physical and biological systems is described. Every physical system is methodologically endowed with a classical information processor what turns every system into an agent being also susceptible to evolution. Biological systems retain this structure as natural extensions of physical systems from which they are built up. Optimization of information flows turns out to be the key element to study the possible emergence of quantum behavior and the unified Darwinian description of physical and biological systems. The Darwinian natural selection scheme is completed by the Lamarckian component in the form of the anticipation of states of surrounding bio-physical systems. (Abstract)

Bromham, Lindell. Curiously the Same: Swapping Tools Between Linguistics and Evolutionary Biology. Biology & Philosophy. Online September, 2017. We cite here because an Australian National University, Canberra, ecological philosopher can trace to an ever-expanding synthesis of such Darwinian dynamics across every area from cosmos to life’s creaturely development onto cultural literacies.

Brown, Joel. Why Darwin Would Have Loved Evolutionary Game Theory. Proceedings of the Royal Society B. Vol. 283/Iss. 1838, 2016. I use evolutionary game theory to define, understand and model therapeutic strategies within the framework of viewing tumors as ecosystems with ecologically and evolutionary dynamic populations of cancer cells. is how the University of Illinois at Chicago biologist describes his research project. As entries in this section try to express, it is vital to grasp and assimilate nature’s actual procreative way of profligate candidates and selective optimization. Although one may question this method, such an evolutionary process is in effect from a multiversal cosmos to metastasizing cells. We then ought to ask, what is being preferred, emerging, chosen – is collaborative medical knowledge a form of the natural genetic code reaching conscious perception, by which it can be fed back to palliate, cure, and prevent future disease?

Humans have marvelled at the fit of form and function, the way organisms' traits seem remarkably suited to their lifestyles and ecologies. While natural selection provides the scientific basis for the fit of form and function, Darwin found certain adaptations vexing or particularly intriguing: sex ratios, sexual selection and altruism. The logic behind these adaptations resides in frequency-dependent selection where the value of a given heritable phenotype (i.e. strategy) to an individual depends upon the strategies of others. Game theory is a branch of mathematics that is uniquely suited to solving such puzzles. Game theory not only applies to matrix games and social games, it also applies to speciation, macroevolution and perhaps even to cancer. I assert that life and natural selection are a game, and that game theory is the appropriate logic for framing and understanding adaptations. Its scope can include behaviours within species, state-dependent strategies (such as male, female and so much more), speciation and coevolution, and expands beyond microevolution to macroevolution. Game theory clarifies aspects of ecological and evolutionary stability in ways useful to understanding eco-evolutionary dynamics, niche construction and ecosystem engineering. (Abstract)

Cabessa, Jeremie and Hava Siegelmann. The Computation Power of Interactive Recurrent Neural Networks. Network: Computation in Neural Systems. 24/4, 2012. University of Massachusetts, Amherst, computational neuroscientists take these cerebral complexities to exemplify how nature evolves, develops and learns. We are then invited to realize that the same dynamical trial and error, feedback to move forward, iterative process is in effect everywhere. See also Turing on Super-Turing and Adaptivity by Hava Siegelmann in Progress in Biophysics and Molecular Biology (113/117, 2013), and search Richard Watson 2014 herein.

In classical computation, rational- and real-weighted recurrent neural networks were shown to be respectively equivalent to and strictly more powerful than the standard Turing machine model. Here, we study the computational power of recurrent neural networks in a more biologically oriented computational framework, capturing the aspects of sequential interactivity and persistence of memory. In this context, we prove that so-called interactive rational- and real-weighted neural networks show the same computational powers as interactive Turing machines and interactive Turing machines with advice, respectively. A mathematical characterization of each of these computational powers is also provided. It follows from these results that interactive real-weighted neural networks can perform uncountably many more translations of information than interactive Turing machines, making them capable of super-Turing capabilities. (Abstract)

This analog information processing model turns out to be capable of capturing the nonlinear dynamical properties that are most relevant to brain dynamics. (997) Indeed, in the brain (or in organic life in general), information is processed in an interactive way, where previous experience must affect the perception of future inputs and older memories themselves may change with response to new inputs. Hence, neural networks should be conceived as performing sequential interactions or communications with their environments and be provided with memory that remains active throughout the whole computational process. Accordingly, we propose to study the computational power of recurrent neural networks from the rising perspective of interactive computation. (997)

Campbell, John O. Bayesian Methods and Universal Darwinism. AIP Conference Proceedings. 1193/40, 2009. At a biannual Bayesian Inference and Maximum Entropy Methods workshop, the University of Canterbury, New Zealand, physicist draws on these latest theories to better understand how a dynamic natural evolution might actually work. As others sense (Watson, Knuth, Mayfield), some generic process involving “populations of probabilities” from which may be chosen better candidates for subsequent iterations and sufficient optimization, seems to be going on. In a conducive spacescape, the same “Darwinian” evolutionary selections can equally apply to the whole cosmos. A 2012 posting by JOC on arXiv (search) expands the case, and the Wikipedia site for Universal Darwinism is a good entry and reference list.

Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a ‘copy with selective retention’ algorithm abstracted from Darwin’s theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes. (Abstract excerpts)

Campbell, John O. Universal Darwinism as a Process of Bayesian Inference. Frontiers in Systems Neuroscience. June, 2016. The Victoria, BC researcher continues his project to express how Darwinian variation and selection may be appreciated as a general feature of not only Earth life, but across a natural evolutionary cosmos.

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. (Abstract excerpts)

Carneiro, Robert. Stellar Evolution and Social Evolution: A Study in Parallel Processes. Grinin, Leonid, et al, eds. Evolution: Cosmic, Biological, and Social. Volgograd: Uchitel Publishing, 2011. The University of Wisconsin anthropologist has been since 1969 the Curator of the American Museum of Natural History. It is then significant that a premier Western scholar recognizes and contributes to this Russian/Eastern genesis vision, which allows him to perceive across a widest stretch that stars and villages seem to form and evolve in parallel ways. He goes on to propose, as a flurry of recent papers (Khaluf 2017) attest to, that a relational repetition seems to occur throughout animal and human realms, indeed a “multilinear” life history from stars to societies.

So there we have it. There are indeed a number of parallels between stellar evolution as astronomers and astrophysicists have revealed it and social evolution as anthropologists have reconstructed it. Both sets of scientists make effective use of the comparative method. Both find in their phenomena distinct sequences and stages of development. Some of these sequences can be termed unilinear, while others are multilinear. Both sets of scientists attempt to lay bare the driving forces underlying the sequences they observe. Both find in the entities they study differential rates of evolution which are closely related to their size. And finally, both astronomy and astrophysics, on the one hand, and anthropology, on the other, see in the evolution of their phenomena a progression from simple, diffuse, and inchoate beginnings to a level of development in which complexity is a common and prominent feature. (81-82)

This article may permit them (anthropologists) to see more clearly that what culture has done is to take up the torch of a universal process which began eons ago with the Big Bang, and which continues, at an accelerated pace, throughout the Universe. This process has seen stars evolve to the point where, in at least one tiny corner of a particular galaxy, conditions developed which allowed a presumptuous primate to arise. (82)

Christian, David. Universal Darwinism and Human History. Grinin, Leonid and Andrey Korotayev, eds.. Evolution: From Big Bang to Nanobots. Volgograd: Uchitel Publishing, 2015. (One does worry over the technical subtitle which is at odds with the overall theme.) The Macquarie University, Sydney historian is the main founder of the popular expansive project to join cosmic and human durations under a common rubric of Big History, so to express their dynamic trajectory and unity. This chapter in the Russian almanac of “universal evolutionism” is an essay to integrate these vistas. But it also contains some persistent contradictions that daunt the effort. While the entire cosmic scenario within which Earth and its human phase are rooted and spring needs to be fully admitted, since only men are involved (in any land, field or journal) it remains an abstraction, no essential identify or purpose of its overall own can be conceived.

Christian curiously applies an excessive use of machinery and mechanism terms, which is off-putting and imperils the well-intended enterprise. To his credit, e.g. he adds a main vector of complexity and information, but as due to “Darwinian machines.” (67) The Three Darwinian Learning Machines section gives a valid trend of a relative creaturely knowledge from contingent mutation and selection, onto individual educations, and a third phase of collective intelligence. This basis leads to a universal Darwinian evolutionism as a cumulative learning process. But once again, any sense of an inherent drive and destiny is not present. See also Christian’s earlier essay The Evolution of Big History: A Short Introduction in Evolution: A Big History Perspective (Uchitel, 2011), second abstract.

This essay discusses Universal Darwinism: the idea that Darwinian mechanisms can explain interesting evolutionary change in many different domains, in both the Humanities and the Natural Sciences. The idea should appeal to Big Historians because it links research into evolutionary change at many different scales. But the detailed workings of Universal Darwinism vary as it drives different vehicles, just as internal combustion engines differ in chain-saws, motor cycles and airplane engines. To extend Darwin's ideas beyond the biological realm, we must disentangle the biological version of the Darwinian mechanism from several other forms. The paper focuses particularly on Universal Darwinism as a form of learning, a way of accumulating information. This will make it easier to make the adjustments needed to explore Darwinian mechanisms in human history. (2015 Abstract)

James Joyce's strange masterpiece, Finnegans Wake, is fractal. You can read it
at many different scales, but you always have the eerie feeling that you are hearing a story you have already heard somewhere else. A mathematician might say the stories are ‘self-similar.’ (61) This paper explores one of these fractal phenomena: ‘Universal Darwinism’. In biology, the Darwinian paradigm describes a distinctive form of evolutionary change that generates adaptive change through repeated copying of selected variants. Universal Darwinism is the idea that similar mechanisms may also work in many other domains. If so, do they always work as they do in biology? Or can we distinguish between a core machinery and the modifications needed to drive it in different environments? (62)

Big history represents a modern scientific form of an ancient project: that of constructing unified, coherent and universal accounts of reality. Such projects can be found within the origin stories of most human societies. But in the late 19thcentury, the universalistic project vanished within both the humanities and the sciences, as scholars in field after field coped with the modern tsunami of information by narrowing the scope of their research. The sciences began to return to larger and more universalistic perspectives from the middle of the 20th century as new unifying paradigms emerged in field after field, and physicists even began talking of ‘Grand Unified Theories’ of everything. New information and new dating techniques made it more reasonable than ever before to attempt scientifically grounded universal histories and such attempts began to reappear in the 1980s. But not until the first decade of the 21st century has that project really begun to take off. (2011 Abstract)

Cirkovic, Milan. The Astrobiological Landscape: Philosophical Foundations of the Study of Cosmic Life. Cambridge: Cambridge University Press, 2012. The Astronomical Observatory of Belgrade and Future of Humanity Institute, Oxford University, astrophysicist achieves an innovative overview upon the revolutionary sense of a fertile evolutionary abode for living, intelligent entities, now of a planetary scale. Again this vista results from an imperative reunion of animate and physical realms and principles. From the latest findings, an organic cosmos is seen to fill itself with friendly “habitable zones” from stars and galaxies onto a “Habitable Universe.” This astrobiology profligacy then infers an extension of natural selection to a “galactic Darwinism.” Altogether a neo-Copernican synthesis is presaged as living, developing systems found everywhere redefine a permissive multiverse. A synopsis appears in the International Journal of Astrobiology, search Branislav Vukotic and Cirkovic. But alas still no inkling of a greater reality and genesis that is growing forth on its ordained own.

Astrobiology is an expanding, interdisciplinary field investigating the origin, evolution and future of life in the universe. Tackling many of the foundational debates of the subject, from discussions of cosmological evolution to detailed reviews of common concepts such as the 'Rare Earth' hypothesis, this volume is the first systematic survey of the philosophical aspects and conundrums in the study of cosmic life. The author's exploration of the increasing number of cross-over problems highlights the relationship between astrobiology and cosmology and presents some of the challenges of multidisciplinary study. Modern physical theories dealing with the multiverse add a further dimension to the debate. (Publisher)

Thus the stage is set. In the remaining chapters, I shall try to muster support for the following tightly interrelated theses: 1. The relationship between cosmology and astrobiology is much deeper than it is usually assumed – besides a similarity in the historical model of development of these two disciplines, there is an increasing number of crossover problems and thematic areas that stem from considerations of Copernicanism and observation selection effects. 2. Such a crossover area is both visualized and heuristically strengthened by the introduction of the astrobiological landscape, describing complexity of life in the most general context. Modern physical theories dealing with the multiverse add an additional level of detail to what is orthodoxly perceived as astrobiological enterprise, encapsulated in the Archipelago of Habitability. 3. Even in its orthodox version, within the well-defined confines of the Milky Way, modern astrobiology offers the prospect of both foundational support and a vast extension of the domain of applicability of the Darwinian biological evolution. (22)

Fernando, Chrisantha, et al.. Selectionist and Evolutionary Approaches to Brain Function. Frontiers in Computational Neuroscience. 6/Art. 24, 2012. Reported more in Intelligent Evolution and Cerebral Form, with Eors Szathmary and Phil Husbands, another contribution that articulates the deep affinity of neural activities with life’s long iterative development. As Richard Watson, Hava Siegelmann, John Mayfield, Steven Frank, and increasing number contend, this achieves a 21st century appreciation of how “natural selection” actually applies. While a winnowing optimization toward “good enough to survive” goes on, the discovery of dynamic, learning-like, algorithms can now provide a prior genetic-like guidance.

Fraix-Burnet, Didier, et al. The Phylogeny of Quasars and the Ontogeny of Their Central Black Holes. Frontiers in Astronomy and Space Science. February, 2017. A latest posting by the Institute of Planetology and Astrophysics of Grenoble natural philosopher about this project, here with Paola Marziani, Padova Astronomical Observatory, Mauro D’Onofrio, University of Padova, and Deborah Dultzin, UNAM Astronomical Institute, to sketch out an evolutionary astrocladistics (Google) for diverse galaxies akin to systematic groupings of organisms. See also their 2017 paper Phylogenetic Analyses of Quasars and Galaxies in this journal, along with Phylogenetic Tools in Astrophysics (1703.00286), The Phylogeny of Quasars (1702.02468), and Concepts of Phylogenetic Classification and Taxonomy (1606.016310 by Fraix-Burnet. For another perception see Cosmic Phylogeny by Paula Jofre, et al. A whole scale Cosmic Cladistics then seems a thought away so as to complete a universe to us developmental genesis.

A latest posting by the Institute of Planetology and Astrophysics of Grenoble natural philosopher about this project, here with Paola Marziani, Padova Astronomical Observatory, Mauro D’Onofrio, University of Padova, and Deborah Dultzin, UNAM Astronomical Institute, to discern and construct an evolutionary astrocladistics (Google) for diverse galaxies akin to systematic groupings of organisms. See also their 2017 paper Phylogenetic Analyses of Quasars and Galaxies in this journal, along with Phylogenetic Tools in Astrophysics (1703.00286), The Phylogeny of Quasars (1702.02468), and Concepts of Phylogenetic Classification and Taxonomy (1606.016310 by Fraix-Burnet. For a further avail see Cosmic Phylogeny by Paula Jofre, et al. A whole scale Cosmic Cladistics then seems a thought away so as to complete a universe to us developmental genesis.

Friston, Karl. The History of the Future of the Bayesian Brain. NeuroImage. 62/1230, 2012. After being immersed for two decades in British and American neuroscience, the now Scientific Director of the Wellcome Trust Center for Neuroimaging surveys the discovery in those years of a cerebral dynamic self-organization, along with a cognitive faculty distinguished by an interactive responsiveness via hierarchical scales in congruence with its greater environment. Such a “Bayesian brain” is busy with optimizing its “beliefs” about any input or reply, so as to minimize any expense of “free energy.” As Friston speaks for the field, the approach, via “statistical physics and information theory,” can be seen to reveal another means to join human and universe.

Thomas Bayes (1701-1761) was a British mathematician and Presbyterian minister. Bayesian Statistics is a subset of the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief or, more specifically, Bayesian probabilities. Such an interpretation is only one of a number of interpretations of probability and there are many other statistical techniques that are not based on "degrees of belief". (Wikipedia)

The future of the Bayesian brain is clear: it is the application of dynamic causal modeling to understand how the brain conforms to the free energy principle. In this context, the Bayesian brain is a corollary of the free energy principle, which says that any self-organizing system (like a brain or neuroimaging community) must maximize the evidence for its own existence, which means it must minimize its free energy using a model of its world. Dynamic causal modeling involves finding models of the brain that have the greatest evidence or the lowest free energy. In short, the future of imaging neuroscience is to refine models of the brain to minimize free energy, where the brain refines models of the world to minimize free energy. This endeavor itself minimizes free energy because our community is itself a self organizing system. (Abstract, 1230)

This means that a Bayesian brain that tries to maximize its evidence is implicitly trying to minimize its entropy. In other words, it resists the second law of thermodynamics and provides a principled explanation for self organization in the face of a natural tendency to disorder. This means the Bayesian brain gracefully accommodates ensemble or population dynamics in evolutionary thinking within a statistical framework. In functionalist terms, such a self organizing system that minimizes its entropy would appear to be making Bayesian inferences about its sensory exchanges with the environment, which, of course, is just the Bayesian brain hypothesis. (1233)

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