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

4. Universal Evolution: A Celestial Expanse

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.

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)

Yildiz, Izzet, et al. From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems. PLoS Computational Biology. 9/9, 2013. Within our Rosetta Cosmos, Max Planck Institute for Human Cognitive and Brain Sciences researchers quantify that evolution has endowed both birds and people with similar modes of coded, informational sound transmission and neural reception. Once again, nature utilizes this same complex organization principles everywhere and everyone.

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. (Abstract)

As a model, we employ a novel Bayesian recognition method of dynamical sensory input such as birdsong and speech. The Bayesian approach first requires building of a so-called generative (internal) model, which is then converted to a learning and recognition model. The key advantage of this approach, as opposed to standard models in both human speech recognition and automatic speech recognition, is that the generative model is formulated as hierarchically structured, nonlinear dynamical systems. This means that one can employ generative models specifically tailored to birdsong or speech recognition. (2)

Zurek, Wojciech. Decoherence and Quantum Darwinism: From Quantum Foundations to Classical Reality. Cambridge: Cambridge University Press 2025.. Cambridge: Cambridge University Press, 2025. The Los Alamos National Laboratory physicist gifts us, after some decades of collegial consideration, with a book length version of his deeply insightful theories. As the quotes say, we seem to abide in and emerge from a stochastic evolutionary process of many called and few (self) chosen writ large, which into the 2020s is being widely realized. One wonders if it even applies to candidate, sentient bioplanets like our own.

The measurement problem has been a central puzzle of quantum theory since its inception, and understanding how the classical world emerges from our essential quantum universe is key to its resolution. Here Zurek builds on the physics of decoherence and introduces the theory of 'Quantum Darwinism' to provide a novel account of the emergence of classical reality. Part II explores decoherence and its role in the quantum-to-classical transition. Part III introduces Quantum Darwinism to explain how an information-theoretic perspective complements, elucidates, and reconciles 20th century interpretations.

Quantum Darwinism is a theory meant to explain the emergence of the classical world from quantum origins by a process akin to Darwinian natural selection induced by the environment whereby many possible quantum states are selected in favor of a stable measured or recorded state.

Zurek, Wojciech. Emergence of the Classical from Within the Quantum Universe. arXiv:2107.03378. The veteran LANL theoretical physicist (search) continues his collegial endeavor to finesse the essence of this deepest, fundamental realm. In some way a John Wheeler-like observer function is a necessary activity, which then involves an informational receive/record aspect. By a wider view, our Earthuman phenomenon seems to serve as a microcosmic agent of universal self-articulation, which yet remains an arduous process of to express what may be actually going on.

Decoherence shows how the openness of quantum systems -- interaction with their environment -- suppresses flagrant manifestations of quantumness. Einselection accounts for the emergence of preferred quasi-classical pointer states. Quantum Darwinism goes beyond decoherence. It posits that the information acquired by the monitoring environment responsible for decoherence is disseminated, in many copies, in the environment, and thus becomes accessible to observers. (Abstract excerpt)

Zurek, Wojciech. Quantum Theory of the Classical: Quantum Jumps, Born’s Rule, and Objective Classical Reality via Quantum Darwinism. arXiv:1807.02092. The LANL physicist and originator of the QD concept that even this basic phase forms many candidate states from which selections are made continues his project, with a growing number of advocates, to develop this considerable insight. It is couched in technical terms in need of editing and arrangement, but contributes to a global perception of a wholly evolutionary cosmos. See also Revealing the Emergence of Classicality in Nitrogen-Vacancy Centers by Thomas Undan, et al including Zurek at 1809.10456, and other entries (Paul Knott) herein.

The LANL physicist and originator of the QD concept that even this basic phase forms many candidate states from which selections are made continues his project, with a growing number of advocates, to develop this considerable insight. It is couched in technical terms in need of editing and arrangement, but contributes to a global perception of a wholly evolutionary cosmos. See also Revealing the Emergence of Classicality in Nitrogen-Vacancy Centers by Thomas Undan, et al including Zurek at 1809.10456, and other entries (Paul Knott) herein.

Zurek, Wojciech. Relative States and the Environment: Einselection, Envariance, Quantum Darwinism, and the Existential Interpretation.. arXiv:0707.2832v1.. Published online February 3, 2008 by the Los Alamos Laboratory physicist as the latest version of his collaborative theory that in relatively sub- or pre- biological realms of quantum phenomena, a selective process also goes on similar to that experienced by classical organisms. Google ‘Quantum Darwinism’ for its website, and see also Philip Ball "Quantum All the Way" in Nature (453/22, 2008). For a later update see Quantum Darwinism by Zurek in Physics Today for October 2014.

Objective existence can be acquired (via quantum Darwinism) only by a relatively small fraction of all degrees of freedom within the quantum Universe. The rest is needed to “keep records.” Clearly, there is only a limited (if large) memory space available for this at any time. This limitation on the total memory available means that not all quantum states that exist or quantum events that happen now “really happens” – only a small fraction of what occurs will be still in the records in the future. So the finite memory capacity of the Universe implies indefiniteness of the present and impermanence of the past: To sum it up, one can extend John Wheeler’s dictum “the past exists only insofar as it is recorded in the present” and say “whatever exists is there only insofar as it is recorded.” (26)

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