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

C. The Information Computation Turn

Stonier, Tom. Information and Meaning. New York, Springer, 1997. A contribution on the fundamental, evolutionary role of content and communication in an emergent cosmos reaching florescence in a global biointelligence.

Taborsky, Edwina, ed. Semiosis, Evolution, Energy. Aachen: Shaker, 1999. Conference papers explore an informational quality lately seen to distinguish a “post-darwinian” emergent evolution founded on self-organized relations. For example, Roberta Kevelson traces this process “from matter to meaning.” To Jesper Hoffmeyer, “our universe has a built-in tendency…to produce organized systems possessing increasingly more semiotic freedom…relative to its material basis.” (110) And Stan Salthe finds that as entropy increases, the universe is not losing coherence but gaining an informational capacity through relational networks.

Terzis, George and Robert Arp, eds. Information and Living Systems: Philosophical and Scientific Perspectives. Cambridge: MIT Press, 2011. While the editors are American philosophers, the papers in The Definition of Life; Information and Biological Organization; and Information and the Biology of Cognition, Value, and Language; sections draw upon an Iberian-Danish nexus in exploration of life’s “biosemiotic” essence. Characteristic chapters could be “The Biosemiotic Approach in Biology” by Joao Queiroz, Claus Emmeche, Kalevi Kull, and Charbel El-Hani, and Alvaro Moreno and Kepa Ruiz-Mirazo’s “The Informational Nature of Biological Causality.” The collection stands as one more attempt to engage the range and ramifications of this evident functional activity of creative conversation. But tacitly within a moribund physical cosmos, sans any sense of a larger context, the essays tend to abstract verbiage, are in need of common translation, unable to imagine a greater genetic genesis being deciphered and discovered.

Information shapes biological organization in fundamental ways and at every organizational level. Because organisms use information—including DNA codes, gene expression, and chemical signaling—to construct, maintain, repair, and replicate themselves, it would seem only natural to use information-related ideas in our attempts to understand the general nature of living systems, the causality by which they operate, the difference between living and inanimate matter, and the emergence, in some biological species, of cognition, emotion, and language. And yet philosophers and scientists have been slow to do so. This volume fills that gap. Information and Living Systems offers a collection of original chapters in which scientists and philosophers discuss the informational nature of biological organization at levels ranging from the genetic to the cognitive and linguistic. (Publisher)

Tkacik, Gasper and William Bialek. Information Processing in Living Systems. arXiv:1412.8752. Institute of Science and Technology Austria, and Princeton University physicists opine that in some deep way the universe is engaged in the optimization of information content. In this view, could one say this endeavor has reached its crucial stage of its conscious recognition by the human/Earth phenomenon.

Life depends as much on the flow of information as on the flow of energy. Here we review the many efforts to make this intuition precise. Starting with the building blocks of information theory, we explore examples where it has been possible to measure, directly, the flow of information in biological networks, or more generally where information theoretic ideas have been used to guide the analysis of experiments. Systems of interest range from single molecules (the sequence diversity in families of proteins) to groups of organisms (the distribution of velocities in flocks of birds), and all scales in between. Many of these analyses are motivated by the idea that biological systems may have evolved to optimize the gathering and representation of information, and we review the experimental evidence for this optimization, again across a wide range of scales. (Abstract)

Vallverdu, Jordi, ed. Thinking Machines and the Philosophy of Computer Science. Hershey, PA: Information Science Reference, 2010. A large volume of proceedings from the Seventh European Conference on Computing and Philosophy held in Barcelona, July 2009. Five sections span Philosophy of Information, Philosophy of Computer Science, Computer and Information Ethics, Simulating Reality?, and Intersections, wherein substantive papers attempt to accommodate and assimilate the 21st century turn to a generative natural realm of software-like “information.” Along with Gordana Dodig-Crnkovic noted above, Walter Riofrio’s “On Biological Computing, Information and Molecular Networks,” “Computing, Philosophy and Reality” by Joseph Brenner, and Klaus Mainzer’s “Challenges of Complex Systems in Cognitive and Complex Systems,” among others, are of much interest.

Varn, Dowman and Jim Crutchfield. What did Erwin Mean? The Physics of Information from the Materials Genomics of Aperiodic Crystals & Water to Molecular Information Catalysts & Life. Philosophical Transactions of the Royal Society. Forthcoming, October, 2015. The paper by UC Davis, Complexity Sciences Center, physicists is online at arXiv:1510.02778. It is also a summary of Jim Crutchfield’s thirty year complex systems endeavor which began at the Santa Fe Institute. At the outset, the contribution ought to be joined with other domains from quantum to social to cosmic as they each and all become reconceived as dynamic, self-organizing networks. Now even their ground phase of “inorganic” passive matter can be seen to possess these same qualities. An historic path is traced from Erwin Schrodinger’s (1887-1961) 1940’s book What is Life?, which posited that organisms and physical nature are both necessarily suffused by an informative materiality, to this mid 2010s collaborative confirmation.

This work is part of a worldwide project as well scoped out in the June 2012 issue of Philosophical Transactions of the Royal Society Beyond Crystals: The Dialectic of Materials and Information by Julyan Cartwright and Alan Mackay (search). Jim Crutchfield’s many publications are listed on his website such as The Evolution of Emergent Computation with Melanie Mitchell (1995 PNAS), Regularities Unseen, Randomness Observed with David Feldman (2003 Chaos 13/1), Between Order and Chaos (2012 Nature Physics 8/1), and Chaotic Crystallography (2015 Current Opinion in Chemical Engineering 7/47) search JC.

Erwin Schrodinger famously and presciently ascribed the vehicle transmitting the hereditary information underlying life to an `aperiodic crystal'. We compare and contrast this, only later discovered to be stored in the linear biomolecule DNA, with the information bearing, layered quasi-one-dimensional materials investigated by the emerging field of chaotic crystallography. Despite differences in functionality, the same information measures capture structure and novelty in both, suggesting an intimate coherence between the information character of biotic and abiotic matter---a broadly applicable physics of information. We review layered solids and consider three examples of how information- and computation-theoretic techniques are being applied to understand their structure. We then illustrate a new Second Law of Thermodynamics that describes information processing in active low-dimensional materials, reviewing Maxwell's Demon and a new class of molecular devices that act as information catalysts. Lastly, we conclude by speculating on how these ideas from informational materials science may impact biology. (Abstract)

We have come a long way from Schrödinger’s prescient insight on aperiodic crystals. We argued, across several rather different scales of space and time and several distinct application domains, that there is an intimate link between the physics of life and understanding the informational basis of biological processes when viewed in terms of life’s constituent complex materials. We noted, along the way, the close connection between new experimental techniques and novel theoretical foundations—a connection necessary for advancing our understanding of biological organization and processes. We argued for the importance of structure and strove to show that we can now directly and quantitatively talk about organization in disordered materials, a consequence of breaking away from viewing crystals as only periodic. These structured-disordered materials, in their ability to store and process information, presumably played a role in the transition from mere molecules to material organizations that became substrates supporting biology. (22)

Quantifying the notion of pattern and formalizing the process of pattern discovery go right to the heart of physical science. Over the past few decades physics’ view of nature’s lack of structure—its unpredictability—underwent a major renovation with the discovery of deterministic chaos, overthrowing two centuries of Laplace’s strict determinism in classical physics. Behind the veil of apparent randomness, though, many processes are highly ordered, following simple rules. Tools adapted from the theories of information and computation have brought physical science to the brink of automatically discovering hidden patterns and quantifying their structural complexity. (Crutchfield 2012, Abstract) There is a tendency, whose laws we are beginning to comprehend, for natural systems to balance order and chaos, to move to the interface between predictability and uncertainty. The present state of evolutionary progress indicates that one needs to go even further and postulate a force that drives in time towards successively more sophisticated and qualitatively different intrinsic computation. (Crutchfield 2012, 23)

Walker, Sara Imari. Top-Down Causation and the Rise of Information in the Emergence of Life. Information. 5/3, 2014. The Arizona State University and ASU Beyond Center physicist and astrobiologist, with colleagues Paul Davies, George Ellis, and others, explains the presence and role of algorithmic processes that serve to originate and drive a complexifying evolution. The approach does remain within the default model of software programs and hardware manifestations. But by this view, living systems can be joined with, and seen as an inherent result of, a fundamentally spontaneous materiality. Through her discussions with Oxford University mathematician Chiara Marletto, this vivifying scenario is seen akin to David Deutsch’s “constructor” version. In any event, a natural, cosmic reality is engaged and articulated that is composed as a doubleness of a mathematical source, and the overt organic entities along with our decipherment which it generates. See also the author's 2015 paper The Descent of Math at arXiv:1505.00312.

Biological systems represent a unique class of physical systems in how they process and manage information. This suggests that changes in the flow and distribution of information played a prominent role in the origin of life. Here I review and expand on an emerging conceptual framework suggesting that the origin of life may be identified as a transition in causal structure and information flow, and detail some of the implications for understanding the early stages chemical evolution. (Abstract)

Top-down causation by information encoded in the current state is one of the most distinctive features of living physical systems. In light of this, we previously suggested that the emergence of life may be associated with a transition in the causal and informational architecture of matter. Within this proposed framework, the debate between genetics-first and metabolism-first scenarios takes on a new dimension: both may be unified under a common information-based descriptive paradigm, where genetics may be thought of in terms of digital information processing and metabolism roughly as a form of analog information processing. Thus the debate on which came first—genetics or metabolism—may be recast as a debate about the informational hardware of the first living systems. (425)

In this review, we have identified life as a unique state of matter distinguishable from other physical states by its causal architecture. Under this view, the transition from non-living to living matter roughly maps to the transition from trivial to non-trivial replication and should therefore correspond to decoupling of “software” (information controlling the dynamics of the chemical system) and “hardware” (the chemical substrate). (435)

An interesting facet of this perspective is that it characterizes life as logically and organizationally distinct from other kinds of dynamical systems, and thus life represents a novel, emergent state of matter. Our usual causal narrative, consisting of the bottom-up action of material entities only, could therefore be only a subset of a broader class of phenomena—including life—that admit immaterial causes through the action of virtual constructors. This viewpoint suggests new thinking as to how life might have arisen on lifeless planet, by shifting emphasis to the origins of computation, control and informational architecture, rather than focusing solely on the onset of Darwinian evolution for example, which does not rigorously define how or when life emerges in a physical system. This framework also permits a more universal view of life, where the same underlying principles would permit understanding of living systems instantiated in different substrates (either artificial or in alternative chemistries) anywhere in the universe. (436)

Walker, Sara Imari, et al, eds. From Matter to Life: Information and Causality. Cambridge: Cambridge University Press, 2017. With Paul Davies and George Ellis as coeditors, this significant collection gathers current notice across many areas of an innately fertile cosmos from which arise sapient organisms. The project began as a 2014 Information, Causality and the Origin of Life workshop at Arizona State University and has now grown to this volume. Its significant theme is an addition of an informational quality and vector, aka J. A. Wheeler’s “It from Bit,” as a novel explanatory source and agency. Wide-ranging considerations are engaged in chapters such as Constructor Theory of Information and Life by Chiara Marletto, Digital and Analogue Information in Organisms by Denis Noble, Causality, Information, and Biological Computation by Hector Zenil, et al, Life’s Informational Hierarchy by Jessica Flack, Major Transitions in Political Order by Simon Dedeo, and especially (How) Did Information Emerge? by Anne-Marie Grisogono. These and more are a stellar array of “methinks” views, but one wonders if an actual “cosmic elephant,” a phenomenal presence on its own, can be imagined and allowed, as some contributors have denied elsewhere. A common translation from many technical abstractions would help, and as this site attempts, a witness of this generative source as a natural, uniVerse to human genetic code.

Recent advances suggest that the concept of information might hold the key to unravelling the mystery of life's nature and origin. Fresh insights from a broad and authoritative range of articulate and respected experts focus on the transition from matter to life, and hence reconcile the deep conceptual schism between the way we describe physical and biological systems. A unique cross-disciplinary perspective, drawing on expertise from philosophy, biology, chemistry, physics, and cognitive and social sciences, provides a new way to look at the deepest questions of our existence. This book addresses the role of information in life, and how it can make a difference to what we know about the world.

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)

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