Four Systems of Change
All foresight practitioners should be able to see both evolutionary and developmental processes occurring in four major systems of change: Cosmology, Biology, Sociology, and Technology. Each of these systems can be understood as a computational “substrate” or platform upon which vast amounts of both evolutionary and developmental complexity have emerged.
Let’s look briefly now at each substrate in turn, using descriptions borrowed from the EDU Blog, to better understand where we are in the universe, at present, and where we are going. The references cited below, in alpha order by author’s name, can be found for those seeking further information on all four systems.
Evo Devo Cosmology. The great story of increasing structural complexity from atoms, stars, galaxies, planets, life, intelligence and civilization is often called “cosmic evolution”. This is a misleading phrase, as cosmic change is actually a story of evolutionary development (evo devo). At the scale of the universe, complex structures emerge in much the same stochastic-yet-also-directional way that a multicellular organism develops. For a system to truly evolve, there needs to be reproduction with variation. Reproduction may happen to our universe eventually, if it is part of a larger environment that physicists call the multiverse. But within our universe itself, development is the most obvious cosmic process, when we view the universe over the largest scales of space and time. All cosmic evolution that occurs, both within local domains of our universe and over its future life cycles, is balanced by overarching and constraining processes of cosmic development.
An evo devo approach to cosmology thus proposes that both evolutionary (unpredictable, creative, experimental) and developmental (predictable, conservative, replicative) dynamics determine the origin, nature, and future of the universe as a complex system. Theories of universal replication, such as cosmological natural selection, allow us to define and test for physical and informational processes that are evolutionary (unpredictable, variational, experimental, and selective) and other processes that are developmental (predictable, predetermined, convergent, hierarchical, or elements of a universal life cycle).
Thermodynamics tells us our universe is aging, and will eventually die. Seeking to discover and better characterize its possible replicative processes, if they exist (Smolin 1999; Vaas 2002; Carr 2009), and to understand whether and how universal intelligence might aid in universal replication (Harrison 1995; Gardner 2000; Balázs 2002), in the same way that genetic and cultural intelligence aids in replication in biological systems, are natural avenues of research for a more evolution and development-aware cosmology.
In physics, examples of unpredictable and creative evolutionary cosmic processes include quantum mechanics and einselectionism (Zurek 2003), quantum cosmology, spontaneous symmetry breaking, and any process best described by nonlinear dynamics or deterministic chaos. In information and computation theory, evolutionary processes may include associative and selectionist networks (Hebbian learning, Hopfield and other neural networks), rapidly-growing diversity regimes (recombinant networks, ‘combinatorial explosions’), and any algorithmic regime subject to Rice’s theorem (undecidability).
By contrast, developmental physics (conservative, convergent, and far-future predictable dynamics) include its special initial and boundary conditions, laws of symmetry and conservation, thermodynamics, general relativity and black hole physics, classical physics, and the principle of least action. As complex systems move from thermodynamic equilibrium toward greater adaptiveness, their energy rate density, one measure of action efficiency, increases. From the emergence of the first large scale structure (protogalaxies) in our universe to today’s digital computers, a subset of complex systems have demonstrated superexponential growth in the energy rate density of their dynamical “metabolisms” and perhaps also in their adaptive intelligence (Chaisson 2002, Aunger 2007). Why this apparent developmental acceleration occurs is not yet clear.
Developmental information theory might include any fine-tuning of fundamental parameters for the emergence of computational complexity or adaptiveness. Likewise, physical limits to computation (Planck scale), and black hole information theory (Holographic principle, Bekenstein bound) would also seem to be part of the predictable developmental nature of information dynamics (“infodynamics”) (Salthe 1993). As our simulation capacity improves, we should be able to increasingly predict specific forms and functions of universal development, or “cosmic convergent evolution” (Flores Martinez 2014).
Many topics in cosmology may benefit from both evolutionary and developmental models. Consider the fine-tuning problem (Rees 2001; Barrow 2007; Vidal 2014). The fundamental physical parameters, laws, and boundary conditions that give rise to predictable forms of cosmic complexity can be considered developmental parameters (Smolin 2004). We can make a direct analogy with the special subset of developmental genes in biological organisms, which are usually far more finely-tuned than other genes, disrupting the organism’s developed complexity with even small changes to their values. The remainder of cosmic parameters and laws may be considered evolutionary parameters. In general, such parameters are far more easily changed, producing phenotypic variety (both in organisms and in universes) but without disrupting somatic development.
The self-organization of complex systems, and their periodic demonstration of hierarchical emergences, or “metasystem transitions” (Turchin 1977) also appears to be both a stochastic (evolutionary) and directional (developmental) process. Neither evolutionary nor developmental models alone seem sufficient to explain self-organization, the origin of life, and the origin of order in our universe. Both approaches seem critical to producing a more accurate and explanatory model of universal form and function.
The emerging field of astrobiology (Lunine 2004, King 2004) seeks to discover, among other things, which processes, including the origin and nature of life, are likely to be developmental (statistically highly probable or inevitable on all Earth-like planets) and which are likely to be contingent and unpredictably different, on all life supporting planets. The study of these two opposing sets of dynamics is presently the most advanced in evo devo biology, the next complex system we must consider. References:
- Aunger, Robert (2007) Major transitions in ‘big’ history, and A rigorous periodization of ‘big’ history. Forecasting & Social Change74(8):1137-1178.
- Balázs, Béla. (2002) The Role of Life in the Cosmological Replication Cycle, Paper from 2002 ISSOL Conference.
- Barrow, John D. et.al. (2007) Fitness of the Cosmos for Life: Biochemistry and Fine-Tuning, Cambridge Astrobiology.
- Carr, Bernard (2009) Universe or Multiverse?, Oxford U. Press.
- Chaisson, Eric (2002) Cosmic Evolution: The Rise of Complexity in Nature, Harvard U. Press.
- Flores Martinez, Claudio L. (2014) SETI in the light of cosmic convergent evolution. Acta Astronautica104(1):341-349.
- Gardner, James N. (2000) The selfish biocosm: Compexity as cosmology. Complexity5(3):34-45.
- Harrison, Edward R. (1995) The Natural Selection of Universes Containing Intelligent Life. A.S. Quarterly Journal36(3):193-203.
- Kauffman, Stuart (1993) Origins of Order: Self-Organization and Selection in Evolution, Oxford U. Press.
- King, Chris (2004) Biocosmology: Cosmic Symmetry-breaking, Bifurcation, Fractality and Biogenesis, Preprint.
- Lunine, Jonathan (2004) Astrobiology: A Multi-Disciplinary Approach, Benjamin Cummings.
- Martinez, Claudio L.F. (2014) SETI in the Light of Cosmic Convergent Evolution, Acta Astronautica104(1):341-349.
- Rees, Martin (2001) Just Six Numbers: THe Deep Forces that Shape the Universe, Basic Books.
- Salthe, Stanley M. (1993) Development and Evolution: Complexity and Change in Biology, MIT Press.
- Smart, J.M. (2008) Evo Devo Universe? Speculations on Cosmic Culture. In: Cosmos and Culture, Dick and Lupisella (Eds.), NASA Press.
- Smolin, Lee (1999) The Life of the Cosmos, Oxford U. Press.
- Smolin, Lee (2004) Cosmological natural selection as the explanation for the complexity of the universe. Physica A340(4):705-713.
- Turchin, Valentin F. (1977) The Phenomenon of Science, Columbia U. Press.
- Vaas, Ruediger (2002) Is there a Darwinian Evolution of the Cosmos?arXiv:gr-qc/0205119.
- Vidal, C. (2014) The Beginning and the End: The Meaning of Life in a Cosmological Perspective, (preprint)
- Zurek, Wojciech H. (2003) Decoherence, einselection, and the quantum origins of the classical. Reviews of Modern Physics75(3)715-775.
Evo Devo Biology. Biological systems engage in evolutionary (variational and selective) process, and they also develop (have heredity, reproduction, a predetermined, predictable and convergent series of hierarchical emergences within a life cycle). In living organisms, these two processes comprise the general model of
“evolution”. Yet evolutionary and developmental processes are actually quite different, even antagonistic in their roles in living systems. Evolutionary process is fundamentally creative and contingent, while developmental process is fundamentally conservative and convergent with respect to the generation and handling of biological information. Both processes are fundamentally necessary to the self-organizing phenomenon we call life, and they may also be fundamental to all complex replicating systems in the universe.
We propose therefore that the phrase “evolutionary development,” or “evo devo,” is a more useful description of organic change than the term “evolution,” which has historically ignored or minimized the role of developmental process (organismic development, group development, environmental development) in guiding and constraining long-range evolutionary processes at all scales. The new generation of “evo-devo” (hyphenated) biologists (Carroll 2006; Kirshner and Gerhart 2006; Callebaut and Rasskin-Gutman 2009; Laublichler and Maienschein 2009; Pigliucci and Muller 2010; Jablonka and Lamb 2014) understand this, as they stress the long-range effects of organismic and environmental developmental constraint on evolutionary process across all of the thirty orders of magnitude of scale inhabited by biological life, from the smallest cells to the planet as a system.
Developmental constraint is obvious in developmental genetics, including homeobox genes, body plans, and various developmental path dependencies that emerge from those genes. But we also find it in our emerging understanding of convergent evolution (McGhee 2011), in models of multi-level selection (Pigliucci and Muller 2010), and in our ecology, which experiences predictable life cycle effects, like ecological succession, developmental ascendancy (Coffman 2006), and panarchy (Gunderson and Holling 2001). Astrobiology (Lunine 2004) also seeks to differentiate
between statistically constrained processes in biological emergence, and those likely to be contingent and unpredictably different on all life-supporting planets.
We can also identify many developmental processes in ecology-culture interactions, including the emergence of cooperativity, self-domestication and niche construction. All intelligent species on Earth inevitably develop culture. Their emerging society permanently alters and constrains their evolutionary selection dynamics. We explore that topic, evo devo sociology, as the third major complex system important to evo devo foresight practice. References:
- Amundson, Ron. (2005) The Changing Role of the Embryo in Evolutionary Thought: Roots of Evo-Devo, Cambridge U. Press.
- Calcott, B. and Sterelny, B. (Eds.) (2011) The Major Transitions in Evolution Revisited, Vienna Series in Theoretical Biology.
- Callebaut, W. and Rasskin-Gutman, D. (2009) Modularity: Development and Evolution of Biology,MIT Press.
- Coffman, James A. (2006) Developmental Ascendancy: From Bottom-up to Top-down Control. Biological Theory1(2):165-178.
- Carroll, Sean B. (2006) Endless Forms Most Beautiful: The New Science of Evo Devo, W.W. Norton.
- Davidson, Eric H. (2006) The Regulatory Genome: Gene Regulatory Networks in Development and Evolution, Academic Press.
- Frank, Steven A. (2009) The Common Patterns of Nature. of Evolutionary Biology 22:1563-1585.
- Gilbert, Scott F. (2013) Developmental Biology, 10th Ed., Sinauer.
- Gunderson L.H. and Holling, C.S. (Eds.) (2001) Panarchy: Transformations in Human and Natural Systems, Island Press.
- Kirschner, M.W. and Gerhart, J.C. (2006), The Plausibility of Life, Yale U. Press.
- Jablonka, E. and Lamb, M.J. (2014) Evolution in Four Dimensions, Bradford.
- Laubichler, M.D. and Maienschein, J. (2009) From Embryology to Evo-Devo: A History of Developmental Evolution, MIT Press.
- Lunine, Jonathan (2004) Astrobiology: A Multi-Disciplinary Approach, Benjamin Cummings.
- McGhee, George (2011) Convergent Evolution: Limited Forms Most Beautiful, MIT Press.
- Newman, S.A. and Bhat, R. (2008) Dynamical patterning modules: physico-genetic determinants of morphological devo and evo. Biol.5(1):015008.
- Nowak, Martin A. (2006) Evolutionary Dynamics, Harvard U. Press.
- Odling-Smee, J. and Laland, K. (Eds.) (2003) Niche Construction: The Neglected Process in Evolution, Princeton U. Press
- Pigliucci, M. and Muller, G.B. (2010) Evolution: The Extended Synthesis, MIT Press.
- Reid, Robert G.B. (2007) Biological Emergences: Evolution by Natural Experiment, MIT Press.
- Smith, John Maynard and Szathmary, Eörs. (1995) The Major Transitions in Evolution, W.H. Freeman.
- Williams, R.J.P. and Frausto da Silva, J.J.R. (2005) The Chemistry of Evolution: The Development of our Ecosystem, Elsevier Science.
Evo Devo Sociology. Evo devo sociology seeks to understand evolutionary and developmental interactions between biology and culture, in humans and all other intelligent animals. One working definition of social evolution can be any biocultural process that involves variation of inherited parameters, intelligent interaction, including competition and cooperation among newly diverse complex adaptive systems, and environmental selection for adaptation on those evolved forms. A working definition of social development can be any biocultural processes that appears to be tracing a statistically predictable life cycle. Life cycles in biology involve inheritance of metastable environmental and developmental parameters, and a set of predictable convergences on the path to future replication.
These definitions can be captured in the phrase “Variation, Interaction, and Selection” (VIS) for evolutionary process, and “Replication and Convergence” (RC) for developmental process. Speaking in general terms, we can say that complex adaptive systems, in society and elsewhere, engage in an RVISC life cycle, where the VIS component is evolutionary, contingent, adaptive, and often unpredictable, and the RC component is developmental, conservative, and potentially statistically predictable (Smart 2008).
In a world where we have few validated models of social change, seeking to understand evolutionary and developmental aspects of human and animal society is a speculative and tentative process today. Both our descriptive and computational skills often do not seem equal to the task. Unlike astrobiology, which is rapidly growing and aided by emerging observational capacities, the field of astrosociology (Dick and Lupisella 2009), which would help us understand which aspects of culture and intelligence are developmental universals, and which aspects are evolutionary contingencies, is today quite small.
Nevertheless, a vast variety of Earth’s species organize their existence in complex social systems, making sociology a promising interdisciplinary endeavor. Evo devo sociology includes anthropology, behavioral science, ethology, ecological psychology, psychological development, sociology, economics, and the emerging study of cultural evolution (words and ideas as replicators undergoing selection, Blackmore 2000). Both animal and human ideas can be usefully divided into those that are contingent, locally adaptive, and largely unpredictable, and others (like science, or democracy) that appear predictable and globally adaptive, even optimal for certain levels of environmental complexity.
Evolutionary processes in human culture might include any that create new variety or contingency in imagination, association, relation, cause, trend or model, and any processes that increase combinatorials among ideas or algorithms. Developmental processes involve the discovery of hidden dependencies, constraints, laws, convergences, or optima. Better understanding of various forms of social development can help us see the boundaries of the more creative and unpredictable processes of social evolution.
Developmental sociology might begin with biologically-based concepts like developmental genetics and developmental psychology, but it also includes social psychology and niche construction (stigmergy), and predictable features of adaptive economies and civilization (Elias 2000; Wright 2001; Pinker 2011; Bernstein 2010; Morris 2014). It includes any examples of cultural convergent evolution, such as the the independent development of universal moral codes and forms of cooperativity (Corning 2005; Bowles and Gintis 2013), and structural or functional convergence within phylogenetically diverse organisms on earth, as in super-organisms like ants (Holldobler and Wilson 2008), termites, and human beings.
As mentioned, evo devo sociology also allows for evidence-based philosophical speculations on the general nature of complex social systems in the universe at large (astrosociology). Science currently has no empirical understanding of non-Earth life, and scant empirical understanding of complex social processes in multicellular organisms, but that is precisely why a more evo devo philosophy of sociology can be of value. Useful speculations can include attempting to better understand the nature of language or symbolic activity as itself a constitutive developmental process of large-brained multicellular organisms (Deacon 2011), and large-scale evolutionary and developmental patterns of social interaction.
On close analysis, oral and written language, human symbols, algorithms, and technology can be considered partly biological and partly “postbiological” (Dick and Lupisella 2009). Human tool use can be analyzed as a case of niche construction, but one that deserves separate treatment from other species uses of technology, like a spider’s web, a termite’s mound, or a beaver’s dam, due to its rapidly improving nature in human society. We consider that topic, evo devo technology, as the fourth and last major system of evo devo foresight. References:
- Allen, C. and Bekoff, M. (1999) Species of Mind: The Philosophy and Biology of Cognitive Ethology, Bradford.
- Aunger, Robert (Ed.) (2000) Darwinizing Culture: The Status of Memetics as a Science, Oxford U. Press.
- Bernstein William (2010) The Birth of Plenty: How Prosperity Was Created, McGraw-Hill.
- Blackmore, Susan (2000) The Meme Machine, Oxford U. Press.
- Bowles, S. and Gintis, H. (2013) A Cooperative Species: Human Reciprocity and its Evolution, Princeton U. Press.
- Conway Morris, Simon (2004) Life’s Solution, Cambridge U. Press.
- Corning, Peter (2005) Holistic Darwinism, U. Chicago Press.
- Deacon, Terrence W. (2011) Incomplete Nature: How Mind Emerged From Matter, W.W. Norton.
- Dick, S. and Lupisella, M. (2009) Cosmos and Culture, Cultural Evolution in Cosmic Context, NASA Press.
- Elias, Norbert (2000) The Civilizing Process, Blackwell.
- Heylighen, F. and Campbell, D.T. (1996) Selection of Organization at the Social Level. World Futures45:181-212.
- Holldobler, B. and Wilson, E.O. (2008) The Superorganism: Understanding Insect Societies, W.W. Norton
- Lifton, R.J. and Olson, E. (2004) Symbolic Immortality. In: Death, Mourning, and Burial, A.C.G.M. Robben (Ed.), Wiley-Blackwell.
- Morris, Ian (2014) The Measure of Civilization: On Social Development, Princeton U. Press.
- Phipps, Carter (2012) Evolutionaries, Harper Perennial.
- Pinker, Steven (2011) The Better Angels of Our Nature: Why Violence Has Declined, Penguin.
- Smart, John. (2008) Evo Devo Universe? Speculations on Cosmic Culture. In: Cosmos and Culture, Dick and Lupisella (Eds.), NASA Press.
- Tyler, Tim. (2011) Memetics: Memes and the Science of Cultural Evolution, CreateSpace.
- Wright, Robert (2001) Nonzero: The Logic of Human Destiny, Pantheon.
Evo Devo Technology. Whenever we consider technology’s replication, variation, interaction, selection, and convergence, as its own complex adaptive system, we differentiate it from the evolutionary development of social ideas and behaviors in culture (including the scientific method), the topic of our previous theme. Since modern human emergence ~100 kya, our technology has been improving exponentially, occasionally at rates that make the rest of human culture appear static in comparison. Since the 1890s, our information technology has been doubling its price-performance capabilities in computing, communication, and storage roughly every two to three years, via miniaturization, materials science, and human ingenuity (Kurzweil 1999, Koh and Magee 2006, Magee 2009, Nagy 2010).
Our technological systems can be usefully analyzed as physical and informational systems that employ both bottom-up, divergent, contingent and unpredictable evolutionary processes, and top-down, convergent, globally-optimized and predictable developmental processes. Our information technology is presently dependent on human culture for its replication and selection, but as it grows in intelligence and adaptability, we can imagine a future time in which it may become even more autonomous than living systems. Some scholars (Blackmore 2008) discuss how ‘temes’ (replicating technological algorithms, in increasingly intelligent machines), are becoming increasingly analogous to ‘memes’ (replicating ideas and behavioral algorithms, in brains), the more brain-like our machines become.
Various endings of Moore’s law, which began in 2005 with the breakdown of Dennard scaling, are for the first time allowing ‘horizontal exponentiation’ in processor connectivity, and the first economical construction of massively parallel, neural-network based, “biologically-inspired” computing machines (Floreano and Mattiusi 2008). Major advances in the neuroscience of learning and memory (Yuste 2010, Liu et al. 2012), and in computational neuroscience (O’Reilly and Munakata 2014) are teaching us how to make truly self-learning and self-programming machines, like human brains. Superhuman performance in several pattern recognition tasks has recently been achieved by self-training “deep learning” computers (Howard 2014). Such work suggests a time in which technology will be a fully autonomous complex system.
Thinking developmentally, the physics principle of least action appears fundamental to technology’s acceleration and adaptive complexification. So too do other developmental factors, including the constructal principle (Bejan and Zane 2012), various technology learning and performance curves (Dutton, 1984), and several of the causes, constraints, trends and cycles identified in technological forecasting (Martino 1992). Scholars of technological invention, diffusion and substitution (Rogers 2003), and of technology’s variation, cultural selection, and independent parallel convergence on a subset of optimal forms and functions (Basalla 1989, Kelly 2011, Johnson 2015) are making early steps toward a future discipline of “astrotechnology.” Such a discipline will eventually tell us which technologies are likely to be developmental universals in intelligent civilizations (from symbolic language, fire, stone tools, the club, and the wheel, to electricity, the digital computer and beyond), and which are likely to be evolutionary, and thus unpredictably different in form and function from one civilization to the next.
Finally, when we attempt to relate accelerating local intelligence to the idea of the universe as a complex system (evo devo cosmology), evo devo thinking can allow us to hypothesize on the role of intelligent life in a possible cosmologic replication cycle (Harrison 1995, Balazs 2002, Gardner 2003), though what that role may be is far from clear today. Nevertheless, models of technological evolution and development can make specific and falsifiable predictions about the future dynamics and role of universal intelligence and its technology. Such models include the expansion hypothesis (Kardashev 1997), the postbiological universe (Dick 2003), the biocosm (Gardner 2003), the transcension hypothesis (Smart 2012) and stellivores (Vidal 2014), and may help us resolve fascinating questions in astrosociology, including the Fermi Paradox (Webb 2015).
We are still early in applying evolutionary and developmental models to technology today, but the scientific, technical, and policy potentials for scholarship and collaboration in this emerging area have never been greater. We hope you’ll join us in discussing them, and our other three research themes, at the EDU blog. References:
- Arthur, W.B. (2009) The Nature of Technology, Free Press.
- Balázs, Béla. (2002) The Role of Life in the Cosmological Replication Cycle, Paper from 2002 ISSOL Conference.
- Basalla, George (1989) The Evolution of Technology,Cambridge Studies in the History of Science.
- Bejan, A. and Zane, J.P. (2012) Design in Nature: The Constructal Law in Biology, Physics, Technology and Society, Anchor.
- Blackmore, Susan (2008) On Memes and ‘Temes’(Video), TED Conference.
- Brynjolfsson, E. and McAffee, A. (2016) The Second Machine Age, W.W. Norton.
- Clarke, Andy (2003) Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence, Oxford U. Press.
- Dick, Steven J. (2003) Cultural evolution, the postbiological universe and SETI. International Journal of Astrobiology2(1):65-74.
- Dutton, J.M. and Thomas, A. (1984) Treating Progress Functions as a Managerial Opportunity. Academy of Management Review,9(2):235-247.
- Floreano, D. and Mattiussi, C. (2008) Bio-Inspired Artificial Intelligence, MIT Press.
- Gardner, James (2003) Biocosm: A New Scientific Theory of Evolution, Inner Ocean.
- Harrison, Edward R. (1995) The Natural Selection of Universes Containing Intelligent Life. Quarterly J. Royal Astronomical Society36:193-203.
- Howard, Jeremy (2014) The Wonderful and Terrifying Implications of Deep Learning(Video), TEDx Brussels
- Johnson, Steven (2015) How We Got to Now, Riverhead.
- Kardashev, Nikolai S. (1997) Cosmology and Civilizations. Astrophysics and Space Science252, Mar 1997.
- Kelly, Kevin (2011) What Technology Wants, Penguin.
- Kurzweil, Ray (1999) The Age of Spiritual Machines: When Computers Exceed Human Intelligence, Penguin.
- Koh, H. and Magee, C.L. (2006) A functional approach for studying technological progress: Application to IT. Forecasting & Social Change73:1061-1083.
- Liu, X. et al. (2012) Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484(7394):381-5.
- Magee, Chris L. (2009) Towards Quantification of the Role of Materials Innovation in Overall Tech Development.Working Paper ESD-WP-2009-09, MIT.
- Martino, Joseph P. (1992) Technological Forecasting for Decision Making, McGraw Hill.
- Miller, James Grier (1978) Living Systems, McGraw-Hill.
- Nagy, Béla et al. (2011) Superexponential Long-term Trends in Information Technology, Forecasting & Social Change73:1061-1083.
- Nagy, Béla, J. et al. (2013) Statistical Basis for Predicting Technological Progress.PLoS ONE 8(2):e52669.
- O’Reilly R.C. and Munakata, Y. (2014) Computational Cognitive Neuroscience, 2nd Ed. (Free Wiki Book).
- Rogers, Everett M. (2003) Diffusion of Innovations, 5th Ed., Free Press.
- Stock, Gregory (1993) Metaman: The Merging of Humans and Machines into a Global Superorganism, Simon & Schuster.
- Smart, John M. (2012) The Transcension Hypothesis. Acta Astronautica78:55-68.
- Vidal, Clement (2014) Chapter 9: High Energy Astrobiology. In: The Beginning and the End, Springer.
- Webb, Stephen (2015) Where is Everybody? Seventy Five Solutions to the Fermi Paradox, Springer.
- Yuste, Rafael. (2010) Dendritic Spines, MIT Press.