What is Evolutionary Development?
Evolutionary development (“evo devo”, or ED) is a minority view of change in science, business, policy, foresight and philosophy today, a simultaneous application of both evolutionary and developmental thinking to the universe and its replicating subsystems. It is derived from evo-devo biology, an emerging set of theoretical and empirical approaches to understanding biological change. Evo-devo theory in biology proposes that evolution and development work in productive tension with each other to produce adaptive change in living systems.
To discriminate between science and systems theory, we will use the hyphenated term “evo-devo” when discussing the scientific discipline of evo-devo biology in this Guide, and the unhyphenated phrase “evo devo” when discussing the systems theory of evolutionary development, which can be applied to all replicating systems within the universe, and to the universe itself.
Whatever else our universe is, and allowing that there are big physical mysteries, like dark matter, dark energy, the substructure of quarks, and the nature of black holes still to be uncovered, reasonable analysis suggests that it is both evolutionary and developmental, or “evo devo”. Like a living organism, it undergoes both experimental, stochastic, divergent, and unpredictable change, a process we can call evolution, and at the same time, programmed, convergent, conservative, and predictable change, a process we can call development.
Evo devo thinking is practiced by anyone who realizes that parts of our future are unpredictable and creative, while other parts are predictable and conservative, and that in the universe, as in life, both processes are always operating at the same time.
To understand the interaction between evolution and development, think of a river. When we look at the river as a complex system, and take an up-close and bottom-up perspective, we are struck by the chaotic, evolutionary path of the stream across the landscape. The flowing water is constantly diverging, exploring all the possibilities available to it. Likewise, the path of any individual water molecule is always chaotic, contingent, and unpredictable. But when we look at the river from a big picture, top-down view, we see many predictable things we can say about it. Rivers flow from a set of higher sources (mountainsides) to a set of predictable lower destinations (lake, ocean, water table). All the water that doesn’t evaporate or get consumed is constrained to that kind of general universal behavior. Both views are valuable.
As our universal physical and information theory advance in coming years, I predict that evo devo thinking, applied on both universal and human scales, will become increasingly essential to understanding our past, present, and future. Why is this kind of thinking so important? Because it tells us how the world works, and that in turn tells what our best strategies are likely to be for the most adaptive foresight, leadership, and action.
For example, complexity scholar and venture capitalist Samuel Arbesman’s Overcomplicated: Technology at the Limits of Comprehension (2016) is an enlightening book exploring some of the ways our modern technologies are becoming like biological systems, which are diverse, always experimenting, always generating errors and small catastrophes, and which are now even beginning to manage their own complexity. As accelerating complexification continues, Arbesman argues that we need to move away from our previous dominance, and current social bias, toward what he calls “physics thinking,” which stresses simplicity, precision, predictability, and generality, toward what he calls “biological thinking,” which embraces diversity, experiments, chaos, error, and unpredictability, and above all, learning from the chaos and mistakes that occur.
In evo devo language, Arbesman is describing the limits of developmental thinking, and the many benefits of evolutionary thinking, as we’ll see. His book is a real contribution to technology leadership, yet I think we can and must say a lot more, to help our leaders anticipate, create, and manage change. We need better definitions and better hypotheses. Physics thinking and theory can be both evolutionary (eg, chaos and complexity) and developmental (e.g., mechanics and relativity), and so too can biology thinking. So Arbesman’s analogy, while it offers a quick insight into two competing views on the future, and tells us why one is far too overused at present, is not precise enough help us in many cases of strategy and action.
To be broadly effective, good leaders, foresighters and managers need a framework for understanding when and how much developmental thinking is useful in complex systems, and where it becomes dangerous, and when and how much evolutionary thinking is useful, and where it breaks down. This chapter, evo devo foresight, offers such a framework. To understand change on our local scale, I firmly believe we need to start with a universal view, where things are simplest and clearest, and work down from there, via UPGO foresight. We also need to start with science and systems theory, then move into the more popular and human-centric of the STEEPS domains. So let’s dive in.
When we look at biological change from a planetary perspective, scholars are increasingly able to recognize that selection operates at multiple levels (genes, cells, organisms, groups, ideas, and technologies). We are also learning about the previously under-recognized importance of development. We are beginning to see that like evolutionary change, development occurs at multiple levels, including the entire universe as a system.
In biology, we are learning the ways biological development directs evolutionary processes. This is represented as evo-devo theory in the picture at right, and in work like Susan Oyama’s developmental systems theory. We are learning that the environment of intelligent organisms undergoes developmental change via niche construction, and so both environment and intelligence must be factored into our understanding of selection. We are also learning the key role that synergies play in the development of structure and function, as outlined by leading synergy scholars like Peter Corning in Holistic Darwinism (2005).
Evo-devo biology is a community of several thousand evolutionary and developmental biologists seeking to improve evolutionary theory by improving our models of the way evolutionary and developmental processes interact in living systems to produce biological processes, morphologies, modules, species, and ecosystems. Evolution: The Extended Synthesis (2010), provides a particularly good introduction to the ways our understanding of biological change is changing. The picture below illustrates that both our 19th century Darwinian model of evolution, with natural selection focused only on the organism, and our 20th century gene-centric view of evolution, popularized in the late 20th century by Stephen Jay Gould and Richard Dawkins, while each great advances for their time, are incomplete accounts of biological change.
Beginning in 1859, Charles Darwin helped us to clearly see what we will call evolutionism in living systems, for the first time. Discovering that humanity was an incremental, experimental product of the natural world was a revolutionary advance in our previously poorly rational and humanocentric beliefs. We owe Darwinism a great debt, and evolutionary approaches remains the best way to describe the vast majority of change in complex systems.
But until we also understand and accept developmentalism, recognizing that the universe not only evolves but develops, and that selection and adaptation can be both evolutionary and developmental, then the purpose and values of the universe, and our place in it will remain high mysteries about which science has little of interest to say. Our science will remain underdeveloped, descriptive without also being prescriptive, and unable to deeply inform our morality and politics. That state of affairs must change in coming years.
In the traditional Darwinian model of biological change, development is included as a subset of evolution. It was mostly ignored in the modern synthesis, which gave us a contingency-dominated view of change. In evo-devo biology and in evo devo systems theory, that way of thinking is simply incorrect. While there is much variation of opinion among evo-devo biologists as to which factors will contribute most to the next synthesis, the large majority would agree that evolution and development are in many ways opposite and equally fundamental processes in complex living systems. Neither can be well understood without reference to its interaction with the other.
The reality, as we see it, is that both biology and the world are evolutionary developmental, meaning that they are always both at the same time, depending on your perspective. As memetics scholar Tim Tyler points out, what scientists typically call evolution includes both merging and joining as well as branching and splitting. For examples of merging and joining in evolution, think of sexual union, endosymbiosis, and any presumably symbiotic emergence of multicellularity that was preceded by networks of protists.
Evo devo theory would say these are all examples of evolutionary development. The branching parts, including the creation of sexes, and the different varieties of prokaryotic cells (which set up the likelihood of eukaryotes developing via endosymbiosis), and the variety of types of protist networks must be driven on every living planet by evolutionary and divergence processes. At the same time, the fact that only two genders contribute genetic material to all known species on Earth may be a developmental optimum, arrived at via evolutionary search but locked in everywhere, once found, due to its value. We do see sterile “third genders” in social insects (bees, ants, etc.), but that too may turn out to be a developmental optimum for such collective organisms. Likewise, endosymbiosis seems primarily developmental, as it offers vastly more dense and powerful bioenergetics for single-celled eukaryotic top predators like Paramecium and that greater energy production allowed more complex functionality to emerge in early amoeboid eukaryotes. Endosymbiosis also allows the emergence of multicellularity. Certain types of multicellularity, in turn also seem primarily developmental (convergent, predictable) in universal terms, as they allow local niche dominance for these inevitably larger organisms, via both physical and informational (intelligence) strategies.
Likewise, all developmental processes involve branching and splitting as well as merging and joining. Consider the way cells divide, differentiate, fan out, and compete during tissue and organ development. But again, evo devo theory would call those branching processes a regulated form of evolution, just as genetic recombination during fertilization is a regulated form of gene reassortment (evolutionary search). The cyclically predictable merging and joining parts are development. Clearly, in developmental biology and psychology, when we know a process takes us to a predictable endpoint, even though it also uses a variety of evolutionary processes to get there, we are comfortable saying that development is the primary process involved. Likewise, in traditional evolutionary biology, when we are modeling things like genetic drift using stochastic models, and seeing diversification and experiment, we are comfortable saying that evolution is the primary process involved, even though several aspects of that diversification are predictable. We can label these two fields “evolutionary biology” and “developmental biology” and see that they are both primarily different yet also inevitably overlapping fields of study.
It might seem confusing to say that both what scientists traditionally call evolution and traditionally call development are each always some mix of each that to be precise, we must call evolutionary development. But the clarity and insight that comes from defining evolution and development in terms of unpredictable and experimental versus predictable and conservative processes, and making that assessment in both physical and informational terms for every system and process we care about, is well worth the mental energy involved.
The evo devo approach acknowledges that two fundamentally opposing processes are always in play in complex systems, and that we don’t necessarily understand the mix of each, and how they play off each other in any system under study. It’s both humbling and more accurate to use the evo devo term, and admit that evolution and development, as we have traditionally defined them, don’t fully separate into neat categories as processes of change. There would be no value in abandoning our traditional scientific terms, evolution and development, in an effort at improving our models. Each are approximately accurate, have been immensely useful, and are quite entrenched. But we can recognize their limitations, and explain the value of redefining them, to a limited degree. We’ll reiterate exactly how we redefine them a few times in this chapter, and why that redefinition is important. For those who don’t think a redefinition is necessary or valuable, versus simply leaving these terms be, please read on.
In evo devo theory, both in biology and in systems theory, there are two key forms of selection and fitness landscapes operating in natural selection – evolutionary selection, which is divergent and treelike, with chaotic attractors, and developmental selection, which is convergent and funnel-like, with standard attractors. Thus natural selection itself is not the kind of random walk that Darwinists have long claimed. It is not just “variation and selection with inheritance”, but something more. It is a mix of both stochastic and directional, programmed change. Evo devo foresight, then, is any attempt to generalize this very valuable evo-devo biological perspective to nonliving replicating complex adaptive systems as well, including solar systems, prebiological chemistry, organizations, societies, technology, and perhaps most interestingly, to the universe itself as a complex system.
In biological systems, development is a process that guides replication through predictable stages of a life cycle. In living systems, developmental genes are a special set of initial conditions and algorithms that have encoded a certain kind of past learning from past life cycles. Together with the stable environment, developmental genes constrain the system to express specific predictable types of future form and function. Once a system is constrained by those special initial conditions, unless one fully understands the way those initial conditions affect the future dynamics of the system, parts of what it does look like self-organization, or what complexity scholars like Stuart Kauffman call “order for free.” Thus the concepts of replication, life cycle, and self organization are commonly associated with predictability in complex systems.
For example, if one breaks up a random sample of large molecules into smaller molecules, and puts them in close proximity, they won’t don’t do much that is predictable. But if one cuts up a virus or a cell into small pieces, and puts those small molecules in close proximity, many will self-assemble again into their original shapes. This looks like improbable levels of predictability and “self-organization”, but it is actually an example of development, at the molecular scale. The system from which those molecules came was a replicator, able to learn over many past replication cycles, that the shapes and charges of a particular sets of molecules have a high probability of maintaining specific forms, even under lots of environmental chaos.
Now ask yourself: If replication of our universe occurs, and any kind of selection or adaptation occurs on the expressed complexity, then we can predict that it must also engage in some form of developmental learning as well. Over time, there will be probability for a particular set of physical parameters and laws to perpetuate themselves. Such parameters, along with the universe they make, become their own motivation for existence, so to speak.
Much of what happens on Earth and in our Universe is unpredictable. Yet there is also astonishing levels of predictability in some of its complex dynamics. Many astrobiologists think, for example, that various kinds of advanced complexity emerge at predictable rates all over the universe, as the universe develops from physics to biology to society to technology. Scholars of convergent evolution also document many astonishing kinds of molecular, organismic and ecological complexity emerging at similar times in widely separated and widely varying environments on Earth. Eyes, jointed limbs, wings, fish fins, brains, math, science, electronic computers, all of these and many more may be inevitable and predictable emergences on planets like ours.
It is arguments like this that have led me to believe it is reasonable to assume that the predictable processes in our universe that scholars call developmental have emerged in a process of past replication. This has led me to explore theories of universe replication that have been proposed by various physicists over the years, and to see if those theories can be reconciled with what we know about other replicating systems within our universe, including living systems. Beginning in the mid 2000s I developed an application of biological evo-devo theory to societies, technologies, and the universe, which I published as a 90 page book precis, “Evo Devo Universe?” (PDF), in an edited volume exploring the diversity and universality of culture, Cosmos and Culture (2008).
Also in 2008, philosopher Clement Vidal and I co-founded a small international interdisciplinary research and discussion community, Evo Devo Universe, to explore evidence for evolutionary and developmental processes that may be occurring at all scales in our universe. We were soon joined by physicist Georgi Georgiev as a co-director, and now also have astrobiologists and complexity scholars as co-directors. The eighty-some interdisciplinary scholars in the EDU community participate in discussions around evolutionary, computational, developmental models of the universe and its subsystems, and we enjoy critiquing them, seeking evidence for or against them, and exploring their variations and implications. We strive to learn from each other about our disciplines, and to be humble and respectful.
Not everyone in our EDU community is of the opinion that our universe is likely to replicate. Not every scholar there thinks the universe undergoes some kind of selection. Not everyone expects that some kind of generic adaptiveness functions for broad classes of complex systems are likely to be formulated in future physical or information theory. But many do harbor these suspicions, and everyone there is on board with the basic idea that there is value in identifying, comparing, and contrasting the unpredictable (“evolutionary”) and predictable (“developmental”) dynamics in every universal subsystem we can identify, including the universe as a system, and asking how those appear to relate to each’s systems ability to adapt, under various tentative definitions of adaptation. That may not sound like it adds much to scientific and systems theory debates, but I believe this is a greatly clarifying way to view complex systems, and I have not yet found this approach as a basis of any other academic community.
If you are a published scholar with an interest in any of our various research themes, you are welcome to apply to join our small, friendly, and learning-oriented community.
Evolutionary Developmental Systems Theory
When we talk about change, “Evolutionary development”, “evo devo” or ED is a term used as a replacement for the more general term “evolution”, whenever any scholar thinks both developmental and nondevelopmental processes may be occurring in any complex system. The hyphenated “evo-devo” is commonly used for living systems, most prominently in evo-devo genetics, and the unhyphenated “evo devo” can be used for the theory of complex systems, whether living or nonliving.
Inspired by the work of evo-devo biologists, evo devo systems theorists look for processes of evolutionary creativity and developmental constraint in complex systems at scales above that of the organism. Evo devo systems theory redefines the term “evolution” to restrict it to the creative, experimental, diversifying processes of system change, which are the dynamical and informational opposite of the conservative, convergent, unifying processes of “development.” In replicating complex systems, evolutionary processes generate new information, and developmental processes conserve old information. In evo devo models, both processes are fundamental to adaptation.
Independently, and via similar reasoning, some scholars occasionally use the term “evolutionary development” as a replacement for “evolution” as it as it includes two opposing concepts — “random” Darwinian evolution and nonrandom development — and thus is a more humble and conservative term whenever one is uncertain whether the change one is talking about is random or directional. For either reason, a small group of ecologists (Salthe 1993), theoretical biologists (Reid 2008), cosmologists (Munitz 1987), complexity theorists (Levin 1998) and systems theorists (Smart 2008) have periodically used the “evolutionary development” term.
Again, evo devo models redefine the word “evolution” to specifically refer to variety-generating, experimental, divergent, and other “soon-unpredictable” processes that generate combinatorial explosions of contingent possibilities. They use the word “development”, to refer to variety-reducing, conservative, convergent, and other “statistically predictable” processes—predictable either if you have the right models and sufficient computation capacity, or if you have experience, having seen a prior life cycle of the developing system in question (a cell, a tree, a human, a stellar system, a galaxy, a universe).
Evolutionary Developmental (“Evo-Devo”) Biology
Since the mid-1990s, the interdisciplinary field of evolutionary developmental, or “evo-devo” biology has emerged to explore the relationship between evolutionary and developmental processes at the scale level of single-celled and multicellular organisms (Steele 1981; Jablonka and Lamb 1995,2005; Raff 1996; Arthur 2000; Wilkins 2001; Hall 2003; Müller and Newman 2003; Verhulst 2003; West-Eberhard 2003; Schlosser and Wagner 2004; Carroll 2005; Callebaut and Rasskin-Gutman 2005). Evo-devo biology includes such issues as:
- how developmental processes evolve
- the developmental basis for homology (similarity of form in species with a common ancestor)
- the process of homoplasy (convergent evolution of form and function in species with unique ancestors)
- the roles of modularity and path dependency in evolutionary and developmental process
- how the environment impacts evolutionary and developmental process.
Conceptual and technical advances in scientific disciplines including comparative phylogenetics, morphology and morphometrics, and statistics are allowing better insights into the evolutionary relationships among organisms, and inferences about how developmental processes influence those relationships. Evo-devo biologists believe developmental processes and genes must themselves act to constrain evolutionary processes, in ways not understood by traditional evolutionary theory, and that both evolutionary diversity and developmental constraint are important to understanding long range “macrobiological” change. Evo-devo genetics is today a rapidly improving field. It is bringing new understanding of both hidden and emergent biological constraint, and a new toolset for understanding such complex, controversial, and potentially scientifically clarifying topics as convergent evolution.
The Riddle of Convergent Evolution
Convergent evolution (CE) is evidence or argument for physical attractors in the phase space of dynamical possibility which guide and constrain contingently adaptive evolutionary processes into statistically predictable future-specific structure or function, in a variety of physical and informational environments. When we look at evolutionary history, the dynamics of several species morphology or function is seen to converge to particular “archetypal forms and functions” in a variety of environments.
Such attractors have been called deep structure, guiding evolutionary process in predictable ways, regardless of local environmental differences. Organismic development depends on specific initial conditions (developmental genes in the “seed”), the emergence of hierarchies of modular structure and function in the unfolding organism, and persistent constancies (physical and chemical laws, stable biomes) in the environment. Likewise, some examples of convergent evolution may be best characterized as ecological, biogeographical, stellar-planetary, or universal evolutionary development (ED) if their emergence can modeled, after adjusting for observer selection, to depend on specific universal initial conditions, emergent hierarchies, and environmental constancies.
A famous example of convergence is found in eyes, which appear to have evolved on Earth from different genetic lineages to work similarly (function) in all species, and in the case of camera eyes, to also look very similar (form) in both vertebrate and invertebrate species, like humans and octopi. One can easily advance the argument that, in universes of our type, eyes, though first created by a process of evolutionary contingency, become an archetype, a kind of general optimization for almost all eye-possessing multicelluar species in Earth-like environments, once they exist. Presumably, the previously rapidly-changing “evolutionary” gene groups that led to eye creation become part of the “developmental” genetic toolkit for eye-possessing species. Such developmental features should become increasingly strongly conserved and eventually, due to path dependency and emergent hierarchies, incapable of being changed without preventing development itself. Proving such genetic convergence arguments with evidence and theory is of course more difficult, yet it is a fertile area of investigation today.
Less-Optimizing Convergence (LOC) Versus Optimizing Convergence (OC)
In our mostly chaotic, contingent, and deeply nonlinear universe, we can predict that many, perhaps even the vast majority, of examples of CE will not be driven by the evolving system’s discovery of some hidden general optimization function, like the discovery of the eye archetype. To understand convergence, we will need some kind of general optimization theory. Let’s consider two necessary features of that theory now.
- We can predict that any optimization that occurs must be on a continuum, from highly-optimizing convergence, which we will refer to simply as optimizing convergence (OC), conferring advantage in all the most competitive and complex environments, to a wide variety of other cases, which we can refer to collectively as less-optimizing convergence (LOC). LOC cases would include convergence that offers only some temporary or local adaptive advantage, to just a few specific species, or in some subset of specialized or less-complex environments, convergence that offers no advantage, or convergence that is deleterious but not fatal. Names for a few general classes of LOC cases have been offered by scholars, including passive convergence, parallel evolution, etc.
- Optimizing convergence can occur via both physical and informational processes. Physically, we might see greater efficiency of employment of physical resources, as in Bejan’s constructal law, or greater density of employment of physical resources for offense or defense, a phenomenon Vermiej calls evolutionary escalation. Informationally, we might see efficiency or density gains via informational substitution for physical processes, what Fuller called ephemeralization, or greater general intelligence (modeling ability), greater immunity, or a more useful collective morality, offering more general and persistent adaptation to a wider range of environments than previous strategies. Intelligence also offers the ability to modify environments to suit the organism, what Odling-Smee calls niche-construction, as humans extensively do today. To understand OC, we will need a theory of optimization that tells us when a physical or informational advantage is likely to be more generally adaptive, particularly in the most complex, competitive and rapidly-changing environments.
Consider eyes again. For their time, eyes were “the leading edge” of general optimization, for animals, in the most morphologically complex (multicellular) environments on earth. Andrew Parker’s light switch theory (In the Blink of an Eye, 2003) proposes that the development of vision in precambrian animals directly caused the Cambrian explosion. This is a fascinating theory, implying an intelligence-driven optimization and acceleration of morphological and functional comlexity. It proposes that eyes created a vastly more competitive, discriminatory, and intelligent evolutionary environment (set of selection pressures) in multicellular evolutionary space. Once they emerged, it is easy to argue that all animals in that intelligence-leading environment needed eyes to survive. Intelligence, in this case, and perhaps generally, appears to be part of a physical and informational optimization function, in the most morphologically and functionally complex environments.
Many other examples of OC can be proposed, in the most physically and informationally complex, and rapidly changing, environments on Earth, including the necessary emergence of eukaryotes, oxidative phosphorylation, multicellularity, nervous systems, bilateral symmetry, jointed limbs, opposable thumbs, tool and language use on land (much faster-improving than aqueous environments), culture, and technology, including machine intelligence. Future science will need better theories of complexity, complexification, and optimization, to deeply understand convergence, and to distingish the much greater variety of examples of less-optimized convergence from the most highly optimized forms.
Optimizing Convergence as Evolutionary Development, On the Appropriate System Scale
When convergence is viewed from the perspective not of the evolving species, but from some larger system scale (the biogeography, the planet, the universe) we can view optimizing convergent evolution as a process of not simply evolution, but of evolutionary development (ED).
When we claim a convergence process is an example of ED, we are not only claiming that some kind of general optimization is occurring. We are also claiming that some kind of developmental process, with predictable convergence, directionality, hierarchy, modularity, a life cycle, and perhaps other features found in biological development, is being followed, at some larger systems level. Thus evolutionary development is an example of complex systems theory.
- Ecology offers several examples of not only evolutionary but apparent developmental change. When we look above the level of species change to ecologies, we can identify predictable patterns of ecological change, including ecological succession.
- Biogeography offers more examples. When we look above ecologies to biogeography, we find scaling laws, like Copes rule, and biogeographic laws like Foster’s rule and Bergmann’s rule, with their predictable processes of convergent and optimizing CE, or evolutionary development. The famous convergence of form seen in placental and marsupial mammals, on separate continents, offers another example of not just evolution, but biogeographic ED.
- Culture change offers more examples. When we look above individual cultures and do cross-cultural comparisons, we find many examples of developmental features at the leading edge of competitiveness, including inventions like fire, language, stone tools, clubs, sticks, levers, written language, hydraulic empires for our first great cities, wheels, electricity, computers. etc. In each of these cases, a high-order convergence has occurred. These and other specific examples of cultural change look not only evolutionary, but evolutionary developmental (ED). Once they exist, there’s no going back, for any culture seeking to stay on the leading edge of physical and informational complexification, and general adaptiveness. We also find many examples of constraint laws that operate in social and economic systems, like physicist and EDU scholar Adrian Bejan’s constructal law.
- Stellar-Planetary change offers more examples. When we look above human culture to our planet and its star, astrophysical theory tells us that the way stars have replicated, and chemically complexified, through three different populations over billions of years, has been not only evolutionary (a variety of randomly arrived at star and planet types and distributions) but evolutionary developmental, involving a progressive drive to complexification in a predictable subset of types. Many astrobiologists and planetologists argue that a subset of chaotic and nonlinear (“evolutionary”) stellar-planetary change has reliably led, with high probability and massive parallelism, to M-class stars and Earth-like planets that are biochemically and geohomeostatically ideal for the development of archaebacterial (geothermal vent) life, and from there to prokaryotes and eukaryotes. See Nick Lane’s The Vital Question (2016) for one such story.
- Universal change offers more examples. When we look beyond stars to galaxies (which do not replicate within this universe) and to the universe as a system, several cosmologists propose that it has not only much change that is evolutionary (random, contingent, experimental), but a large subset that appears developmental. If the universe and its galaxies are a replicative system in the multiverse, as some cosmologists have proposed, such special initial conditions and constancies may have themselves self-organized in an iterative and selective process, just as biological developmental parameters have self-organized, in biological systems over multiple replications. For more on the latter idea, see our wiki page cosmological natural selection (fecund universes). The fine-tuned universe hypothesis also offers one of several examples that the initial conditions of our universe seem self-organized for the emergence of internal complexity and its persistence over billions of years. As in biological genes, only a handful of which are developmental, highly conserved, and finely-tuned, only a handful of these universal parameters seem improbably finely tuned, to a degree far beyond that we would expect through obvious observer-selection effects. See Martin Rees, Just Six Numbers, 1999 for one such account.
Our universe is not only generating local variation, creativity, and difference, it is also developing toward a small set (in our present understanding) of currently-predictable destinations. While there is much about cosmogony that remains unclear, we know that dark energy is accelerating complex galactic groups away from each other, that total entropy increases, and that an increasing fraction of the mass-energy of our universe will end up in black holes. The better we understand the conservative and predictable features of our universe, and can distinguish them from creative and unpredictable ones, the better we may understand evolutionary and developmental processes at all scales.
If universal evolutionary development is occurring, future science must show that each successive environment in the developmental hierarchy inherits certain initial conditions and physical constancies from the environment that preceded it, back to the birth of the universe, and that some of these initial conditions and constancies act to predictably constrain the future dynamics of each successive environment, to some degree. These constraints have been called developmental portals by some scholars. M-class stars and organic chemistry may be necessary portals to planets capable of generating life. Fats, proteins, and nucleic acids may be necessary portals to cells. Eyes may be necessary portals to higher nervous systems, etc.
As we saw in Chapter 2, another example of predictable developmental signal, across all of these environments, may be the ever-faster complexification we see in the historical record of the most physically and informationally complex locations in our universe, since the emergence of M-class stars, Earth-like planets, and almost simultaneously, on our planet, life. This acceleration was famously summarized in Carl Sagan’s metaphor of the Cosmic Calendar. Ever since August, on this calendar metaphor, leading-edge complexity environments have become exponentially faster, more complex, and more intelligent, on average, on Earth. Sagan said this phenomenon, which we can call acceleration studies, was an understudied area of science, in need of better understanding. See Sagan’s The Dragons of Eden (1977) for his original account. It our hope that better models of early universe, astrophysical, chemical, biological, psychological, social, economic, technological, and other evolutionary development will help us understand our universe’s emergence record of ever faster and more physically- and informationally-complex local environments.
Evo Devo Models Require Advances in a Variety of Theories
If universal evolutionary development is occurring, future science must show that each successive environment in the developmental hierarchy inherits certain initial conditions and physical constancies from the environment that preceded it, back to the birth of the universe, and that some of these initial conditions and constancies act to predictably constrain the future dynamics of each successive environment, to some degree. These constraints have been called developmental portals by some scholars. M-class stars and organic chemistry may be necessary portals to planets capable of generating life. Fats, proteins, and nucleic acids may be necessary portals to cells. Eyes may be necessary portals to higher nervous systems, etc.
From a landscape (phase space) perspective, if ED is occurring, as the evolutionary “search” landscape gets more diverse and complex, certain portions must convert into developmental funnels, then portals. Occasionally, these portals must also work together to produce a metasystem transition (a higher level of order or control), a new level of ED hierarchy. Both the landscape’s tendency to produce funnels/portals as complexity emerges, and the number of portals (lower is generally better) are two obvious ways to maintain developmental control in any evolutionary (chaotic, creative, locally unpredictable) system.
In evo devo models, alternative chemistries for life, periodically sought by astrobiologists (see Goodwin 2014) if they continue to be undiscovered by observation or simulation (we have been imagining them for decades, so far with little to back them up), would be more evidence indicating a universe with a high level of ED (self-organizing) constraint on the life transition. Such constraint might be due to strong or weak multiversal selection for life and intelligence with both evo and devo properties, over many past cyclings of our universe.
In addition to better simulation capacity, progress in any theory of evolutionary development will require better:
- Complex systems theory – Seeing the appropriate system and scale at which ED is occurring.
- Evo-devo theory – Better understanding organismic ED, modularity, reaction-diffusion systems, gene-protein regulatory networks, intelligence, immunity, morality, and other ED features of living systems, both individually and as collectives. This will require advances in evo-devo genetics, theoretical morphology, paleontology, evolutionary (developmental) biology and psychology, anthropology, sociology, economics, and many other fields.
- Adaptation theory – Moving beyond the MES (modern evolutionary synthesis) to EDSO (evo devo self-organization) to a better understanding of the sources of adapted order.
- Optimization theory – Reliably differentiating less-optimized convergence (LOC) and optimized convergence (OC), in the emerging study of convergent evolution, via better definitions, tools, data, models and optimization functions
- Acceleration theory – Understanding accelerating change, in ED terms. When it happens as a physical process, acceleration always seems to involve both densification and miniaturization of critical adaptive processes in complex systems. Acceleration also happens via informational or computational processes as well. For that we may need a better intelligence theory.
- Intelligence theory – We need advances in such intelligence-related topics as:
- Intelligence substitution – Understanding precisely when information, or a computational process, can substitute for a physical process, and either retain or improve adaptiveness for the system under study. Some scholars call this dematerialization, or ephemeralization. Along with densification, dematerialization is an obvious driver of acceleration.
- SOE partitioning – Adaptation and intelligence always exist in three interacting subsystems: seeds (with evo and devo initial conditions), organisms (which engage in a life cycle), and the selective Environment (some scholars call this ambient intelligence). Because of niche-construction (intelligence always alters its local environment), environments essentially replicate along with seeds and organisms (think of the replication we see in city structure and function) and are a full partner with organisms and seeds in the ED of intelligence.
- Hierarchy theory – Seeing the ED trajectory for the system as a whole. Stan Salthe’s work on subsumptive hierarchies is an excellent example. Hierarchy theory (Salthe 1985,1993) tells us how each new hierarchy is more constrained than the latter. While we traditionally think of intelligence in an evolutionary role (increasing diversity and options), hierarchies tell us the ways that new “higher” systems are more constrained than the ones from which they emerged. Chemistry uses only a subset of physical laws, and has new emergent constraints, biology constrains chemistry, society constrains biology, and so on. The emergence of constraining morality in social collectives is a good example of cultural hierarchy.
- Life cycle theory – Seeing the full replicative cycle of the developing system. If we can predict the remaining stages of the life cycle in any system, aided by comparisons with other evo devo systems, we can see its developmental futures, in broad outline at least. Its evolutionary futures, of course, remain intrinsically unpredictable at the same time. Both predictable and unpredictable process are perennially found in complex systems, whether an organism, a culture, a star, a galaxy, or a universe.
Building better hypotheses and theory of evolutionary and developmental processes will be an immense amount of work. But this path may be the only viable way forward (a conceptual developmental portal), and if validated, the benefits we stand to gain, via better collective foresight, also seem comparatively immense.
Shortcomings of the Word “Evolution”, and of the Modern Evolutionary Synthesis
In scientific literature, the term “evolution” is used to describe any process of growth or change that involves the accumulation of historical information, in either living and nonliving complex systems (Myers 2009). When we restrict the term to refer to biology, and modern forms of Darwinian evolution, it is used to describe cumulative inherited change, via descent with modification from preexisting organisms. A classic conceptual model of Darwinian evolution, often taught in undergraduate classes, is the acronym VIST (Russell 2006). Evolutionary change is proposed to happen via Variation, with Inheritance, and (Natural) Selection, over long amounts of Time.
While it is a good start, there are two basic problems with the VIST model:
- VIST does not explicitly consider cumulative Replication, the organism’s life cycle, as a contributor to biological change. It is implicitly considered as the factor of “Time” in the VIST model. But it is not Time that causes biological change. Organic change occurs via cumulative cycles of Replication (of the organism), partly guided by Inheritance factors (genes, brains, and other information carriers, or “seeds”), and Selection (in the environment). In all three of these interacting systems (organisms, seeds, and the environment) we find processes of Variation (evolutionary processes) and Convergence (developmental processes), working together in service to adaptation. Considered together, these five factors give us a RISVC model of change. As we will see, replication is also the best word to start with in any model of the self-organization of complex adaptive systems, whether we are discussing replicating suns creating organic chemistry, replicating chemicals creating cells, replicating cells creating organisms, replicating organisms creating ideas, replicating ideas creating self-replicating machines, or any other complex adaptive system. Adaptation, learning, and intelligence always begins with replication, of some kind of “organism” (system).
- VIST does not explicitly include the concept of developmental (Convergence) genes and processes, or describe the way they act in opposition to processes of variation within the organism. Developmental genes and processes are those that keep the organism on a convergent, conservative life and reproduction cycle. Their fundamental role is Convergence, funneling the organism toward a series of future-specific states. Variation, within the organism or within the environment, is the “enemy of development.” It must be overcome by Convergence, if the organism is to develop in a predictable way. Unfortunately, both classical Darwinism and modern evolutionary theory deprioritize the influence of organismic development on macrobiological change. Perhaps because of this, they also don’t consider that the natural environment itself may be developing, and be a product of past replications. As a result, the MES, our current standard in biological investigations, is biased toward the idea of an “accidental” universe, and “random” experimentation and diversity as a primary cause of macrobiological change.
Evo devo models, whether in biology or in other replicating systems, help us eliminate the biases of both original Darwinian evolutionary theory (VIST, white oval at right), and of modern evolutionary theory (light oval at right), which both view diversification as the prime source of adaptiveness, but ignore or minimize the converging, conserving role of development, and the possibility of development on scales far larger than the organism. They also offer us a broad understanding of evo devo self-organization as the natural source of adapted complexity, in all replicating systems. As we’ll discuss shortly, understanding self-organization in turn shows us why challenges to Darwinism that have been launched by groups like the “intelligent design” community are more in line with supernatural belief, not science. They are typically motivated by belief in an “intelligent designer.” But if the universe replicates, as several cosmologists propose, parsimony and evidence both argue that evo devo self-organization, via many past replications in a selective environment, not intelligent design, is the source of the intelligence we see.
After we have done our best to adjust for observer-selection effects, we still see many highly unreasonable examples of adaptedness for complexification, in the laws and processes of our universe as a system. The phenomenon of accelerating change, the fine-tuned universe hypothesis, the Gaia hypothesis (in a more rigorous form) all come to mind. To explain such unreasonable adaptedness we should think first of replicative self organization under selection, not design. After all, such self-organization is our best model for the source of the intelligence that is reading this page, right now.
The RVISC Model – Replicative Self-organization via Evolutionary Development
In any evo devo model of complex systems, we find three major processes of change:
- Processes that manage Variation, divergence, and experiment (“evolutionary” processes).
- Processes that manage Convergence, are conserved, and guide the system thorough future-specific stages of form and function (“developmental” processes)
- Processes that are adaptive (“evo devo” processes). These processes are always some blend of the first two fundamental types. In the RISVC model, adaptive processes can be further divided into Replication (Organism/Complex System) processes, Inheritance (Seed, Genes) processes, and Selection (Environment) processes. We can color them purple to indicate they are an evo devo blend.
The RISVC model of self organization via evolutionary development proposes that Replication in complex systems (Organisms), using Inheritance (Seeds, Genes), and Selection (Environments) is the source of adaptive order, and that such replication always involves “tree-like” evolutionary processes driven by Variation (creation of new information) and “funnel-like” developmental processes driven by Convergence (conservation of old information).
These evo devo processes act in parallel, and sometimes in opposition to each other, in service to adaptation. Consider how all Replicating organisms are sometimes driven to variation, and sometimes to convergence. Inheritance units (seeds, genes) sometimes duplicate (think of gene duplication) and vary, and sometimes converge (with gene loss). Selection in the environment sometimes favors diversity, and sometimes favors a particular phenotype. In the RISVC model, evo devo replication under selection is the root source of adapted order. Environmental selection alone is not sufficient.
There is more we must say about the environment. The more complex the organism, the more those organisms use their intelligence to niche-construct (alter, or engage in “stigmergy”) their local environment to make it more suitable to adaptation. Historically metastable features of the local environment are also used by genes to reliably guide the evolving and developing organism to its future destinations. Environments may also replicate, on some higher systems level, just as organisms and seeds replicate. This happens, for example, when we replicate an urban architecture or idea-complex (like capitalism or democracy), when stars replicate, when continents drift apart, or if our universe itself replicates. Thus our selective environment is a lot more similar, both dynamically and informationally, to organisms and seeds than is commonly understood in Darwinian models. In an evo devo model, adapted intelligence for any replicating system is always partitioned (SOE partitioning) between these three core actors, Seed, Organism, and Environment (Smart 2008).
Again, in evo devo theory, adaptive processes are not called “evolutionary” but rather “evolutionary developmental” or evo devo, to remind us that they are always a balance between diverging evolutionary and converging developmental processes. This language change helps us correct a major bias of standard Darwinian models, which ignore or minimize convergence. Even today, the study of convergent evolution (planetary, biogeographic, and ecosystem development) remains controversial and understudied in evolutionary (developmental) biology. Use of the evo devo term also communicates our humility and ignorance when we are asked whether evolutionary or developmental process are presently dominating in any particular system or environment. We usually don’t know which processes are most in control of either physical or informational dynamics, at first glance. Some degree of study, modeling, and data collection is often required to see where the system is presently headed, process by process.
As we come to understand the complex phenomenon of convergent evolution, on myriad system levels (physical, chemical, genetic, morphological, functional, algorithmic, cognitive, technological, etc.), we will rectify the historical biases that the Modern Evolutionary Synthesis have perpetuated with respect to our presumably living in a “random”, “directionless” and “purposeless” universe. To do this, we will need what Pigliucci and Müller (2010), call an Extended Evolutionary Synthesis (EES), one that includes both evo-devo and evo devo perspectives, better science and simulations, and much more.
Do We Live in an Evo Devo Universe (EDU)? – The EDU Hypothesis
Since I was twelve years old, I’ve suspected that parts our universe, most particularly the continuously accelerating parts, have many statistically predictable aspects to the futures they are rapidly building. As an adult in my twenties, taking my first undergraduate classes in physics, chemistry and molecular biology, I learned how to reconcile this view with the much more prevalent view of the future as random, contingent, and largely unpredictable.
It turns out that all replicating complex systems can be viewed from two fundamentally different perspectives. When we look at the system up close, whether it is a star, a prebiotic chemistry, a cell, or an organism, we see much about it that is locally unpredictable. Yet when we look at the same system either from larger scale, or over a longer time frame, long enough to see its replication cycle, we see much about it that is predictable–even when we don’t yet know any of the math or causal forces behind its predictability.
Think of an acorn. Once you’ve seen one acorn grow into an oak tree, you learn that the shape of the acorn seed tells you that it will make an oak tree. Once you’ve planted more than one acorn, you know, in advance, that most of the structural and molecular details of each oak tree will remain contingent, “random,” and unpredictable. But you also know much about its future that is predictable. That predictability, in biology, is called “development.” The unpredictable parts we can call “evolution”, in an evo devo model.
If our universe is a replicating system, it is very much like an oak tree. The more we learn about the shape of the seed that created our universe, its nurturing environment (multiverse), and the “organism” itself, the more we’ll know about both our evolutionary futures–what will stay unpredictable, and about our developmental future–what predictable and constraining “portals” and “destinies” lie ahead.
Our intelligence allows us to take these larger scale and longer time frame views on our reality, even though we are physically stuck, because of the way our universe is self-organized, with its severe travel constraints, in one small corner of this particular universe. That big picture perspective is one of the many great things intelligence does for us–it lets us move our viewpoint to all aspects of any system we can model, either in our heads or in our computers. Using that perspective, we can also see that our computers are becoming the new leading local intelligence. They will predictably zoom past us in their adaptiveness very soon now, in a cosmic timeframe.
The evo devo universe (EDU) hypothesis (Smart 2008, PDF) proposes that our universe has two fundamental drives, to evolve (vary, diverge, create, experiment) and to develop (converge to a predictable life cycle). In the RISVC model of self-organization for complex systems, these two drives are carried out via three adaptive components: Replication (Organism), Inheritance (Seed, Genes) and Selection (Environment). In replicating living systems, and presumably also in the universe as a system, adaptive intelligence of any replicating system always lives in, and is partitioned between, all three of these adaptive components, the initiating Seed, the replicating Organism, and the Environment (SOE Partitioning)
If our universe has these similarities to living systems, and replicates in some multiversal environment, we can predict that development at all system scales (organismic, ecological, biogeographic, cultural, technological, universal, etc.) will act as a constraint on evolution at all system scales. Likewise, we can expect that evolution, via preferential replicative selection, will continually and slowly change future development, again at all scales. If this view of the universe is valid, there is much we will continue to learn from evo devo processes in biological organisms, the most complex and adaptive systems on our planet, which will tell us more about how our universe works as well.
Whenever we can discover and validate evolutionary process and structure, we can better describe evolutionary possibilities for complex systems in our universe. Likewise, wherever we can find and model developmental process, we can predict or guess developmental constraints on those systems, and where they are striving to go. More generally, and most auspiciously for our moral and intellectual lives, we will better understand more of the evo and devo “purposes” or “telos” for ourselves, our societies, and the universe. We can better understand our natural drives to both evolutionary and developmental process (to create/innovate and to conserve/sustain), and perhaps, what a good balance between these two fundamental processes should be.
The “Tape of Life” (“Identical Earths”) Experiment – Ecological, Biogeographic, & Planetary ED
If life emerges on two similar Earth-like planets, in either in reality or in a good simulation, by definition the evolutionary aspects will typically turn out differently in the two environments, and the developmental aspects will turn out the same. This is called the “Tape of Life” experiment, and it is commonly discussed in the philosophy of biology and by some of the more systems-oriented evolutionary (developmental) biologists.
Beginning in the 1970s, leading evolutionary theorist Stephen Jay Gould (1977,2002) famously predicted that little of life’s functions and morphologies on another similar Earth would turn out the same as those presently found on our Earth. He expected a few broad similarities, in kingdoms and some phyla, but most species would turn out very differently, in his view. Beginning in the 1990’s, Simon Conway Morris (1998,2004) famously argued the opposite, that most functions and many morphologies would turn out the same, optimized for replication and adaptation in this particular Earth environment. In the decades since, some biologists and most astrobiologists have migrated from Gould’s to Conway Morris’s camp, though the dividing line between predictable and unpredictable remains a productive and contentious debate.
Convergent evolution, in any potentially replicating system, at all universal scales, can be productively modeled as a process of evolutionary development. Genetic evolutionary development, organismic evolutionary development, species evolutionary development, ecosystem evolutionary development, cultural and technological evolutionary development, planetary and universal evolutionary development. The simplest phrase to encompass all these and other types is “universal evolutionary development”. Applied to the universe, evo devo theory argues that both universal evolution (useful diversity) and universal development (useful similarity) must be aspects of any universal biology that some scientists and systems theorists (Mariscal 2016) are presently seeking. Though we seek simplicity in our models, discussing either alone may lead to oversimplistic views of how adaptation actually occurs.
We must also recognize that just as in biological evo devo, our science and simulation skills will be unable to predict many of the developmental similarities (“convergent evolutionary developments”) that emerge between two parametrically identical universes, two Earth-like planets, two similar but biogeographically separated continents, two highly similar cities or organizations, two genetically identical twins, separated at birth, or even two dividing cells.
Fortunately, the latter examples, and others, have happened many times on Earth. So we can look to these “natural experiments” to better understand processes of development, at all scales. As our science and simulation capacity gets better, we can also develop better and more predictive models of how our physical universe evolved and developed.
In a few of our more advanced biotechnological prosthetics (eg, cochlear and vision implants, even hippocampal “chips”), our software and hardware models are good enough to substitute for the biological system without significant loss of function. We can hope that this intelligence substitution will also serve us as we learn to simulate universes in our future computers as well.
If so, we will increasingly be able to predict ED in at least two major ways. By discovering more natural experiments, at all scales, and by simulating the emergence of those experiments, at a level sufficient for the simulation to substitute for the physical process.
The “Tape of the Cosmos” (“Identical Universes”) Experiment – Universal ED
As we did in Chapter 2, let’s look again at convergent evolution on the largest scale we can presently imagine: our universe. In Carl Sagan’s famous Cosmic Calendar metaphor of change (1977,1980, picture right), we see that earlier stages of hierarchical evolutionary development, involving the emergence of large scale structure, galaxies, and stelliferous and planetary change, are highly isomorphic and convergent, across the universe. Simply looking at the night sky shows us these amazing levels of convergence. In the last century, physicists have worked out many of the reasons this convergence is evolutionary developmental. It is written into the initial conditions and emergent laws of our particular universe.
Are the features of our universe that have accelerated on Earth since the emergence of life, seen in latter half of the Cosmic Calendar metaphor (“August” and afterward), also found convergently throughout the universe? Is this convergence on multilocal complexity acceleration in our universe strong, happening with high frequency, as a developmental process, or is it random and happening weakly, as an evolutionary process? In other words, should we expect Earthlike acceleration in a multitude of special environments, such as those found on habitable planets around M-class stars? These are questions of universal ED. Astrophysicists and astrobiologists hope to answer such questions, by theory and observation, in coming years.
Now consider genetically identical twins. Most molecular and tissue-level aspects of two genetically identical twins look different when you view them up close (different fingerprints, organ microstructure, ideas, etc.). Those are “evolutionary” differences in an evo devo model. They are locally unique in myriad ways, either because the twins genetic systems aren’t capable of ensuring perfect identicalness, or because there are adaptive (eg, immunity) advantages to this local diversity. Genes are not a blueprint, but a recipe for building local complexity in a way that allows contingent local diversity, yet is also robust enough to local molecular chaos that each twin is reliably guided toward a set of useful far-future destinations in structure and function. All the aspects of the two biological biological twins that turn out the same, we call “developmental.”
Now consider that if our universe replicates, and its emergent features and intelligence undergo some form of selection in the multiverse, this twin model helps us to define evo devo terms like universal evolution (variation between universes) and universal development (similarity between universes). Cosmology models typically assume that if our multiverse had two parametrically identical universes (universes with the same fundamental parameters and initial and boundary conditions of our universe), some aspects of those universes would turn out the same and some would turn out differently. Astrophysics guides our universe toward future-varying (evolutionary) and future-determined (developmental) form and function, at the same time. We can conduct “simulation experiments” today to explore these divergences and convergences between two model universes, but our science remains incomplete, and our simulations still do not capture all the reality they attempt to model. Yet if we live in an evo devo universe, these virtual experiments will get ever more predictive, and they’ll eventually convince even the most die-hard believers in contingency that we have a set of highly constrained futures ahead of us.
The Fine-Tuned Universe Hypothesis – Early Evidence for Universal ED
The fine-tuned universe hypothesis (Barrow and Tipler 1986; Rees 1999,2001) can be understood as an example of universal development. In most organisms, you can change many genes and generate phenotypically different organisms, but they will still develop. We can call those “evolutionary” genes. But there is a subset of genes that are highly conserved in evolutionary history, and highly resistant to change. Nudge them just a bit, and you don’t get viable development.
In the same way, while our universe simulation capacity is still emerging, and the physical theory is not yet complete, we know that among the known 26 or so fundamental parameters of our universe, most can be changed and simulations will still produce viable universes. We can call these the universe’s “evolutionary” parameters in its initiating “seed” or “genome” in an evo devo model. At the same time, there are a special subset of parameters that seem improbably precisely tuned (one, the cosmological constant, apparently even to 120 orders of magnitude), to work with the other finely-tuned parameters to produce universes capable of rich internal complexity and longevity.
When we nudge any of these precisely-tuned parameters in our simulations, we don’t get viable universes. We can obviously call those “developmental” parameters, in an evo devo model. They seem exactly analogous to the very small subset of developmental genes in organisms. Edit any of those, and you never get viable organisms. They’ve been self-organized, over vast numbers of previous cycles, to work together to conserve the developmental forms, functions, hierarchies, and life cycle of the organism.
Cosmologist Lee Smolin and his hypothesis of cosmological natural selection (CNS) (1992,1997) offers one example of a self-organized, evo devo approach to explaining the emergence of cosmological complexity. We can imagine many others that are also consistent with evo devo models. If this analogy between replicating organisms and universes holds up, models like Smolin’s CNS, in some variation, will continue to gain theoretical and empirical support. The better we understand and can simulate the operation of evolutionary and developmental parameters in living systems, the better we should be able to understand and simulate them in universes as well. Both look like finite and replicating systems, in an evo devo model.
Why “Intelligent Design” and “Creation Science” Are Not Reputable Science
This analysis should help explain why the Evo Devo Universe research community does not associate with scholars affiliated with the Discovery Institute or other “intelligent design” communities, or any of the even less reputable scholars of “creation science.” A minority of members in these communities, like Michael Denton, can be argued to be pursuing science, but most are motivated by supernatural belief, and are constructing models and hypotheses that seek to justify that belief. This religious belief has led the more activist members of these communities to a variety of objectionable political acts, like seeking “equal treatment” for their evidence-poor hypotheses in our public high schools.
Supernaturalism takes many forms, some quite subtle. Even otherwise deeply insightful works, like EDU scholar and complexity theorist James Gardner’s Biocosm (2003), ostensibly an attempt to “split the difference” between a God-created and self-organized universe, run into trouble when they speculate that our universe may have been rationally constructed (read: “intelligently designed”) by “godlike” entities in a previous cycle. Such models simply don’t fit with all materialist experience to date with respect to intelligence’s role in replicating systems within our own universe. Consider that all our present attempts to “rationally design” our own environment, are intelligence-guided guesses, in any honest analysis. Human engineering is an evo devo process, perhaps 95% creativity/experiment, and 5% discovery/optimization (to guess a ratio), not omniscient “design”.
Adapted intelligence has always had a useful but very minor influence on RISVC cycles, in every system we’ve seen so far in our universe, and no intelligence ever becomes “godlike”. If we live in an evo devo universe, it is easy to argue that our future must continue to become rapidly computationally opaque to any finite and physical beings, the further ahead they look into their own futures. Combinatorial explosions of possibilities always limit our foresight. No matter how advanced we become, the intelligences within this universe seem destined to remain evo devo gardeners, finite beings with “free will” (self-unpredictable evolutionary futures), not gods.
If our universe replicates, as many cosmologists now propose, evolutionary developmental self-organization is an entirely sufficient model to explain our universe’s improbably fine-tuned initial conditions (possibly tuned for the adaptive emergence of internal complexity), our improbably self-correcting geophysical environment (the “Gaia hypothesis”, in its more rigorous form), our continually accelerating levels of adapted complexity on Earth (Sagan’s “Cosmic calendar” metaphor), or any of the other particularly astonishing aspects of our complexity emergence story so far.
Just as life’s incredibly adapted complexity self-organized over many evo devo cycles, and just as everything that is complex and adaptive inside our universe is a replicating system, it is most parsimonious to assume that our universe is a replicating evo devo system as well. We have no need to invoke supernatural entities, and we have found no credible evidence, in our five hundred year epic of science advancement, for an intelligent designer.
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