Evolutionary development (evo devo, ED)

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This page offers a general systems definition of the phrase "evolutionary development". Its primary author is EDU community co-founder, systems theorist John M. Smart. The EDU community seeks to reconcile evolutionary, developmental, physical, informational, and adaptive approaches to universal structure, function, and complexity, and its members hold many different views. Alternative definitions for this phrase may appear here, as they are proposed. For more on our community, see our homepage.

Definition and overview

An evo devo lifecycle
“Evolutionary development”, “evo devo” or “ED” is a term that can be used as a replacement for the more general term “evolution”, whenever any scholar thinks that both experimental, creative, contingent, stochastic, and increasingly unpredictable or “evolutionary” processes, and conservative, convergent, statistically deterministic (probabilistically predictable) or “developmental” processes, including replication, may be simultaneously contributing to selection and adaptation 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 any potentially replicating and adapting complex system (star, prebiotic system, gene, cell, meme (concept), behavior, technology), whether living or nonliving. Occasionally, the hyphenated term “eco-evo-devo” is used to place evo-devo biology within ecological systems with their own evo devo dynamics (e.g., Pigliucci 2007; Gilbert, Bosch, and Ledón-Rettig 2015). This is controversial, since the conventional neo-Darwinian Modern Synthesis does not recognize multi-level selection, and maintains that ecological dynamics are secondary to species competition. But the rise of theoretical and systems ecology and its models, including ecological energetics, panarchy, and ascendancy, supports the idea that ecologies themselves both evolve and develop. Finally, future evo devo models may require what Lucia Jacobs refers to as “cog-evo-devo” (Jacobs 2012), the recognition that both information and cognition evolve and develop, are causal agents in the dynamics of complex replicators, and are increasingly important in determining their future as higher intelligence emerges

Inspired by the work of evo-devo biologists, evo devo systems theorists look for processes of evolutionary creativity and developmental constraint in replicating complex systems at scales above and below that of the organism. Evo devo systems theory redefines the term “evolution” to restrict it to stochastic, information-creative, experimental, diversifying, and nonhierarchical processes of system change, which are the dynamical and informational opposite of the predictable, information-conservative, convergent, unifying, and hierarchical processes of “development.” In replicating biological and other complex systems, evolutionary processes generate new information, and developmental processes conserve old information. Both processes can be differentiated in any replicating complex system, and both are presumably fundamental to adaptation, and the ways each system encodes representations (models, intelligence) of itself and its environment.

Independently, and via similar reasoning, some scholars occasionally use the term "evolutionary development" as a replacement for "evolution" as it as it juxtaposes two dynamically- and informationally-opposing concepts — "random", unguided Darwinian evolution and nonrandom, guided development — and thus is a more conservative and humble descriptive term to use when one is uncertain whether the change one is talking about is random or directional. For either reason, a small but growing group of ecologists (Salthe 1993), biologists (Losos 2017), paleontologists (Conway-Morris 2004,2015) theoretical biologists (Reid 2008), cosmologists (Munitz 1987), complexity theorists (Levin 1998) and systems theorists (Smart 2008,2012) find it valuable to use the “evolutionary development” term.

Again, evo devo models redefine the word “evolution” to refer specifically 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, the right perspective (often a collective, big picture, or long-term view) or if you have empirical 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).

The start of the journal Evolution & Development in 1999 signaled rise of evo-devo biology to a formal subdiscipline. Evo devo systems science and philosophy presently has no journal. If it did, Bio-Inspired Complexity Science and Philosophy (BCS&P) would be a reasonable title. This would be a journal within which evo devo cosmology, culture, and biology, living systems theory, cog-evo-devo, eco-evo-devo and related topics might be modeled and critiqued. BCS&P is the self-description of our EDU research and discussion community.

Two polar categories, and tensions

Common evo and devo terms

Table 1 introduces sets of two polar (equal and opposite) word pairs that can be associated with evolutionary and developmental processes in a range of complex systems. As you look them over, think of all the events, processes, and systems you have previously described with these words. These and similar words are often useful starts at categorizing social, economic, and technological events and processes into one of two camps (Smart 2008).

Some systems operate by chance, others by necessity. Some processes are random, others predestined. Some events are indeterminate, others predetermined. Some processes are segregating, others integrating. Some act bottom-up, others are top-down. Some systems appear to be branching, others funnelling. Some changes look reversible, others irreversible. Some are generating novelty, others conserving sameness. Some are exploring possibilities, others running into constraints. Some promote variability, others stability. Some degrade hierarchies, others create hierarchy. In the organization, good foresight and strategy requires a continual balance between divergent (innovative, experimental) and convergent (predictive, conservative) thinking. We can see these twin tensions, and their mixture, in all the ways humans have yet invented for knowing the world.

In the twentieth century, we learned that even our scientific laws fall neatly into these two categories. From our reference frame, not only have we discovered deterministic (developmental) types of laws that precisely describe the far future, like the equations of classical mechanics and relativity, we’ve also found stochastic and statistical (evolutionary) physical laws, like quantum mechanics, thermodynamics, and nuclear physics.

We have learned we can view physical and informational systems as either deterministic or stochastic, depending on the analytical reference frame we adopt. Deterministic laws are highly conserved and predictable at the individual level (i.e., the laws of motion for individual objects), yet become unpredictable at the collective level (i.e., the N-body problem in physics). Stochastic laws are random, novel, and creative at the individual level (the quantum state or entropy of any particular system, the decay of any particular nucleus), and yet are probabilistically predictable at the collective level.

Many social, economic, and political processes historically alternate between unpredictable and divergent (evolutionary) and predictable and convergent (developmental) phases. For every social issue, we can find processes simultaneously generating "evolutionary" variety and "developmental" convergence, in comparative analyses of different cities, counties, states, countries, or regions. For example, regarding economic inequality, we find great "evolutionary" variations, country by country, in the levels and quality of social services available to each citizen, and in the cycles of increasing or decreasing inequality. Yet we also find a long-term "developmental" trend of predictably increasing total economic inequality (relative and absolute rich-poor divides) the greater the flow rates of capital, goods, and information in any societies we analyze (Bejan and Errera 2017).

The two opposing perspectives and tensions of evolution and development (unpredictability or predictability) appear to be equally fundamentally useful ways to view the world.

The RISVC model: Replicative self-organization via evolutionary development

RISVC - A Conceptual Model of Replicative Self Organization via Evolutionary Development

In the evo devo model of complex systems, we find three major processes of change:

  1. "Evolutionary" processes that manage Variation, divergence, and experiment.
  2. "Developmental" processes that manage Convergence, are conserved, and guide the system thorough future-specific stages of form and function.
  3. "Evo Devo" processes that are successfully replicative, allow inheritance, and are Adaptive. 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) processes, Inheritance (Seed, Genes) processes, and Selection (Environment) processes.

The RISVC model of self organization via evolutionary development (Smart 2016) 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.

Evolutionary development in organisms: The 95/5 rule

Evo-devo genetics is improving our phylogenetic systematics (taxonomies)

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 levels 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.

The best work in evo-devo recognizes that natural (environmental) selection is a subtractive process. Natural selection reduces diversity (Johnson 2001). The fundamental role of evolution can be hypothesized as cumulative mechanisms that generate experimental ("good bet") types of diversity, to improve the odds of survival under environmental selection. Evolutionary systems do this in an increasingly information-driven and intelligent way as organic complexity grows (Shapiro 2011). Living systems continually sense their internal states and environment, and they react to catastrophe and stress with bursts of poorly-predictable, information-driven innovation (a divergent form of "intelligence"), a pattern called punctuated equilibrium. The fundamental role of development can be hypothesized as cumulative mechanisms that conserve and execute a small subset of (in-principle) predictable processes that have worked in the past to guarantee replication, under a range of chaotic internal and external environmental conditions. Developmental systems encode future-predictive probabilistic models of themselves and their environment, models which we assume follow the rules of Bayesian probability in nervous systems and presumably even in single celled organisms. Developmental prediction (a convergent form of "intelligence") is generated from special initial conditions (developmental genes), tuned via informational constancies that exist in genes, developing organisms, and the environment.

In other words, natural selection can be argued to be a composite of two more fundamental kinds of selection. Evolutionary selection biases the system for ever more useful, intelligence-guided innovation when needed, and developmental selection biases the system for continued complexity conservation and successful replication. We must see both of these selective and often opposing processes, apparently at work at scales in every system that replicates, to truly understand biological change.

There is more we can say about developmental selection. It is critically dependent on a small subset of control parameters, or genes. While about half of metazoan genes are expressed in such processes as organ development, less than 20% of these (thus less than 10% of all genes) are substantially regulated during expression (Yi 2010). A further subset of our genome, roughly 5% of DNA in human, mouse, and rat, is highly conserved across these and other metazoan species. This 5% of our genome typically cannot be changed without stopping, or causing major deleterious effects to, processes of development. The majority of this highly conserved DNA, or 3.5%, is noncoding, yet presumably also constrains functional expression (Wagman and Stephens 2004). A subset of this conserved DNA is sometimes referred to as the developmental genetic toolkit (DGT), or less accurately, the evo-devo gene tookit. These genes include the Hox genes which determine animal body plans, and they often involve initial choices in spatial, dynamical, and informational form and function that commit the organism to a particular developmental path. In other words, they create path dependencies, a key kind of constraint. Thus a subset of all metazoan genomes have become very finely tuned, over many past replications, for the production of complex, path-dependent modularity, hierarchy and life cycle. Presumably, the other 95% of these genomes can change and generate diversity without such immediately deleterious effects.

Thus all genomes can be categorized into two groups, of conserved and unconserved genes, and we can propose that all highly conserved genes which are also highly tuned (highly sensitive to change, with deleterious effect) are the core constraints on development itself. I call this observation the 95/5 rule, and have found early evidence for it in a variety of complex systems (Smart 2008). The rule proposes that some small subset of developmental parameters are always top-down causal, involving essentially one-way information flow (in this case, developmental genes to organism). They can no longer be easily changed, they can only be added to, as organisms get more complex. The remainder of the genome can be considered evolutionary, whether it controls evolutionary or developmental process, as all of those genes can be altered by two-way information flow with the environment, with feedback. But per the 95/5 rule, a small, and highly-tuned set of top-down constraints must always exist, in any evo devo system.

A major implication of the 95/5 rule is that development will typically grow more constraining on evolutionary processes over time, in the most complex evo devo systems. As development becomes more complex, it progressively limits evolutionary possibilities via both additional sources of one-way information flow (more developmental genes and regulatory processes) causing additional predictable convergences on future form and function, and via the evolutionary discovery of environmental optima (convergent evolution), some of which presumably become conserved as new developmental genes in the next replication cycle.

Our developmental genetic toolkit, for example, is larger than a worms, but contains mostly the same genes, plus new ones. We cannot typically alter the original conserved genes, we can only add to them. The only way out of this growing constraint, for the most complex systems, is for development to move evolutionary processes to a new level of hierarchy in complexity. For example, no new animal body plans have emerged since the mid-Cambrian, 470mya. All the most useful varieties have found their niches. But Hox gene duplication, to create complex brain plans at the end of the Cambrian allowed a profusion of new brain types to emerge thereafter. Perhaps 10 mya, from one of those experiments, a new "hierarchy" of social-symbolic evolution began, inside hominid brains. Far more recently still, a new "hierarchy" of extra-biological (technological) evolution began in earnest with mass production in the 20th century, and is continuing ever faster as automation and AI emerge today.

But in the far future, even the creation of new and more innovative substrates seems likely to eventually end, as only a fixed number of new hierarchies may be producible in any physical universe. Thus it may be the fate of all universal systems, and of the universe itself, to eventually become fully developed and ergodic (incapable of generating novelty or new learning). At that point, only replication (a new universe seed, in a new environment) would allow the fully developed system to enter more complex domains (Salthe 1985,1993,2010).

Thus there are a variety of levels of hierarchy at which evo devo concepts can be applied, and evo-devo biologists believe developmental processes and genes must themselves act to constrain evolutionary processes, in ways not yet 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 adaptation, and such complex, controversial, and potentially scientifically clarifying topics as convergent evolution.

The riddle of development and the challenge to cosmology

Proliferation, migration, and differentiation in embryonic development.
There is nothing in science more magnificent and more mysterious than biological development, including genetic, embryonic, organismic, and psychological development. How is it that developing organisms can reliably converge on far-future form and function (from the molecular perspective), under chaotic and variable environmental conditions? How is this done with just a small percentage of highly-conserved developmental genes? Development employs stochastic, contingent, and selectionist processes, presumably ranging from quantum to macroscopic scales, in service to statistically deterministic, modular, hierarchical and cyclic emergent change, from conception to organism, and from organism to reproduction, senescence, and death (recycling). Our mathematical models of development are incomplete today, but they continue to make progress. Our models of evolution, of random genetic reassortment and selection in populations, are much more advanced. Development also involves teleology, or the assumption of goal-driven, end-seeking behavior, including successful replication. For these and other reasons, most scientists have focused on the idea that our universe may be evolving, while ignoring the idea that it may also be developing. This oversight, more than any other, has motivated the creation of the EDU commmunity. The great challenge to cosmology today is to change this state of affairs, to learn from biology to better understand universal change.

Biologically-inspired hypotheses for cosmological change offer us a number of predictive models of the dynamics and large-scale properties of the universe. This is necessary for establishing the value of ED explanatory approach, but only falsifiable predictions can establish (or negate) its legitimacy. Unfortunately, falsifiablity is not easy in our present level of cosmological understanding. Whenever these hypotheses appear (and we shall see some below) we may need to investigate many details before concluding that that the hypothesis is impossible or unfeasible. In such circumstances, it is best not to jump to negative conclusions on the basis of the greater familiarity that science has had to date with mechanistic, bottom-up reductionism. Biological development itself shows us that fantastic complexity can be built, in the right environment and with the right parameters, via primarily holistic, top-down causal developmental processes.

Like living systems, our universe broadly exhibits both stochastic and deterministic components, in all historical epochs and at all levels of scale. It has a definite birth and it is inevitably senescing toward heat death. The idea that we live in an “evo devo universe,” one that has self-organized over past replications both to generate multilocal evolutionary variation (preselected diversity), and to convergently develop and pass to future generations selected aspects of its accumulated complexity ("intelligence") is an obvious hypothesis. Yet very few cosmologists or physicists, even in the community that theorizes universal replication and the multiverse, has entertained the idea that our universe may be both evolving and developing (engaging in goal-driven, teleological, directional change and a replicative life cycle). There is a reasonable frequency of discussion, in the cosmology and astrophysics literature, of the idea of universal evolution. But none of it takes an evo devo approach. We find plenty of random, Monte Carlo models of change, applied to our universe's initial conditions (eg. various chaotic inflationary multiverse models; Linde 1992), but no models in which adaptive complexity emerges via evolutionary development in replicating universes in the multiverse, just as it does in all living replicators, and in several nonliving ones, such as hierarchical prebiotic chemistries on the path to RNA, and hierarchical populations of increasingly chemically complex stars. Even our best current models of universal replication, like Lee Smolin's cosmological natural selection, do not yet use the concept of universal development, or refer to development literature, or to any theories of intelligence. Yet intelligence is an emergent property that it is reasonable to expect may need to be accounted for in our future cosmology.

Our current cosmological models are implicitly developmental, but do not yet use biologically-inspired models of cosmic evolution, development, and replication. This is due to multiple reasons, notably the lack of multidisciplinary communication among the fields, but also due to the extreme predominance of reductionist, bottom-up explanatory approaches in physical science. Only recently has the academic climate began to change, with calls for a balanced approach, incorporating some holistic and top-down elements (e.g., Vidal 2010; Ellis, Noble, and O’Connor 2012; Ellis 2015; Adams et al. 2016). In some ways it is a return to the ancient organicist tradition, in which philosophers found value in comparing the physical universe with a living organism (for example, Book 4 of the Meditations of Marcus Aurelius).

Organisms are evolutionary, and most of their genes recombine and change to generate diversity, but they are also developmental, and a small subset of their DNA, on the order of 5% per the 95/5 rule, cannot be changed without disastrous effects on development. This DNA has become very finely tuned, over many past replications, for the production of complex, path-dependent modularity, hierarchy and life cycle in all complex metazoans.

In the same fashion, a handful of our universe's fundamental parameters appear breathtakingly finely tuned, in their mathematical values, for producing stable, long-lived, complex universes. Just as we observe in all living systems, the most parsimonious explanation for this incredible developmental fine tuning is a history of past universal replication, under some sort of selection, and with path dependency (conserved inheritance) of those parameters that express developments that aid in universal selection. In living systems, developed properties like intelligence, immunity, and morality strongly alter previously locally contingent environmental selection processes toward organism improvement and survival. It is an obvious hypothesis that the evolutionary development of such emergent properties may be conserved in the universe as well, if it is also a replicating system under selection. Yet at present, the scientists exploring the fine-tuned universe problem presently do not use the phrase "universal development." Instead, we find fine-tuning research disproportionately dominated by intelligent design creationists championing the idea of fine-tuning as "evidence for God". Perhaps as a result, the field remains professionally controversial for orthodox science, and a minority of astrophysicists, seeking to debunk theists, argue that fine-tuning, beyond the weak anthropic principle (observer-selection effects) doesn't exist (Adams 2008; Stenger 2011; Carroll 2016). But since the anthropic principle was first clearly articulated in cosmology (Carter 1974), another community of scientists have offered evidence that such tuning appears baked into our standard model of physics and empirically observed cosmology (Barrow and Tipler 1986; Rees 1999; Smolin 2006,2012).

The primary bias that exists in our cosmological models today is not observer selection bias, which is real but overrated. The primary bias at present is our failure to consider the concept of universal development, the idea that our universe's special initial conditions and stunning internal complexity are likely self-organized, via evolutionary development, just as our initial conditions and complexity have self-organized in all living systems. If our universe is a replicator, then evo devo self-organization is the most parsimonious explanation for the surprising levels of fine-tuning, massive parallelism, and fitness for life we find in our universe, not randomness alone, and not "design." See the fine-tuned universe hypothesis - early evidence for universal evolutionary development below for further discussion.

Our leading scientific theories of universal change are presently missing the concept of evolutionary development—the best model we presently know for managing complexity. To correct this oversight, cosmologists, astrophysicists, geochemists, astrobiologists, information theorists, philosophers, scholars of Big History and other scientists considering universal change must improve their understanding of biological evolution, biological development and evo-devo biology, and consider how they may well apply to the universe as a complex system. So too must our scholars of long-range biological, social, and technological change consider how their theories and models may be improved by a better understanding of processes of universal evolution and development. For more on this perspective, see Humanity Rising: Why Evolutionary Developmentalism Will Inherit the Future (Smart 2015).

Do we live in an evo devo universe? The EDU hypothesis

Acorn and oak tree
The developmental 'tree of differentiation' of tissue types. As important as Darwin's tree of speciation, perhaps. To see it, we have to take an intra-organismic view. Some universal change surely works like this. Until we have good models for it, we are in the dark.

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, with its characteristic leaves, shape, and behavioral proclivities. 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, diversity-generating 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, moving from a highly defined initial seed, to a very flexible, undifferentiated, and totipotent embryo, as we see in our universe's pregalactic era, then to an increasingly specified and constrained set of outcomes, like the increasingly terminally differentiated structure of the oak tree, or the terminally differentiated tissue types that emerge in a developing embryo, as depicted on a "tree of differentiation" (picture right), an organismic equivalent to the Darwinian "tree of life". 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 "terminal destinies" lie ahead, both for us and for all intelligent life.

Our intelligence allows us to take these larger scale and longer time frame views on our reality, even though we are physically stuck in one small corner of our universe, due to astronomical distances between the most complex bits. The scale and isotropy of our particular universe, and its severe migration and communication constraints, may also have been self-organized by the universe to maximize the local evolutionary variety of each intelligence prior to contact. If universal intelligence plays a nonrandom role in universal replication, as it does in living systems, a bio-inspired case for the emergence of our kind of massively parallel yet apparently intelligence-compartmentalized universe can be made, as well as the prediction that a mechanism must exist for all end-of-universe intelligences to eventually be able to compare and contrast their incomplete yet different models of reality. Taking such big picture perspectives as thought experiments is one of many great things intelligence does for us. It allows us to regularly shift our viewpoint to all aspects of any system we can model, either in our heads or in our computers. In a cosmic perspective, we can also see that our computers are rapidly becoming the new leading local intelligence. They seem likely to soon exceed us in their adaptiveness, immunity, and intelligence, and become a fully autonomous form of postbiological life. This transition to a new level of hierarchical complexity (and presumably, consciousness) may predictably occur on all planets that harbor intelligent biological life.

Darwin's tree of life (speciation), 1837. The current leading scientific view is that this (increasing unpredictability and contingency) is how almost all change occurs. That view ignores the hypothesis of concurrent universal development.

The evo devo universe (EDU) hypothesis (Smart 2008) 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 selective Environment.

The cosmological natural selection (CNS) hypothesis (Smolin 1992,1997,2004), in which our universe replicates via black holes, is one such evo devo model. In CNS, black holes can be considered the "seeds", the universe the "organism," and the multiverse the "environment." CNS remains controversial (see Vaas 1998 for one review of questions to be resolved), but has much to recommend it as well. See Gardner and Conlon (2013) for an evolutionary biological approach to CNS using the Price equation to model selection for black hole replication. Much more such interesting work will be needed if CNS is to explain more of the universe's evolutionary and developmental structure and function from an evo devo frame.

If our universe has these general 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, to better understand 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 pursue both evolutionary goals (eg, to create/innovate/experiment) and developmental process (to conserve/sustain/discover), and seek to harness these two apparently fundamental processes to greater individual, organizational, and societal adaptiveness. One perspective on what that might look like can be found in (Smart 2016).

The fine-tuned universe hypothesis: Early evidence for universal ED

Universe systematics must partition into evo and devo processes, if our universe is an evo devo system (Smart 2008)

The fine-tuned universe hypothesis (Rees 1999,2001) can be best understood as an important and early example of universal evolutionary 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 (Smolin 1997). 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.

The proposition that our universe's laws are finely-tuned for various evo devo outcomes can be made in a variety of ways. These claims can be considered various forms of the anthropic principle. In its most useful variation, the anthropic principle is the idea that our universe's initial conditions and laws seem improbably biased toward the production of intelligent observers (Barrow and Tipler 1986). But there is a more fundamental bias that must be considered when evaluating fine-tuning models, the bias that results from observer-selection effects. Is the particular kind of physical and mathematical universe we live in logically necessary, if there are intelligent observers around to ask questions about it?

It does seem plausible that we must have a quantum universe in order to have a universe with observers. So that level of observer-selection bias does seem to exist, at least. In perhaps the best-known "weirdness" of quantum physics, we observers alter physical reality (quantum states) by the manner in which we choose to observe them. But the apparent necessity for quantum physics in our observable universe in no way tells us that fine tuning doesn't exist. The kind of quantum physics we have, and its relation to the rest of our physics, may be tuned for the necessary emergence of not just observers, but of intelligent observers, of "mind." Quantum physics doesn't presently integrate fully with other physical and mathematical features of our universe, such as the fundamental parameters, general relativity, symmetry, information, and meaning (whatever that is). So we don't know yet what our most fundamental universal theories are. As we don't yet have the ability to definitively answer such questions at present, the fine-tuning debates will continue, and continue to be productive.

Most physicists were strongly opposed to the teleological (purposeful, directional) idea of fine-tuning, when the Barrow and Tipler book emerged in 1986. Today, many leading physicists, like Leonard Susskind (2006) and Steven Weinberg (2007), now argue that multiverse models offer us the simplest explanation (principle of parsimony) for the mathematically improbable levels of fine tuning we find in several of our fundamental physical constants, and as a result, in the strengths and nature of the four forces in our Standard Model of Physics. So we have seen a shift of many leaders in the physics community toward multiverse explanations of fine-tuning, and thus an implicit recognition that our universe's apparent fine-tuning is a real problem that must be addressed. In the most dramatic example presently known, the empirically observed value of our universe's cosmological constant appears to be tuned to one part in ten to the power of one hundred and twenty. Any imperceptibly small change in that constant would lead to either near-immediate collapse or destructive inflation of the early universe. Likewise, Planck's constant, electromagnetism, the nuclear weak and strong forces, the gravitational constant, the neutron-proton mass difference, and the masses of particles in early inflation after the Big Bang, and several other aspects of our physical universe appear fine-tuned for the production of long lived universes that support high levels of emergent complexity.

But there's even more to explain, because not only does our universe support improbably high levels of emergent complexity and mind, it supports continuous accelerating complexification in special environments. We find a variety of self-stabilizing features, like the Gaia hypothesis (in its more rigorous form), that are improbably life-stabilizing on the early Earth. Life's accelerating complexification reliably produced many social tool users, and in humans, an accelerating intelligence, immunity, and morality in recent millennia. Consider how both the frequency and severity of global social violence have statistically declined over human history, even as our potential for committing acts of violence at scale, via science and technology, has steadily grown (Pinker 2010). Empirically, this self-stabilizing aspect of complexification, in the geophysical processes of Earth, the intelligence produced by life, and in human civilization, seems unlikely in a randomly generated universe.

We should also explain the impressive simplicity and comprehensibility of (most of) the mathematics that underlies nature. According to Leslie (2013), Gottfried Leibniz (1686) was perhaps the first to argue that while some mathematical equation could be found to fit any curve one might draw, the vast majority of the set of possible curves and equations would be exceedingly complex. Similarly to Leibniz, Vilenkin (2006) argued that one would expect "horrendously large and cumbersome" mathematics underlying a typical randomly derived universe in a multiverse ensemble. Yet we find only a very special subset of mathematics and physics in our universe, a subset that is unreasonably effective and simple for human minds to understand (Wigner 1960), and one apparently highly tuned for the support of universal complexity.

Finally, we should explain why our universe appears to use massive parallelism in its production of intelligent civilizations, and keeps them spatially separated for the majority of their evolutionary development. It's as if the universe seeks to promote maximum evolutionary diversity in each civilization, while developmentally guiding each of them to a future in which their evolutionary learning might be instantly shared (Smart 2012). In short, there's a lot of apparent tuning that needs explaining.

If our universe is a replicator, and intelligence plays a role in its replication, good arguments can be made for the adaptive value of many of its structural and functional features. Evo devo cosmological natural selection for intelligence (CNS-I), over many past replications, may be the most parsimonous explanation for the kind of universe we presently inhabit.

Here are four important levels of evo devo based (replicative, self-organizing) fine-tuning models:

  • Level I. Our universe appears fine-tuned (self-organized) for the emergence of complex, long-lived universes and black holes (Smolin 1997, Rees 1999, Gardner and Conlon 2013).
  • Level II. Our universe appears fine-tuned for the fecund (multilocal) emergence of M-class stars and biological life (Henderson 1913, Barrow etal. 2008, Lewis and Barnes 2016).
  • Level III. Our universe may be fine-tuned for the fecund and accelerating emergence of intelligent life (Piel 1972, Sagan 1977, Moravec 1979, Dick 1996, Kurzweil 1999,2005).
  • Level IV. Our universe may be fine tuned for the fecund and accelerating emergence of increasingly intelligent, immune, and moral (defending evo devo values) forms of complex life (Smart 2008,2012,2016).

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 fine-tuned cosmological complexity. We can imagine many others that are also consistent with evo devo models. Particularly promising given their functional relationship to biological evo-devo models, are models and hypotheses of CNS-I (CNS with Intelligence), Levels III and IV above (see Price 2017 for one example).

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.

The riddle of convergent evolution: Limited forms most beautiful

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, species morphology or function is often seen to converge to particular "archetypal forms and functions" in a variety of environments.

Human vs octopi camera eyes (Ogura etal 2004)

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 bias, 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 (humans have a blindspot, however, as our eyes evolved via a different evolutionary developmental history than invertebrates, see picture left). One can easily advance the argument that, in universes of our type, eyes, though first created by a process of evolutionary contingency, become a developmental archetype, an adaptive optimization for the great majority of multicellular species in Earth-like environments.

Presumably, the previously rapidly-changing "evolutionary" gene groups that led to eye creation become part of an increasingly conserved "developmental" genetic toolkit for all eye-possessing species in environments where eyes are adaptive. Eventually, due to both path dependency and emergent hierarchies, some of subset of these gene groups should be 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.

Charles Darwin ended his foundational text on evolution, On the Origin of Species (Darwin 1859) with a well-known phrase, predicting "endless forms most beautiful" continuing to evolve. But as George McGhee describes in a well-titled book, Convergent Evolution: Limited Forms Most Beautiful (McGhee 2011), preexisting physical and informational optima in our particular universe mandate that only a limited subset of forms and functions will ever emerge in biological evolution. Creative evolutionary process is continually reconverging to such forms, driven there by functional (environmental) and developmental (genetic) constraints. Better understanding and modeling convergence is one of the great challenges of modern evolutionary biology.

Less-optimizing convergence (LOC) versus optimizing convergence (OC)

Adaptive landscapes allow both local and global optima

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 in parameter space, 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.

  1. 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.
  2. 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, the escalation hypothesis (Vermeij 1987). 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 biologists call 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. We also need to know whether there are any other paths that can lead, in a competitive timeframe, toward a competitively superior new form of adaptiveness. If not, then we may have discovered a developmental portal, a global optimum that represents a bottleneck, a singular pathway toward greater adaptation at the leading edge of local complexity. Organic chemistry, RNA, photosynthesis, and oxidative phosphorylation are all potential examples of portals that all universal life must pass through first, on the way toward greater adaptive complexity. They may be the only global optima on their landscapes, at the relevant timeframes, that will allow the creation of vastly greater adaptive complexity.

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 complexity. 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 visible animals in that intelligence-leading environment needed eyes, or other highly effective defensive strategies, 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, bioluminescence, 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 distinguish the much greater variety of examples of less-optimized convergence from the most highly optimized forms.

Optimizing convergence as accelerating and stabilizing evolutionary development, on many system scales

Embryogenesis is an evo devo process.

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 evolutionary developmental process, with both "random" and creative evolutionary search, and predictable convergence, directionality, hierarchy, modularity, life cycle, and perhaps other features found in biological development, is being followed, at some larger systems level. Viewed from the perspective of the individual actors (molecules), we see mostly stochastic and contingent processes, along with a few convergent and hierarchical processes, in such evolutionary and developmental phenomena as embryogenesis. Evo devo models assume such a process is going on even at the universal scale, and thus that some examples of convergence can be better understood in complex systems theory as not simply evolution, but evolutionary development.

  • Universal change offers many examples of not only evolutionary but apparent developmental change. 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.
  • Stellar-Planetary change offers more examples. When we look down from early universal change to the stelliferous era, and the genesis of our life-permissive 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.
  • Biogeography and Ecology offer more examples of not only evolutionary but apparent developmental change. In 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. For many more examples, including intelligence traits, see Conway-Morris (2004,2015), McGhee (2011), Losos (2017) and our list of examples of convergent evolution in species morphology and function. In ecologies, we can identify predictable patterns of ecological change, including ecological succession, ascendancy, and panarchy.
  • Culture, Science, and Technology change offers yet 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 parallel invention and/or convergent development of archetypal scientific and technological inventions like fire, language, stone tools, clubs, sticks, levers, written language, mathematics, hydraulic empires for our first great cities, wheels, electricity, computers, artifical neural networks, 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 these archetypes and algorithms 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, and more generally, the least action principle (Georgiev etal. 2015).

In each of these rough hierarchies of complexity, 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.

There are two more curious features of this predictable developmental trajectory, across all of these environments, that should now be mentioned.

  1. The first is the ever-faster complexification rates seen 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.
  1. The second is the increasingly informationally stable (immune, antifragile) nature of complexity in ever more complex environments. If a particularly adaptive (convergent) species is wiped out by an asteroid, the time to recreate its useful unique information, and the probability of its specific recreation, both seem low. Yet if a particularly adaptive (convergent) culture was wiped out, the time to recreate its useful unique information, and the probability of its specific recreation, both seem significantly higher (Dartnell 2014). There is something about culture, science, and technology that seems likely to make the information they produce more stable to destruction via environmental fluctuations. Perhaps it is simply that increasing intelligence allows increasing adaptive strategies for survival. Some kind of nonlinear input-to-state stability (a form of Lyapunov stability) may emerge as intelligence's potential to moderate inputs grows. Perhaps it is that increasingly intelligent systems tend to have both more immunity and morality (both have been proposed as subtypes of intelligence), though this view is controversial, given recent human history with advanced technology. Or perhaps it is simply that increasing intelligence allows new forms of niche construction (environmental engineering), which afford the ability to move one's core complexity to an entirely new substrate with more intrinsic stability. My bet would be on the latter as the most powerful of many potential reasons for growing stability during complexification. Several scholars have argued that humanity appears just a few decades away from being able to port its essential complexity, in body and mind, into a technological substrate (substrate shift). Such postbiological entities seem likely to be vastly more stable to destruction via any imaginable universal process, and far more redundant, than today's bio-dependent culture, science, and technology. Such entities should be able to control molecular nanotech, fusion energy, and perhaps even subatomic processes (femtotech), and no longer require either planets or functioning stars to maintain their existence (Smart 2008). Due to accelerating change, such stable new entities also seem likely to arrive much earlier than most of us presently expect.

Both of these features, acceleration and progressive informational stability, suggest that today's current models of existential risks are likely to be overstating the near term risk to our species of many apparent species-threatening events (for a detailed review of such risks, see Bostrom and Ćirković 2008). The time for which such risks actually threaten our informational complexity seems to be rapidly decaying. We appear to be on the edge of entering a far more stable substrate for life and intelligence, in a cosmologically insignificant fraction of future time.

Observation selection effects: The challenge of assessing them for fine-tuning and convergence

Any form of reasoning about traits or properties which constitute observers, or that are logically or physically necessary for the existence of observers, is subject to observation-selection effects and biases. The importance of these selection effects and biases has only recently begun to be fully appreciated. For example, the number of small bodies’ (asteroids and comets) impacts in Earth’s history is constrained by our existence at the present time through the “anthropic shadow” effect (Ćirković, Sandberg, and Bostrom 2010).

Several detailed reviews of observation selection effects exist (for example, see Bostrom 2002). Observer selection arguments and models are often used to critique the fine-tuned universe hypothesis. Unfortunately, our cosmological models remain quite primitive, so it is easy to argue that either fine tuning or observer selection bias are more important in such models. But even more fundamentally, as I have argued before (Smart 2008) all observer selection models in common use depend on a random observer self-sampling assumption, a random distribution of possible universes, or some other random, Monte Carlo-style mathematical framework in their evaluations. In other words, they assume we live in an essentially random, evolutionary universe, and this is a major limit to their utility.

Consider that if we actually live in an evo devo universe, such math must itself be incorrect. If our universe is not just randomly (contingently) evolving, but it also nonrandomly developing, then some subset of its critical probability distributions (informational and dynamical) will continually be skewed in the direction of the universe's developmental trajectory, given its special initial conditions and constraints. As complexity and hierarchy grow in local environments, those environments will further bias a special subset of locally constraining, nonrandom developmental processes to occur. Such developmental biases may be why accelerating complexification occurs in special environments (acceleration bias) and why spontaneous abortions (miscarriages) are so frequent early in gestational development, but so rare late in development, with miscarriage frequency in humans declining from 40% of all conceptions to 0.1% of pregnancies at 42 weeks of gestation (Rosenstein 2012). Presumably, the more developmentally complex both the fetus and the gestational environment become, the less often that any survivable random perturbations of a standard size or duration are disruptive (stability bias).

Math that describes an increasing developmental bias toward both acceleration and toward growing informational stability during complexification in special environments is the kind of math we may need to properly model developmental processes, and to properly understand complex observers. Such observers are not random, they are privileged, the more complex they are. We surely do not live in an anthropic universe, if by that we mean one self-organized to produce biological humans. But we may well live in a noetic (intelligent) universe, one that is self-organized to produce accelerating and increasingly stable intelligent observers (Hoyle 1983; Gardner 2007).

Even the frequency of evolutionary convergence, versus presumably much more commonplace evolutionary contingency in biological change, is a complex issue we don't yet agree upon. In the Signor-Lipps Effect, because rarer species are much less common in the fossil record, and the record itself is so sparsely sampled, rarer species will seem to disappear long before their actual time of extinction, simply due to chance. This makes the timing and speed of mass extinctions, and the ancestry of specific genera both much harder to determine from paleontology alone. As one consequence, quick and dramatic extinctions can go undetected, as they may look gradual due to selective sampling of a poor record, a classic observation selection effect (Signor and Lipps 1982).

Unfortunately, even the consequences of known biases like this are not clear. On one hand, if extreme ecological perturbations have overturned entire faunas via mass extinction events at much greater rates than we presently appreciate, and more rapidly switching Earth's macroevolutionary regimes (Jablonski 1986), then the contingency that life is subject to due to “normal” evolution may be less than is currently theorized. Our environment, not evolution itself, may be our greatest source of contingency. On the other hand, it has long been argued that environmental catastrophes themselves act as a major (and perhaps even the primary) catalyst for evolutionary innovation (Gerhart and Kirschner 2005; Bhullar 2017). We know that immune systems depend on catastrophe to get stronger, so to evolution may depend on it to innovate. Without periodic catastrophe, some biologists have argued that evolutionary contingency must steadily reduce with time (Salthe 1993; Shapiro 2011). In stable niches, stabilizing genes presumably gain the upper hand over innovating genes. At other times, evolutionary innovation itself has provided the catastrophic environmental change, spurring further evolutionary innovation, as in the Great Oxygenation Event, dieoffs due to oxygen-excreting cyanobacteria, the End-Ediacaran extinction of large sessile organisms due to the emergence of mobile sighted animals during the Cambrian Explosion, and the Permian-Triassic extinction, perhaps precipitated by the emergence of Methanosarcina, a methane-synthesizing archaea (Ray 2017).

Geerat Vermeij has offered a particularly interesting and relevant argument supporting high frequency of convergence in evolution based on the logical asymmetry between singular and multiple events in the incomplete terrestrial fossil record (Vermeij 2006). If some specific character, call it Z, demonstrably evolved multiple times in different lineages in different ecological conditions, this is clearly an argument for the convergent nature of Z. However, if Z evolved only once to the best of our knowledge, it is still not an argument for uniqueness, since the incompleteness of our record may hide repeated instances of the independent evolution of Z. This logical asymmetry has intriguing consequences when a wider set of various important innovative characters scattered over all of the history of life are analyzed from a Bayesian point of view. Vermeij was able to show that the alleged singular innovations tend to be either more ancient or to appear in small clades. Small clades, in turn, would be invisible in the fossil record if located in more distant past; thus, purported unique innovations in small clades might indeed be only the latest instances of what is in fact multiple, convergent evolution of an advantageous character.

Vermeij concludes that few innovations are ever truly unique: “Purportedly unique innovations either arose from the union and integration of previously independent components or belong to classes of functionally similar innovations. Claims of singularity are therefore not well supported by the available evidence. Details of initial conditions, evolutionary pathways, phenotypes, and timing are contingent, but important ecological, functional, and directional aspects of the history of life are replicable and predictable.” (Vermeij 2006, p. 1804) Insofar as key evolutionary innovations are largely determined by universal principles of physics and economics, they will lead to widely-to-universally useful designs. This is the classic view of environment-driven convergence.

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.

A genetic adaptive landscape
Search basins and portal paths, many at similar fitness levels (Crutchfield and van Nimwegen 2002)

From an adaptive landscape (phase space) perspective, if ED is occurring, as the evolutionary “search” landscape gets more diverse and locally complex, certain portions must convert into developmental funnels, then portals. These portals must also work together to periodically 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.

Fortunately, evo-devo genetics models, as they seek to differentiate developmental and "evolutionary" gene fitness landscapes, will have to turn to such models as our theory, tools, and data advance. Unfortunately, there are many problems with current adaptive and fitness landscape models in depicting the hyperspace of structure and function, and as critics of the adaptive landscape metaphor point out (Kaplan 2008) few models incorporate any concept of probability of movement across the landscape. In useful landscape models, potential portals would have to emerge as persistent, and theoretically globally optimal peaks (or in a more useful depiction, valleys) on adaptive fitness landscapes (picture left). These models will eventually have to evolve into network-based models with search basins and portal paths, which include both "evolutionary" tangles of similar-fitness landscapes and portals (Crutchfield and van Nimwegen 2002, picture right) as well as regions that use portals to predictably transition to globally optimal, hierarchical and developmental forms, landscape locations offering the greatest resource (space, time, energy, matter) efficiency or density of adaptation.

Protein folding funnel (biomolecular evo devo)

Another field that will help evo devo models advance will be protein folding, which already use funnel (portal) landscapes to depict 2D to 3D transformation of protein structure, involving both energy minimization and information production or conservation, a key example of biomolecular evolutionary development. 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) as 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. Speculative proposals like the transcension hypothesis (Smart 2011) and the stellivore hypothesis (Vidal 2016) extrapolate accelerating densification trends in adaptive systems to their universal limit, a black hole. Acceleration also happens via informational or computational processes as well. For that we may need a better intelligence theory.
  • Intelligence theory (cog evo devo) - The Baldwin effect is the recognition, beginning with James M. Baldwin in 1896, that learned behavior affects an organisms reproductive success. It is a modest start in understanding learning and intelligence in evo devo systems, but we must go much farther. The better we understand the evo and devo roles for such cognitive processes as intelligence, immunity, and morality in bioadaptation (and today we often do not) the better we may understand their adaptive role for the universe as a replicator. To build a better intelligence theory, we will need advances in such intelligence-related topics as:
    • Intelligence as immunity - Strategies for modeling and differentiating self from other, and passively and actively countering degradation and predation are among life's most critical intelligence processes. Antifragility is a closely related concept, a set of adaptive strategies that makes the immune system both smarter and stronger when it is appropriately stressed. Chronic stress and stress avoidance weakens immunity, while cyclic stress and right-sized catastrophes strengthen immunity.
    • Intelligence as empathy-morality (collective intelligence) - Collectives figure out positive sum games, rules and algorithms (morality, ethics) that involve not just self- and world-modeling but other-modeling and empathizing, which makes the collective smarter and more adaptive than the sum of previously isolated intelligences. As humans and other intelligent animals have varying degrees of both intra- and inter-group and inter-species ethics, it is reasonable to presume some degree of developmental (universal) ethics will accompany interplanetary civilization contact as well.
    • Intelligence as foresight - Organisms use their intelligence to generate foresight, which improves both their chances of survival and their power over their environment. They practice foresight based on models of self and world encoded in the structure and function of their genes, organism, and environment. There are three major types of foresight: probable, possible, and preferable futures. As intelligence develops, organisms get better at prediction. They also get better at evolutionary innovation, which becomes a bit less trial-and-error, and more intelligence-guided. For one information-centric view of evolutionary process, see Shapiro 2011. Finally, they get better at generating preferable futures, shared plans and goals that increase collaboration and collective capacity to survive.
    • 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.
    • Intelligence 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.
  • Information theory - Convergent evolution in biology can be modeled as the result of networks made up by biomolecules or other agents that are organized and structured by information hierarchies emerging via top-down causation. The emergence of modularity and of functional equivalence classes in suboperations or subroutines – both in biological and technological systems – can be explained via such information hierarchies. Top-down causation describes the process whereby higher levels of emergent informational organization in structural hierarchies constrain the dynamics of lower levels of organization. In a typical reductionist paradigm it is assumed that purely physical effects entirely determine the dynamics of lower levels of organization and, by extension, at higher levels as well. But an emerging school of investigators hypothesize that the transition from non-life to life, abiogenesis, requires a top-down transition in causation and information flow (Flack 2017; Walker et al. 2017).
  • 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) to fully understanding such scientific challenges as convergent evolution, galactic development, and the origin of life. If validated, the benefits we stand to gain, via better collective foresight, also seem comparatively immense.

Evolutionary developmental adaptation: From autopoesis to the evo devo universe hypothesis

Autopoesis is a term introduced by Chilean biologists Humberto Maturana and Francisco Varela (1974,1980) to describe the self-reproducing and self-maintaining chemistry of living cells. It became popular with a handful of complex systems theorists in the late 20th century to describe the capacity of complex systems, and their network of subsystems, to self-reproduce and self-maintain. Autopoesis scholars sought to find general systems rules applicable to a variety of self-reproducing systems, including not only living systems, but to ideas, behaviors, algorithms, and technologies. Autopoetic models were among the first to seek a general physical and information theory of adaptation applicable to all stably reproducing complex systems. They were also among the first to argue that a general information theory, including a theory of cumulative intelligence of the replicator and its environment, would be needed to understand both self-reproduction and self-maintenance in complex adaptive systems.

While they made little scientific progress at the time, they were focused on what we might call the right question: the physical and informational sources of adaptation, and the ways adaptation changes over time in complex environments which themselves may be engaged in replication as well. At the least complex end of the spectrum, all stable replicating systems depend on the emergence of some set of predictable action-reaction couplings to their environment. Stars are autopoetic systems, dependent on physical action-reaction processes. Moving up the chain of information-production rate density (a form of complexity), a variety of self-reproducing prebiotic systems (clays, RNA, protein polymers) are dependent on not only physical but also chemical action-reaction processes. On the path to life, certain self-replicating chemical systems developed autocatalytic networks (Kauffman 1993), and some developed sensory-motor cognitive Bayesian (predictive) chemical networks, including memory networks, observable in single-celled organisms like Paramecium, Amoeba or Stentor (Bray 2011). Eventually, a subset of these replicators developed multicellularity, another subset developed specialized neural networks, and a subset of those, self-awareness. Understanding these increasingly complex set of adaptations, and the necessary emergence of life and mind as adaptations in certain environments, is one of the main challenges of modern science.

Scholars in such complementary fields as the origin of life (Smith and Morowitz 2016; Pross 2014), computational astrobiology (Pohorille 2012), artificial life and information theory (Adami 2016), evolutionary escalation (Vermeij 1987), top-down causation (Walker et al. 2017) and evo-devo theory (see above) have all made progress in recent decades in understanding how successful cumulative replication changes both the replicator and its local environment. Continued progress in such fields will be critical to developing better models of adaptation.

Neo-Darwinian models of evolutionary adaptation, such as the adaptive landscape theory of Sewall Wright (1932) seek to model adaptation as phenotypic fitness to the environment, in some genotypic, morphological, or functional parameter space. Such models, while they have been tremendously useful, are also deeply incomplete, as they do not allow that the environment itself may change in predictable and highly-nonrandom ways over time, as the growth of intelligence influences local environments, as described in the phenomenon of stigmergy/niche construction (Odling-Smee et al. 2003). They also do not consider that the selective environment itself may be both evolving and developing over time, changing the nature of selection and adaptation.

But if our universe itself is a replicator, as the evo devo universe hypothesis (Smart 2008) proposes, then it too is a selective environment that is not just evolving (experimenting, diversifying), it is also developing (complexifying, and engaged in a life cycle). From the perspective of biological evo-devo theory, much of this environmental complexification is both constraining, directional, progressive, and in-principle predictable, just as biological development is in-principle statistically predictable (though not always so in practice). If the universe is an evo devo replicator, at least some kinds of local environmental complexification will function to protect the replication and self-maintenance of the system (the universe). Any evolutionary (experimental, creative, contingent) activity that occurs within a developing organism must be increasingly constrained as that organism develops, in service to the organism's self-replication and self-maintenance. If our universe is an evolutionary developmental system, the local adaptive landscape must constantly be shifting toward certain developmental attractors, as evo devo complexity grows in certain local environments.

As with intrauniversal replicators, mechanisms that guide and protect universal replication may be very simple action-reaction physical and informational processes, such as those that statistically guarantee stellar replication, or the replication of prebiotic chemicals. Alternatively, depending on the extent of prior universal replication, such mechanisms may have started simple, but by now may be informationally complex developmental processes, similar to those found in developmental genes in living organisms, involving the emergence of planetary-scale social and technological processes that are functionally similar to biological intelligence, immunity, and morality. Such top-down causal informational mechanisms would be expected to guide the multi-local emergence of life and mind in the universe, as part of its self-maintaining process. From a functional perspective, mind might inevitably emerge in a universal replicator, just as it has in biological replicators, if intrauniversal intelligence plays a usefully nonrandom role in universal replication.

In summary, evo-devo biology may offer us the most complex and rigorous model for understanding not only convergent evolution in universal evolutionary development, but how adaptation itself must change in a universe that is itself a replicator. Once certain critical biological advantages, like eyes, emerge and are strongly adaptive in an environment, all the most successful replicators in those environments will have to employ that advantage. Once certain critical technological advantages, like digital computers and machine intelligence, emerge and are adaptive in an environment, a subset of replicators (individuals, organizations, societies) must use those technologies if they wish to remain at the leading edge of adaptation. In this view only evo-devo biology, and its successive processes of molecular, genetic, physiologic, and psychological evolutionary development, offer us a sufficiently complex analogy for understanding how adaptation may change in the universe, if it too is a self-reproducing, self-maintaining evo devo system.

The “tape of life” (“identical Earths”) experiment: Simulating ecological, biogeographic, and planetary ED

Cartoon of an evolutionary developmental Earth

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 (1998,2004,2015) 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 unpredictable or predictable processes alone will lead to insufficient views of how adaptation actually occurs. We must learn how they blend, and when those blends are adaptive.

We must also recognize that just as in biological evo devo, our science and simulation skills will be insufficiently advanced 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: Simulating Universal ED

The Cosmic Calendar: 13.7 Billion Years of Universal History On a 12 Month Calendar, Showing Universal Acceleration. Lovely CC image by Wikipedia author Eric Fisk

Let’s look now 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 observable morphological, functional, and informational features of our universe that have clearly accelerated on Earth since the emergence of life, as depicted from August afterward in the Cosmic Calendar metaphor (picture right), 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 genetically identical twins that turn out the same, we call “developmental.”

A phylogenetic tree (simulation experiment)

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), 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.

Today we can conduct primitive “simulation experiments” to explore the divergences and convergences we see in two model universes, but our science remains incomplete, and our simulations still do not capture all the reality they attempt to model. Fortunately, our experiments in simulating evo-devo phylogenetics (picture left) are going to lead the way to better simulations of all kinds of evo devo systems. If we live in an evo devo universe, our simulation 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.

Ambiguity of the word “evolution”, and the modern evolutionary synthesis

In the 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 or 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 three basic problems with the VIST model:

  1. VIST does not explicitly consider the concept of development, and of developmental genes and processes, which act 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.
  2. VIST does not explicitly consider cumulative Replication, and its growing constraints on organizational change, in any substrate, over cumulative life cycles. Replication is implicitly considered as the factor of “Time” in the classic VIST model. But it is not Time that causes biological change. Organic change occurs via cumulative and increasingly ergodic cycles of Replication (of the organism), within any substrate, as 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).
  3. VIST doesn't recognize that the natural environment may itself be not only evolving but also developing, if our universe is itself a replicator. As a result, the Modern Evolutionary Synthesis, our current standard in biological investigations, is biased toward the idea of an “accidental” universe, and “random” experimentation and diversity as the primary (or in some views, exclusive) cause of macrobiological change.
Conceptual schematic of Darwinian evolution (1859, white oval), the Modern Evolutionary Synthesis (post-1940, light gray oval) and current concepts in the coming Extended Evolutionary Synthesis (post-2000, dark gray oval) (Pigliucci and Müller 2010)

Evo devo models, whether in biology or in other replicating systems, help us eliminate the biases of both the original Darwinian VIST view of evolution (white oval at right) and of modern evolutionary theory (light gray oval), 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 also 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, evolutionary constraint laws (like the constructal law and various scaling laws), terminal differentiation of morphological complexity, the fine-tuned universe hypothesis, the presumed fecundity of Earth-like planets, the collective morality of social animals, and the Gaia hypothesis (in a more rigorous form) all come to mind. To explain such unreasonable adaptedness for complexification in our universe 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.

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 (MES) 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.

A global group of scientists exploring the EES, led by evolutionary biologists Kevin Laland and Tobias Uller, can be found at ExtendedEvolutionarySynthesis.com. Recently funded with an $8M grant from the Templeton Foundation, this group is doing very important work in developing a meta-Darwinian (including and going beyond Darwinism) view of change. Another group of scientists who share this perspective, led by biochemist and molecular biologist James Shapiro and physiologist Denis Noble, can be found at The Third Way of Evolution. This latter website is admirable, but not entirely error-free. As biologist Jerry Coyne points out at the Richard Dawkins Foundation, the web editor of the Third Way website, Raju Pookottil, who does not have biology training, once argued that life "carries the hallmarks of design." That is a useful critique (see my section on the Fallacy of Intelligent Design below) but Coyne's post also ignores the far more important contributions of the many eminent meta-Darwinist scholars listed at the website. Both of these, poor thinking and ultraorthodoxy with respect to evolutionary process, are common responses that we must guard against.

"Ultra-Darwinists" like Coyne and Dawkins have attracted this particular label because they appear to believe, and they often defend with an aggressive certainty, the ultraorthodox position that contingent evolutionary selection (neo-Darwinism) can be the only force driving macrobiological change. Though Darwinism has deep evidence behind it, and its models appear to aptly describe the vast majority of organic change, they also seem insufficient to explain a small subset of phenomena that appear developmental, not evolutionary. That subset includes the development of intelligence, and the increasingly nonrandom guidance of evolutionary innovation in intelligent systems (Smart 2008). In a similar way, the vast majority of change we can sample at the molecular scale in biology seems locally stochastic, randomly selectionist, and diversity generating ("evolutionary"), yet a small subset also seems deterministic, convergent, and predictively selectionist ("developmental"), particularly when viewed from larger or longer-range spatial, temporal, energetic, and material (STEM) scales.

The fallacy of “intelligent design”: Why ID, supernaturalism and religion are outside the scope of our community

This analysis should help explain why the Evo Devo Universe (EDU) 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 biochemist Michael Denton, can be argued to be pursuing science, but most are motivated by declared or undeclared 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. Given that there are fully naturalistic explanations for the sources of "design" we see in living systems, and given the intelligent design communities position on mixing religion and science, we seek to maintain a separation from the scholars of intelligent design, and to provide a fully naturalist scientific and philosophical community for those scientists who do not wish to engage in discussions of the supernatural.

This is not to argue that religious belief or religious community are not fundamentally important and useful as personal practices and social institutions. We all hold beliefs with respect to fundamental metaphysical questions. Besides the traditional religions, atheism, agnosticism, possibilianism, universism (my personal belief), and others are all fundamental belief systems. Many scholars have observed that religion was our first science, that religion always ventures first into areas of moral prescription where science cannot easily tread, that religious community has provided invaluable guidance and public benefit for millennia, and that our most socially successful religions continue to reform their beliefs and practices to be congruent with accelerating scientific knowledge.

As the futurist Ray Kurzweil has predicted, it is even reasonable to expect that self-aware artificial intelligences, when they eventually emerge, must evolve and develop their own set of religious beliefs (philosophies of universal purpose, meaning and value), as there will remain many areas of reality about which they will know little. No intelligence is ever godlike, and we all must have sets of unproven beliefs, and communities that share those beliefs. But as good practitioners of science and natural philosophy, we should attempt to make our beliefs explicit and public, and seek to test them with evidence and experiment in whatever partial ways we can. We should also strive to keep those beliefs and supernatural philosophies separate from the practice of science, and where we cannot separate them we should declare our influences. For these reasons then, and not for any anti-religious bias, discussion of religious belief is outside the scope of the EDU community.

Some have also argued (wrongly, in our view) that scholars in evolutionary convergence like Simon Conway-Morris, who accept funding from and publish with the Templeton Foundation and Templeton Press, which promote dialog between science and religion, have had their science tainted as a result of their personal religious beliefs. We say judge the science for its own sake, and leave religious discussions for religious practice, as science has no accepted framework to address them. Conway-Morris's science appears unimpeachable, to our eye at least.

Supernaturalism takes many forms, some quite subtle. Even otherwise deeply insightful works, like EDU scholar and complexity theorist James Gardner's Biocosm (2003), which some critics read as 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, must be more accurately characterized as intelligence-guided guesses at more adaptive forms and functions. Human science and engineering are always evo devo process, like all other natural processes. They might be 95% bottom-up creativity/experiment/serendipity, and 5% top-down discovery/optimization/prediction, if we were to guess a very rough ratio. They are never a fully top-down or future-omniscient "design". The universe, and all evo devo systems, are far too chaotic and contingent to allow such omniscient foresight.

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" as opposed to omniscient engineers, 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, perhaps even tuned for the robust emergence of adaptive complexity and intelligence, our improbably self-correcting geophysical environment, a subset of claims made in the Gaia hypothesis, 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.

Research questions

  • How do we best improve our physical theories of unpredictable evolutionary and predictable developmental process?
  • What improvements to complex systems theory, evo-devo theory, adaptation theory, optimization theory, acceleration theory, intelligence theory, hierarchy theory, life cycle theory, and other topics will help us better define and compare evo and devo process in all replicating complex systems?
  • How can we better define evolutionary and developmental process as sources of intelligence, in seeds (containing initiating evo and devo parameters), organisms, and environments?
  • What evo and devo goals (purposes, telos) can we discover for intelligent complex systems?
  • To what extent can we find modularity, reaction-diffusion systems, and other features of organismic ED in ecosystem ED? In biogeographical ED? In stellar-planetary ED? In galactic ED?
  • When we do not have the science or computing power to simulate biological ED, but must observe it ab initio (for the first time, as we must do in an organism we’ve never seen before, or if our universe is an ED system) what empirical tools and tests allow us to infer the developmental processes and emergences that may be coming next, at all stages of hierarchy and life cycle?
  • How do we best improve our models and tests, especially falsification tests, for the universe as an evo devo system?


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