What is Evolutionary Development?
Evolutionary development (“evo devo”) is a minority view of change in science, business, policy, foresight and philosophy today, a simultaneous application of both evolutionary and developmental thinking to the universe and its replicating subsystems. It is derived from evo-devo biology, an emerging set of theoretical and empirical approaches to understanding biological change. Evo-devo theory in biology proposes that evolution and development work in productive tension with each other to produce adaptive change in living systems.
To discriminate between science and systems theory, we will use the hyphenated term “evo-devo” when discussing the scientific discipline of evo-devo biology in this Guide, and the unhyphenated phrase “evo devo” when discussing the systems theory of evolutionary development, which can be applied to all replicating systems within the universe, and to the universe itself.
Whatever else our universe is, and allowing that there are big physical mysteries, like dark matter, dark energy, the substructure of quarks, and the nature of black holes still to be uncovered, reasonable analysis suggests that it is both evolutionary and developmental, or “evo devo”. Like a living organism, it undergoes both experimental, stochastic, divergent, and unpredictable change, a process we can call evolution, and at the same time, programmed, convergent, conservative, and predictable change, a process we can call development.
Evo devo thinking is practiced by anyone who realizes that parts of our future are unpredictable and creative, while other parts are predictable and conservative, and that in the universe, as in life, both processes are always operating at the same time.
To understand the interaction between evolution and development, think of a river. When we look at the river as a complex system, and take an up-close and bottom-up perspective, we are struck by the chaotic, evolutionary path of the stream across the landscape. The flowing water is constantly diverging, exploring all the possibilities available to it. Likewise, the path of any individual water molecule is always chaotic, contingent, and unpredictable. But when we look at the river from a big picture, top-down view, we see many predictable things we can say about it. Rivers flow from a set of higher sources (mountainsides) to a set of predictable lower destinations (lake, ocean, water table). All the water that doesn’t evaporate or get consumed is constrained to that kind of general universal behavior. Both views are valuable.
As our universal physical and information theory advance in coming years, I predict that evo devo thinking, applied on both universal and human scales, will become increasingly essential to understanding our past, present, and future. Why is this kind of thinking so important? Because it tells us how the world works, and that in turn tells what our best strategies are likely to be for the most adaptive foresight, leadership, and action.
For example, complexity scholar and venture capitalist Samuel Arbesman’s Overcomplicated: Technology at the Limits of Comprehension (2016) is an enlightening book exploring some of the ways our modern technologies are becoming like biological systems, which are diverse, always experimenting, always generating errors and small catastrophes, and which are now even beginning to manage their own complexity. As accelerating complexification continues, Arbesman argues that we need to move away from our previous dominance, and current social bias, toward what he calls “physics thinking,” which stresses simplicity, precision, predictability, and generality, toward what he calls “biological thinking,” which embraces diversity, experiments, chaos, error, and unpredictability, and above all, learning from the chaos and mistakes that occur.
In evo devo language, Arbesman is describing the limits of developmental thinking, and the many benefits of evolutionary thinking, as we’ll see. His book is a real contribution to technology leadership, yet I think we can and must say a lot more, to help our leaders anticipate, create, and manage change. We need better definitions and better hypotheses. Physics thinking and theory can be both evolutionary (eg, chaos and complexity) and developmental (e.g., mechanics and relativity), and so too can biology thinking. So Arbesman’s analogy, while it offers a quick insight into two competing views on the future, and tells us why one is far too overused at present, is not precise enough help us in many cases of strategy and action.
To be broadly effective, good leaders, foresighters and managers need a framework for understanding when and how much developmental thinking is useful in complex systems, and where it becomes dangerous, and when and how much evolutionary thinking is useful, and where it breaks down. This chapter, evo devo foresight, offers such a framework. To understand change on our local scale, I firmly believe we need to start with a universal view, where things are simplest and clearest, and work down from there, via UPGO foresight. We also need to start with science and systems theory, then move into the more popular and human-centric of the STEEPS domains. So let’s dive in.
When we look at biological change from a planetary perspective, scholars are increasingly able to recognize that selection operates at multiple levels (genes, cells, organisms, groups, ideas, and technologies). We are also learning about the previously under-recognized importance of development. We are beginning to see that like evolutionary change, development occurs at multiple levels, including the entire universe as a system.
In biology, we are learning the ways biological development directs evolutionary processes. This is represented as evo-devo theory in the picture at right, and in work like Susan Oyama’s developmental systems theory. We are learning that the environment of intelligent organisms undergoes developmental change via niche construction, and so both environment and intelligence must be factored into our understanding of selection. We are also learning the key role that synergies play in the development of structure and function, as outlined by leading synergy scholars like Peter Corning in Holistic Darwinism (2005).
Evo-devo biology is a community of several thousand evolutionary and developmental biologists seeking to improve evolutionary theory by improving our models of the way evolutionary and developmental processes interact in living systems to produce biological processes, morphologies, modules, species, and ecosystems. Evolution: The Extended Synthesis (2010), provides a particularly good introduction to the ways our understanding of biological change is changing. The picture below illustrates that both our 19th century Darwinian model of evolution, with natural selection focused only on the organism, and our 20th century gene-centric view of evolution, popularized in the late 20th century by Stephen Jay Gould and Richard Dawkins, while each great advances for their time, are incomplete accounts of biological change.
Beginning in 1859, Charles Darwin helped us to clearly see what we will call evolutionism in living systems, for the first time. Discovering that humanity was an incremental, experimental product of the natural world was a revolutionary advance in our previously poorly rational and humanocentric beliefs. We owe Darwinism a great debt, and evolutionary approaches remains the best way to describe the vast majority of change in complex systems.
But until we also understand and accept developmentalism, recognizing that the universe not only evolves but develops, and that selection and adaptation can be both evolutionary and developmental, then the purpose and values of the universe, and our place in it will remain high mysteries about which science has little of interest to say. Our science will remain underdeveloped, descriptive without also being prescriptive, and unable to deeply inform our morality and politics. That state of affairs must change in coming years.
In the traditional Darwinian model of biological change, development is included as a subset of evolution. It was mostly ignored in the modern synthesis, which gave us a contingency-dominated view of change. In evo-devo biology and in evo devo systems theory, that way of thinking is simply incorrect. While there is much variation of opinion among evo-devo biologists as to which factors will contribute most to the next synthesis, the large majority would agree that evolution and development are in many ways opposite and equally fundamental processes in complex living systems. Neither can be well understood without reference to its interaction with the other.
The reality, as we see it, is that both biology and the world are evolutionary developmental, meaning that they are always both at the same time, depending on your perspective. As memetics scholar Tim Tyler points out, what scientists typically call evolution includes both merging and joining as well as branching and splitting. For examples of merging and joining in evolution, think of sexual union, endosymbiosis, and any presumably symbiotic emergence of multicellularity that was preceded by networks of protists.
Evo devo theory would say these are all examples of evolutionary development. The branching parts, including the creation of sexes, and the different varieties of prokaryotic cells (which set up the likelihood of eukaryotes developing via endosymbiosis), and the variety of types of protist networks must be driven on every living planet by evolutionary and divergence processes. At the same time, the fact that only two genders contribute genetic material to all known species on Earth may be a developmental optimum, arrived at via evolutionary search but locked in everywhere, once found, due to its value. We do see sterile “third genders” in social insects (bees, ants, etc.), but that too may turn out to be a developmental optimum for such collective organisms. Likewise, endosymbiosis seems primarily developmental, as it offers vastly more dense and powerful bioenergetics for single-celled eukaryotic top predators like Paramecium and that greater energy production allowed more complex functionality to emerge in early amoeboid eukaryotes. Endosymbiosis also allows the emergence of multicellularity. Certain types of multicellularity, in turn also seem primarily developmental (convergent, predictable) in universal terms, as they allow local niche dominance for these inevitably larger organisms, via both physical and informational (intelligence) strategies.
Likewise, all developmental processes involve branching and splitting as well as merging and joining. Consider the way cells divide, differentiate, fan out, and compete during tissue and organ development. But again, evo devo theory would call those branching processes a regulated form of evolution, just as genetic recombination during fertilization is a regulated form of gene reassortment (evolutionary search). The cyclically predictable merging and joining parts are development. Clearly, in developmental biology and psychology, when we know a process takes us to a predictable endpoint, even though it also uses a variety of evolutionary processes to get there, we are comfortable saying that development is the primary process involved. Likewise, in traditional evolutionary biology, when we are modeling things like genetic drift using stochastic models, and seeing diversification and experiment, we are comfortable saying that evolution is the primary process involved, even though several aspects of that diversification are predictable. We can label these two fields “evolutionary biology” and “developmental biology” and see that they are both primarily different yet also inevitably overlapping fields of study.
It might seem confusing to say that both what scientists traditionally call evolution and traditionally call development are each always some mix of each that to be precise, we must call evolutionary development. But the clarity and insight that comes from defining evolution and development in terms of unpredictable and experimental versus predictable and conservative processes, and making that assessment in both physical and informational terms for every system and process we care about, is well worth the mental energy involved.
The evo devo approach acknowledges that two fundamentally opposing processes are always in play in complex systems, and that we don’t necessarily understand the mix of each, and how they play off each other in any system under study. It’s both humbling and more accurate to use the evo devo term, and admit that evolution and development, as we have traditionally defined them, don’t fully separate into neat categories as processes of change. There would be no value in abandoning our traditional scientific terms, evolution and development, in an effort at improving our models. Each are approximately accurate, have been immensely useful, and are quite entrenched. But we can recognize their limitations, and explain the value of redefining them, to a limited degree. We’ll reiterate exactly how we redefine them a few times in this chapter, and why that redefinition is important. For those who don’t think a redefinition is necessary or valuable, versus simply leaving these terms be, please read on.
In evo devo theory, both in biology and in systems theory, there are two key forms of selection and fitness landscapes operating in natural selection – evolutionary selection, which is divergent and treelike, with chaotic attractors, and developmental selection, which is convergent and funnel-like, with standard attractors. Thus natural selection itself is not the kind of random walk that Darwinists have long claimed. It is not just “variation and selection with inheritance”, but something more. It is a mix of both stochastic and directional, programmed change. Evo devo foresight, then, is any attempt to generalize this very valuable evo-devo biological perspective to nonliving replicating complex adaptive systems as well, including solar systems, prebiological chemistry, organizations, societies, technology, and perhaps most interestingly, to the universe itself as a complex system.
In biological systems, development is a process that guides replication through predictable stages of a life cycle. In living systems, developmental genes are a special set of initial conditions and algorithms that have encoded a certain kind of past learning from past life cycles. Together with the stable environment, developmental genes constrain the system to express specific predictable types of future form and function. Once a system is constrained by those special initial conditions, unless one fully understands the way those initial conditions affect the future dynamics of the system, parts of what it does look like self-organization, or what complexity scholars like Stuart Kauffman call “order for free.” Thus the concepts of replication, life cycle, and self organization are commonly associated with predictability in complex systems.
For example, if one breaks up a random sample of large molecules into smaller molecules, and puts them in close proximity, they won’t don’t do much that is predictable. But if one cuts up a virus or a cell into small pieces, and puts those small molecules in close proximity, many will self-assemble again into their original shapes. This looks like improbable levels of predictability and “self-organization”, but it is actually an example of development, at the molecular scale. The system from which those molecules came was a replicator, able to learn over many past replication cycles, that the shapes and charges of a particular sets of molecules have a high probability of maintaining specific forms, even under lots of environmental chaos.
Now ask yourself: If replication of our universe occurs, and any kind of selection or adaptation occurs on the expressed complexity, then we can predict that it must also engage in some form of developmental learning as well. Over time, there will be probability for a particular set of physical parameters and laws to perpetuate themselves. Such parameters, along with the universe they make, become their own motivation for existence, so to speak.
Much of what happens on Earth and in our Universe is unpredictable. Yet there is also astonishing levels of predictability in some of its complex dynamics. Many astrobiologists think, for example, that various kinds of advanced complexity emerge at predictable rates all over the universe, as the universe develops from physics to biology to society to technology. Scholars of convergent evolution also document many astonishing kinds of molecular, organismic and ecological complexity emerging at similar times in widely separated and widely varying environments on Earth. Eyes, jointed limbs, wings, fish fins, brains, math, science, electronic computers, all of these and many more may be inevitable and predictable emergences on planets like ours.
It is arguments like this that have led me to believe it is reasonable to assume that the predictable processes in our universe that scholars call developmental have emerged in a process of past replication. This has led me to explore theories of universe replication that have been proposed by various physicists over the years, and to see if those theories can be reconciled with what we know about other replicating systems within our universe, including living systems. Beginning in the mid 2000s I developed an application of biological evo-devo theory to societies, technologies, and the universe, which I published as a 90 page book precis, “Evo Devo Universe?” (PDF), in an edited volume exploring the diversity and universality of culture, Cosmos and Culture (2008).
Also in 2008, philosopher Clement Vidal and I co-founded a small international interdisciplinary research and discussion community, Evo Devo Universe, to explore evidence for evolutionary and developmental processes that may be occurring at all scales in our universe. We were soon joined by physicist Georgi Georgiev as a co-director, and now also have astrobiologists and complexity scholars as co-directors. The eighty-some interdisciplinary scholars in the EDU community participate in discussions around evolutionary, computational, developmental models of the universe and its subsystems, and we enjoy critiquing them, seeking evidence for or against them, and exploring their variations and implications. We strive to learn from each other about our disciplines, and to be humble and respectful.
Not everyone in our EDU community is of the opinion that our universe is likely to replicate. Not every scholar there thinks the universe undergoes some kind of selection. Not everyone expects that some kind of generic adaptiveness functions for broad classes of complex systems are likely to be formulated in future physical or information theory. But many do harbor these suspicions, and everyone there is on board with the basic idea that there is value in identifying, comparing, and contrasting the unpredictable (“evolutionary”) and predictable (“developmental”) dynamics in every universal subsystem we can identify, including the universe as a system, and asking how those appear to relate to each’s systems ability to adapt, under various tentative definitions of adaptation. That may not sound like it adds much to scientific and systems theory debates, but I believe this is a greatly clarifying way to view complex systems, and I have not yet found this approach as a basis of any other academic community.
If you are a published scholar with an interest in any of our various research themes, you are welcome to apply to join our small, friendly, and learning-oriented community.