I. Universal Evo Devo: A Model
Seek simplicity and distrust it. – Alfred North Whitehead
This chapter will offer a set of simple models of universal change, and our role within the universe, as a product of the universe. As a collection, we can call them models of universal evolutionary development, or universal evo devo for short. Many models, including these, have a kind of elegant simplicity, yet we should always distrust that simplicity at first, until it is either validated or disproven by future science. As we are exposed to the world, our often simple hypotheses, beliefs, and models usually come quickly, as tentative explanations for what we see around us. Only later can we verify them.
The author of the quote above, Alfred North Whitehead was a great mathematician, logician, and process philosopher of the 20th century. His major philosophical work was Process and Reality, 1929. Process philosophy regards process, which constantly regulates and generates change, as the most fundamental aspect of reality. Some versions of process philosophy (the best versions, in my view) are also called a “philosophy of organism”, as they view processes that regulate organic systems, like evolution, development, replication, adaptation, and intelligence, as processes that also apply to, and can be studied in, the universe as a system.
In this chapter we’ll consider some of the ways the universe is like an organic system, and why that view is likely to be central to understanding concepts we care deeply about, like adaptation, intelligence, and purpose (goals, morality, values) for that intelligence. Taking the larger view turns out to be very helpful to understanding the future of what some scholars and futurists call the “world system“. We may think, for example, that plutocrats, both the benevolent and the self-obsessed kind, are a powerful force controlling the nature of modern society, and they are. But their power and influence is negligible in comparison to the universe itself, and the natural processes, like dematerization and densification, that the universe appears to be using, especially via science, technology, and entrepreneurship, to allow our civilization to explore evolutionary options, and move us in a particular set of developmental directions.
In this larger view, we’ll see early evidence and argument that our universe is a system that began as a kind of seed (with special constraining parameters and laws) is now unfolding as a kind of organism (with predictable stages of youth, maturity, senescence, and death, as in all organisms), and is evolving in and will be recycled into an environment (a system supporting ours and perhaps other universes, that physicists call the multiverse). In living systems, their full complexity and intelligence is always spread out between, and exists in all three of these partially separate systems, seed (a fertilized egg and its genes), organism (our bodies), and environment (our ecosystem). We therefore should suspect that this SOE partitioning also occurs for the universe as well. If true, we can’t expect to fully understand our current universe by considering it as an isolated system. We must also infer what it started as (its initial parameters and laws), and what is nurturing it (the multiverse), where our universe will ultimately be recycled back into, when its useful life is over.
I was introduced to Whitehead’s work in college. He was the mentor of my own most influential college mentor, James Grier Miller. Miller was a theorist of living systems. Miller wrote perhaps the most widely known 20th century work on biological systems theory, Living Systems, 1978. Living Systems was a major effort at finding common laws and processes shared by living systems at all scales, from cells to societies to the Earth as a single ecosystem. In classes and private meetings with him, Miller encouraged me to develop a systems theory (a set of prescientific hypotheses) that better accounted for a topic I’ve been interested in since childhood, the curious and largely-ignored phenomenon of accelerating change. Both this and the last chapter are modest efforts in that regard.
This is our second chapter on universal foresight, the sciences and systems theories of change. It assumes a world view we can call adaptive foresight, the proposition that environmental adaptation and selection are the central mechanisms that complex systems use to survive, thrive, and navigate the future. Much foresight is generated by intelligent systems every day, but only a subset of it is ultimately adaptive. Seeing how adaptiveness emerges is our goal in the following pages.
The model we will offer is called evo devo foresight. We introduced this model in Chapter 2 in our discussion of natural intelligence and natural security. This model is incomplete and approximate, as all models are. But I think it is also a useful step forward in understanding the nature of the future, from a universal view. It is our best current effort towards a general science of foresight, as called for by Alvin Toffler in Future Shock in 1970, and by H.G. Wells in a famous radio speech, “Wanted: Professors of Foresight!,” in 1932.
The model claims that universal change, and the complex adaptive systems that emerge within the universe, can be understood as being driven by two fundamental processes: evolution and development. To make this claim, our definition of the terms evolution and development will differ just a bit from the standard definitions, as we will see. These two fundamental processes can be also be considered an interacting mix, a third perspective that we call “evo devo”.
The vast majority of the changes that occur in complex systems on a daily basis (we will argue 95% of observable changes, to a first approximation) are chaotic, variety-producing, locally adaptive, and unpredictable—changes we can call “evolutionary” in our model. But a special subset of changes and trends (we will argue 5%, on average, to a first approximation) always seem just the opposite, stabilizing, convergently unifying, globally adaptive and predictable—changes we can call “developmental”. Increasing ephemeralization, increasing technological intelligence, increasing intimacy of human-machine and physical-virtual interface, and increasing speed of change are but a few of such globally predictable trends in technological development.
For an understanding of the difference between evolutionary and developmental process, consider two genetically identical twins. They are always chaotically unique at the molecular scale (fingerprints, brain wiring, organ microstructure, memories, the combination of alleles they make available for fertilization via their reproductive systems), but they are also always convergently similar at the whole system scale (with many common physical, process, and personality attributes). These similarities occur because a small and special subset of developmental genes constrain each twin to these convergent developmental outcomes, even as they experience highly unpredictable and adaptive interactions every step of the way, on the molecular scale.
Every replicating complex system, from stars to planets to organisms to societies to technologies, appears to have this combination of mostly unpredictable and variety-producing “evolutionary” paths, adaptive natural selection on these varied paths, and convergence on a very small subset of probabilistically predictable global “developmental” trajectories. The latter occur because the complex system is not only driven by evolutionary “possibility”, but also by developmental “constraint” from the very outset. Evolutionary processes generate new information and developmental processes conserve old information that act together, and often in tension and opposition to each other, to stabilize and improve the replicator’s survival.
Consider how these two processes interact in a few universal systems.
- In physics, for example, we cannot tell what the unique nonlinear and quantum “evolutionary” interactions will occur in any physical system at the quantum scale, but we know that physical systems are also constrained by predictable global dynamics describable by relativity, thermodynamics, and classical mechanics.
- In chemistry, we cannot say what molecular evolutionary patterns will occur in any warm pond, but we are on the verge of understanding that in certain special high-energy flow environments, specifically undersea hydrothermal vents, RNA and lipids must replicate, and life must emerge, presumably on all Earth-like planets.
- In the weather on Earth, we cannot say whether it will rain or not on any particular day, but given a statistically significant sample of weather patterns from anywhere on its surface, we can confidently describe the envelope of expected weather, as Earth’s climate is also constrained by physics, geography, and stellar habitat into a predictable set of global behaviors.
- In human social and technology evolution, we cannot say whether any particular society or technology will chart a specific path, but we can say that certain positive sum social developments like increasing collective morality and democracy (Wright 2000), and technological developments like accelerating computation (Kurzweil 1999) and public transparency (Brin 1998), have advanced so predictably on average, while not necessarily in any specific case, that they seem likely to be “developmentally determined”, for our world system as a whole.
In a dynamical cartoon for evo devo system change, we can say that chaotic and varied motion of a set of interacting marbles around a fitness landscape (evolution, strange attractors), will exhibit mostly chaotic and unpredictable adaptive paths. But at the same time, all marbles will converge on a few special points at the bottom of the bowl (development, standard attractors), as these points are global developmental optima that are dependent on the systems particular dynamical constraints (laws of physics, chemistry, biology, and complexity that limit the system’s global behavior) Despite a day-to-day norm of uncertainty and variety, the marble’s (agent’s) long-term fate has a few inevitabilities that were encoded within it from the beginning. The agents find a pre-existing computational optima hidden in the realm of all possibilities. They expresses a developmental form that was waiting for the emergent environmental complexity to be sufficiently advanced, just as in biological development.
Convergent evolution offers us many concrete examples of planet-scale rather than organism-scale process of development. Dolphins and fish both move efficiently through water and share a similar shapes (streamlined bodies, fins), but these shapes are independently evolved. This evolutionary convergence (developmental attractor) occurs because both types of animals must move rapidly through the same physical environment, water. Thus they must find the same solution, but through very different initial paths. The emergence and dominance of bilateral symmetry, eyes, jointed limbs, grasping appendages, and many, many other convergent forms and functions can be understood as inevitable, once one understands how such forms and functions must be dominantly adaptive in such environments.
The Three Ps of foresight, used throughout this Guide, is another key piece of the evo devo model. The Three Ps are the three most fundamental ways that intelligent systems–living organisms and their machines—act to perceive the future. For most of our clients, the language of probable, possible, and preferable futures is generally the best way to do our foresight work, rather than using science-derived terms developmental, evolutionary, and evo devo, as we will do in this chapter.
To really understand how to apply the Three Ps, and to get practice with differentiating between unpredictable and predictable processes in every system, at every scale, serious foresight practitioners need to understand evo devo foresight. Helping foresight professionals and their clients to become both evolutionist and developmentalist in their thinking is key to a balanced use of the Three Ps. By the time you’ve finished this chapter, you may not agree that as much of the world is as developmental (predictable) as we argue here, but you will almost certainly agree that some of it is developmental, and likely more than you saw before.
This is the longest chapter in this Guide because, as we will see, both current majority science and the field of professional foresight are biased to see most change through an evolution-exclusive lens. Expanding that lens to include development will require offering you a bit more evidence and argument than in typical chapters, and I hope you’ll consider it worth the effort. If by the end of this chapter you see more potential developmental signature in the data of universal, biological, social, and technological systems than you did at the beginning, and feel more confidence in looking for that signature, your new foresight capacity will make the effort of learning the model a success.
Of course, any potential process, emergence, or future that that we think may be developmental needs to eventually be validated. Our insights, intuition, arguments, evidence, bets, strategies, and systems theories about the future always arrive first, and the science arrives later, if at all. Fortunately, in a world of ever-growing social intelligence, our foresight never needs to be perfect, just good enough to maintain adaptiveness.
The evo devo foresight model is more physically and informationally grounded, quantifiable, and evidence-based than any other foresight model I know. It can be applied to both human systems and to the universe at large. It is my candidate for the most useful and rigorous approach to foresight scholarship and validation available to us at the present time. As our science and simulation abilities advance, the claims made in this chapter will be increasingly validated or falsified. In the meantime, we do the best that we can with the tools that we have.
Do you recall our discussion of biocentric bias in Chapter 2? We claimed a better understanding of universal foresight can help us understand which of our future human stories, like the continued inevitable and rapid growth of general AI, and exponentially better computer hardware, software, and nanotechnology, are supported by universal trends, and which stories, like human space exploration, germline genetic engineering, and superlongevity of biological humans are just wishful fantasies. I’m hopeful that after skimming this chapter, you’ll develop a better intuition about which things the universe is guiding us toward, and will allow as near-term outcomes, and which things are highly unlikely to emerge as we would wish them to, even though they are emotionally appealing and satisfying stories to tell.
The main paradigm, the mental construct that science presently uses most to understand our universe, is theoretical physics. It has helped us understand much about space, time, energy, and matter, which we can call STEM, but does not presently strongly explain or predict the emergence of information, computation, life and mind. We have good models of the physical processes in the universe, but still very poor ones with respect to complex phenomena like information, knowledge, intelligence, morality, and consciousness, why they arise, how they can and must interact with the physical universe, now and in the future. So when we talk about these information-related phenomena in this chapter, we are generally engaging in prescientific speculation and systems theory. Yet I think with the right models, we can produce very useful speculations and predictions, which can be increasingly tested and validated as our science develops.
Biological systems are the most complex of all adaptive systems in the known universe, so better understanding how their change is regulated is I think the best place to start in developing a deeper understanding of complexity in all its forms. In biological systems, the discipline of evo-devo biology studies how evolutionary development, or evo-devo, guides the production of ordered, complex, adaptive, and intelligent structures. In living systems we can distinguish evolutionary processes which are stochastic, variety-creating, divergent, and contingently adaptive and developmental processes which produce convergent and systemically statistically predictable structures and trajectories in a hierarchical replication cycle, from seed, to adult, to reproduction, aging, and death.
If our universe is also a cycling, evolutionary developmental system, as several scientists and philosophers now suspect, that means our universe is also moving predictably from seed, to adult, to reproduction and death. Physicist Lee Smolin in his masterful Life of the Cosmos (1997) offers one of the most detailed examples of such a model, which he calls cosmological natural selection. But his model is missing a few obvious elements as well, such as the influence of intelligence on selection. Complexity scholar James Gardner calls it the “biocosm hypothesis”, the idea that the universe is both like a living system, and friendly to the production of life and intelligence. I propose calling it an evo devo universe, as it allows us to apply the most useful terms we have in biology, evolution and development, to all replicating complex systems, and to see what we gain from that approach.
If it is both evolutionary and developmental, our universe must exhibit both unpredictable evolutionary creativity over its lifespan, and also many predictable and constraining developmental constraints, function, and futures, as it grows to “adulthood,” reproduces, and dies, within a still poorly understood environment that physicists call the multiverse. If we live in an evo devo universe, our finely-tuned fundamental physical constants, physical laws, and boundary conditions are most likely to have self-organized their present values, through many prior cycles in the multiverse, to maximize the multilocal production of unique adaptive intelligence, just as cycling living systems have self-organized their genetic and phenotypic complexity to adapt in their environments. Fortunately, these claims about the evo devo nature of all life and of the universe itself are all increasingly simulation-testable, as our science and computing power continue to advance. So far, as simple models like Smolin’s have proposed, there is no reason to doubt that this kind of cyclic self-organization is central to all of our most complex adaptive systems.
We’ll look over some of the evidence for this life-like view of the universe and many of its replicating subsystems in this chapter, and its implications for foresight practice. Again, as we will see, if we live in an evo devo universe, then the process of self-organization, via past cycles of evolution and development is most likely to have created our present life-like and life-friendly universe. If our universe has this similarity to life, then intelligence is something the universe is self-organized to protect and improve. Since intelligence has a very limited influence on evolution and development in any particular replication in living systems, it would be wise to assume this holds for the universe as well. In other words, if the universe is life-like, it wasn’t “designed” by God-like beings, but rather it self-organized to its present state over many previous cyclings, just as we humans did in our own evolutionary development on Earth.
As science learns to see our universe and its intelligences as not only as evolutionary but also as developmental systems, we’ll move beyond the currently popular “random accident”, “purposeless” and “null hypothesis” views of universal change and its relationship to intelligence, and recognize that a subset of future intelligence processes and destinations are statistically implicit in our physics, waiting patiently to emerge as civilization complexifies. These future complexity states are “in the genes” of the universe we were born into. As our knowledge of evolutionary developmentalism grows, our foresight will greatly improve, and we will gain new moral responsibilities, both to predict and to help developmental processes unfold, and at the same time, to increase our evolutionary free choice and diversity of unpredictable paths.
This chapter will be a bit abstract and technical at times, but we hope you find it helpful, as it addresses that most fundamental of foresight questions: How can we best discriminate between unpredictable and predictable futures? The payoff comes when we begin to suspect that key parts of our future really are predictable, in a probabilistic way, parts that both scientists and lay foresight practitioners will increasingly be able to see in coming years.
First we will introduce the evo devo model, and see why it is such a clarifying way to view universal change. We’ll also briefly consider limitations and dangers of the hypothesis, and how we can increasingly test it to validate or invalidate its components and assumptions.
We’ll also consider what this model teaches us about our purpose and place in the universe. We will propose that as humanity continues its incredible, accelerating rise, our leaders, planners, and builders must become evolutionary developmentalists if we are to learn to see reality through the universe’s eyes, not just our own. Learning to see, accept, and better manage all the hidden development ahead of us, and bringing our personal ego, fears, and illusions of control back down to fit historical reality, are among the greatest challenges of our era.
Second, we will turn to Evo Devo foresight, focusing most of our attention not on evolutionary foresight, which has been well described in many other foresight books, but on developmental foresight, the nascent study of changes that seem statistically very likely to occur in coming years. Developmental foresight helps foresight practitioners to better understand the probable future, in more ways than we ever have before. This will give us context for understanding why many of the accelerating social and technological developments discussed in Chapter 2 appear inevitable, if intelligent life persists on this planet.
Finally, we’ll look briefly at evo devo approaches to activist politics and policy, in a section we call evo devo activism. If we live in an evo devo society, what kinds of activism should we be focusing on? What does progress look like, in evo devo terms, and what are our duties as activists to catalyze positive change? These are each emerging schools of foresight and activism discussed in Chapters 1, 2, and in this chapter. I look forward to further community collaboration and critique of this model of change, and its many implications in values and action.
I hope you’ll find an evo devo approach to personal, organizational, and global activism to be as helpful in your own journey as I have, so far, in mine.