Innovation, Intelligence, Interdependence, Immunity, and Sustainability (I4S): An Evo Devo View of Change

As we’ll argue in this chapter and in Chapter 11 (Evo Devo Foresight), progress, of at least five special kinds, appears to be built into the informational and physical dynamics of our complex universe. If we are evo devo systems, then both “evo” and devo” purposes, working in continual tension and opposition with each other, are at the center of human purpose, due to the nature of our universe itself. One of the main developmental responsibilities of intelligent systems is to better understand, measure, and manage that progress, and to help the universe do whatever it has been self-organized to do. One of our main evolutionary responsibilities is to continually create new things, and run new experiments, creating kinds of progress that the universe never anticipated.

This is a protopian view of change, a view that argues we can make progress toward objectively, universally better futures. Utopias can never exist, and history shows how dangerous it is for any of us to hold up any system of belief or of governance as a perfected ideal. We’re always imperfect, and can always get better and more adaptive. These five goals, then, should not be considered any kind of final model. But they may be a step toward a more specific and universal view of how complex systems improve and adapt, whether they are biological, organizational, or sociotechnological systems.When we recognize that progress of various kinds can occur, and that we have a moral obligation to get better at seeing, measuring and managing it, we are able to anticipate, create, and manage better than ever before.

If we are evo devo systems, we can model and measure progress as the pursuit of Five Goals or abilities that seem particularly fundamental ways to increase evolutionary or developmental adaptiveness. In brief, these goals are:

1. Innovation. Growing freedom, creativity, experiment, play, re-creation, awe, fun, and unpredictable novelty, especially under stress.
2. Intelligence. Growing mental and physical abilities, wealth, and rights that are “freedoms to”.
3. Interdependence. Growing connectedness, love, understanding, ethics, cooperation.
4. Immunity. Growing security, power, stability, fairness, and rights that are “freedoms from”.
5. Sustainability. Growing order, truth, science, data, rationality, and predictability of the life cycle.

We can also associate at least two social values with each of these goals, giving us Ten Values that may be particularly universal to adaptive societies.

I4S, the Five Goals and Ten Values, is a protopian world view, and from where I sit, it is the most valuable way we have today to see, and increasingly measure, what gets better, and how. When we also incorporate the VUCA and CIBA world views, which help us to understand losers and winners on the way to better societal futures, we may have a minimally comprehensive set of perspectives for understanding change.

Intelligence Theory (Cog Evo Devo): The Five Goals as Five Forms of Intelligence

The Baldwin effect is the recognition, beginning with James M. Baldwin in 1896, that learned behavior affects an organism’s reproductive success. It is a modest start in understanding how learning and intelligence affect selection and adaptation in living systems, but we must go much farther. We must come to understand all complex adaptive systems as intelligent evo devo systems, balancing unpredictable and creative evolutionary processes against predictable and conservative developmental processes, at all scales.

The better we can understand the evo and devo roles for cognitive processes in biological replicators, a future discipline that Lucia Jacobs calls cog evo devo, the better we will understand the true nature of intelligence in selection and adaptation. I would go further than this, and say that cog evo devo will also allow us to better understand the roles of intelligence in other replicating systems, including society, with its replicating ideas and rules, and technology, with its replicating algorithms and architectures.

Let’s look more closely now at the Five Goals, innovation, intelligence, interdependence, immunity, and sustainability, as five forms of intelligence in complex systems:

• Intelligence as innovation (exploratory intelligence) – Evolutionary process is the hallmark of this type of intelligence. As James Shapiro, in Evolution (2011) and others propose, living systems use genetic, somatic, and cognitive systems to harness stochasticity to generate selectable variety (experiments, possible futures), particularly under stress or after catastrophe. They seek to do this in increasingly clever (“good bet”) ways, the smarter they become. Evolutionary innovation is nonrandomly guided by intelligence, particularly in the “next adjacent” action and feedback cycle. At the same time, the complexity generated becomes rapidly unpredictable the farther ahead any intelligence looks.
• Intelligence as intelligence (representation intelligence) – Most fundamentally, intelligence is a process of informational representation of environmental reality, as Fischler and Firschein say in Intelligence (1987). Informational representation (modeling) can be argued to be a dominantly divergent, evolutionary process. Our neural models conform to regularities of their environments, but they also generate astounding numbers of exploratory representations, only a fraction of which are universal (predictable) or adaptive. Think of imagination, fiction, or theoretical math, most of which has no known application. Being “intelligent” is also no guarantee of being adaptive. Indeed, those with too much of this single ability may be maladaptive.
• Intelligence as interdependence (empathic-ethical intelligence) – Organisms engage in positive sum games, rules and algorithms (morality, ethics), involving not just self- and world-modeling but collective competition and cooperation, coordinated by other-modeling and other-feeling (empathy). Complex interdependent organisms develop culture, which evolves and develops independently from the individual, both faster and more resiliently, and allows them to view and optimize outcomes from either personal or group perspectives (which may conflict). A variety of universal evolutionary and developmental ethics (algorithms that protect collective adaptation and intelligence) may apply to all complex cultures. For more on how emergent synergies (interdependences) may have driven major transitions in evolutionary development, see Corning and Szathmáry 2015.
• Intelligence as immunity (security intelligence) – Organisms use many strategies for differentiating self from other, and passively and actively countering degradation and predation. Chronic stress and stress avoidance both weaken immunity, but right-sized cyclic stress and catastrophes both build immune system capacity and accelerate evolutionary innovation. Nick Taleb’s concept of antifragility argues this for organizations, as does the catalytic catastrophe hypothesis. If there are universally discoverable “natural security” architectures and strategies (many ways to fail, but only a few ways to survive), as I would predict, then immunity can be classed as a dominantly convergent and developmental process.
• Intelligence as sustainability (predictive intelligence) – Developmental process itself is the hallmark of this type of intelligence. Organisms use their intelligence not just to explore possible (innovation, intelligence) and preferable (interdependent, immune) futures, but to build predictive, and presumably Bayesian, models of probable futures. A subset of these predictive models are encoded in an organisms developmental genes, in emergent properties of their soma, in their environment, and in more complex organisms, culture. The growth of knowledge, common sense, science, and all the processes of development that predict, but do not protect (immunity) can all be considered sustainability. These processes grow “truth” and understanding. This form of intelligence is in constant tension with innovation, which can rapidly cause both poorly understood and dangerous forms of complexity to emerge.

Consider the particularly fundamental nature of these five systems in living beings. We can see all five of these processes acting in all living replicators, from viruses on up. We typically don’t think of as living or self-improving, but perhaps we should. We think of viruses as parasites on cells, but the RNA that some of our viruses are based on was likely our first self-improving molecular replicator, a “parasite on Earth”, on the path to the first cells. We think that viruses cause as much genetic diversity in metazoans as sex does. All human bodies have roughly ten times more viruses in them than bacteria, at any moment, and roughly ten times more bacteria than eukaryotic cells. See Carl Zimmer’s lovely A Planet of Viruses (2015) for more. What we typically think of as “human”, then, is just the tip, the capstone, of a much larger and deeper pyramid of evo devo replicators, stretching back all the way to the first replicators on Earth.

As all five of these forms of intelligence evolve and develop in evo devo systems, we can argue that they are self-selecting. We can’t understand natural selection, in intelligent systems, without recognizing that certain forms of nature become increasingly intelligent, and that intelligence changes the nature of their environment, themselves, and their selection and adaptation processes. In this sense, then, we can argue that our universe is not just a Darwinistic (selective, adaptive ) universe, it is a Noetic (intelligence-accumulating) universe.

In other words, I will argue that our universe appears to be organized to make intelligence growth, in these five forms, a centrally important process. If universe itself is a system with finite lifespan that replicates in the multiverse, as we will argue in Chapter 11 then it may be most accurate to propose that our universe has self-organized to be intelligence accumulating, because intelligence itself, when we define it in evo devo terms, appears to allow for a particularly adaptive kind of selection, in all complex systems that replicate.

We shall see if our cosmology and science continue to develop toward a view that we live in a replicating, intelligence-accumulating, evo devo universe. If it does, I would predict that something like the Five Goals model will move from systems theory into the realm of physical and informational science. For now, it is simply philosophy. I hope you find it as useful as I have in recent years.

The Best Simple Definition of a Human Being 

Let’s zoom out for a minute, to place these concepts in a broader context. As we argue in our chapter on Evo Devo Foresight, the best single definition of a human being appears to be any organism, on any planet, that learns to use technology, with ethics and empathy, both cooperatively and competitively in social groups (cooptition) to become something more than its biological self. We are innovators and sustainers, at our very core.

Lots of animals use technology. But only humans use it in a runaway fashion, continually innovating and improving it, and changing our culture and ourselves in the process. Four million years ago, prehuman Australophithecines lived largely in the trees. These environments were complex, requiring lots of spatial prediction and physical dexterity. We were also surely minor tool users, the way primates use minor tools today, like sticks to get grubs out of tree trunks. But whenever we came down from the trees, as we had to during droughts, were constantly being eaten by leopards on the African savannah. There are Australopithecus skulls with holes in their heads that perfectly fit leopard teeth. They probably grabbed us by the head, the way a cat grabs a mouse, and ate us in large numbers. We weren’t human, yet.

But over roughly the next two million years, a number of small and large changes occurred, that together created early humans. In my view, two groups of changes seem particularly important. Together, these two give us the best simple definition a human being. Neither alone is sufficient.

Homo habilis hunting with Oldowan hand axes, 2 million years ago. Illustration: Angus McBride

First, a branch of prehumans became physically dextrous enough and mentally sophisticated enough to begin using defensive and offensive technology. We began holding sharp rocks, and presumably, also large clubs. The Oldowan hand axe, named after the Olduvai Gorge in Tanzania, is the most famous of an entire Oldowan tool set that was produced by Homo habilis, the tool using human, the first of our species, emerging roughly two to three million years ago. Hand axes are rocks that are round on one end, easy to grip, and sharp on the other. The sharp part is made by striking certain kinds of rocks with other rocks. They are a very powerful tool, but the tool alone isn’t enough to change ouspecies.

Second, we began using these new tools cooperatively and competitively, in large social groups. Oldowan hand axes were not just used by early humans, they were mass produced by them. We’ve found thousands in one location alone, by a prehistoric lakeshore. Imagine what was going in the minds of that first human builder. They wanted to share this power, as widely as they could.  Once we became smart enough to work together, spreading the benefit of our technology to others of our kind, we were off to the races. Leopards could no longer easily predate a Homo habilis that only went out in groups, with many of us holding rocks or clubs. At the same time, we began using these rocks to hunt together, as in the picture at right. Competition, and greater returns to the winners, allowed us to drive the fastest improvements in tool use. But that competition had to be positive sum to do so, as Robert Wright argues in his masterpiece, Nonzero (2000). Ethical and empathic cooperation and competition, or cooptition, was the critical second factor in our Great Leap to humanity.

As I see it, humanity has always needed advances in both mind (tools) and heart (ethics and empathy, cooptition) in order to progress.

Understanding the Most Recent Great Leap in Human Society

GDP per Capita in Western Europe, 1000-2000 CE. Image: ASF

Let’s look now at our most recent Great Leap, the Industrial Revolution. Again, two groups of causes have been offered, by various authors, for the state switch we see in the J-curve, GDP per capita in Western Europe, 1000-2000 CE, in the figure at right.

1. Science and Technology

• The invention of algebra (in Iran in 800s), then calculus (in Europe in 1600s)
• The rise of science (theory and experiment) in Europe in the 1600s
• Cheaper food, energy, and resources
• More powerful (internal combustion) stronger (steel) faster (electric) machines

2. Societal Ethics, Rules, and Economics

• Property rights, corporate charters, patents, capital, and the rule of law
• The reemergence of democracy (Greece 500 BCE) in the US and Europe (1700s)
• Education (often via mass military conscripting) and middle class expansion
• New cultural attitudes (pro-innovation) and beliefs (Enlightenment values, reform Protestantism)

Again, note that advances in mind (science, machines) and heart (ethics, empathy, positive sum cooperation and competition) are the two most frequent themes, if we were to pick just two.

An I4S view, and a deeper analysis, would separate these two groups further, into the five goals of innovation, intelligence, interdependence, immunity, and sustainability. All five factors seem fundamentally important to any Great Leap, but as with the rise of humans themselves, I think we can also say that new forms of ethical cooperation and competition are always the most central and critical enablers.

William Bernstein’s lovely The Birth of Plenty (2010) proposes the Industrial Revolution was due to four advances:  property rights and common law, scientific rationalism, capital markets, and technology. I would argue that it was the ethical-legal and cultural attitude shifts, partly addressed in his first three factors, that were the central enablers. Books like Dierdre McCloskey’s Bourgeois Equality (2016) and Steven Pinker’s Enlightenment Now (2018) describe the changes in our societal ethics and empathy that allowed the revolution to occur. Beginning in the Enlightenment (1600-1800), we began to talk less about conquerors and kings, and more about good leaders, entrepreneurs, scientists, inventors, educators, artists, and activists. We began to see we can innovate and make progress here on Earth. New ethics of thrift, persistence, self-restraint, and risky innovation, and new empathy for others, may have lifted us faster than any other force.

As we’ll see in this chapter, we are now transitioning into an Intelligence Revolution, driven by our increasingly biologically-inspired and self-improving computing machines, and substantially faster exponential growth in the wealth, productivity, and innovation capacity of our leading cities and corporations. Again, it will be better ethics and empathy that will be central to the transition.

As our technological abundance grows, we need to learn how to create a new kind of state, with new ethics and empathy, as Ryan Avent says in The Wealth of Humans (2016). We will need to move away from the market as the central metaphor of the state, to something like the family. Families all involve widely different people learning to get along, and ideally with everyone trying to help everyone become their best selves. They have unconditional love and empathy, and conditional ethics. Work within families, like the work we assign to our kids, is done not just for the financial benefits we get and give, but for the social benefits, the life lessons, self-actualization, and purpose that good work, chosen by us, provides.

In our current market-dominated state, wealth will increasingly flow to those employing these advanced technologies, widening economic and political inequality. As firms require far fewer people to produce ever cheaper and more powerful products and services, they will contribute to ever-growing technological unemployment. These new tools will increasingly be used by bad actors for new forms of crime, fraud, and manipulation, and protecting our security, privacy, and freedoms has become increasingly complex and important.

If we have a deficit of anything today, it’s a recognition of how much better our world could be, both today and tomorrow, if we were more carefully and honestly studying how our leading complex systems have always self-improved, via advancing their capacities in innovation, intelligence, interdependence, immunity, and sustainability, and tried to aid these natural universal processes. We could see and guide this accelerating adaptiveness, and become more tolerant of failure, as long as we learn from it, because we have no perfect foresight and must continually run small experiments to get to a better world. Increasingly intelligent innovation, failure and review are the evolutionary price of progress. We cannot avoid paying that price, we can only delay it, and delay our own progress as a result.

If we are serious about improving our foresight, we must become acceleration-aware, or in short “accelaware“, so we see that complexification has always accelerated, in special ways, and that adaptation and progress have always occurred with the survivors, on average, when we define and measure them appropriately. When we see acceleration and adaptation, we can jump tracks to higher exponential growth modes, with appropriate social regulations, and solve our our current problems in far better and more rapidly improving ways than we do today. We can avoid currently tremendous and tragic amounts of pain, conflict, waste, and loss.

Let’s look now at the phenomenon of accelerating change, to see the kind of world we live in today. It is a different world than the one most of us have been told is our reality. I hope to help you see it, and its major opportunities and substantially lesser perils (lesser because they tend to impact individuals, or individual societies, rather than the whole system), with new eyes.