Chapter 11. Evo Devo Foresight: Unpredictable and Predictable Futures

Ten Values of Social Progress: An Evo Devo Model 

In Chapter 7, we explored how our universe is “running up”, creating negentropy, or information and intelligence, at the same time as it is “running down”, creating ever-increasing entropy, ultimately resulting in its death and a need to be recycled. We argued that the universe’s information potential is always going up, and its energy potential always going down. This negentropy increasingly has causal force on the physical reality around it, chaining it to the goals of the complex systems that arise.

Five Goals of Complex Systems – An Evo Devo Model

Let us return now to that very important question, the nature of social progress. Evo devo models can help us to better define this slippery concept, so we can begin to measure it, see it, and pursue strategies to advance it in our lives. We made our first effort at defining social progress in Chapter 7, introducing the Five Goals of Complex Systems (I4S). Recall that we can think of all five of these goals as kinds of intelligence, another useful term for negentropy.

We will see in this chapter that the paradoxical phrases Sustainable Innovation and Immune Intelligence, or better yet, Immune, Empathic, and Ethical Intelligence, are each excellent summaries for the kind of special complexity that creates and sustains accelerating change. In this view Interdependence, or what Peter Corning calls synergy, is the central property of adaptive systems. Adaptive systems are all leading the future, by managing competing and cooperating entities toward universally valuable shared visions and actions.

We’ll now see how we can use the Five Goals/Behaviors to derive a tentative set of Ten Values of Social Progress, the most detailed set of values we will recommend for social foresight work in this Guide. To my mind, these ten values seem to be particularly universal. They offer us guideposts for where we want to go, as individuals, as societies, and as a universal system. Learning how to see them, measure them, balance them, and advance them seems critical to the future of social foresight and leadership.

Adaptive human social systems can be argued to be pursuing and balancing at least Ten Universal Values. These can also be understood as goals, behaviors, abilities, drives, purposes, and trends, depending on our point of view.

In our leading systems, the universe is continually creating, and seeking to create more of both evolutionary and developmental goals, as follows:

Evolutionary Values: Freedom, Creativity, InsightDiversity, Empathy.

Developmental Values: Order, TruthPower, Security, Ethics.

These Ten Values represent each of the Five Goals, in groups of evo devo pairs, as follows:

Here are the five evo devo pairs, with a bit more of the qualities of each:

  1. More Empathy (love, compassion, understanding, connectedness).
  2. More Ethics (morality, fairness, synergy, positive-sumness, interdependence).
  3. More Insight (dematerializationvirtualization, modeling, consciousness, intelligence).
  4. More Power (densification, wealth, strength, STEM compression (exponential production efficiency & density).
  5. More Diversity (information, individuation, specialization, difference, independence).
  6. More Security (awareness, protection, safety, risk management, immunity).
  7. More Freedom (bottom-upness, indeterminacy, options, uncertainty).
  8. More Order (top-downness, structure, regulation, constraint).
  9. More Creativity (unpredictability, novelty, imagination, fiction, experiment, innovation).
  10. More Truth (predictability, optimization, accuracy, inertia, sustainability).

As with the Five Goals, the closer each of these five value pairs are to the center of the Evo Devo Values Distribution, the more they should be thought of as complementary (adaptive) rather than oppositional values. We represent this perspective in the Adaptive Leadership Model, covered in Chapter 24 (Self-Leadership and Universal Values) and Chapter 5 (Group Leadership). Note that this Three P’s model (picture right) classifies Empathy and Ethics and Insight and Power as centrally adaptive (evo devo) values. While these values pairs often act in opposition to each other, they aren’t conceptual opposites. Likewise, two of the values of Intelligence and Immunity, Diversity and Security, are not exact opposites, but their functions are central to evolutionary and developmental processes, so they are best classified as oppositional rather than adaptive value pairs. Freedom and Order and Creativity and Truth are also classical opposites. All of these values, and values sets, must be balanced against each other in good strategy and leadership.

There are certainly more values that matter to society than just these ten. It would be easy to add more values in evo devo pairs, associating each pair with one or more of the Five Goals. Recall our Table 1, with a long list of evo devo value pairs. The Five Goals can also be used to organize other lists of social values we find in the literature. But in my work so far, I’ve found sharply declining utility to adding additional values to these core ten.

When we limit ourselves to just two values for each of our five goals, that offers us two complementary and slightly different ways to understand how each of these goals works in society. That exercise will yield something like this model, which I suggest is sufficiently comprehensive and concise for much of our social analysis.

It is easy to argue that all of these ten values are continually increasing in our most successful, adaptive societies. Everyone who has bet against these particular values growing measurably in all our leading societies, over the long term, has ended up being wrong.

Having the right frame of analysis is important to evaluating these values. For example, the diversity of spoken human languages is presently rapidly decreasing as globalization, connectivity and AI advance. So from one perspective, it looks like the social diversity value is being harmed. But if we include all the new languages and algorithms that machines are using to communicate with each other and with people, and the informational diversity that that has resulted from these processes, we can argue that the diversity of information, of forms of communication, and of kinds of intelligence is today much greater than it was just a few decades ago, before IT-aided globalization took off in the 1980s. Our conclusions thus depend on our assumptions and perspective.

Note that Empathy and Ethics, or right-feeling and right-thinking, are our two most adaptive values in this model. Thus we can color them purple, as they are at the heart of all intelligent adaptive systems. A simple bacterial replicator has some kind of feelings (empathy), beginning with a desire to move toward food and away from danger, and also some kind of right thinking (morality), encoded in their genetic and cellular intelligence, for how to best act in relation to their environment. We can also broaden our perspective on adaptation to consider three potentially universal forms of intelligence: (individual) intelligence, (collective) interdependence, and (defensive) immunity, and color all three in purple whenever that broader view seems more useful to our analysis. That is the most common perspective we take in this Guide.

As Empathy and Ethics are the two most central evo devo values, it is perhaps hardest to see either of this pair as being more evolutionary or more developmental. Yet when we think of Empathy as an ability to connect to the entire (evolutionary) diversity of minds and views, and Ethics as a search for more optimal (developmental) constraints on our thought and action, we can color them green and blue to remind us of ways they oppose each other. In our own lives, we can think of many times where our feelings for others override our desire to engage in right action, and vice versa, times when our morality makes us act against our feelings for another.

Let’s now explore each of the Ten Values in a bit more detail in evo devo terms. We’ll also offer a few more synonyms and descriptions for each, so you can better see, predict, and guide them as they grow in yourself, your teams, your client organizations, and in society.

As above, we’ll list the evolutionary version of each value/goal pair first. Recall that the 95/5 Rule tells us the first value in each pair is responsible for the most change by far, and is usually the easiest to see. But remember that good foresight process usually starts by doing the reverse of this, trying to forecast the developmental version of each of these value pairs first. The latter is often harder to see, but is more predictable, once we have the right definitions. Only after thinking hard about a developmental value should we contemplate its evolutionary partner, which is far more diverse, experimental, contingent, and possibility oriented. Starting with developmental thinking helps us to constrain evolutionary thinking to that subset that is most worth doing.

  1. Empathy/Love/Compassion/Understanding/Connectedness is perhaps the most fundamental feature of successful evolutionary systems. All living systems need feelings, which begin with pleasure/pain sensing and motivation, to navigate to more adaptive states. This requires an intuitive understanding of as much of the relevant environment as possible. The more advanced this value gets, the more that feeling becomes a sense of love, compassion, and connectedness to all things. Whether we call it empathy or love, great philosophers and spiritual leaders argue that this value is at the heart of all life. We all know people who are strong in this value, and they are the glue that binds our groups together, helping us to get better at feeling others mental states as accurately and strongly as we feel ourselves. If you want to develop greater empathy and understanding toward another person, do the 36 Questions, developed by social psychologists to help you progressively understand and empathize with (“love”) any person you do this work with.
  1. Ethics/Morality/Fairness/Synergy/Positive-Sumness/Interdependence is perhaps the most fundamental feature of successful developmental systems. If empathy is the “heart” of complexity, ethics is the “brain”. Moral insights act to funnel and constrain, rather than branch and diverge, all living systems. The emergence of cognitive norms in collectives of previously independent individuals and tribes, the advance of globalization and technological linkages between all people, and the positive sum rulesets we develop for social, political, economic, and engineering interaction are all forms of growing cognitive interdependence during biological, social, and technological development. Yet consider how growing interdependence also reduces many kinds of information growth, by limiting the probabilities of many types of outcomes. This value is truly a constraint we don’t fully appreciate. Read Pinker’s Better Angels of Our Nature (2012) to see how much less violent the twin forces growing digitally-aided ethics and empathy have made modern humanity, on average, versus our ancestors just a few generations back. This civilizing process is inexorable, and when we don’t see it we don’t recognize all the ways we can make it even stronger now, rather than later.
  1. Insight/Dematerialization/Virtualization/Modeling/Consciousness/Intelligence is a second key feature of evolutionary systems, one particularly prominent at the leading edge of complexity. Leading systems dematerialize, or create informational representations or simulations of the world. This kind of intelligence is experimental and diversity-creating, but not necessarily adaptive. Think of all the evil or dysfunctional intelligences in human history. It also isn’t necessarily more complex. Think of all the intelligences of all the different variety of species on Earth. Many have simplified (think of a parasite) to find their best local niche. Yet curiously, the most adaptive local simulations increasingly augment and substitute for processes in the physical world. The typical human mind is a great example of that. A smartphone is as well. Just as there are billions of human minds and smartphones, there are also likely billions of extremely dematerialized civilizations in our universe as well. In this view, the universe isn’t a simulation, as in Moravec and Bostrom’s Simulation Argument, but rather a massively parallel simulation system, with each local civilization (simulation) being very limited and incomplete. But just like Darwin’s tree of life, the more dematerialization we get on Earth, the more predictable diversity of intelligences exist. It is also predictable that we all spend more time “in our minds” rather than “in our bodies,” doing more thinking and less doing, as time goes on. Fortunately, a subset of these intelligences will grow measurably more intelligent, interdependent, and immune over time.
  1. Power/Densification/Wealth/Speed/STEM Compression (Exponential Production Efficiency & Density) is a second key feature of leading developmental systems, also particularly prominent at the leading edge of complexity. They keep accelerating in their resource density and efficiency. Think of the computational densification that occurs in human synapses as a child grows from baby to adult. Or the increasing efficiency of movement and purpose in older versus younger individuals, and the increasing economic value of labor in a well-developing worker in any trade. Think also of all the social and technological densifications (STEM compression) described in Chapter 7, as leading societies have moved to increasingly fast and efficient forms of wealth, from barter to gold to currency to bits, and from agriculture to empires to industrial cities to corporate economies to our increasingly intelligent machines. It is easy to densify a system in a way that reduces its intelligence. Conversely, you can make a system more intelligent without greatly improving its resource efficiency or density. The ideal strategy is often one that advances both goals at the same time.
  1. Diversity/Information/Individuation/Specialization/Difference/Independence is a third key feature of evolutionary systems. Think of Darwin’s tree of life, and all the diversity of its species. Information is perhaps the most basic kind of diversity. Information has been called a “difference that makes a difference”. Information generation is perhaps the easiest to measure of all of these variables, at least in digital systems, as bits. Information also grows exponentially, as human and machine civilization continue to advance on Earth. When we think of Big Data, we can think of digital information as the “soil” or “pattern” that needs to exist before any pattern recognition system (intelligence) can arise. Think also of how cognitive diversity, both within our own mind (“multiple intelligences” and perspectives), on our teams, and in organizations and societies is also a fundamental kind of system intelligence. Every bit of information is kind of like an individual, it is something unique and specialized. When we grow specialization and division of labor in society, we are growing not only evolutionary diversity, but information, which can lead us to greater forms of complexity, computation, and intelligence. Clearly, a balance must be struck between information and diversity generation, which is inherently conflict-generating and potentially disruptive, and security/awareness/protection/immunity in complex systems. Pursue either of these goals too exclusively and you’ll threaten the other. Great managers find strategies that grow diversity and security at the same time.
  1. Security/Awareness/Protection/Safety/Risk Management/Immunity is a third key feature of developmental systems. All complex systems have not only intelligence, they have various types of immune systems, designed to protect that intelligence from disruption. In human beings, our immune systems are the second most complex systems, in terms of numbers of genes involved in their creation and maintenance, after our brains. Yet we often forget this illuminating fact, when we design our teams, organizations, and institutional processes. Risk management and security are usually afterthoughts. Look at all the pain the personal computer industry inflicted on the world, when it released personal computers that had no immune systems in the 1980s, inviting every curious high school student to become a black hat hacker. In organisations and societies, immune systems are all those processes and features we use to ensure security, to defend ourselves, and to police rulebreakers. Yet as important as these systems are, we see how they also must be balanced against their evolutionary counterparts. Too much diversity/individuation/experimentation will threaten immunity/protection, and vice versa. For examples of too much immunity, and how it causes diversity destruction, think of autoimmune diseases, or every degenerative process in human beings made worse by inflammation, a very poorly intelligent, nonspecific immune system response.
  1. Freedom/Bottom-Upness/Indeterminacy/Options/Uncertainty is a fourth key feature of evolutionary systems. In dynamics, the term degrees of freedom describes the number of independent ways a system can move, and in statistics, the number of variables that remain free to vary in a complex system. We can call the freedom available to a system its indeterminacy, options, or uncertainty. The degree to which any system is run bottom-up, with local agents determining their own courses of action, is one key measure of how much potential freedom or indeterminacy that system has. The more bottom-up it is, the freer and more creative it can be. This doesn’t mean it is always freer in its actual behaviors. It may also be highly integrated and restrained by, for example, its morality, or by regulations, from doing various actions. But in general, the more bottom-up a system is, the more potential options and uncertainty, and the more the number of possible states, thoughts, or actions it can occupy.
  1. Order/Top-Downness/Structure/Regulation/Constraint is a fourth key feature of developmental systems. Order opposes freedom, but it can also enhance it as well. As every large organization knows, too much structure and regulation can keep employees from exercising freedoms, taking risks, and finding valuable new things. Too little regulation will expose it to catastrophe (security failure). The challenge is to get the order structured in a way that guides bottom-up activity, where most change always occurs, to focus its attention and energy more on certain freedoms than others. This happens well when we enact laws (regulations, rights) that give us “freedom from” various social negatives (discrimination, crime, poverty, disease) and which guarantee “freedoms to” do various positive sum collective actions (own property, compete, vote). An organization that incentivizes appropriate bottom-up behavior (employee ideation, ownership, leadership), by creating appropriate top-down order (incentives, platforms, policies) to support such activity will advance both the exercise of individual freedoms and the growth of useful order at the same time. But as the 95/5 Rule would argue, we typically don’t need more laws, just better laws. The most effective systems run almost entirely in bottom-up mode, with the exception of critical top-down processes and constraints. When should you run your own mind, your organization, or your society in a top-down versus a bottom-up mode? Both are quite valuable, at different times for different reasons. Under threat, we predictably turn to top-down control in our societies, at first. Think of post-9/11 America. In conditions of plenty and relative safety, we predictably swing back to more bottom-upness and freedom for ourselves and our societies. To accomplish big things, we always need some top-down hierarchy. Think of the leaders of the Occupy movement, which fizzled predictably due to their utopian nonhierarchical (entirely bottom-up) organizational structure, versus organizations like Syriza in Greece, which had very similar goals but a pragmatic approach to building both top-down hierarchy and bottom-up innovation together. Because of Syriza’s understanding of the need for order, and because Greece itself is a more constitutionally democratic (bottom-up oriented) country the the US, being a parliamentary democracy, they soon became a leading political party, and as such they’ve provided many more options (freedoms) for Greece’s poor than Occupy ever could.
  1. Creativity/Unpredictability/Novelty/Imagination/Fiction/Experiment/Innovation is a fifth key feature of evolutionary systems. Innovations typically begin as risk-taking experiments. The more you empower any complex system to run experiments, do rationally-guided trial and error learning, the more chance it has of growing adaptive intelligence. But as an evolutionary system, lots of mistakes, and a few catastrophes, are inevitably going to occur along the way. As psychologist Alison Gopnik tells us, experiments are how children learn about the world. She notes that until we build biologically-inspired AIs and robots that are flexible enough to run constant creative experiments, both in their minds and in the world, they won’t be able to learn like us, and we have no grounds to expect them to become human-surpassing. Fortunately, we’re now beginning to build machines that experiment more like we do, and most computer scientists see this is the best way forward. At the same time, we must also see the dangers of being too creativity and innovation centric. Think of our out-of-control growth in consumerism, where we buy all kinds of useless fluff just because it is the “new thing.” Think of the way we easily live in filter bubble fictions, imaginary worlds that fit with our biases but not with evidence. Think of the way we binge watch fiction that is often repetitive and of low educational value. Such tendencies threaten the sustainability of our societies and our environment. A great bookstore will have half of its space dedicated to fiction (creative what-ifs), and half to truth (nonfiction). A great manager will inspire us both to more creative imagination and experimentation, and to gaining more truth/predictability/sustainability as well.
  1. Truth/Predictability/Optimization/Accuracy/Inertia/Sustainability is a fifth key feature of developmental systems. They are always searching for truth, as a kind of convergent optimization of informational possibilities around one high-probability representation. Like morality, truth also has its own inertia, once it is discovered. It persists, and has increasing influence, the more intelligent the environment around it comes. Whenever society discovers any truths, through science, the more it is bound by the constraints those truths reveal. Sustainability is the ultimate optimization that complex systems must achieve. All of them must complete their life cycle and replicate, so they can build on what they have learned. For organizations, sustainability means knowing enough truths about the operating environment to continue as a going concern, to continue to develop. Great managers are always seeking to advance both team and organizational innovation and sustainability. As we’ve said earlier, “sustainable innovation”, is a paradoxical phrase that aptly describes the competing drives of evo devo systems, and the nature of life.

Twelve Traits of Complex Systems

As we’ll see in the next section, there is also an eleventh condition, Incompleteness, that we can think of as key to the nature and future of evo devo systems. Incompleteness is not an ability or goal, but rather a persistent state of goal-seeking systems. Yet it seems so important to remember that it deserves to be listed first, above the Ten Values, as the “roof” of a house of adaptiveness, as in the picture at right.

We can also see how each of these values fits within a culturally universal Right/Left political model. Some folks on the Left, having a bit too much empathy, can seek the false security of a socialist state, robbing individuals of the benefits and challenges of self-actualization, and stronger personal ethics (individual moral intelligence) development. Some folks on the Right, having too little empathy, would put the entire responsibility on the individual, not recognizing that people have widely varying capacities to make good ethical decisions, and much of that capacity is determined by the quality of the environment in which they were raised. Likewise with ethics. Some folks on the left, being too social ethics (vs. individual ethics) oriented, seek to overregulate, turning everything into an ethical optimum, even when we don’t have any idea what the optimum is, and both society and individuals would benefit most from greater experimentation and data generation. Some folks on the right, being too ethically underdeveloped, seek to argue that there is no moral requirement for how individuals, corporations, or the state should act toward fellow citizens.

Remember also our model for how Left and Right political parties split the domains of economic and social freedoms and regulations. The Left typically wants social freedom and economic regulation. The Right typically wants social regulation and economic freedom. Thus we see that these Left/Right labels are not just random, but something very deep, that we should expect on all Earthlike planets. Perhaps most importantly, this evo devo analysis helps us understand where our political center lies. That lets us work toward strategies that will bring us back to a dynamically balancing center. The center is where we likely belong, most of the time.

Consider now how pursuing the evolutionary goals above is usually the most effective way to get more of the developmental goals described above. Likewise, consider how improving our developmental goals and abilities, over successive replications of complex systems, will usually be the most effective way to improve our evolutionary goals and abilities. The two sets of goals depend upon, and are often in tension or opposition with, each other.

Notice also that two of these goals, Insight and Power, are closely associated with two terms we introduced in Chapter 7, Dematerialization and Densification, or “D&D,” the twin races that complex systems run toward more virtual and physical “inner space”. The Ten Values show us that societies are not simply dematerializing or densifying. They continually make complex tradeoffs between all ten of these values, as they seek to grow their adaptiveness. While D&D is particularly insightful for looking at complex systems from a simplified, universal perspective, we need a more complex model when we discuss acceleration in social systems, to begin to properly address society’s complexity.

The physicist John Wheeler is among those who have proposed that our universe is fundamentally computational, that “It” emerges from “Bit”, via quantum mechanics. Dematerialization includes that perspective, and says something more. The more “bits” we have, the more we focus on manipulating them, over manipulating atoms. Densification, in turn, tells us we use atoms increasingly densely, microscopically, rapidly, productively, and efficiently, for creating more information, wealth, and intelligence (bits).

The philosopher Alfred North Whitehead, the mentor of my own mentor, systems theorist James Grier Miller, was a great advocate of the hypothesis of panpsychism, the idea that “all adaptive matter models external reality”, to the greatest extent that it can. In other words, adaptive systems have a mental dimension, and this mental dimension grows with the complexity of the physical system. Conserving and advancing various kinds of intelligence seems to be a basic drive of the universe, one that must be accounted for in our future science and cosmology, if it is to become truly inclusive and predictive.

Recall also that Intelligence uses many different representation systems, like emotion, morality, cognition, and consciousness. For a classic introduction to intelligence as various forms of informational representation, see Fischler and Firschein’s Intelligence (1987), which compares the representation systems used by such different complex adaptive structures as eyes, brains, and computers. Chapter 7 described how it grows exponentially, and is now doing so particularly rapidly in machine intelligence, and its performance on various benchmarks. In my view, intelligent machines are the most important developmental outcome we can expect on Earth in the history of our species. They are a truly new evolutionary development, a key complexity transition we can expect on all Earth-like planets, whether we recognize it today or not.

When we see our universe and its intelligences as not only evolutionary but also developmental systems, we move beyond the “random accident”, “purposeless” views of universal change offered by many current scholars to explain reality. For an example of that, see Alan Lightman’s Sydney award winning essay, “The Accidental Universe,” Harpers 2011. We’ll also move beyond our sterile “null hypothesis” perspective on the likely relation between the laws of our physical universe and its accelerating intelligences.

If our universe is evo devo, we can extrapolate from current early evidence for Earthlike planet ubiquity in our galaxy to predict that it has self-organized to  protect and accelerate the emergence of an astronomically large number of local intelligences. That would make our universe the most massively parallel computational system we know of, one with great similarity to the massive parallelism and diversity that we find in the many neural network mindsets that we use to argue with ourselves, over competing courses of action, within our own brains. All that might be required for evo devo tuning for intelligence to happen would be for it to play a small but usefully nonrandom role in the survival and adaptiveness of the universe that generates it, just as it does in other replicating complex adaptive systems, such as bacteria replicating in an ecosystem, bonobos replicating in the Congo, or Babbage’s idea (meme) of the computer, replicating in modern society.

It’s obvious that any one of these Ten Values can be overvalued, at the cost of the others. Consider Security. It’s easy to overprotect a child, an adult, or an organization, or to overchallenge them in an effort to build their immunity. It’s also easy to slip into extremism. Extreme top-down rules like socially shunning anyone who tries to leave a group (Mormons), or even violently attacking the leavers (Chimpanzees, Mafia, Drug Cartels) will make that group much more inertial and immune, but will reduce its moral interdependence with the rest of society, and lower its internal freedom and innovation. Many other examples of social imbalances can be given.

Consider also Truth. Good foresight is always trying to find and tell new possible truths, and grow the sphere of the known, even at the risk of social consequences to the teller. This Guide seeks to advance our truthful understanding of who we are, by starting with exponential and evo devo foresight, even though some of these stories are controversial, a bit threatening or disturbing. If we have sufficient empathy and morality, we will try to be sensitive in telling our stories, ideally mainly to those who seem ready to hear them. We often tell “white lies” in our own personal attempts to serve the other values, like empathy, security, freedom, or preserving a diversity of options for others. But because we value truth, we’ll continue to tell the best stories we find, until they can be knocked down and replaced with better ones. As long as your stories seem to be weebles, they deserve to be responsibly told, in a search for more truth. But we must remember that truth seeking, like every other goal, is always in balance with all the other goals.

In addition to balancing these Ten Values during over the life of any system, there are life cycle effects we need to be aware of as well. All evo devo systems move from the left to the right side of the eight values as they age and get more developed. Eventually they become senescent, a kind of physical and mental overdetermination (loss of resiliency) and brittleness that leads to their eventual death and recycling, in order to reestablish their balance with the environment. For more on this perennial balance between senescence and renewal via replication in complex systems, see Stan Salthe’s Development and Evolution (1993). Salthe is a member of our Evo Devo Universe research community, and someone whose work has greatly inspired my own.

In other words, all evo devo systems eventually fall into an Overdevelopment Trap, shifting from the left to the right side of the value sets listed above. They reduce their dematerialization and prefer further densification, even when it reduces their intelligence. They reduce their freedom and diversity and favor security and order too much for their own good.

There are two ways to avoid this trap. The first is to “square the curve” of normal aging, to slow down and push off overdevelopment and senescence until the latest possible time in the natural life cycle. In any ideal organism, organization, or society, senescence comes only at the very end of a long and healthy mature life. If we divide our lifespan into “healthspan” and “frailspan”, in the most healthy evolutionary development we want our frailspan to be compressed to the shortest possible time at the end of our life. All the wheels should come off the wagon in a very short time period, with everything working great beforehand.

The second way out of the trap, of course, is to rejuvenate, recycle or replicate. If we can’t rejuvenate, we need to packaging our best learning into a new replicator (seed) which becomes a new organism with even more indeterminacy (freedom, bottom-upness) than we had at birth. When parents have children, and try to give them more freedom and options than they had, they are engaging in this perennial renewal strategy. Every complex system needs some form of renewal every so often, in order to get out of the trap of overdevelopment.

Fortunately current research suggests most older people today don’t fall into this trap as much as we might think. Several studies argue that we get more open minded in many ways with age, at least for a long while during our late adulthood. I don’t know if the growing open mindedness of older adults is a culturally recent phenomenon, due to the rejuvenating effect of an ever more complex environment, as seen in the Flynn effect in Chapter 2. It may be a general feature of normal human development that people get more open minded as they age, perhaps far into old age. In other words, mental senescence may be the exception, not the rule, in late adulthood. But regardless of the normal chain of events, we all know older people who have fallen into the trap of overdevelopment, becoming much too predictable, conservative, pessimistic, and rigid their beliefs for their own good. Figuring out how to avoid this trap, and to rejuvenate, recycle, or replicate as we, our organizations and societies age is a great area of future research.

There are many frames from which to view social complexity, and we’ve offered our most complex one here. Unfortunately, our science and information theory are not yet advanced enough to prove or disprove many of the speculations we’ve just engaged in, and they are certainly wrong in many parts. What matters most is whether they are more useful, for any of us, than the frameworks we presently use to navigate the world. Even tentative ideas on these issues, if they are useful, can change our lives for the better, and greatly improve our effectiveness. I hope you find it useful in your own journey.

Comments
  • Oleksiy Teselkin
    Reply

    Great point! Indeed, we have been quite good at squaring our senescence curve, like this: http://joshmitteldorf.scienceblog.com/files/2014/12/Squaring-the-curve.png

    Zero progress has been achieved, however, at extending our ‘species limit’ (i.e, at untangling the tails of survival curves https://thefunambulistdotnet.files.wordpress.com/2013/03/life-expectancy.png). If biotechnologies to rejuvenate the brain become available towards this end, this would – indeed – transition personalities (which are dependent on the network structure). Done slowly, however, how different would this be from our natural process of personality change? Most would agree that often our own ‘self’ from 10, 20, 30, 40 years ago is more different from our current ‘self’ than any randomly chosen developmentally unrelated ‘selves’ of similar age.

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