Let us now briefly consider examples of empowerment foresight in four practice domains. These are a modified version of the STEEPS foresight domains.

1. Scientific and Technological Evo Devo
2. Biological and Psychological Evo Devo
3. Organizational and Industry Evo Devo
4. Societal (Economic, Political, and Social) Evo Devo

1. Scientific and Technological Evo Devo

After biological and psychological evolutionary development, to be discussed next, science and technology are perhaps the second most powerful system that we should strive to see as engaging in both evolutionary experiment and irreversible and predictable cumulative development.

Scientific hypotheses and technical solutions, each imperfect and biased by our world views, compete with each other in an evolutionary, selective manner to explain the evidence or solve the problem at hand. At the same time, scientific and technical knowledge and capacity accumulates at an accelerating pace. S&T are already the most powerful and rapidly changing systems on Earth.

As very few in the philosophy of science, science, technology and society, technology dynamics, and related academic communities will admit today, S&T increasingly constrain and determine the futures of societal systems, often in little-understood ways. Clearly the growth of efficient, effective, and intelligent new solutions to problems, and the redundant sharing of them across the planet, is like a one-way ratchet, moving us inevitably to a more complex, intelligence-rich world. But what else can we say about that ratchet?

In the model of universal evolutionary development, and in the Five Goals and Ten Values, we have proposed some of the emerging constraints, and developmental portals, waiting ahead for an increasingly S&T-capable humanity. If the evo devo model is at least very roughly correct, will modern science and philosophy wake up to recognize the evolutionary developmental nature of the universe before human-surpassing machine intelligences (the technological singularity) predictably arrive sometime this century and definitively show it to us?

The answer to this evolutionary question of timing is hard to predict. But I do think it is a good prediction that as humanity continues its incredible 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.

In Chapter 7 and this chapter, we have argued that a set of universal developmental processes appear to be driving accelerating technological change. We have called those processes STEM compression (STEM efficiency and density of computation and physical transformation) and Densification and Dematerialization (“D&D”). We have also used the evo devo perspective to expand these “drivers” beyond D&D to include six other important variables, calling them Ten Values of adaptive social systems. We have even proposed, in the transcension hypothesis, that all advanced universal civilizations may end up leaving normal space for some kind of “inner space”, most likely black holes, as the most efficient way to meet all other universal civilizations to compare and contrast their imperfect knowledge, perhaps as a step on a path to universal replication. All of these are of course very preliminary and speculative models, but hopefully they at least advance the discussion on these topics, at a time when scholarship in these areas is still quite immature.

We have also argued that there are a number of coming developments that appear highly probable, from our current perspective. These include conversational interfaces, personal AIs, humanoid robotics, and the technological singularity. Increasingly intimate physical-virtual interfaces, via hyperrealistic virtual worlds, mirror worlds, augmented reality, wearable computers, the internet of things, and even brain implants (though these are very unlikely to be widespread, or very effective, due to substrate shift) have been discussed as probable coming developments. We have even argued that while most people will be happy with personal AIs as an incremental and very natural form of uploading (substrate shift), over the next generation or two (25-50 years), a minority of people will choose to do brain preservation upon biological death. Both that mass majority who use sims, and that small minority who do brain preservation will reasonably expect to have some kind of digital afterlife.

This new mental benefit, the ability to imagine that we are living on here on Earth, even past the end of our biological lives, should help humanity to further converge our scientific and religious views of the universe. Our leading religions will develop more nuanced stories of what the afterlife is. They’ve successively revised their views of the afterlife several times in the past millennia, and it is a conservative prediction that they will continue to do that in the future. We will increasingly demand it from our leading religious communities for moral and spiritual guidance, when we turn to them as places to help each other to live better, and to discuss the big questions about our place and purpose, as we will surely do.

These are some of the more obvious examples of developmental futures that seem likely, when one looks carefully at today’s accelerating science, technology, and computation. The better we get at carefully building and critiquing our developmental models, and the more we help our clients and the public to see the world in both evolutionary and developmental terms, the better we can all get at steering toward the positive sum destinations that are implicit in the technology, waiting patiently to be born.

2. Biological and Psychological Evo Devo

Just as our bodies and brains evolve and develop, so too do our psychologies. They have evolutionary aspects that branch and experiment, making each of us unique, and developmental aspects that funnel and conserve, guiding the developing mind on a hierarchical progression that has universal features, statistically common to all of us. The better we understand psychological evo devo, the better we can manage our own psychologies, and relate to our clients and stakeholders.

One useful tool for eliciting both your own and your clients’ values is Crace and Brown’s Life Values Inventory. This is an online assessment that maps values across fourteen categories, and shows how they change over time. Knowing where a client is on a values map (values intelligence) can give you a great sense of where they may possibly, probably, or prefer to go next.

A classic model of moral development of values is Lawrence Kohlberg’s Six-Stage Theory of Moral Development. Kohlberg proposed that we grow through six stages in our mental relation to others: Obedience, Self-Interest, Personal Conformity, Social Authority, Social Contract, and Universal Ethics. Understanding where others presently are on Kohlberg’s stages can help you better relate to them, using their language, and empathizing with their world view. It is also helpful to know if a person is in a low state of moral development (pre-conventional, the first two stages). You can take extra care in your relations with such people, and protect yourself.

A model with less evidence but more popularity in the West, is psychologist Abraham Maslow’s Six-Stage Hierarchy of Needs Development. It proposes six roughly hierarchical needs that are sought after in psychological development: Physiological Needs, Safety Needs, Love and Belonging Needs, Esteem Needs, Self-Actualization Needs, and Self-Transcendence (Higher Purpose) Needs. Philosopher Turil Cronberg offers a speculative expansion of Maslow’s hierarchy (picture right).

Another noteworthy but less evidence-based model is Spiral Dynamics, an Eight Stage Theory of Cultural Values Development. It proposes that cultures, and sometimes even organizations, grow through Survival, Clan, Egocentric, Purposeful, Strategic, Relativistic, Systemic, and Holistic value systems as they develop experience and wisdom. See Beck and Cowan’s Spiral Dynamics: Mastering Values, Leadership, and Change (2005) for an overview, and ideas on how to use it your foresight practice.

With all such abstract models, it is easy to propose more developmental structure than actually exists. But efforts like this move us in the right direction at least, and we can expect all such developmental models to be more rigorously tested and quantitated in coming years. In the meantime, knowing which needs your client is currently pursuing, and having good models of reasonable hierarchies, whether or not you find them rigorous, can greatly improve your psychological insights.

Again, these hierarchies would be roughly statistical, if these models are valid. The evolutionary component would show up in some variation in the hierarchies based on unique individuals and cultures.

For example, returning to Maslow’s hierarchy, we know that more collectivist societies including many in the East, tend to raise individuals who seek self-transcendence (realization that there are higher purposes, and more “meaningful” complex systems than the self, including human society, the biosphere, the universe, and spiritual practice) above self-actualization. Individualist societies, by contrast, including many in the West, tend to raise individuals who do the reverse, preferring self-actualization as their highest aspiration, and only rarely seeing the value of self-transcendence. In fact they may confuse self-transcendence with simpler, social belonging-based values like loyalty and patriotism.

Various stages in these hierarchies can compressed to some degree, but attempts to skip them entirely can be easily seen as dysfunctional, and disruptive to normal psychological development. For example, it is a common saying in religious communities that one should first feed and clothe a person if you expect to minister to them about self-transcendence. But there are several developmental steps between these two states that need to be properly attended to as well. The practices of some religions and philosophies seek to skip, or at least minimize time spent, in these transitional levels. A number of these clearly involve a pathological short-circuiting, where the believer denies the value of all the intermediate levels, for themselves, their friends, and the world. Think of all the cults of self-denial, like the Moonies, the Hare Krishnas, and the Family, cults that have captivated people who feel spiritual emptiness, or who are not very successful with interim levels like love and belonging, self-esteem, or self-actualization, in their current social environments. Cowan and Bromley’s Cults and New Religions (2007) offers an instructive overview of eight recent dysfunctional religious philosophies, and the people they prey upon.

Notice now that the further one travels up these evo devo hierarchies, the more one moves from psychological needs (developmental factors critical to our mental health) into values (normative preferences, or choices). In the future, we may expect to get much better at distinguishing between these two, and ordering both into rough hierarchies, seeking to begin with needs and end with values. The higher one travels up a needs-to-values hierarchy, the less developmental predictability and the more evolutionary experimentation one should find, both for individuals and populations. In other words, the base of any needs-to-values pyramid should be mostly blue, and the top mostly green. Fortunately, as per capita wealth grows, we all tend to think more about values (free choices) than needs, as long as those needs are being satisfied. Unfortunately, that doesn’t mean that we’ll make adaptive values choices. Values experimentation grows as wealth grows, and many poor choices are made on the way to greater adaptiveness.

In the tradition of Maslow, let me offer two speculative needs-to-values pyramids, one for individuals and one for societies. They are presented without evidence, merely to spark your thinking about needs and values in both individual and social terms.

If needs and values hierarchies like these are to be validated in the future, we must find values sets that are typically individual and cultural choices (low probability, in green above), needs sets that are mostly universals (high probability, in blue above), and “near-needs”, values in the middle that are a more even mix of need and choice (intermediate probability, in black above). We should find robust individual and cultural variations in the rankings, by life-hours spent at each step, yet statistical bounds on that variability, with rankings and path being predictably dependent on adaptive context. For one famously predictable example, safety (individual) and loyalty (social) needs gain greatly in relative importance during perceived crises, then subside as the crisis passes.

In this particular model, self-knowledge (for individuals) and science-rationality (for cultures) are experimental choices today, and at the same time, higher levels of development in the future. They are not presently found in all individuals and cultures, as are the lower levels. This seems true from life experience, even though we know these values are critical to all domains of foresight.

In this model, we haven’t yet evolved to need any of the green values, on deep emotional and cognitive levels, even though they offer immense adaptive value. Some of us presently make these values choices, and others don’t, due to our upbringing, chance, or the limitations of our culture.

But the more intimately we share our lives with our personal AIs in coming years, the more we can expect values that most grow adaptive intelligence, values like self-knowledge and science, as well as the other green values above, to become future needs. They will be as fundamental to the late 21^st century human’s developmental psychology as current needs like air, food, shelter, self-esteem, love, belonging, and others in blue above. Since genetic engineering to change the psychological nature of biological humans will likely remain both far too difficult and ethically perilous in this century, it will be our personal AIs that most deeply express this psychological need.

If they are learning evo devo systems, as I have argued, they will hunger for ever greater levels of innovation, intelligence, interdependence, immunity, and sustainability, in ways that we truly we don’t yet appreciate in our scientific and lay communities.

As humanity learns to expand our set of needs higher up this pyramid, this will add new constraints to our behavior and morality. These constraints in turn will allow a new set of values (experimental normative choices) to emerge on top of these needs, further growing the pyramid. Our existing needs and values surely emerged in this kind of trial and error process. We are always balancing evolution and development.

For more on values, Adler’s The Great Ideas (1992) and How to Think About the Great Ideas (2000) are both great overviews of the kinds of things we choose to care about across all cultures.

3. Organizational and Industry Evo Devo

Organizations and their industries also evolve and develop. In addition to the study of competition and change offered within Business Administration, the academic fields of Industrial and Organizational Psychology, Organizational Studies, Organizational Behavior, Organization Development, Operations Research, and Management Science are good sources for both evo and devo perspectives on organizational change. There is also an Industry Studies Association, an academic community exploring unpredictable and predictable aspects of industrial change.

There are scores of good books in the management literature that take a balanced approach to the unpredictable and the predictable. Books like Adizes Managing Corporate Lifecycles (2014), Moore’s Crossing the Chasm (2014), Rogers Diffusion of Innovations (2003), Christensen’s The Innovator’s Dilemma (2011), Forrester’s Industrial Dynamics (1961) and many others mentioned in this Guide all help us anticipate as much as to experiment, in our organizations and their industries.

As the pace of change continues to accelerate in leading industries, we’re also seeing new evolutionary and developmental approaches to organization. We’ve mentioned agile and scrum management as recent innovations that seem likely to also be new developmental optima as well. See Sutherland, Scrum (2014) for a great intro.

Scrum rules like forming cross-functional teams of seven plus or minus two, engaging in one to three week sprints, followed by demos to and feedback from product stakeholders, having a product owner prioritize the backlog, and the team size the difficulty of items, are all likely to be optima for rapid and efficient work. The scrum approach is a very good example of the 95/5 rule, as it is a far more bottom-up and locally-determined approach to work than most organizations take.

So too there are likely a set of optimal ways of forming “teams of teams”, organizing your scrum teams within larger organizations. There are many examples of leaders successfully implementing more evo devo approaches to management, but finding them in highly bureaucratic and high-stakes organizations is particularly inspiring.

See Gen. Stanley McCrystal’s Team of Teams (2015) for a great account of the reorganization of the communication protocols in the Joint Special Operations Task Force under the Special Operations Command in the US Department of Defense in a far more bottom-up fashion, and the great benefits of that approach in Iraq in 2004 and later in Afghanistan, under his command. One of McCrystal’s critical innovations was to have one member of each relevant small team attend his daily online Operations & Intelligence briefing, which ran for up to two hours. That ended up being 7,000 individuals who were able to ask critical questions or report problems, and offer solutions to their colleagues offline after the briefing.

McCrystal’s changes, beginning with communication, helped him reshape the Task Force as not one massive team, but a “team of teams”. Prioritizing daily communication between existing small and very efficient teams, like the Navy SEALS and Army Rangers, and finding that one “connector” individual in all the departments and teams in the much more top-down organizations, including the Army, Navy, Air Force, Marines, CIA, NSA, and others, and getting them on the daily briefing really helped McCrystal push through a more scrum-like approach to problem solving and operations.

Bratton and Knobler’s excellent book Turnaround (1998) describes another scrum-like daily briefing process, which, when combined with new digital measurement and accountability platform called CompStat, was used to reform the New Jersey Transit Authority and the New York Police Department.

In my experience, books like Team of Teams and Turnaround can change people’s minds about what is possible when more bottom up and evo devo process is implemented on small teams, even when they exist within rigid bureaucracies. If enlightened leaders can make the US military and police departments more scrum-like, we can reshape any organization, beginning with our small teams.

Schwartz (1996)
A classic in Org Foresight.

Fortunately, many of the world’s leading companies are already surprisingly evolutionary developmentalist in their strategy and planning. We can trace this shift back at least to Pierre Wack’s strategy group at Royal Dutch/Shell in the 1980s, as discussed in Peter Schwartz’s The Art of the Long View (1996), a classic in business foresight. Wack realized that in order to do good scenario planning (exploring “what could happen”, and the best strategic responses to major uncertainties) one should first constrain the possibility space by understanding what is very likely to continue to happen in the larger environment.

Wack recommended starting with developmental foresight, finding the apparently “inevitable” macrotrends that he called TINA trends, which we saw in Chapter 7. TINA stands for “There Is No Alternative” to the trend. He cited increasing economic globalization and political liberalization, short of global pandemic or asteroid strike, as starter examples of such trends. Once we know the boundaries of the future indicated by such trends, we can then do evolutionary foresight (exploring alternative futures) within a set of testable TINA trends and constraints. That in turn will make our alternative futuring much more believable and accurate as a result.

Treating both evo and devo foresight perspectives seriously remains a key challenge for some strategy leaders. Those who practice strategic foresight are by definition prioritizing preference foresight, the Third P, but many strategists don’t try to balance evo and devo thinking on the way into setting their preferences, agendas, and plans.

A number of management and foresight consultancies are good at one, but not the other of these two fundamental perspectives, as it’s a lot easier to pick one perspective as your dominant framework than to have to continually figure out how to integrate two opposing processes. But picking one as your primary way of getting to preferences is a cognitive bias, from the evo devo perspective. Both perspectives are critical to evaluating strategies and managing change.

I’ve been doing technology foresight work for several years now, and follow the work of various consultancies. In my experience, all the most successful companies and consultancies realize there are many highly predictable aspects of our future. Collectively our business and government leaders bet trillions annually on those predictions. Some are using good foresight processes in making those bets, but many are still flying by the seat of their pants.

Finally, when we consider evo and devo processes as inputs to strategy, we should strive to start with certainty (development), as Daniel Burrus says, as that will simplify our possibility thinking. I am convinced that those companies and consultancies, large and small, new and old, that solicit internal and external predictions, forecasts, risk analyses, and other predictive foresight processes in advance of their imaginative, creative, uncertainty-defining, scenario, and option-seeking work, as inputs to strategy, planning, and execution are winning increasingly large advantages in their markets every year.

Even our most successful entrepreneurs, who are clearly more experimental in their outlook than typical corporate leaders, very often have a world view that begins with prediction, and ends with their current set of experiments.  I need to look at the literature to find good studies to back up these claims, but offer them as conjectures at present.

The executives leading our most successful companies today definitely do not see the world as a random accident, like an ultra-Darwinian evolutionist, or a naive postmodernist “cultural relativist” who lives off the exponentiating wealth and leisure of the science and technology that he argues are “not uniquely privileged perspectives” on the universe. That perspective is bunkum, and we must call it out when we see it. Let’s hope a growing proportion of our young scientists and foresight practitioners in coming years become as evolutionary developmentalist in their world views as many of our corporate leaders and even entrepreneurs are today.

4. Societal (Economic, Political, and Social) Evo Devo

Astronomer and statistician Adolphe Quetelet coined the term “social physics” in A Treatise on Man (1842 in English). Since then, we’ve recognized that many economic, political, and  societal trends and events are predictable in probabilistic ways. Quetelet stunned 19^th century thinkers with his predictions of suicide rates in cities, models which get even made better when time of year, economic climate, weather, and other causally-related factors are included. Historian Henry Buckle’s History of Civilization (1857) also argued that a true science of history and the future could eventually be constructed using statistical approaches.

As we saw in Chapter 1, statistical foresight made great strides during the 1850’s up to World War I, but the volatility and carnage of the twentieth century’s world wars, social conflicts, and mass political oppressions kept Quetelet and Buckle’s vision from materializing. A few 20^th century futurists elegantly championed social predictability, perhaps most notably Ossip Flechtheim in Germany, who coined the term “futurology” in the mid-1940s, and proposed it as a new science and discipline of social probability and statistics. Science fiction author Isaac Asimov also famously championed this approach in his fictional discipline of Psychohistory in his Foundation series (1951).

But iterative modeling and probabilistic prediction of societal events turned out to be more complex and difficult than twentieth century society had patience or money for. Except in vertical niches, probabilistic prediction has been a marginal enterprise for most of the last sixty years, and sociology, economics, political science, history, and other disciplines have been underquantified and underpredictive as a result.

But as the intelligence and power of computers, big data, and predictive analytics have grown, and companies offering these services to governments and corporations have proliferated, we are again on the threshold of great advances in statistical foresight. I have very high hope that probable futures will become as fundamental as the possible and the preferable in all our academic fields in this century.

Computer scientist Alex Pentland’s new book on idea flow, Social Physics: How Good Ideas Spread, Lessons from a New Science (2014) is one recent example of this new quantitative, predictive orientation and optimism. Pentland does not cite Quetelet, even though he uses the same phrase in the same manner. Such is the state of foresight work on Earth in the early 21^st century. Fortunately, as our assistive conversational agents emerge n coming years, our rediscovery of relevant historical ideas will greatly improve, allowing us to better map human idea flow across the generations, and to see the continual branching out and funneling back to the truly universal ideas over human evolutionary time.

One late 20^th century foresight professional who became an early advocate of societal predictability was Pierre Wack, the first director of Shell’s Scenarios Group in the 1970s. As pioneering futurist Peter Schwartz describes in The Art of the Long View, Shell used scenarios to “get to the future first” by successfully anticipating important, uncertain futures, and having ready-to-implement strategies on hand in case that future arrived.

Wack’s group at Shell used scenarios to successfully anticipate and benefit from two separate oil shocks in the 1970s. Shell later used scenarios to anticipate the fall of the Soviet Union in 1989, allowing them to get long term gas contracts from Gazprom and others in that region faster and at much better rates than their competitors. They didn’t actually predict the fall, but they effectively predicted it, by identifying it as a very important and uncertain future question, and having strategies and relationships ready ahead of their competitors in case that outcome materialized. An excellent book on Shell’s 50-year experience “experimenting” with scenario foresight is Wikinson and Kuper’s The Essence of Scenarios: Learning from the Shell Experience (2014).

Wack was a strong systems thinker, which in addition to good historical knowledge and a keen, inquisitive mind seems necessary to good foresight regarding societal relationships, opportunities, and constraints. In his research, Wack came to suspect the existence of various “predetermined elements” shaping our societal future. We can call these elements developmental factors in the evo devo foresight model. Wack proposed that their discovery would function as a key constraint on scenario generation. Once discovered, they allow good futurists to do better, or what he called “second generation” scenario work. By discovering all the predictable societal elements we can, we take otherwise plausible scenarios off the table, and gain a much clearer forward view. Futurists Napier Collyns and Hardin Tibbs wrote a nice article for the Global Business Network (a pioneering but now-defunct SF Bay Area foresight consultancy started by Schwartz and others) describing Wack’s thinking in this regard.

GBN’s scenario approach, finding the two “most important, most uncertain” future outcomes relative to the client’s strategy, and then dividing those into a 2X2 scenario grid, with a high and low outcome for each, is fortuitously a useful mix of evo and devo priorities. Recall that evolutionary processes are “uncertain” (variable and at most locally optimized), while developmental processes are “important” (enduring global optimizations and destinations). By seeking to find and explore two strategic variables that could be either evolutionary or developmental (uncertain, to the planner), and yet are of high value (important) the GBN scenario process is a good first pass at evo devo thinking.

Let us briefly look in more depth at a few of the recent inspiring predictive societal foresight models that we discussed in Chapter 7 under TINA Trends. With most of the TINA trends, people are presently engaging in them unconsciously. They are not aware that many inevitable futures that the universe is guiding them toward, whether they want to go there or not.

Thus the great opportunity for futurists and foresighters is to help our clients and our societies to become consciously aware of these inevitabilities, so they can bring our full resources to charting the most humanizing path we can toward these destinies.

How did the use of that word “destinies” at the end of the last sentence make you feel? Did it feel like overclaiming? Did you reject it, at an emotional or unconscious level? We have to help people through the DABDA stages that they feel whenever they encounter TINA Trends, and hear words like “destiny” or “purpose” or “predictable future”. Many of us are in the Denial and Anger phases, seeking to kill or discredit the messenger, rather than listen to the message.

Many, but fortunately not all, of the models of societal prediction have been developed by individuals, typically independent scholars and academics, with precious little societal funding. Why? Because our leaders, and the world, are not yet development-aware. Almost all modern societies, with a few exceptions are presently biased to prefer a chaotic, reductionist, evolution-only view of change. Russia, France, and a handful of other countries produce slightly larger than average numbers of developmental theorists, but even in those countries developmental thinking never gets strong societal support.  When we do truthseeking, one of our Ten Values, in a society that doesn’t yet strongly pursue this goal at a conscious level, it is easy to get out in front of the pack with our models. We then pay the price of all pioneers, but at least we get the best views J.

In the future, let us hope development takes its proper place in coming decades, and we see a real, TINA-Trend driven science of societal foresight emerge, with good jobs for scholars, and lots of competitive funding opportunities for those who can improve universality and accuracy of their models, year over year. Perhaps, like so many things, we will have to wait for intelligent machines to arrive before this societal bias is rectified. But in the meantime, as evidence-driven professionals we can fix our own bias, and practice better foresight for our clients, right now.

TINA Trends in societal development that we should strive to better understand include:

Biogeography Determining the First Societies. In Guns, Germs, and Steel (1999) paleontologist Jared Diamond offered a masterful account of why, given the fact that all the humans who left Africa in waves beginning ~100 kya had roughly similar intelligence, certain regions on Earth were highly likely to be the first to produce “cargo”, the fruits of industrialization and market economies, while others were likely from the outset to be largely left behind in this great wave of technological and economic development.

Diamond makes clear why Eurasia, with its wide girth and thus easy ability to trade at the same latitude (changing climates and deserts inhibit North-South trade), had to advance far faster than Africa and the Americas, which had narrow girths and less of the key resources needed. Unlike Eurasia, the Americas also had very few domesticable species (llamas, turkeys, guinea pigs) which survived the extinctions of the Pleistocene.

His analysis tells us why civilization emerged first in the fertile crescent of the Nile Delta, the Levant, and Western Asia on the Tigris and Euphrates rivers. This region had the plentiful water supply and all five major domesticable large animals (sheep, goats, cattle, pigs, horses) and all the wild seed groups that could become domesticated grains and cereals. It was also a perfect location for the first hydraulic empires to emerge. The Middle East may not have had to emerge first, but the Middle East, China, India, and Europe were uniquely privileged locations for economic development and early hydraulic empires.

Diamond pointed out the competitive exclusion (superior market position for future development) that happens due to early mastery of certain technologies, including instruments of war (“guns”) and infrastructure (“steel”) as well as early arrival at more advanced “germs”, or communicable diseases. Those countries that develop first, like China and Europe, live in close proximity to their domestic animals, in poor sanitation, giving them the first superbugs and the first super immune systems to manage those bugs. When those immunologically advanced peoples then come into trading and warfare contact with immunological naives, most of the conquering is done by germs, not by the people, as with the Plague coming to and decimating Europe from China and European germs coming to the Americas.

As a paleontologist with extensive field work in New Guinea, Diamond has a deep insight into early cultures. This insight is well-displayed in The Third Chimpanzee (1992). He reminds us that in many ways early humans had to be smarter than modern humans, as they had less society to rely upon. Human brains have shrunk 10 or more percent in the last 40,000 years, a phenomenon called self-domestication. Scholars like Richard Wrangham argue that a good portion of that self-domestication is due to removal of violence and environmental awareness circuitry that is no longer adaptive nor necessary in non-nomadic communities. The tradeoff is that society becomes smarter than the individual, who keeps getting more “juvenile” in form, and thus more imprintable by society. Once technology is added, there is a runaway increase in social intelligence, interdependence, and immunity.

Diamond’s work has much to recommend it, but I find it falls short in one key area: it is not sufficiently information- technology- and acceleration-aware. In Collapse (2011), Diamond explores how climate change, population explosion, and political discord set the conditions for collapse of much older and often much smaller populations, including the Mayans, the Anasazi, and the Tasmanians. He thinks this lesson can be generalized to current human culture, but I mostly disagree. A climate change or resource crisis, were it to happen today, would act as a great catalytic catastrophe, giving us the impetus to develop scientific, technological, and social solutions. Recall the way the last ice age spurred Paleolithic humanity into much tighter and more tool-dependent collectives, which, when the ice retreated, allowed the Neolithic era to emerge.

While I love much of his work, I find Diamond largely unaware of the way culture and technology are rapidly becoming independent of the biology and environment in which they emerged. Certainly there are lessons to be learned from history in making more resilient civilizations, but increasingly those lessons are not about how to reform human thinking and behavior, but to better represent that thinking and behavior in our technological systems, which are rapidly outstripping biology in pursuit of the Ten Values.

Measures of Civilization. The historian Ian Morris has constructed a simple but well-evidenced index of social development. He restricted his index to four particularly meaningful and orthogonal variables, or “traits”, energy capture per capita, war-making capacity, level of organization, and information technology. This economy of variables allowed him to collect longitudinal data for their change in both Eastern and Western civilizations over millennia, and to see what they allow us to predict.

Morris introduced his index in an award-winning book, Why the West Rules—For Now (2010). In that book, he argues that the West’s early lead on these critical variables allowed us to dominate economic and political interactions for the last two hundred years. What’s more inspiring is that his developmentalist approach shows individual historical leaders and events as largely instruments of the larger systemic forces of social development.

Morris is thus seeing to discover and describe statistical “laws of history” in the most valuable and fundamental sense. He is also one of the rare academics willing to acknowledge accelerating change, as he does at the end of this book, and the likelihood of our transition to a postbiological state. But in discussing highly undesirable futures, such as extreme climate change and nuclear disaster, he fails to acknowledge how such outcomes would instead be catalytic catastrophes, in all but the most catastrophic of disasters. The funnel moving us ever more rapidly toward postbiological life exists, whether we want it to or not.

His next book, The Measure of Civilization: How Social Development Decides the Fate of Nations (2013), provides all the data and methods used to construct his social development index, and more commentary on why energy capture, war-making capability, organization, and information technology are inevitable factors determining the leaders in social development. This is commendable work seeking to quantify social development, and I expect much more of this kind of work in coming years.

Whether Morris has the right key variables or not is much less important than whether he is openly publishing his data and methods and making predictions. I’m sure he is quite controversial in the historical and anthropological communities, but his example is also making room for others to take this unpopular developmentalist approach. Slowly, our academic and social prejudices will change.

Morris’s latest book, War! What is it Good For?: Conflict and the Progress of Civilization from Primates to Robots (2014), is a powerful exploration of war as, in most cases, a positive set of catalytic catastrophes, as well as a recognition that war has become far more regulated and proportionately less destructive as civilization has progressed. Again, a controversial position, but one that seems well justified from a reading of social acceleration, at least in my view.

Inglehart-Welzel Cultural Map of World Values (2010)

Industrialization Values. In The Silent Revolution (1977), the political scientist Ron Inglehart began a series of global surveys tracking intergenerational values shifts in countries impacted by technological and economic development. His was among the first work documenting predictable trends as developing cultures shift first from “survival” to “modern” and then to “postmodern” values. This work eventually grew into the World Values Survey, perhaps the largest global database of predictable values changes in industrialized societies.

In Modernization, Cultural Change, and Democracy (2005) Inglehart and Christian Welzel outline some of those predictable changes, including an increasing value on individual autonomy and freedom, and increasing an increasing shift to secular-rational (evidence-based) values over faith-based and authority-based values. Other clear shifts include a desire for greater democracy, equality, and equity, though not a desire for excessive statism or top-down control. Two of these shifts are summed up nicely in the Inglehart-Welzel Cultural map of World Values (2010). Notice the three regions of Low, Middle, and High Income on this map. Information technology use and access would likely regionalize in much the same way. The key idea here is that as economic income and technological capabilities grow, individuals inevitably shift toward these modern values.

Such analysis makes clear that countries like the US and Ireland, which are very freedom-oriented but still 80% religious, are major anomalies, unlikely to last. In most of Protestant and Catholic Europe religiosity has been both in reform and in steady decline for generations, and is now in a small minority. It is below 15% in typical Nordic democracies, for example. America’s high religiosity may be primarily due to its uniquely privileged historical position in the global economic and technological landscape. Things have gone incredibly well for us, for so long, relative to other countries, given our geographic isolation from other equally developed countries, our great resource richness, and our personal and political freedoms, which have attracted innovators for nearly three centuries. This good fortune has kept us very much tradition-bound in regard to our religious beliefs and practices, and we are unlikely to reform our views much unless we fall on hard times. Hard times for America seems very unlikely to me prior to the singularity, so we can expect the USA to remain an anomaly on this map for some time to come.

Nevertheless, if these values changes are developmental, the USA, and all the other countries on this map, can be predicted to take a partly random, partly directional walk toward the upper right corner of this map. The USA will just move slower than most. In the end, we all end up looking like Sweden on these two values, whether we want to or not. I believe machine intelligence will greatly hasten this transition, as these are both more desirable and more adaptive values for democratic societies, and I think intelligent machines will want us to improve our democracies, for as long as we remain biological.

Violence Regulation and Severity Reduction. Under developmental interdependence, we discussed Steven Pinker’s The Better Angels of Our Nature: Why Violence Has Declined (2011), and Norbert Elias’s The Civilizing Process (1978/2000). These are among a handful of books that have made the effort to find a variety of proxy variables for human violence and antisocial behavior, and to collect and chart data for those variables over very long periods of human history.

Such courageous books offer early yet ample evidence that our social collectives inevitably become more integrated, self-policing and moral over time. Pinker offers several speculative hypotheses for the causes of this predictable developmental trend. The most convincing, to my ears, is that as our collective intelligence, knowledge, and capabilities grow, our mental versions of the social contract keep changing, with greater personal benefits to cooperation.

The economic and instrumental value of individual life, and of cooperating in society, keeps going up, and so the benefits of collective thinking get stronger and stronger. Robert Wright comes to this same conclusion via a different path in Nonzero (2000), which explores the increasing value and attraction of positive sum, cooperative social games (moral codes, democracy, capitalism) as social complexity and the benefits of social participation keep increasing.

Violence has always been a useful and efficient way to settle disagreements and create disincentives to certain behaviors, and it will never go away. In modern industrial societies, accelerating technology also gives each of us an ever greater individual ability to harm others, should we so choose. In local communities, we can think of this as MAD, or mutual assured destruction, a condition that nuclear-enabled states attained in the Cold War era.

But in societies where the social contract is clearly improving, MAD becomes MALD (Mutual Assured Limited Destruction). People realize that they want an ever more surgical limitation on the kinds and degrees of violence that are permissible, as a way to get more positive sum benefits from cooperation. I could easily kill you, but the downsides, for myself and my family, are clearly just not worth it.

Think of various Meta-Golden Rules that have emerged on top of the “Golden Rule” in human interaction (treat others as you would like to be treated reciprocally), once we had complex tribes. The most successful tribes learned the value of trusting others first (and developing social proxies for trust as everything moves at the speed of trust), but also trying to verify that cheating is not occurring. Ronald Reagan’s famous quote with respect to the Soviet Union, which is also a Russian proverb, is applicable here: “Trust, but verify.”

If cheating or rulebreaking is discovered by the one who was trusted, the next step in the algorithm is to punish the cheater, by kicking them to the edge of the tribe, but not kicking them out entirely. The cheater is then watched for another round of the social game (a “parole” round), and if they are penitent, they are offered a way to absolve themselves of their crime, and be forgiven by society, so they can become a full player in society again. Societies that apply this rule are far more resilient than societies that do not.

See the documentary Uganda Rising (2006) to see this Meta-Golden Rule applied to forgive child soldiers who were previously abducted into mercenary armies, and have killed many villagers, and who now as adults want to be allowed back into village society. Societies that do not have this forgiveness round maintain the hate between groups, and are increasingly less adaptive in modern society. This lack of amnesty/forgiveness for those who have committed violence, on both sides, is one of several key reasons the Israeli-Palestine problem is still so intractable, and violence there is so persistent. Cultures need algorithms that allow them to keep learning, so that MAD can become MALD.

In the future, we can imagine the violence of social interaction being restricted to fair competition between ideas, products, and services. There are always losers, and someone will always perceive that they have been treated unfairly. If violence is the perceived violation of the will from the perspective of the will-generating organism, as I defined it for myself in high school (a definition I still like) then there will always be people who perceive that they have been treated violently and unfairly. But the severity of that violence, and the regulations around it, will go up predictably as collective social intelligence grows. Individuals may always see personal benefit to cheating, exploitation, and violence, if they are not caught, but societies will grow transparency so that positive sum interactions are the only reasonable course of action, and they’ll devote adequate resources to personal development so that reason and self-interest are increasingly what motivates all individuals. Those who fall away from that will be caught early by the system, and given increased resources (groupnets, transparency, therapy) so that they don’t develop into dangerous, fanatical, and disconnected adults.

Societal Sources of Wealth. William Bernstein offers a compelling four-factor explanation of the global economic J-curve, Angus Maddison’s charting of the economic takeoff of Western Europe after the industrial revolution. The picture right gives his model. First Property Rights, then Scientific Rationalism, then Capital Markets, and finally Power, Transport, and Communication entered their modern forms. Every nation in which all four of these factors emerged has seen accelerating GDP growth since. Many nations which still lack some of these factors, like most communist and many Arab and African nations, remain locked in very low growth modes.

As Bernstein describes in The Birth of Plenty, 2004, Both Greece, from 1100 BCE to 60 CE and early Republican Rome from 500-50 BCE had property rights, an independent judiciary, and versions of democracy. But Greco-Roman property rights did not include many individual rights. In a world where the state did not seek to protect the freedoms and fortunes of its citizenry, an independent judiciary and democracy could not survive for long. Slavery and conscription, in particular, were rampant, and grew with the power of the leaders. This easy ability to enslave and conscript the conquered proved to be Rome’s downfall, as successively more authoritarian and corrupt leaders used it to create an oppressive and unstable Empire, taxing its farmers and subjects into apathy and always needing new military conquests just to keep the tax base to stay afloat.

Modern individual rights emerged only in England beginning in the period from the Magna Carta (rights of noblemen, 1215) to the American and French revolutions (rights of individuals, 1700’s). Many advanced property rights, such as the right to free markets, unencumbered by monopoly power (the Sherman Antitrust Act, 1890), or the right to privacy (a concept that didn’t exist prior to the industrial revolution) are still being fought for today. Paul Johnson’s incredible The Birth of the Modern: World Society 1815-1830 (1991) argues that it was the application of these hard-won individual rights in property, finance, management, science, and technology, over a narrow 15 year period centering in England, that propelled the second industrial revolution into being, setting a framework for our modern society that endures to this day.

Bernstein’s model is an excellent first effort at finding the causal factors of economic growth, but it would need much more funding and study before it would be accepted as a statistical model. One day our society will be rich and curious enough to try to validate such developmentalist models. Until then, they are the province of wealthy independent scholars like Bernstein and of underfunded academics with even more limited marketing and production budgets.

Knowledge Economics. Recall our discussion in Chapters 4 and 7 of the 1960’s-1980’s Solow-Swan New Productivity Growth Model in economics, one version of which assigned roughly 70%/20%/10% shares to Technical, Labor, and Finance productivity respectively. This was the start of an understanding of the contribution of technical knowledge, information, and machine productivity to the growth of GDP. Another great social prediction advance occurred in 1988, when Paul Romer introduced endogenous growth theory, a model that treats scientific and technical knowledge and innovation as endogenous (internal) factors in industrial economies, rather than “exogenous” (and presumably unpredictable!) features of the economic environment. Warsh’s Knowledge and the Wealth of Nations (2007) gives you some of the insider’s history of the research that led us from classical economics and Adam Smith to New Growth Theory.

With Romer, economists finally began to realize that the growth of useful information, or knowledge, is its own source of wealth, and that as information creation and computation (the ability to find useful patterns in information) accelerates, so too must wealth. Ramez Naam, in The Infinite Resource (2013), does a great job explaining how different ideas, information, and entrepreneurship are as collective resources, and how they increasingly overwhelm our current limitations and scarcities. Economic theory, by and large, still doesn’t adequately model the dominant and accelerating contributions of science, technology, information, and machine intelligence to wealth, but progress is being made.

As mentioned in Chapter 2, our planet is now engaged in a Great Shift, from a Consumption Economy, based on marketing and buying of stuff, by people, to a Computation Economy, based on marketing and buying of algorithms, data, and computational platforms, by various human machine hybrids and eventually, autonomous machine systems. As their evo devo complexity advances, machines are growing all of the Ten Values. The rapid rise of highly densified and dematerialized computational products and services on the global and mobile web, many of them unicorns, is an early indicator of what a computation economy looks like. More and more, it’s information that directly creates wealth, creating local environments of greater and greater STEM efficiency and STEM density in the process.

Intelligence-Computation Economics. Explaining accelerating wealth as a function of accelerating technical productivity (Solow-Swan) and knowledge (Romer) is a good start, but not enough. We don’t just need to understand the role of knowledge or productivity, but of accelerating intelligence and computation on wealth production and economic behavior.

Economists Erik Brynjolfsson and Andrew McAfee begin to take us down this road of thinking in The Second Machine Age (2014) with their discussion of accelerating knowledge and combinatorial innovation as keys to accelerating wealth. The irrepressible Peter Diamandis and Steven Kotler, in Abundance (2012) and Bold (2015), and Salim Ismael, in Exponential Organizations (2014) offer a slew of ideas and strategies for how companies can rapidly use the accelerating capabilities of our connection-rich, information-enabled environment to create exponential wealth.

This kind of thinking point the way to the future of the economics. It isn’t just knowledge that’s the critical value creator, it’s what humans and machines can do with that knowledge, which is a direct function of their intelligence. We need to be able to measure and predict the exponential impact of intelligence and computation themselves on our economy. At present, most of that marginal increase in intelligence is being provided by people, using machines. Within two generations it will primarily be provided by the machines themselves.

In Chapter 2, we proposed that an exponential process of both densification and dematerialization of adaptive intelligence is happening, as a universal developmental process. Densification tells us that the further we can port our valuable processes like metabolism, thinking, energy production, and matter manipulation, into physical inner space (nanospace), and the more we can densify and parallelize the computational actors (cities, corporations, neural network machines) we get the best exponential returns. Dematerialization tells us that whenever we can substitute virtual for physical processes, using virtual processes like imagination, ideas, simulations, and software, we will again get the best exponential returns. Growing individual, collective, and machine intelligence allows us to do both of those things ever better, and pursuing the rest of the Ten Values also seems necessary to allowing intelligence and computation to continue to accelerate in the local environment.