Drivers of social change


John L. Casti

Roger D. Jones

Michael J. Pennock




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Trends and Transitions

At a random moment in time, the generic behavior of any social system is to be in a trending pattern. In other words, if you ask how will “things” (e.g., the GDP of an economy, the financial market averages, the political climate) look tomorrow, the answer is that they will be just a bit better or a bit worse than today, depend- ing on whether the trend at the moment is moving up or down. This is a large part of what makes trend-following so appealing: it’s easy and it’s almost always right—except when it isn’t! Those moments when it isn’t are rare (infinitesimally small in the set of all time points, actually) and the event is usually surprising within the context of the situation in which the question about the future arises. These special moments when the current trend is rolling over from one trend to another are the critical points of the process. And if that rolling over involves great social damage in terms of lives lost, dollars spent, and/or existential angst, we call the transition from the current trend to the new one an X-event. In the natural sciences, especially physics, such a transition is often associated with a “flip” from one qualitatively different type of structure or form of behavior to another, as with the phase transition from water to ice or to steam.

A central question arising from the above scenario is whether we can we predict where the critical points will occur. In situations where you have a large database of past observations about the process and/or a dynamical model that you believe in for the system’s behavior, then you can sometimes use tools of probability and statistics and/or dynamical system theory to identify these points with a modicum of precision. This is often the case in the natural sci- ences, but it’s almost never the case in the social domain. In the human sphere, we generally have too little data and/or no believable model, at least no data or model for the kinds of “shocks” that can send humankind back to a preindus- trial way of life. In short, we are generally dealing with “unknown unknowns.”

In this X-events regime, it’s unlikely that we’ll ever be able to predict the location of the critical points with the same sort of accuracy and reliability that we’re accustomed to in the natural sciences. As we noted in the last chapter, this is due to the fact that events, X- or otherwise, are always a combination of context, which determines the space of possible events, and a random trigger that picks a particular event out of that spectrum of possibilities as the one that’s actually realized. In other words, at any given time the context, which is always dynamically shifting, admits a variety of possible events that might be realized. The one event that is in fact actually observed/experienced at the next time moment is determined by a random “shove” that sends the system into one “attractor” from the set of all possible events the context admits. Since by its very nature a random trigger has no pattern, it cannot be forecast. Hence, the specific event that turns up cannot be forecast either. Note that this does not


mean that every possibility is equally likely. It simply means that while some possible events are more likely to be seen than others, the random factor can step in to give rise to a realized event that is a priori unlikely, thus surprising.

The problem with speaking here about “likelihoods” is that this terminol- ogy has built into it the assumption that there is some probability distribution available for evaluating the relative likelihood of the occurrence of the possible events. But when it comes to the X-events regime, where there are neither data nor models, this assumption simply falls apart. There may indeed exist such a probability distribution. But if so, it resides in some platonic universe beyond space and time, not in the universe we actually inhabit. So what to do? How do we characterize and measure risk in an environment in which probability the- ory, statistics, and dynamical system theory cannot be effectively employed?

We have shown earlier that a way out of this no-data quandary is to focus on the drivers of context change—the change in landscape. The argument presented earlier is that the two principal drivers are the social mood, which drives the spectrum of possible events that might ensue from the current sit- uation, and the complexity gap between interacting systems (plus the random trigger) that picks out the event that is actually realized from the possibilities. A quantum theorist would recognize this set up immediately: the social mood is analogous to the Schrö dinger wave function, while the complexity gap plays the role of the agent/observer that collapses the wave function into a single point, the event actually measured/observed.

The social mood and complexity gap are what we might term “con- text-free” drivers of context and change, since they are not dependent on the particular type of event we’re considering or even the specific time and place where the current trend is unfolding. They exist independent of these factors. On the other hand, we have “context-dependent” factors that also act to impact what events actually occur. Some authors term such factors “drivers” as well, although for our purposes they play a role as second-level drivers that serve more as drivers of the context-free drivers that we are focusing on. A very good example of this phenomenon was presented by noted Harvard historian Niall Ferguson in an article on turning points in history published in the New York Times at the end of 2012. It’s worth briefly summarizing Ferguson’s argument in order to show the distinction between the two types of drivers, context-free social mood/complexity gap, and context-dependent drivers of those drivers.


Ferguson begins with the metaphor that history is like an oil tanker: it doesn’t turn on a dime. He then says that what does change suddenly on an oil tanker is the emotions of the crew (cf. social mood!). “Nine hundred ninety-nine days


out of a thousand the crew obeys their orders and does their work. But very occasionally there is a drama. The men mutiny and the captain is clapped into irons. Or pirates board the ship. Such events are what historians love to study and call ‘revolutions.’ Still the ship plows onward.” So a system theorist’s criti- cal point is an historian’s revolution.

In his account, Ferguson goes on to note what he calls six slow-acting driv- ers of historical change. They are:

1. Technologicalinnovation;
2. The spread of ideas and institutions;
3. The tendency of even good political systems to degenerate; 4. Demographics;
5. Supplies of essential commodities;
6. Climate change.

What almost jumps off the page when reading this list is that every single item on the list is actually an event, or more generally, a constellation of events taking place over an extended period of time. So unlike the drivers of social mood and complexity gaps, which are rather abstract and do not pertain to any specific event or cluster of events, Ferguson’s list of drivers is more like a list of slowly-unfolding events. Under that interpretation, we could think of them as events driven by mood and complexity. In other words, a consequence of the deeper drivers, not the cause. Of course, in Ferguson’s story, his drivers are indeed the cause of historical change; in our setting, they are the effect of drivers at a deeper level, not causes at all.

The point of this observation is that social mood and complexity gaps are always present and do not depend on context, at all. On the other hand, the type of drivers Ferguson lists come and go, and his list would almost surely be different if it had been prepared, say, a hundred or five hundred years ago. It’s in this sense that we call them context-dependent drivers, not context-free. The take-home lesson from this observation is that time scales matter. A slow- ly-unfolding event can be seen as an event at one timescale, but as a driver of an event taking place on a shorter timescale.

To return to Ferguson’s list, all the events on that list are ultimately driven by social mood and complexity gaps simply because those two drivers are always present. On the other hand, a long-timescale event like technological innovation can indeed serve as a driver of the shorter-timescale phenomenon of, say, the emergence—and the end—of globalization.

More worrying is the third item on the list, the tendency of political sys- tems to degenerate. Almost all studies of institutional quality show that in the majority of Western countries there has been a serious decline in the rule of law. And not just the rule of law, but in trust in institutions in general. Accord- ing to Ferguson, this slow flow of events is driving much of the slowdown in


economic growth and productivity in Western countries since the turn of the millennium.

We’ll return to this theme of drivers of historical change, both context-free and context dependent, in a later section, where examples will be given in more detail of both types. But beforehand, let’s first have a look at the five stages of the collapse of human systems identified by Russian emigre, engineer, and social analyst Yuri Orlov. In particular, we want to explore how a combination of social mood and complexity overload give rise to these stages.


In her 1969 book On Death and Dying, psychiatrist Elisabeth Kubler-Ross identified five stages of grief that constitute the process of coming to terms with death for terminally ill patients and their family. These five stages begin with denial, where the patient imagines a false reality in which the impending death is absent. Once the patient recognizes that denial will not make the sit- uation disappear, the second stage of anger appears, in which people either get angry with themselves or with those close to them. They strike out with angry statements, such as “It’s not fair,” “Why is this happening to me?” and so forth. After venting their anger, the patients enter a bargaining stage, where the indi- vidual hopes to make a deal for a bit longer life with some higher power. For instance, the patient may say I’ll reform my life, reconcile with my family, give my worldly belongings for a few more years of life. When this stage runs its course, depression sets in. Here the patient shifts into a mode of thinking that involves things losing any meaning. Basically, they say, “I’m going to die soon, so why care about anything?” Finally, the individual comes to acceptance of their impending death, and begins thinking how to use the time remaining to them to prepare themselves and their loved ones for their mortality.

In around 2008, Yuri Orlov noted that many commentators were using the Kubler-Ross stages of grief as a way of structuring the process of humanity’s “death” through global ecological mismanagement. Orlov noted that the col- lapse of societies also seemed to take place in five collapse states, each of which involves a loss of faith in some taken-for-granted social structure that humans rely upon for the functioning of everyday life. James Quinn described Orlov’s five stages in the following way:




Stage 3: Political Collapse Faith that “the government will take care of you” is lost. As official attempts to mitigate widespread loss of access to commer- cial sources of survival necessities fail to make a difference, the political establishment loses legitimacy and relevance.


Stage 5: Cultural Collapse Faith in the goodness of humanity is lost. People lose their capacity for “kindness, generosity, consideration, affection, hon- esty, hospitality, compassion, charity.” Families disband and compete as individuals struggle for scarce resources. The new motto becomes, “May you die today so that I can die tomorrow.”

As Quinn further notes, these stages do not follow in lock-step, one imme- diately after the other. They are overlapping, so that elements of one stage are still unfolding as the next stage begins. For instance, the first three stages are already apparent in the United States today. The initial stage exploded on to the world stage in fall 2008 and is still unfolding as the global financial system continues to melt down. Once the financial collapse gets into full swing, com- mercial collapse will soon follow since ready and reliable credit is an essential element in keeping the supply chain of goods flowing from the manufacturing regions in Asia to the shelves of Wal-Mart and The Gap. We are also now seeing bits and pieces of the stage of political collapse making itself felt, as the legit- imacy of government and its leaders is increasingly discredited by the voting public as each day goes by.

We draw attention to the italicized passages in the descriptions of the five stages above. Everyday words like “risk assessment,” “hoarding,” “breakdown,” “legitimacy,” “internal conflicts,” “disbanding,” and “competition” are pre- cisely the descriptors that one would associate with the types of events we can expect to see when the overall social mood is strongly negative and/or the com- plexity gaps in social infrastructures are stretched to their breaking point. So for a critical observer of today’s social, political, financial, and cultural struc- tures, these words come easily off the tongue. Negative social mood, essentially fear of the future, is rampant worldwide. And the concomitant stretching of the infrastructures of everyday life beyond sustainable levels is equally evident.

So the general relationship between Orlov’s five stages of collapse and the context-free drivers of social mood and complexity overload is rather clear. But what about the context-dependent drivers outlined by Ferguson? For example, which of the five stages of collapse are driven by the spread of ideas


and institutions? Which are most impacted by changing demographics? And which can be laid at the doorstep of climate change?

As we described them above, each of Orlov’s five stages of collapse involves a loss of faith in something. But the only item on Ferguson’s list that relates to a loss of faith is the second one, the degeneration of even good political systems. This is essentially the same as Orlov’s third-stage collapse of the political system. But if we regard this as a driver, the cause of something, not a consequence, then we can ask what a political collapse causes. Basically, it drives the stage 4 social collapse.

One might argue that from a Western perspective an external driver like technological innovation is analogous to a “collapse” of certain parts of the Western commercial sector, perhaps computing or even more directly high-tech manufacturing. The same argument might be used to link Ferguson’s spread of ideas and institutions to a collapse of Western domination in math and science by young people. But this analogy is a weak one, at best. So if our concern rests with understanding the reasons societies collapse, the context-free drivers are clear, while the context-dependent drivers give insight into the particular col- lapse of the Western world as we’re experiencing it today. When the Eastern world that’s now reemerging as the dominant global social structure begins its own collapse a century or two from now, it remains to be seen whether the particular context-dependent drivers identified by Ferguson will still be the rel- evant drivers for that upcoming collapse. In his book Collapse, Jared Diamond argues that some of those same elements play a role in almost every societal col- lapse. A driver like climate change appears on both Diamond’s and Ferguson’s lists. But, then, a context-free driver like complexity overload also appears on both our list and Diamond’s—but not on Ferguson’s. On balance, it appears that we can say that both types of drivers enter into the collapse of every civilization. But the context-dependent list will likely be different in each case, while the context-free drivers are universal across both space and time.

Our goal in this chapter is to underscore the way social mood and complex- ity overload/mismatch enter into the dynamics of not only civilizations, but other long-term social processes in the economic and geopolitical arenas. We now do that by way of several mini-chapters focusing on the processes of globalization, finance, global population change, and the degeneration of political systems.


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