THE CONTEXT AND THE TRIGGER

To understand extreme events (X-events) and how they occur, we first have to understand the way events, in general, take place. A good picture for this process is to imagine that you are walking in the mountains, where the landscape consists of hills, valleys, mountain peaks, plateaus, and flat, lowland terrain. At any moment, you occupy a position in this landscape. The event of immediate concern is where you will be at the next moment. Unless you happen to be standing on the edge of a cliff or on the top of a sharp mountain peak, your next step will not change your position much. But if you are near the edge of a cliff or on a mountain peak, even the smallest step in the wrong direction will change your life dramatically and very likely not for the better. In fact, such a small step for a man (or woman) may well be the last step. So there are two kinds of locations, or points, here in this mountainous terrain: an ordinary point, from which a small step doesn’t change your situation much at all, and a critical point, where even a minor step in the wrong direction can lead to a major discontinuity in your life.

To bring this landscape metaphor into closer contact with the realities of human life, imagine now that the landscape is not static, but is dynamically shifting and undulating at every moment. This means that you might think that you’re standing in the middle of a plateau, but while you’re contemplating your next step from there the plateau may morph into a mountain peak—without you even noticing. Now if you don’t recognize this shift and take it into account in deciding which direction to step next, well . . .

The dynamically changing landscape of events is what we’ll term the context of events. It is this geometry that defines the space of possible events and the likelihood of which one will actually be realized at the next moment. Since the context is continually changing, so is the set of possibilities and likelihoods. So what is it that picks out one of those possibilities and turns it into the reality that you actually experience at the next moment? That catalyst is what we call a random trigger. This trigger is like that famous butterfly in the Amazonian rain forest, flapping its wings today and giving rise to a tornado in Topeka tomorrow.

Think of the beginning of the so-called Arab Spring in 2011. At that time, North African countries were poised for major social change. In our geomet- ric terms, they were sitting on a mountaintop waiting for a random trigger to push them into one of the valleys below. The bigger valleys represented revolu- tionary changes involving ouster of long-standing, repressive political regimes. Smaller valleys represented harsh actions by the regimes to retain their power. Governments like the Mubarak regime in Egypt did things like totally shutting down the Internet to stifle communication among the demonstrators, in effect changing the context and trying to make the government’s valleys larger (more likely). Who could have said that it would be the event of a fruit seller burning himself up on a street in Tunisia that would serve as the random shove sending that country into one of the valleys of regime change? Answer: No one. That immolation in Tunisia could not possibly have been predicted. And even if it could have been forecast, it would have been simply impossible to say that its consequences would lead to the major shift in political power that we still see unfolding today in Tunisia, Egypt, and elsewhere in the region. Such is the power of a random nudge of the right sort at the right time. As the old saying goes, timing is everything in life. And in death too, it seems.

Thus, we see that fortune’s formula for any kind of event, ordinary or extreme, is

Event = Context + Random trigger

Since the trigger is random, this means it has no discernible pattern or structure. Therefore, it cannot be forecast. So any hope we have for predicting, or even anticipating, an event rests upon our being able to understand the context and how it shifts over the course of time. An important part of the argument of this book is to provide concepts and tools for doing just that.

The 800-pound gorilla in the room when it comes to forecasting X-events is not simply the random trigger. That randomness is a problem in forecasting any event, X- or otherwise. But in situations where we have a lot of data avail- able on past occurrences of an event, it’s often the case that the different ran- dom triggers acting each time one of the events takes place tend to cancel each other out over a sufficiently large number of occurrences. So the real problem with X-events is that by their very nature they are rare; in fact, we are often faced with trying to evaluate the likelihood of an event that may have never happened before. In that case, the standard tools of probability and statistics are powerless to help in assessing what is and is not likely and by how much. So what to do?

In what follows, instead of taking the top-down approach of looking at the events themselves as is the custom in conventional risk analysis, we take a bottom-up perspective and examine the context of the event and the situations where that context tells us we’re in the yellow zone of impending danger of the occurrence of an X-event. In other words, we abandon the idea of actually forecasting the event and look to concepts and tools for anticipating it. For this, we need to understand the drivers that create the ever-changing landscape, which in turn tells us when we are near the edge of a cliff or on a mountain peak instead of resting on safe and solid ground.

Let’s first be clear about the types of X-events we’ll be focusing on in this book. Our concern is with collective human social events, not the kinds of events like the Toba super volcano or the Chicxulub asteroid thrown our way by nature. Stock market crashes, changing trends in popular culture like styles in fashion or films, shifts in political ideologies, or the outbreak of war are our concern. These are X-events that involve the collective action of groups of people, not the actions of a single person. And the impact of the occurrence of this type of X-event is felt throughout an entire group or society. Our goal will be to present ideas for how the context of these types of X-events changes over the course of time, and how those changes strongly bias the nature of the event itself. This disclaimer now being on the record, let’s proceed to the business at hand.

Over the past years, we have discovered two principal drivers of the dynamics that shape the context of events. The first is a structural driver, what we will term the “complexity gaps” between subsystems in interaction. The second is a behavioral driver stemming from the collective “mood,” or beliefs, of a group or society. In broad terms then the drivers of how context changes involve both systemic features, the complexity gaps, and mass psychological elements, associated with what we call the overall social mood. These two factors taken together create the shifting context.

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