Der Komplexitätsforscher John Casti bietet in seinem letzten Buch „Confronting Complexity – X-Events, Resilience, and Human Progress“ wieder zahlreiche Blickwinkel auf die Themen „Komplexität & Resilienz„. Besonders die Betrachtung der fünf Stufen eines gesellschaftlichen Niedergangs/Kollapses sollten zum Nachdenken anregen, vor allem, da wir uns zwischen Stufe 3 und 4 befinden. Im Bezug auf die Entwicklungen im Stromversorgungssystem sollten vor allem die Erfahrungen aus der Modellierung eine Warnung sein: Eine zu hektische Intervention führt immer zum Kollaps; Der Abbau der Momentanreserve führt automatisch zu einem solchen Verhalten, da mit der elektronischen Gegensteuerung genau ein solches Verhalten erzeugt wird.
Kurzbeschreibung: The book you are seeing on your screen may look like a normal book; it is not. It is a conversation in which you are a participant. The book does not offer pat answers to hard questions. In fact, it barely even gives definition to hard questions. Rather, this book presents that stage in which science is most challenging and, arguably, most interesting—the period of identifying just what the problems and issues are. That is why we solicit your help in writing this story—the story of extreme events in social systems.
The participants in this book-writing enterprise are independent thinkers who wish to understand the forces impinging on social systems and the systems’ often dramatic and extreme responses to those forces. Extreme events, the sudden and discontinuous response of social systems to these forces, are what we for shorthand term X-Events. X-events We imagine the reader to be a person who wants to intelligently manage his or her actions and behaviors in the midst of an X-event—in short, to manage an organization in chaos. And not only manage, but be a beneficiary of that event. Explicitly, we understand that there are no simple answers to social questions. But but there is at least a gestalt that can help an individual anticipate and manage X-events. The program outlined here is to build the gestalt by total immersion in the topic—by examining the issues from many perspectives.
Hier einige Auszüge
No one perspective is sufficient to capture the entire picture. But patterns begin to emerge when questions are asked from several points of view.
Event = Context + Random trigger
X-events: by their very nature they are rare
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.
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.
… there was a common cause, something he called “complexity overload”
Tainter then argues that when the next problem appears, the same process leads to yet another layer of structure and so on, until finally a point is reached when all the resources of the society are being consumed maintaining the existing governmental and social structures. In particular, no resources remain to deal with the next problem when it arises.
The only way to avoid the crash is to voluntarily “downsize” the gap by decreasing the complexity of the more complex system or increasing the complexity of the less complex one. But humans are not generally in a downsizing frame of mind, so what typically happens is we continue to extend the gap until nature, or human nature, steps in and says if you won’t voluntarily reduce the gap, I’ll do it for you.
Upon first hearing the term “resilience”, many people think it’s just another word for “stability”.
The second caution is to recognize that there is no such thing as a system being “resilient” in an absolute sense. There is only the relative notion of being resilient with respect to a particular type of shock. So a system might be resilient to a breakdown of the internet but totally vulnerable to, say, a major hurricane or a power failure. So when we speak about resilience in this book, it is always tacitly assumed that there is a particular type of shock in the back- ground. The reader should keep both these points in mind: resilience is not a synonym for stability and it is a relative concept, not an absolute one.
By this we mean that the system can to take advantage of the new environment that the shock creates, in function even better than before the shock occurred. We’ll a few of this from the corporate world in a moment. For now, let’s list the properties that a resilient system displays. We call these the “Four As”:
Awareness: The system monitors early warning signals for the type; that could threaten it, and is able to take action to “batten down the hatches” if any of those shocks seems imminent.
Assimilation: The system is able to survive the shock, perhaps by maybe by absorbing the shock into its operation (assimilation) or some other means. But survival of the shock is a necessary condition for a system to be resilient.
Agility: The system is able to survey the changed landscape that the shock creates, and is capable of deploying its resources to filling one or another the “niches” the shock opens up.
Adaptativity: The system is ready to change its way of doing business if it finds a new niche that offers greater potential for growth and development the from its pre-shock activity.
The system’s resilience is only as strong as its weakest link.
Instead of waiting until the shock occurs and then trying to formulate a plan in real-time for dealing with the event, how can we get ready for tomorrow today?
The higher the temperature gradient, the higher the complexity mismatch in the systems.
Systems in contact can experience a complexity mismatch that leads to sudden shifts in the systems that bring the complexity of the systems and drivers more in line with each other.
With increasing driver intensity the system makes discrete jumps to ever more complex structures.
Social systems are constantly adapting and changing — in other words, spontaneously reconfiguring themselves.
We can no longer focus only on the probability of the trigger event, we also need to consider the structure of the system in question and how it evolves over time.
If modeling the probability of the trigger event is not sufficient, we need to look for changes in landscape.
One way to characterize the landscape for a social system is the “mood” of the population. That is the collective effect of everyone’s beliefs about the future. When the social mood is positive, people generally have positive expectations for the future and act accordingly (e.g., expand businesses, hire workers, have children, buy houses, etc.) In contrast, when the social mood is negative, they make the opposite choices. Thus, social mood biases the social system’s response to a random event. This would suggest that the social mood forms at least part of the social system’s landscape. The interesting aspect of social mood is its seeming potential for feedback loops. For example, positive expectations about the future state of the economy may lead to decisions that improve the state of the economy. Which, of course, reinforces the belief that the economy is improving, and leads to further decisions that improve the economy. For negative social mood, the reverse happens. So for a social system, one way to monitor shifts in the landscape would be to monitor social mood.
Resilience is all about being able to overcome the unexpected.
Resilience refers to the ability of a system to recover and even benefit from a disruption. Up to now we have discussed the stability or robustness of a system against a disruption. That is the ability of the system to absorb the disruption without significant consequence. Stable systems return to their original state after a disruption. Resilience, on the other hand, relates to recovery after a significant event has occurred. Resilient systems do not necessarily return to their original state after a disruption. may move to a different state, but the system keeps functioning. For example, the robustness of the financial system refers to its ability to absorb problems and keep operating. Tie resilience of the financial system refers to its ability to resume normal operations — perhaps with a different structure — after it has collapsed and failed. As with robustness, it is more intuitive to define resilience for a physical system than a social system.
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.
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.
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 situation, and the complexity gap between interacting systems (plus the random trigger) that picks out the event that is actually realized from the possibilities.
The social mood and complexity gap are what we might term “context-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.
In his account, Ferguson goes on to note what he calls six slow-acting drivers of historical change. They are:
- Technological innovation;
- The spread of ideas and institutions;
- The tendency of even good political systems to degenerate;
- Supplies of essential commodities;
- Climate change.
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.
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.
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 1: Financial Collapse Faith in “business as usual” is lost. The future is no longer assumed to resemble the past in any way that allows risk to be assessed and financial assets to be guaranteed. Financial institutions become insolvent; savings are wiped out and access to capital is lost.
Stage 2: Commercial Collapse Faith that “the market shall provide” is lost. Money is devalued and/or becomes scarce, commodities are hoarded, import and retail chains break down and widespread shortages of survival necessities become the norm.
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 commercial sources of survival necessities fail to make a difference, the political establishment loses legitimacy and relevance.
Stage 4: Social Collapse Faith that “your people will take care of you” is lost, as social institutions, be they charities or other groups that rush to fill the power vacuum, run out of resources or fail through internal conflict.
Stage 5: Cultural Collapse Faith in the goodness of humanity is lost. People lose their capacity for “kindness, generosity, consideration, affection, honesty, 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 we described them above, each of Orlov’s five stages of collapse involves a loss of faith in something.
Basically, this solution is the well-known process of “bureaucratic creep”, as we outlined in the opening chapter. As problems accumulate, the bloat of bureaucracy increases to the point where the entire resources of the organization are consumed in just maintaining its existing structure. When the next problem comes along, the organization falls off the ”complexity cliff” and simply collapses. Many times this complexity cliff shows up when two (or more) systems are in interaction. The gap in complexity between them becomes too big to sustain and an X-event emerges to close it.
Currently, countries like Russia are in a demographic death spiral, and as aging and population decline becomes pro-gressively more pronounced, economies and tax revenues will implode across Europe and East Asia.
People around the world are losing faith in their governments.
So the exit option is no longer available, especially if you believe you already live in the best country on earth. If you believe in the exit option, the alternative is not to reform government, but to get rid of it.
Palmer and his colleagues at the Santa Fe Institute in Santa Fe, New Mexico developed a virtual stock market populated by virtual agents. Each of the agents could change its investment rules over time. They found that when agents could only update their investment rules slowly, then the market converged to the rational expectations hypothesis because the bad rules were weeded out over time, However, if the agents could update their investment rules quickly, then the virtual stock market exhibited bubbles and crashes just like the real stock market because bad rules could feed off each other.
Even in a bubble market, the fact that the market is in a bubble may be widely recognized. Yet this does not cause an immediate crash. Why? Timescale is important.
Observations, however, indicate that there is usually asymmetry between optimism and pessimism. Optimism builds slowly, while pessimism occurs after an extreme event.</p><p>
I’m saying to be a hero means you step across the line and are willing to make a sacrifice. So heroes always are making a sacrifice. Heroes always take a risk. Heroes are always deviant, Heroes are always doing something that most people don’t and we want to change — I want to democratize heroism to say any of us can be a hero. Philip Zimbardo
So graduating from college at the wrong time, economically speaking, means that the graduates will never fully recover and move into higher-paying jobs after the economy comes back to life.
The graveyard of history is filled with the corpses of forecasts that were simply wrong.
In either the Piketty or Brynjolfsson paradigms, though, the essential element is a growing complexity gap between one segment of the workforce (the supermanagers or skilled elite) and the rest (the worker bees or the unskilled). This gap widens daily. And as it does, the social stresses grow in just the same way that the stresses increase between two tectonic plates as they slowly move in different directions. In both cases, the stresses ultimately must be released. The typical form of that release is a rapid break or crash. In the social realm, that usually takes the form of a revolution of some type; in nature, the form is an earthquake. And in both cases, a huge amount of damage is done to the social or physical structure. Of course, the landscape that remains after the crash has many new hills and valleys that the surviving “organisms” can exploit.
A revolution is coming — a revolution which will be peaceful if we are wise enough; compassionate if we care enough; successful if we are fortunate enough — But a revolution which is coming whether we will it or not. We can affect its character; we cannot alter its inevitability. Robert Kennedy, Speech in the United States Senate (9 May 1966)
However, it is well known that people tend to overweight low-probability events and underweight “high-probability”.
“Once the human mind has set out to do something, or has gotten into the habit of doing something, changing it is very hard.” And it’s not simply that people resist change. What’s astounding is how many companies drive at top speed into the brick wall of corporate disaster.
… it’s only when the situation reaches the point that the firm can’t make the payroll or make a loan payment or the cheques start to bounce that the sense of urgency becomes great enough that people become ready to change.
Change is risky, since it involves doing something that isn’t already working.
When opportunity knocks you have there to open the door.
A constellation of individual mistakes and misunderstandings combine to create a horrific accident that could otherwise have been avoided.
The real problem with complex systems, though, is that the very complexity of the system generally means that failures occur when many things go wrong at once.
Throughout the nineteenth century, when there was a laissez-faire mentality and insufficient regulation, you had one crisis after another. Each crisis brought about some reform. That is how central banking developed. George Soros
Anchoring is a cognitive bias that describes the common human tendency to rely too heavily on the first piece of information offered (the “anchor”) when making decisions.
Stability refers to a system that only changes its behavior in reaction to changed circumstances, but does not change its underlying structure at all. It remains the very same system as it was prior to the disturbance. In Holling’s work and most of what we see today going under the rubric of resilience, an essential part of a system being resilient is that it be able and ready to restructure and reconfigure itself in reaction to a changing environment.
Putting all these factors together, we will use the following definition of resilience in what follows in this chapter. For simplicity, we’ll call it the Four As:
Awareness: A resilient system should be monitoring early-warning signals for X-events, and be able to take action to “batten down the hatches” if the X-event seems imminent;
Assimilation: A resilient system will be able to survive the X-event. Maybe the survival will be by resistance to the event, maybe by absorbing the shock into the system’s operation, or through some other survival mechanism. But survival is a necessary condition for being resilient;
Agility: A resilient system is able to survey the new landscape that the X-event creates and have the ability to evaluate its resources and see how to deploy them to fill one or another of the new niches that the event opens up;
Adaptivity: The resilient system should be ready to change its way of doing business if it finds that a new niche offers greater potential for growth, development, and survival than its pre-shock activities.
Since the sine qua non of the entire notion of resilience rests upon the occurrence of an X-event, and since there are many, many different types of X-events, it follows that any idea of system resilience is necessarily context-de-pendent. A system may be very resilient to, say, the shock of an earthquake but totally vulnerable to the shock of a solar storm.
As an X-event itself is, by definition, rare and surprising, it’s a pretty tricky business creating the infrastructure needed to be resilient against all such contingencies. In fact, it’s impossible.
Resilience is all about being able to overcome the unexpected.
Conventional risk analysis typically proceeds by identifying all the different arts of shocks that a system might experience, calculating the probabilities of each of these shocks, subtracting each of these numbers from 1, and then (assuming the shocks are independent!) multiplying the resulting numbers together to obtain the likelihood that no shocks will occur. If that number is too small, steps then should be taken to reduce one or another of the initial probabilities to get the final result down to an acceptable level. Of course, this is a caricature of the actual process of everyday risk analysis. But it already underscores many of the problems in using such an analysis to address resilience. First of all, individual shocks are seldom, if ever, independent.
The 2011 Japanese earthquake shows that not only are X-events rare and surprising, but that the event itself is generally a cascade of smaller events that ultimately creates the X-event that we want to be resilient against.
So when it comes to resilience against an X-event, we have to abandon the notion that the “event” is a single entity; rather, it is almost always a sequence of sub-events that are later gathered together under one label. But the actions taken to be resilient to that headline event must take into account the entire sequence.
Another element entering into the resilience equation is time. X-events will happen regardless of how much energy, effort, and money we put in to prevent them. This means that there are two types of planning required in order to be able to absorb the shock of the event and come back stronger than ever. One is the planning done for protection before the X-event occurs, mitigation if you like. Tie other is planning how to restructure and reconfigure the system after the event has run its course. Each of these factors merits its own slice four attention.
Society must take in the message that there’s no such thing as a risk-free world.
Implementing a resilience-based program won’t be easy, though. Most government officials, particularly those in the security end of things, regard the whole idea of resilience as “defeatist”. Flynn remarks, “They believe our job is to prevent these things from happening. What we have seen is that we keep having big events that are profoundly disruptive and that we are woefully underprepared to deal with.” Like a lot of other lessons that are “obvious”, there’s little, if any, hope of converting such officials to a new way of viewing the world. They have too much at stake in protecting the old ways to even consider a change of such magnitude. Rather, one has to begin the process by educating those whose minds are not already cast in concrete. Enter Thomas Homer-Dixon. Homer-Dixon is a professor of international affairs at the University of Waterloo in Canada and a keen observer of the way complexity has entered into the way the world now works. He says that when he lectures to audiences of even educated people — businessmen, civil servants, and social scientists — the audience has a tendency to believe that systems always tend toward an equilibrium and that small causes give rise to only small effects. This is the very type of thinking that comes from classical physics, a la Newton. It is also the type of thinking that the world is just risky, not inherently uncertain. To combat this view of the way things work, we require a sea change in attitudes and teaching of the principles of complex systems early on in the academic curriculum.
Once the matter of exactly what X-event is of concern is settled, we can bring out the Four As: Awareness, Assimilation, Agility, and Adaptivity and assign an integer from 0 to 10 to each category, reflecting how well-prepared we are for the X-event in the activities described by that category.
The biggest risk is not taking any risk. In a world that is changing really quickly, the only strategy that is guaranteed to fail is not taking risks.
Awareness, Assimilation, Agility, and Adaptivity. Awareness: What are the early warning signs of an impending climate-change X-event? Assimilation: What survival mechanisms are available to get through significant climate change? Agility: What tools are available to survey the post X-event landscape in order to deploy resources? Adaptativity: What might be the potential opportunities of climate change?
We saw that a simple temperature difference can lead to exotic and complex phenomena such as convection zones, weather patterns, hurricane formation, and beta gyres. The transition from one level of complexity to a greater level of complexity was sudden—an X-event.
The first thing we need to do is to understand the threat as much as possible;
CO, behaves like glass; it is transparent to visible light, but opaque to longer-wavelength infrared light. A layer of C02 therefore behaves like a greenhouse, hence the name „greenhouse gas.“ The visible light passes through the C02 layer on its way to the Earth’s surface. At the surface, the light interacts with water and solids. Light is re-emitted by the surface in the form of infrared light, which is trapped in the atmosphere by the C02 layer. Tie infrared light heats the surface of the Earth as the light is further absorbed.</p>