Everyone knows that one of the primary imperatives of life (at all levels) is to increase security, reduce threats and reduce unpredictability. Our early upbringing, our education and all the adult experience that follows is largely devoted to these objectives. If there’s one thing that motivates us, it’s to avoid chaos. Whether it’s the appearance of threats, insecurity, unexpected obstacles, digression, and breakdown, whether it’s interruption of our plan of the day or being diverted from our life-course — we hate chaos. Every time events start spinning out of control, which is always a possibility, we are reminded of how easily we become attached to the expectation of smooth and unimpeded execution of our plans. We are also quite familiar with the choices and the behaviors we find most natural and comforting. These are the moments when we feel conscious and connected.
Despite all our efforts, chaos and disorder are guaranteed parts of life. We know that if we avoid such turbulence, we can reduce suffering. We spend a great deal of time either attempting to control the appearance of or devising ways of responding to chaos. Because we know our control is limited, that strategy becomes part of our lifetime project of self-improvement, a recurring meditation on how we meet impermanence and loss.
Just because we may find ourselves in the middle of chaos, we don’t have to be chaos. But no matter what we do, we are cruising down the river of predictability toward the waterfalls of unpredictability throughout life. There is much that reminds us that nothing in life is guaranteed. And yet, no matter our past, our education, our general comfort in life, chaos seems never to be very far away. That’s not such bad news. Trungpa Rinpoche called chaos very good news.
Chaos theory and what we call chaos in our lives may be two different things. Chaos theory says ordered nonlinear processes produce outcomes which are not directly determined by prior events. Causality is unpredictable. This is true at the micro scale of the individual, or the macro scale of the planetary or the universe level. It is also true at the cellular, atomic, and even the quantum level of phenomena. It says that the smallest changes, events that may be far beneath our direct awareness (or detection) in any system can produce huge changes later. The classic illustration of this principle is the butterfly flapping its wings in Argentina causing a tornado in Texas three weeks later (Edward Lorenz, 1963). That’s the nature of chaos. Moreover, the difficulty in predicting the future is that we don’t know which events determine the outcomes we are most interested in.
Chaos theory is the science of surprises, of the nonlinear and the unpredictable. It teaches us to expect the unexpected. While traditional science deals with supposedly predictable phenomena like gravity, electricity, or chemical reactions, Chaos [and Complexity] Theory deal with nonlinear things that are effectively impossible to predict or control, like turbulence, weather, the stock market, or brain states.—Fractal Foundation
So really, an event that appears to have a direct and identifiable cause may be entirely beyond our capacity to prevent because its root cause is much smaller or much older than what appears before us in the moment. Our efforts to control events cannot take all these hidden ‘causes’ into account and are therefore practically useless. Since chaos theory primarily looks at small-scale systems with a small number of variables, like what happens when a rubber ball is shot against a wall or when a hinged pendulum swings, the pattern of resulting events may seem to lend a predictability to them. In this sense, determinism, the predictability of future events, is also a feature of chaos. Investigations of chaos examine the zones of predictability and randomness in the behavior of such small systems.
Complexity
Complexity theory is concerned with larger systems with many, including unknown, variables. Complex systems are dynamic in the sense that there is likely to be feedback between subunits. As demonstrated by chaos theory, small changes in a dynamic system, such as when you force your way up the down escalator in Macys on December 24, can have larger (and unpredictable) consequences (fisticuffs?)—illustrating the relationship between a small change and the larger outcome.
Examining and predicting events in larger social systems becomes far more complicated because there are so many more variables operating. Most importantly, complexity theory examines the self-organizing nature of ordered nonlinear processes, which is to say, there is a constant expression of intrinsic intelligence, adapting to internal and external influences to achieve equilibrium at a higher order of complexity. Hence, events are unpredictable.
Social systems made of many subunits undergoing a unique evolution are both complex and dynamic. They are deterministic in some sense because some of the underlying systems operate in generally predictable ways, such as human physiology or photosynthesis. Without any perturbations of their operation, evolution might even take a predictable course. But environmental perturbations are occurring all the time, so clutching for predictability is an attempt to reduce a complex unpredictable system to a more deterministic (predictable) system. This is a denial of the intrinsic properties of complex systems to respond to changing conditions and thus (to a degree) determine their own future.
The unique evolutionary path of any individual subunit of a social system follows the constant and unpredictable influence of ‘external’ events, large and small. The evolution of physiology, brain function, and even the physical boundaries (of cells or the skin) of any individual are always under reformation because the variables influencing that system and the automatic decisions made by any sub-systems of that individual are also always adapting and reforming. The complexity of situational and long-term patterns of response render predictability under most circumstances impossible. That’s a good thing because it means that system is not a machine. It means the diversity of adaptation is not limited by rigid rules. In such a case, the adaptive capacity of the larger system is enhanced.
For the complex, unpredictable nonlinear composite system we call a human, chaos is the unscientific name we might use when uncertainty becomes unmanageable. Circumstances impinging on our survival are going beyond the existing database of adaptive capacities created and embedded over a lifetime. Whatever our dominant patterns of decision-making may be, our secure handholds are lost. We are not in control. There is no default stabilizing act. Immediate adaptation using all our intelligence is required to determine a path forward that appears to restore order. We call this resilience. But such decisions are not guaranteed to work. Adaptation to such instability necessarily becomes a continuous, rapid, ongoing process of trial, error, learning and integration. How successful we are at adaptation is determined by the rewards that follow, unless we already have some record of successful adaptation supported by previous actions. But in every instance of unpredicted unpredictability, past performance is no guarantee of future results.
We are less comfortable with uncertainty and tend not to view the unexpected as an unforeseen opportunity. So, we develop strategies to support our preferred version of reality, reducing the probability of unanticipated events disrupting our plans or expectations. At the same time, trying to improve what is by setting an objective of creating what is not yet is also a complicating factor in the flow of decisions based on the best data we can verify. The deeply embedded social imperative that says we are on a continuous and lifelong trajectory of improvement (a micro version of the macro growth imperative) establishes a pre-existing bias in the way we interpret events. With such a bias, events may appear to be facilitating or blocking our pre-determined objective. The appearance of such randomness in a system is why we give the name chaos to unexpected or unexplained events.
Emergence
Complex systems are very different [from] the systems studied in Chaos Theory. They contain constituent parts that interact with and adapt to each other over time. Perhaps the most important feature of complex systems, which is a key differentiator from chaotic systems, is the concept of emergence. Emergence “breaks” the idea of determinism because it means the outcome of some interaction can be inherently unpredictable. In large systems, macro features often emerge that cannot be traced back to any event or agent.
Nature is a complex system. There are a virtually infinite number of complex subsystems nested within the whole. Nature’s response to the evolutionary challenge of continuous adaptation to environmental stress is to reorganize itself at a higher level of complexity, thereby transcending the immediate condition and expressing the intelligence gained from exposure to those conditions. Emergence is the unpredictable flow of such self-organizing events arising from the ongoing synthesis of predictable interactions at all levels of the natural world. Humans are included in that process, constantly becoming our own versions of emergence. Each adjusted level of organization transcends and includes the previous state. When a flock of birds settled in a tree are suddenly alerted to danger, that all birds will take flight may be predictable. But how the alert is detected and transmitted, which birds lead, which direction they go and how the flock organizes in flight are all subject to ongoing refinement.
The intrinsic nature of emergence is a spontaneous self-organizing interactive expression of intrinsic intelligence. Its most elemental stirrings may not be conscious, but there is no superseding intelligence, no memory, no sense of past or future, no sense of ‘other’ in its application. What we see at all scales of life are creative responses arising from creation knowing itself, acting as itself because it cannot do otherwise. It is the most intimate character of life, a constant flow of resilience, independent of rationality, beyond any specific identifiable cause. Emergence is a spontaneous, natural creative phenomenon. We may identify it in any number of situations involving living entities of all types, from the hot crushing pressures of deep ocean trenches to the rarified atmospheres of icy peaks. Life exhibits all manner of adaptive strategies. But we also empirically understand that unpredictability is inherent to all of it. We may imagine or even sense that whatever we call ‘emergence’ among our fellow humans is a phenomenon beyond reckoning, beyond comprehending, until sometime after the fact. We expend mental energy, emotional or psychic energy responding to the incomprehensible or to gain insight into the ineffable.
Greg Fisher elaborates the meaning of emergence this way: Physics or chemistry can determine the properties of a single hydrogen or oxygen molecule, but the properties of water cannot be predicted from that knowledge because water is more than the sum of its parts. All living complex systems are more than the sum of their parts. That’s why emergence is even possible. In most cases, ‘reasoning upward’ (predicting the properties of water knowing only the properties of its components) is not possible. Water organizes itself under radically different circumstances in ways that are not predictable merely from the knowledge of its component molecules. As water is central to how all species organize themselves and express adaptive capacities, its presence (or absence) has highly variable long-term effects on social organization and culture as well.
All living systems must adapt to changing conditions of life, from system-level to subsystems, to the microscopic and even the molecular or the atomic level. For many, those adaptations occur at a rate slower than the pace of change. But regardless of the scale, living systems possess a natural ‘computational capacity,’ a self-reflective capacity to absorb environmental information and determine what is the most advantageous response. In the case of climate change, we may run any number of computational simulations, but the ability of science to predict how humans will adapt to the empirical impacts of climate change or the long-term ingestion of pollutants or micro-toxicities remains rather shallow. Those changes are only now appearing on a mass scale, haunting us with their monstrous portent.
Stay tuned for part II of this post.
Spontaneous Presence 