These interdependent shifts toward greater and ever-increasing levels of complexity become recognizable as very compressed frames of coadaptation and reciprocal exchange, which are then coded into a coevolving network of agents. In many cases, these frames are most fully expressed through the algorithms of artificial environments and technologies designed to improve human adaptation to the rapidly occurring changes in the biosphere. These algorithms then create intuitive and sophisticated relationships with high degrees of efficacy that increase overall habitat literacy and reduce operational risk and levels of environmental exposure. The resiliency phase of the Anthropocene will benefit from upward trajectories in innovation and technology and will follow more closely the principles of industrial ecology. These principles support the types of dyadic, reciprocal exchange and coevolution of interdependent complex adaptive systems that can create more robust and resilient development in five main centers of gravity and global systemic risk — economic, environmental, geopolitical, societal and technological — as illustrated in Global Risks 2014 report | World Economic Forum – Global Risks 2014 report. As this report indicates, it is essential to understand humans as intricately and irreversibly involved in the extraordinarily complex coevolution of the Earth System, and all future global change and development must be based on the following foundational premise:
The term Earth System refers to the suite of interacting physical, chemical and biological global-scale cycles and energy fluxes that provide the life support system for life at the surface of the planet. This definition includes humans, our societies and activities; thus, humans are not an outside force perturbing an otherwise natural system but rather an integral and interacting part of the Earth System itself. Global change refers to both the biophysical and socioeconomic changes that are altering the structure and functioning of the Earth System. Global change includes alterations in a wide range of global scale phenomena: land use and land cover, urbanization, globalization, coastal ecosystems, atmospheric composition, riverine flow, nitrogen cycle, carbon cycle, physical climate, marine food chains, biological diversity, population, economy, resource use, energy, transport, communication and so on (Steffen, et. al., 2004).
The field of complexity science looks at systems – be they meteorological, biological, physical, even economic or cultural – and the inherent usually unpredictable, structure that emerges from them. How do the parts give rise to collective behaviors? How do systems interact with their environments? At some point, perhaps at high density, a chaotic system of individuals undergoes a transition to order. And with this order, the complex system becomes highly adaptive, with a heightened capacity to respond to a constantly changing and unpredictable world (Suzuki, 2007, p. 29).
Holland (1995) explains the concept of complex adaptive systems (cas), and presents general modification times for adaptation by natural and artificial systems to changes in structure or strategy based on system experience. For example, modifications to change occur in the central nervous system in a matter of seconds to hours while the immune system responds in hours to days. Some agents and networks can take months to years to respond to change, while many species can take days to centuries to evolve. Most ecosystems respond in years to millennia, while others respond more quickly.
The following analysis of complex adaptive systems by Holland (1995) describes in detail how individual agents and networks can understand the properties of natural and built complex adaptive systems:
Complex adaptive systems (cas) are quite different from most systems that have been studied scientifically because they exhibit coherence under change, via conditional action and anticipation, and they do so without central direction. At the same time, it would appear that cas has lever points, wherein small amounts of input produce large directed change (Holland, 1995, p. 39).
Evolution “remembers” combinations of building blocks that increase fitness. The building blocks that recur generation after generation are those that have survived in the contexts in which they have been tested. These contexts are provided by (1) other building blocks and (2) the environmental niche(s) the species inhabits. There is actually an extensive hierarchy that is continually tested at every level. The building blocks that we observe are by and large, the robust building blocks. Evolution continually generates and selects building blocks at all levels, selected combinations of established building blocks at one level becoming the building blocks of the next higher level. Evolution continually innovates, but at each level it conserves the elements that are recombined to yield the innovations. When a new building block is discovered at some level, it usually opens a whole range of possibilities because of the potential for new combinations with other extant building blocks. Tremendous changes and advances can then ensue (Holland, 1995, p. 80).
Holland’s description of cas diversity and dynamics also appears to strengthen the natural occurring correlations between natural and built systems: The diversity of cas is a dynamic pattern, often persistent and coherent and a pattern of interactions disturbed by the extinction of component agents often reasserts itself, though the new agents may differ in detail form the old. The diversity observed in cas is the product of progressive adaptations. Each new adaptation opens the possibility for further interaction and niches. Agents that participate in cyclic flows cause the system to retain resources. The resources so retained can be further exploited – they offer new niches to be exploited by new kinds of agents. Parts of a cas that exploit these possibilities, particularly parts that further enhance recycling will thrive. Parts that fail to do so will lose their resources to those that do. This is natural selection writ large. It is a process that leads to increasing diversity through increasing recycling (Holland, 1995, p. 30). In cas the flows through these networks vary over time; moreover, nodes and connections can appear and disappear as the agents adapt or fail to adapt. Thus, neither the flows nor the networks are fixed in time. They are patterns that reflect changing adaptations as time elapses and experience accumulates (Holland, 1995, p. 23).
Holland’s cas flows are the circulatory system of vibrant, healthy habitats that are formed through the reciprocal exchanges between the biotic and abiotic, the natural and the built environment and the interactions between an infinite number of agents and networks. These exchanges create a complex level of viability and thrivability that become part of the adaptive dialogue flows that are recycled and refreshed until they create evolutionary individual and organizational building blocks, while nonviable concepts and actions flow out of the system as unfit and unusable. These flows rely upon perpetual reciprocal exchange and feedback, with reciprocity as the oxygenation that breathes life into the future habitability of the Earth System.
2 thoughts on ““Hidden order: How complexity builds adaptation””
In the spirit of further reading suggestions . . .
As it happens I am reading A Thousand Years of Nonlinear History, by Manuel De Landa (New York: Zone Books, 1997); it does not look like he cites Holland, but he is certainly drawing on the kind of thinking Holland presents. De Landa applies complex systems theory (if that is the proper name for it) primarily to European history from 1000 – 2000 CE, describing the emergence of economic and political systems (in the parts I have read so far!). The book predates the use of the term Anthropocene, but it offers an extremely rich interdisciplinary platform, informed by systems theory, on which to build an understanding that integrates natural and social science approaches.
Also, I came upon an article I mean to post on at some point: Gillings & Hagan-Lawson, “The cost of living in the Anthropocene,” Earth Perspectives 2014, 1:2 (doi:10.1186/2194-6434-1-2). The argument here is that anticipated disruption of the relative stability of the Holocene means that the future will be less predictable, raising the costs of habitation. My question, in relation to Holland, is this: does the theory he offers actually allow for greater predictive power, so that it might decrease uncertainty, even in a less stable climate regime? Is the promise of resiliency something like, even in the face of instability, the outputs of the systems we rely on to survive can remain relatively stable? More generally, what is the relationship among habitability, stability, and predictability? Does the development of complexity theory mean that a tight relation between stability and predictability is loosened, thus loosening the relationship between stability and habitability?
My view of Holland is that he believes complex adaptive systems are more anticipatory than predictive, and they become more robust and resilient with each new level of evolutionary change and added adaptive capacity. Since greater uncertainty and climate chaos are now observable and verified by volumes of rigorous data sets, this data becomes the algorithms and system inputs for a given operational habitat(s). These inputs then provide the knowledge flows between agents and among networks who work to develop dynamic responses to anticipated gradual and/or rapid system change. So, like Holland’s building blocks, these responses should lead to enhanced anticipatory capabilities and relatively greater system stability for future levels of adaptation and evolution as they exist in a system’s output. Yes, complexity theory implies that “tight relations” or bonds between habitat modalities such as stability and predictability can actually produce system incoherence as the exogenous shocks to a system become increasingly complex, varied and unpredictable. Both Holland and Suzuki suggest that coherence can emerge from highly adaptive systems — especially at very high Anthropogenic input density — but this must occur, as it does in the natural world, through self-organization, and without predetermined or central organization.