processPhotography - Introducing Complexity

Most of the things we normally have to deal with understanding are complex, fuzzy, messy, changing, and in fact poorly delineated. We don't actually know where the boundaries of them are, let alone being able to make clear questions about them. We spend a lot of our time as ordinary humans navigating through complicated situations with one another that require constant negotiation, and constant new attempts to understand. Brian Eno

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Complexity provides a conceptual framework with which to view the world. Through it, we acknowledge that we are surrounded by things which are dynamic, changing, entangled, and interconnected. The study of complexity asks us to think in an holistic way, focusing on "the connections between things rather than the things themselves"._Lucas Complexity challenges some of the assumptions at the core of classical thinking which follows principles of determinism, reductionism, objectivity and rationality. It challenges us to move away from "our intellectual habit of thinking linearly"_{De Landa}.

Complexity is an emerging transdisciplinary field of research that studies complex systems. Complex systems have previously been studied in various fields including cybernetics, economics, sociology, anthropology, cognitive science, chemistry, mathematics, physics, systems science etc. Complexity itself is now the focus of much of the research in these fields - to explore what complexity is and what it means for understanding the systems that make up our universe. Complexity is approached in different ways, applied to a diverse range of scenarios, using a variety of methodologies to achieve a multitude of results!

Complex Uncertainty

Complex systems are open, which means they depend on and contribute to their environment. Because of this it is difficult to define the borders of complex systems. "Instead of being characteristic of the system itself, the scope of the system is usually determined by the purpose of the description of the system, and thus often influenced by the position of the observer". _{Cilliers 2004 p4} Therefore "a distinction made by one observer in one context may no longer be meaningful - or even possible - for another observer or in another context". _{Gershenson & Heylighen}

Gershenson and Heylighen present two examples to illustrate the point, the first is Heisenberg's Uncertainty Principle. In classical science, particles and waves are mutually exclusive. In quantum mechanics, however, an electron can appear to have the characteristics of a particle in some circumstances and the attributes of a wave in other circumstances. Particles and waves are complementary - "they are jointly necessary to characterize the electron but they can never be seen together, since the observation set-up necessary to distinguish "particle-like" properties is incompatible with the observation set-up for "wave-like" properties"._{Gershenson & Heylighen}

The second example Gershenson and Heylighen use is that of the rabbit-duck illusion. It demonstrates that while both the rabbit and the duck are equally recognizable in the drawing, they cannot be viewed simultaneously - rather there is a switching between the interpretations. "Complementary properties, like the rabbit and the duck gestalts, are distinct yet joined together. But while we see the one, we cannot see the other!"_{Gershenson & Heylighen}. Both, however, are still necessary to describe the illusion.

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These examples highlight the fact that is there is no best model but rather models for different purposes and different contexts."With a classical way of thinking, we can spend all our efforts trying to decide what is the system. Complex thinking, on the other hand, allows us to contemplate different representations at the same time.... in order to have a less-incomplete understanding of the system"_{Gershenson & Heylighen}. In this way, problems are tackled by choosing a representation that is most appropriate for the context, remaining aware that a different problem may require a completely different model.

Approaching a complex system as a physicist is quite different from approaching a complex system as a cognitive scientist. Hans Daellenbach differentiates between technical complexity - the type found within a physical, mathematical or computational problem; and human/social complexity_Daellenbach. Others use the terms hard and soft to describe systems and methodologies. Hard system methodologies seek quantitative results while soft system methodologies deal with qualitative properties and solutions. Soft systems, which are of interest to this research project, relate to human and social, socio-technical and socio-economic systems. The good news is, as Paul Cilliers points out, complex systems do have characteristics that exist outside of the point of view of the observer_{Cilliers 2005 p3}. Furthermore, Complexity is not limited to scientific areas and can "usefully be employed in considering many personal and social situations where complex interactions and difficult decisions need to be evaluated"_Lucas. It is more important and useful for this research project, to outline the basic concepts and characteristics of complexity in broad terms.

More is Different

What makes complex systems complex, is that the parts making up the whole don't sum up in any simple fashion. Rather they interact with each other, and interacting even quite simple components can generate bewildering behaviour. _{Watts 2003 p25-26}

Complex systems consist of a large number of elements which interact dynamically. The interactions of elements simultaneously influences and is influenced by, many others. These interactions over time, change the system itself. A study of the discrete elements of a complex system ignores the interactions between elements and therefore does not assist in understanding the system as a whole. As Paul Cilliers explains:

One of the most important scientific tools has always been the analytical method. If something is too complex to be grasped as a whole, it is divided into manageable units which can be analysed separately and then put together again. However, the study of complex systems has uncovered a fundamental flaw in the analytical method. A complex system is not constituted merely by the sum of its parts. In 'cutting up' a system, the analytical method destroys what it seeks to understand._{Cilliers 2005 p2}

The Whole is Different than the Sum of its Parts

The interactions of complex systems are nonlinear. In a linear system cause equals effect. In a non-linear system the effects themselves can also be causes, which generated further and varied effects, in turn becoming further causes and so on. The whole is therefore quite different than the sum of its parts.

Non-linearity can be understood as the effect of a casual loop, where effects or outputs are fed back into the causes and inputs of the process. Complex systems are characterized by networks of such causal loops. In a complex, the interdependencies are such that a component A will affect a component B, but B will in general also affect A, directly or indirectly._{Gershenson & Heylighen}

Feedback loops can be positive or negative - they can amplify or dampen the impact on the system. The effects of feedback loops and sensitivity to initial conditions have been popularised in chaos theory as the Butterfly Effect. In a positive or amplifying feedback loop, the flapping of the wings of a butterfly in Brazil can grow into a tornado in Texas. Fundamentally, it is the idea that small fluctuations or small changes can have large effects or impacts on a system. Had the butterfly not flapped its wings, the trajectory of the system might have been very different. The inverse is also possible as is evident with a dampening or negative feedback loop. Large causes or changes can be counter-acted having very little impact on the system.

As you would now expect, a complex system will "typically exhibit a tangle of interconnected positive and negative feedback loops, where the effects of any change in a component cascade through an increasing number of connected components, in part feeding back, positively and/or negatively, into initial components"_{Gershenson & Heylighen}.

Bottom Up

Complex systems have the ability to self-organize. Self-organizing systems search by themselves for solutions and stability. Self-organization emerges out of interactions at the level of the system and is therefore not evident when studying individual components of the system. Self-organizing systems are intrinsically open and flexible. They adjust and adapt to change easily and without central control. "The organization is distributed over all the participating components and their connections"_{Gershenson & Heylighen}. This is why self-organization is also known as bottom-up organization.

Self-organization occurs when components of a system are attracted to a particular state. As stable configurations of the system, these states are known as attractors. Complex systems can have multiple attractors which give the system different possible behaviours. "If the system wanders...it would look to us as random, but if its behaviour is pinned down to a few states, then it will look ordered"_{De Landa}. Attractors are considered the local destinies of complex systems.

Not only do complex systems evolve through time, their past is co-responsible for their present behaviour. The attractors and behaviour of a complex system is determined by its initial conditions (its history) and the subsequent perturbations (disturbances). Self-organization itself is also a non-linear process since small disturbances can effect which different attractors the system enters. Once an attractor is reached, major perturbations can have little or no effect.

Self-organization "thrives on randomness or indeterminacy", the more changes the system encounters the quicker it reaches an attractor, the more stable the attractor will be. Gershenson and Heylighen call this the order from noise principle. If changes are too strong, organization will be destroyed and so systems evolve at "the edge of chaos". Edge of Chaos is describes as "a self-maintaining balance between areas of stability (static) and areas of change (chaotic)"_{Lucas & Milov 1997}.

Where there is sufficient variation to make a system creative and flexible, but not enough to make it wholly chaotic._{Gershenson & Heylighen}

Complex systems also operate under conditions far from equilibrium. Complex systems interact with their environment because they require a constant flow of energy to maintain organization and ensure survival. Equilibrium is another word for death._{Cilliers 2005 p4}

Local Information, Global Wisdom

In a complex system no one element has all the information. "Each element in the system is ignorant of the behaviour of the system as a whole, it responds only to the information that is available to it locally"_{Cilliers 2005 p4}. Elements think locally, and act locally but their collective action produces the global intelligence of the system. This is known as distributed intelligence.

Different components participate in different ways to the overall gathering and processing of information, thus collectively solving the problems posed by any perceived deviation between the present and the desired situation._{Gershenson & Heylighen}

Classical thinking assumes all the information is required to make a rational decision. The complexity approach "notes not only that indeterminacy is unavoidable, but that having too much information is as bad as having too little"_{Gershenson & Heylighen}. Complex systems only require information with respect to goals.

More than the Sum of the Parts

Emergence is another phenomenon of complex systems which is linked to the concept of self-organization. Emergence is the resulting higher level sophistication that arises from lower level rules or interactions in a system. Typically, self-organizing systems display emergent properties. Emergent properties are generally unpredictable at a lower level which means that the individual elements themselves are not aware of the higher-level patterns or order they are generating in the system. It is also why emergent properties cannot be studied by being reduced to the sum of its parts.

Another way of phrasing emergence is "Micromotives combine to form macrobehaviour"_{Johnson 2002 p90}. Again, local interactions or rule lead to global structure in the system but its a structure that couldn't be predicted from the interactions/rules. As Steven Johnson says, complex systems get their smarts from below and grow smarter over time.

Holistic Complexity View

Complex systems do not exist in isolation - they interact with their environment; they can emerge as the result of sub-systems; as well as form part of other larger complex systems. When considering the interactions of the elements, we also need to consider the interactions of the emergent systems properties - the higher level features; interdependencies between these elements and emergent properties; the multiple nested levels and hierarchies that can form; and if the elements and properties are included in other systems.

Reductionism and Complexity

Although Complexity thinking can be viewed to be in opposition to Classical reductionism, it has been suggested that “system thinking embraces the values of reductionist science to understand the parts, and the constructivist perspectives which seek to understand the wholes, and more so, the understanding of the complex relationships that enable “parts” to become “wholes” _WikiPedia. Chris Lucas highlights the idea of synthesis:

We combine the parts to form larger systems and look at the overall properties that then become evident. Looking at the whole is an holistic viewpoint, but because we also include the parts then we can study how the properties of the whole emerge from the parts._Lucas

With these general principles of complexity, we can transform our understanding of the vast array of systems that surround our contemporary existence. In relation to this research project, complexity offers new ways of approaching photography - that acknowledge the dynamic and changing interactions; and the multitude of elements and variables that constitute the photographic system as a whole. Complexity provides the basis for completely new understandings and explorations of photography.