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p.1 ABSTRACT. The science of complexity is based on a new way of thinking that stands in sharp contrast to the philosophy underlying Newtonian science, which is based on reductionism, determinism, and objective knowledge... Systems theory replaced reductionism by a scientifically based holism... the paradigm of complexity still needs to be fully assimilated by philosophy...
p.1 Complexity is perhaps the most essential characteristic of our present society... a change in any component may affect virtually any other component, and that in a mostly unpredictable manner.
The traditional scientific method, which is based on analysis, isolation, and the gathering of complete information about a phenomenon, is incapable to deal with [JLJ - 'of dealing with' is a nit-picking grammar improvement] such complex interdependencies.
p.2 A basic function of philosophy is to analyse and criticise the implicit assumptions behind our thinking... As such, philosophy can help us to clarify the principles of thought that characterise complexity science and distinguish it from its predecessors.
p.4 In essence, the philosophy of Newtonian science is one of simplicity: the complexity of the world is only apparent; to deal with it you need to analyse phenomena into their simplest components. Once you have done that, their evolution will turn out to be perfectly regular, reversible and predictable, while the knowledge you gained will merely be a reflection of that pre-existing order.
p.5 The first challenges to reductionism and its denial of creative change appeared in the beginning of the twentieth century in the work of process philosophers, such as Bergson, Teilhard, Whitehead, and in particular Smuts (1926), who coined the word holism which he defined as the tendency of a whole to be greater than the sum of its parts. This raises the question what precisely it is that the whole has more.
In present terminology, we would say that a whole has emergent properties, i.e. properties that cannot be reduced to the properties of the parts.
p.6 on closer scrutiny practically all of the properties that matter to us in everyday life... turn out to be emergent.
p.7 Systems theory considers both directions, the downward direction of reduction or analysis, and the upward direction of holism or emergence, as equally important for understanding the true nature of the system. It does not deny the utility of the analytical method, but complements it... not only is the behavior of the whole determined by the properties of its parts (“upwards causation”), but the behavior of the parts is to some degree constrained by the properties of the whole (“downward causation” (Campbell, 1974)).
p.8 Because of the dependencies between components, the properties of these components can no longer vary independently: they have to obey certain relationships. This makes much of the individual properties irrelevant, while shifting the focus to the state of their relationship, which will now define a new type of “emergent” property.
p.8 The greater the variety of perturbations the system has to cope with, the greater the variety of compensating actions it should be able to perform (Ashby’s (1964) law of requisite variety), and the greater the knowledge or intelligence the system will need in order to know which action to perform in which circumstances.
p.8 According to cybernetics, knowledge is intrinsically subjective; it is merely an imperfect tool used by an intelligent agent to help it achieve its personal goals (Heylighen & Joslyn, 2001; Maturana & Varela, 1992). Such an agent not only does not need an objective reflection of reality, it can never achieve one.
p.9 "second-order cybernetics”... Its main thesis is that we, as observers, are also cybernetic systems. This means that our knowledge is a subjective construction, not an objective reflection of reality. Therefore, the emphasis has to shift from the apparently objective systems around us to the cognitive and social processes by which we construct our subjective models of those systems. This constitutes a major break with traditional systems theory, which implicitly assumed that there is an objective structure or organization in the systems we investigate (Bunge, 1979). This departure was reinforced by the concepts of autonomy, autopoiesis (Maturana & Varela, 1979) and self-organization, that were introduced to characterise natural, living systems in contrast to artificial, engineered systems. These imply that the structure of a system is not given, but developed by the system itself, as a means to survive and adapt to a complex and changing environment.
The rift became even larger when it became clear that many systems, and in particular social systems, do not have any clear structure, function or organization, but consist of a tangle of partly competing, partly co-operating, or simply mutually ignoring subsystems.
p.11 From evolutionary theory, complexity science has learned that agents typically are ignorant about their wider environment or the long-term effects of their actions: they reach their goals basically by trial-and-error, which is equivalent to blind variation followed by natural selection of the agents, actions or rules for action that best achieve fitness.
p.11 even when the agents are intelligent and knowledgeable enough to select apparently rational or cooperative actions, they - like us - are intrinsically uncertain about the remote effects of their actions.
This limited range of rational anticipation is reflected at the deepest level by the principle of locality: agents only interact with (and thus get the chance to “know”) a small number of other agents which form their local neighbourhood. Yet, in the longer term these local actions typically have global consequences, affecting the complex system as a whole. Such global effects are by definition unexpected at the agent level, and in that sense emergent: they could not have been inferred from the local rules (properties) that determine the agents’ behavior.
p.11 Eating a zebra may be an obvious solution to the lion’s problem of hunger, but that action will be resisted by the zebra.
p.12 agents will co-evolve: they constantly adapt to the changes made by other agents, but through this modify the others’ environment, thus forcing them to adapt as well (cf. Kauffman, 1995). This results in an on-going process of mutual adaptation, which in biology is elegantly expressed by metaphors such as an “arms race” or the “Red Queen principle”.
p.12 since actions are local, their effects can only propagate step by step to more remote agents, thus diffusing across the whole network formed by the agents and their relationships of interaction. The same action will in general have multiple effects in different parts of the network at different times. Some of those causal chains will close in on themselves, feeding back into the conditions that started the chain. This makes the system intrinsically non-linear. This means that there is no proportionality between cause and effect. On the one hand, small fluctuations may be amplified to large, global effects by positive feedback
p.12 self-organization: the system spontaneously arranges its components and their interactions into a sustainable, global structure that tries to maximize overall fitness, without need for an external or internal designer or controller (Heylighen, 2002; Kauffman, 1995). When we focus on the relation between the system and the environment, we may call it adaptation (Holland, 1996): whatever the pressures imposed by the environment, the system will adjust its structure in order to cope with them.
p.13 The co-evolution of many, interacting agents... seems able to explain the emergence of organization in any domain or context
p.13 the basic principle is simple: each agent through trial-and-error tries to achieve a situation that maximises its fitness within the environment. However, because the agent cannot foresee all the consequences, actions will generally collide with the actions of other agents, thus reaping a less than optimal result.
p.13 the organization of such a complex system is not frozen, but flexible... all complex systems created through self-organization and evolution are intrinsically adaptive, since they cannot rely on a fixed plan or blueprint to tell them how they should behave.
p.14 The intrinsic uncertainty, which appeared like a weakness, actually turns out to be a strength, since it forces the system to have sufficient reserves or redundancy and to constantly try out new things so as to be prepared for any eventuality.
p.15 Modernism can be characterised, in Lyotard’s words (1988: xxiv), as a search for a single coherent meta-narrative, i.e. to find the language of the world, the one way in which to describe it correctly and completely. This can only be a reductive strategy, something which reduces the complexity and the diversity of the world to a finite number of essential features. If the central argument of postmodernism is a rejection of this dream of modernism, then postmodernism can be characterised in general as a way of thinking which is sensitive to the complexity of the world.
p.15 [Derrida, Limited Inc, 1988] “If things were simple, word would have gotten around”
p.17 Complexity theory argues that, since we cannot give a complete description of a complex system, we also cannot devise an unchanging and non-provisional set of rules to control the behaviour of that system.
p.18-19 For centuries, the world view underlying science has been Newtonian. The corresponding philosophy has been variously called reductionism, mechanicism or modernism. Ontologically, it reduces all phenomena to movements of independent, material particles governed by deterministic laws. Epistemologically, it holds the promise of complete, objective and certain knowledge of past and future. However, it ignores or even denies any idea of value, ethics, or creative processes, describing the universe as merely a complicated clockwork mechanism.
Over the past century, various scientific developments have challenged this simplistic picture, gradually replacing it by one that is complex at the core. First, Heisenberg’s uncertainty principle in quantum mechanics, followed by the notion of chaos in non-linear dynamics, showed that the world is intrinsically unpredictable. Then, systems theory gave a scientific foundation to the ideas of holism and emergence. Cybernetics, in parallel with postmodern social science, showed that knowledge is intrinsically subjective. Together with the theories of self-organization and biological evolution, they moreover made us aware that regularity or organization is not given, but emerges dynamically out of a tangle of conflicting forces and random fluctuations, a process aptly summarized as “order out of chaos” (Prigogine & Stengers, 1984).
These different approaches are now starting to become integrated under the heading of “complexity science”.
p.19 the complexity paradigm still needs to be assimilated by academic philosophy.
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