The MC2 Project [Machines of Collective Conscience] A possible walk, up to Life-like Complexity and Behaviour, from bottom, basic and simple bio-inspired heuristics - a walk, up into the morphogenesis of information. Vitorino Ramos, Leonel Moura "[...]QUESTION_HUMAN >If Control’s control is absolute, why does Control need to control? ANSWER_CONTROL >Control..., needs time. QUESTION_HUMAN >Is Control controlled by his need to control ? ANSWER_CONTROL >Yes. QUESTION_HUMAN >Why is Control need Humans, has you call them ? ANSWER_CONTROL >Wait ! Wait...! Time are lending me...; Death needs time like a Junkie... needs Junk. QUESTION_HUMAN >And what does Death need time for ? ANSWER_CONTROL >The answer is so simple ! Death needs time for what it kills to grow in ! [...]" in Dead City Radio, William S. Burroughs / John Cale , 1990. Imagine a "machine" where there is no pre-commitment to any particular representational scheme: the desired behaviour is distributed and roughly specified simultaneously among many parts, but there is minimal specification of the mechanism required to generate that behaviour, i.e. the global behaviour evolves from the many relations of multiple simple behaviours. A machine that lives to and from/with Synergy. The emergence of complex behaviour in a system consisting of interacting simple elements is among the most fascinating phenomena of our world. Examples can be found in almost every field of today’s scientific interest, ranging from coherent pattern formation in physical and chemical systems, to the motion of swarms of animals in biology, and the behavior of social groups. In the life and social sciences, one is usually convinced that the evolution of social systems is determined by numerous factors, difficult to grasp, such as cultural, sociological, economic, political, ecological, etc. However, in recent years, the development of the interdisciplinary fields "science of complexity", along with "artificial life" (aLife), has lead to the insight, that complex dynamic processes may also result from simple interactions. Moreover, at a certain level of abstraction, one can also find many common features between complex structures in very different fields. For instance, it is an old idea that society is in a number of respects similar to an organism, a living system with its cells, metabolic circuits and systems. As an example, the army functions like an immune system, protecting the organism from invaders, while the government functions like the brain, steering the whole and making decisions. In this metaphor, different organizations or institutions play the role of organs, each fulfilling its particular function in keeping the system alive, an idea that can be traced back at least as far as Aristotle, being a major inspiration for the founding fathers of sociology, such as Comte, Durkheim and especially Spencer. The organismic view of society has much less appeal to contemporary theorists. Their models of society are much more interactive, open-ended, and non-deterministic than those of earlier sociologists, and they have learned to recognize the intrinsic complexity and unpredictability of society. The static, centralized, hierarchical structure with its rigid division of labor that seems to underlie the older organismic models appears poorly suited for understanding the intricacies of our fast-evolving society. Moreover, a vision of society where individuals are merely little cells subordinated to a collective system has unpleasant connotations to the totalitarian states created in the last century. As a result, the organismic model is at present generally discredit in sociology. Similarly, biology has traditionally started at the top, viewing a living organism as a complex biochemical machine, and has worked analytically down from there through the hierarchy of biological organization - decomposing a living organism into organs, tissues, cells, organelles, and finally molecules - in its pursuit of the mechanisms of life. Analysis means ‘the separation of an intellectual or substantial whole into constituents for individual study’ (that is, by top-down reductionist approaches). By composing our individual understandings of the dissected component parts of living organisms, traditional biology has provided us with a broad picture of the mechanics of life on Earth. In the meantime, however, new scientific developments have done away with rigid, mechanistic views of organisms. As pointed by Langton, there is more to life than mechanics - there is also dynamics. Life depends critically on principles of dynamical self-organization that have remained largely untouched by traditional analytic methods. There is a simple explanation for this - these self-organized dynamics are fundamentally non-linear phenomena, and non-linear phenomena in general depend critically on the interactions between parts: they necessarily disappear when parts are treated in isolation from one another, which is the basis for any analytic method. Rather, non-linear phenomena are most appropriately treated by a synthetic approach, where synthesis means "the combining of separate elements or substances to form a coherent whole". In non-linear systems, the parts must be treated in each other’s presence, rather than independently from one another, because they behave very differently in each other’s presence than we would expect from a study of the parts in isolation. Of course, there is no universally agreed definition of life. The concept covers a cluster of properties, most of which are themselves philosophically problematic: self-organization, emergence, autonomy, growth, development, reproduction, evolution, adaptation, responsiveness, and metabolism. Scientists differ about the relative importance of these properties, although it is generally agreed that the possession of most (not necessarily all) of them suffices for something to be regarded as alive. Similarly, when studying living systems, biologists no longer focus on the static structures of their anatomy, but on the multitude of interacting processes that allow the organism to adapt to an ever changing environment. Recently, the variety of ideas and methods that is commonly grouped under the header of "the sciences of complexity" along with Artificial Life, has led to understanding that organisms are self-organizing, adaptive systems. Most processes in such systems are decentralized, non-deterministic and in constant flux. They thrive on noise, chaos and creativity. Their collective swarm-intelligence emerges out of the free interactions between individually autonomous components. Rather than take living things apart, Artificial Life attempts to put living things together within a bottom-up approach, that is, beyond life-as-we-know-it into the realm of life-as-it-could-be, generating lifelike behaviour, and focusing on the problem of creating behaviour generators, inspired on the nature itself, even if the results (what emerges from the process) have no analogues in the natural world. The key insight into the natural method of behaviour generation is gained by noting that nature is fundamentally parallel. This is reflected in the "architecture" of natural living organisms, which consist of many millions of parts, each one of which has its own behavioural repertoire. Living systems are highly distributed and quite massively parallel. Artificial Intelligence (AI) and aLife are each concerned with the application of computers to the study of complex, natural phenomena. Apart from traditional and symbolic top-down AI in the sixties and seventies, both are nowadays concerned with generating complex behaviour, in a bottom-up manner, turning their attention from the mechanics of phenomena to the logic of it. The first computational approach to the generation of lifelike behaviour was due to the mathematician John Von Neumann. In the words of his colleague Arthur W. Burks, Von Neumann was interested in the general question: [...] What kind of logical organization is sufficient for an automaton to reproduce itself ? This question is not precise and admits to trivial versions as well as interesting ones. Von Neumann had the familiar natural phenomenon of self-reproduction in mind when he posed it, but he was trying to simulate the self-reproduction of a natural system at the level of genetics and biochemistry. He wished to abstract from the natural self-reproduction problem its logical form [...] This approach is the first to capture the essence of Artificial Life (replace, for instance, references to ’self-reproduction’ in the above with references to any other biological phenomena). From this "kinematic model" of Von Neumann, a genuine self-reproduction mechanism implemented in the sixties, Stan Ulam suggested an appropriate formalism where the logical form of the process is completely distinguish from the material counterpart, which has come to be know as a Cellular Automata (CA). In brief, a CA consists of a regular lattice of (many) finite automata, which are the simplest formal models of machines. A finite automata can be in only one of a finite number of states at any given time, and its transition between states from one time-step to the next are governed by a state-transition table: given a certain input and a certain internal state, the state-transition table specifies the state to be adopted by the finite automata at the next time step. In a CA, the necessary input is derived from the states of the automata at neighbouring lattice-points. Thus the state of an cellular automata at time t+1 is a function of the states of the automata itself and its immediate neighbours at time t. All the finite automata in the lattice (group of cells) obey the same transition-table (rule table) and every cell changes his state at the same instant, time-step after time-step. CA’s are a good example of the kind of computational paradigm sought after by Artificial Life: bottom-up, parallel, local determination of behaviour with minimal specification, and emerging complex phenomena from simple rules. In order to study any natural phenomena, scientists are turning to a separation. A need to separate the notion of a formal specification of a machine (any that will reproduce the phenomena itself) - that is, a specification of the logical structure of the machine - from the notion of a formal specification of a machines’s behaviour - that is, a specification of transitions that the machine will undergo. In general, we cannot derive behaviours from structure, nor can we derive structure from behaviours. So instead, in order to determine the behaviour of some machines and coupled phenomena, there is no recourse but to run them and see how they behave. This has consequences for the methods by which we (or nature) go about generating behaviour generators themselves, and from which any evolutionary and adaptive process seems to be essential. For instance, the most salient characteristic of living systems, from the behaviour generation point of view, is the genotype/phenotype distinction. The distinction is essentially one between a specification of machinery - the genotype - and the behaviour of that machinery - the phenotype. The genotype is the complete set of genetic instructions encoded in the linear sequence of nucleotide bases that makes an organism’s DNA. The phenotype is the physical organism itself - the structures that emerge in space and time as the result of the interpretation of the genotype of a particular environment. The process by which the phenotype develops through time under the direction of the genotype is called morphogenesis. Simulation plays an essential role in the study of morphogenesis. This was anticipated as early as 1952 by Turing, who wrote: [...] The difficulties are such that one cannot hope to have any very embracing theory of such processes, beyond the statement of equations. It might be possible, however, to treat a few particular cases in detail with the aid of a digital computer. This method has the advantage that it is not so necessary to make simplifying assumptions as it is when doing a more theoretical type of analysis [...] <- Main contact