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Fostering Creative Emergences in Artificial Cultures

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Proc. of the Alife XII Conference, Odense, Denmark, 2010 669Fostering Creative Emergences in Artificial Cultures Nicholas Gessler ISIS (Information Science & Information Studies), Duke University [email protected] Abstract Empirically, culture is that complex whole which results from the interaction of a multitude of ideas, individuals, behaviors, groups, artifacts, workplaces and architectures, each distributed uniquely and differentially in space and time. Artificial culture is the program of describing, understanding and explaining such human complex systems in computer simulations. Several recent conferences in evolutionary computation (i.e. dynamical hierarchies, computational synthesis, and dynamic ontology) have focused on the problem of automatically creating novel and compounded emergences in natural and artificial worlds. This paper reviews current progress toward that goal from the perspective of an anthropologist. Cultural Complexity Each culture is as different as are its members. Moreover, the minds of individual members of a culture are often filled with different and competing thoughts. To further complicate matters, cognition is unevenly distributed not only among people, but also among their behaviors and the products of their technology. Culture is the totality that emerges, through complex webs of mutual causation at increasing levels of complexity, through dynamical hierarchical synthesis, from such seemingly dissimilar things: Ideas, and other atomic particles of human culture, often seem to have a life of their own – organization, mutation, reproduction, spreading, and dying. In spite of several bold attempts to construct theories of cultural evolution, an adequate theory remains elusive. The financial incentive to understand any patterns governing fads and fashion is enormous, and because cultural evolution has contributed so much to the uniqueness of human nature, the scientific motivation is equally great. (Taylor & Jefferson, quoted in Gessler, 2003). Culture shifts… with kaleidoscopic variety, and is characterized internally not by uniformity, but by diversity of both individuals and groups, many… in continuous and overt conflict in one sub-system and in active cooperation in another. (Wallace, 1961:28). Humans create their cognitive powers by creating the environments in which they exercise those powers. (Hutchins, 1995:xvi). More formally, we might define culture as a complex network of activity through multidimensional multiagent webs of mutual causation, a computational process that is both massively parallel and simultaneous. Culture is the emergent product of the variety of beliefs held by a single individual and the variety of individual behaviors that constitute a society. Complexities of this kind are everywhere and everywhere they defy casual description. Although complex adaptive systems are largely intractable to traditional discursive and mathematical representations, the "new sciences of complexity" offer some fresh alternatives. Beginning about 1950, we created computational languages for describing, explaining and understanding these dynamic technicalities. Artificial culture1 is a program that extends the trajectory that began with distributed artificial intelligence and grew from artificial life to artificial society, towards a new social scientific practice. Creative, critical, experimental and empirically informed, artificial culture is the project of describing the technical complexities of culture in computational terms. Much existing discursive and mathematical cultural theory may be amenable to translation; much may need to be completely reformulated. In short, we need to encode a population of agents, along with their social and physical environments, inside simulations. This enables us to begin to describe, understand and explain the complex causal web of biological and cultural evolutionary processes that distinguished us as humans from our primate ancestors. Experiments of this kind allow us to evaluate the nature of alternative counterfactual "what if” scenarios by observing the entailments of different initial patterns of similarity and difference, different constellations of individual and group (local and global) interaction and different degrees of ideational and material agency. Inspired by the epistemological convergence between evolution and computation (e.g. Rozenberg, et al. 2010), such investigations offer rich new insights into cultural complexity: the individual and society (local versus global), distributed cultural cognition (including the intermediation between humans and their technologies) and the coevolution of the unlimited variety of cultural things-that-think2 and things-that-work. Vital to understanding the evolution of culture is understanding networks of trust, secrecy and deception, the human practice of judging the reliability of other individuals in exchanging matter and information, the practice that builds 1 A term originally suggested by Michael Dyer. 2 A phrase originated by the MIT Media Lab.Proc. of the Alife XII Conference, Odense, Denmark, 2010 670reputation. Artificial culture enables us to describe and experiment with the coevolution of seemingly disparate processes in natural culture and it suggests to us some new critical perspectives from which to evaluate our methods of anthropological inquiry. Metaphors and Media Although cultural evolution clearly outpaces genetic evolution in the natural world, it does so only to the degree in which it is freed from the constraints of biological materiality. Cultural change, considered as the reproductive cycle, takes place in seconds, minutes, days, years or decades, whereas human biological change takes at least a decade and a half. In the natural biological and cultural worlds the media of evolutionary transmission behave quite differently: genes reproduce slowly; ideas reproduce quickly. In the artificial world of the computer, whether modeled on a cultural or genetic metaphor, the medium through which evolution unfolds is essentially the same for both. The generations over which evolution unfolds are constrained by the same system clock. Although cultural evolution proceeds more quickly than biological evolution in the natural world, there is no a priori reason to believe that cultural processes will be quicker than genetic ones when evolution runs in simulation. Computational algorithms


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