Introduction
Having already discussed the use of unified computational intelligence to learn and to adapt, this book now investigates its ability to seek. Computational social science modeling allows heightened understanding of the dynamics of complex systems in ways that the traditional analytical approaches could not. In this way, unified computational intelligence algorithms can power models unlike anything computable using a static or mathematical approach. Agent-based modeling, using agents whose intelligence includes full-blown creativity thanks to their ability to learn and to adapt, is revealing information about ourselves and the world that has never before been supported. From how elephants mourn their dead to how pandemics spread to large-scale financial market models, these techniques are giving humanity a way to seek that used to be only the purview of mystics and philosophers. In domains where the unified approaches to learning and adapting prove advantageous, their combined ability to assist in seeking may be great.
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© 2010 Springer-Verlag Berlin Heidelberg
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Seiffertt, J., Wunsch, D.C. (2010). Unified Computational Intelligence in Social Science. In: Unified Computational Intelligence for Complex Systems. Evolutionary Learning and Optimization, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03180-9_7
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DOI: https://doi.org/10.1007/978-3-642-03180-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03179-3
Online ISBN: 978-3-642-03180-9
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