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Socio-Ecological Systems

  • Claudio Cioffi-RevillaEmail author
Reference work entry

Abstract

This chapter provides a unified framework for understanding the ecological triad of coupled human, artificial, and natural systems and processes, based on convergence among the social, engineering, and natural sciences and enabled by computing technology. The framework explains the rise and future of the Anthropocene epoch and a convergence-based approach to civilization, among other phenomena, with roots in earlier paradigms, such as complex adaptive systems and coupled socio-ecological systems, which in turn extend and advance prior knowledge on general systems and cybernetics. The vision is that of a unified science of humans, artifacts, and nature, with a system-of-systems architecture and supported by universal formalisms, systems principles, and object-based computational models calibrated with real-world data.

Keywords

Coupled human-artificial-natural systems Complex adaptive systems Computational social science Computational ecology Convergence Anthropocene Agent-based models Climate change Complexity science 

Notes

Acknowledgments

This chapter is dedicated to the memory of Herbert A. Simon and Elinor Ostrom, twentieth-century visionaries of a unified science of coupled human, artificial, and natural systems (“Simon’s Triad”). Funding for this study was provided by the US National Science Foundation under grant no. IIS-1125171 and by the Center for Social Complexity at George Mason University. I am grateful to Bill Bainbridge, Dan Rogers, and Rob Axtell for comments on an earlier draft. Jeff Bassett, Ken De Jong, Tim Gulden, Ates Hailegiorgis, Bill Kennedy, Sean Luke, Paul Schopf, and members of the MURI and CDI teams provided earlier discussions.

References

  1. Cioffi-Revilla C (2002) Invariance and universality in social agent-based simulations. Proc Natl Acad Sci U S A 99(Suppl 3(14)):7314–7316Google Scholar
  2. Cioffi-Revilla C (2009) Simplicity and reality in computational modeling of politics. Comput Math Organ Theory 15(1):26–46Google Scholar
  3. Cioffi-Revilla C (2014a) Introduction to computational social science: principles and applications. Springer, HeidelbergCrossRefzbMATHGoogle Scholar
  4. Cioffi-Revilla C (2014b) Theoretical analysis of Amdahl’s Law for agent-based simulations using the nabladot operator. In Scarano V, Cordasco G, de Chiara R, Erra U (eds) Proceedings of the 2nd workshop on parallel and distributed agent-based simulations (PADABS’14), Porto, 25 August 2014. Springer, London/HeidelbergGoogle Scholar
  5. Connor WR (2003) Why we need independent centers for advanced study. Chron High Educ 49(19):B10Google Scholar
  6. Crooks AT (2012) The use of agent-based modelling for studying the social and physical environment of cities. In: De Roo G, Hiller J, Van Wezemael J (eds) Complexity and planning: systems, assemblages and simulations. Ashgate, Burlington, pp 385–408Google Scholar
  7. Francois C (2004) International encyclopedia of systems and cybernetics. K G Saur, MunichCrossRefGoogle Scholar
  8. Friedenthal S, Moore A, Steiner R (2014) A practical guide to SysML: the systems modeling language, 3rd edn. MK/OMG Press, WalthamGoogle Scholar
  9. Helbing D (ed) (2012) Social self-organization: agent-based simulations and experiments to study emergent social behavior. Springer, HeidelbergGoogle Scholar
  10. Helsloot I, Boin A, Jacobs B, Comfort LK (eds) (2012) Mega-crises: understanding the prospects, nature, characteristics and the effects of cataclysmic events. Charles C. Thomas Publisher, SpringfieldGoogle Scholar
  11. Heppenstall AJ, Crooks AT, See LM, Batty M (eds) (2012) Agent-based models of geographical systems. Springer, New YorkGoogle Scholar
  12. IPCC (Intergovernmental Panel on Climate Change) (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge, UKGoogle Scholar
  13. Jones EC, Murphy AD (eds) (2009) The political economy of hazards and disasters. Altamira Press, New YorkGoogle Scholar
  14. Liu J, Mooney H, Hull V, Li S (2015) Systems integration for global sustainability. Science 347:963–973Google Scholar
  15. Luke S (2014) Multiagent simulation and the MASON library. Department of Computer Science, George Mason University. Available from http://cs.gmu.edu/~eclab/projects/mason/
  16. Miller JH, Page SE (2007) Complex adaptive systems: an introduction to computational models of social life. Princeton University Press, PrincetonzbMATHGoogle Scholar
  17. Mitchell M (2009) Complexity: a guided tour. Oxford University Press, Oxford, UKzbMATHGoogle Scholar
  18. Moore WH, Siegel DA (2013) A mathematics course for political and social research. Princeton University Press, PrincetonCrossRefzbMATHGoogle Scholar
  19. National Geographic (2009) Concise history of science and invention. An Illustrated Time Line, Washington, DCGoogle Scholar
  20. Nikolai C, Madey G (2009) Tools of the trade: a survey of various agent based modeling platforms. J Artif Societies Soc Simul 12(2): article no. 2Google Scholar
  21. Ostrom E (2009) A general framework for analyzing sustainability of socio-ecological systems. Science 325:419–422MathSciNetCrossRefzbMATHGoogle Scholar
  22. Pearl J (2000) Causality: modeling, reasoning, and inference. University Press, Cambridge, UKzbMATHGoogle Scholar
  23. Perry RW, Quarantelli EL (2005) What is a disaster? International Research Committee on Disasters, Newark, DelawareGoogle Scholar
  24. Roco MC, Bainbridge WS (2002) Converging technologies for improving human performance. Springer, HeidelbergGoogle Scholar
  25. Roco MC, Bainbridge WS (2013) Convergence of knowledge, technology, and society: beyond convergence of nano-bio-info-cognitive technologies. Springer, HeidelbergCrossRefGoogle Scholar
  26. Rogers DJ, Cioffi-Revilla C, Linford SJ (2014) The sustainability of wealth among nomads: an agent-based approach. In: Barceló JA, Bogdanovic I (eds) Mathematics in archaeology. Science Publishers, EnfieldGoogle Scholar
  27. Rundle JB, Turcotte DL, Kline W (eds) (1996) Reduction and predictability of natural disasters. Addison-Wesley, ReadingGoogle Scholar
  28. Simon HA (1996[1969]) The sciences of the artificial. MIT Press, Cambridge, MAGoogle Scholar
  29. Wolfram S (2015) http://www.wolfram.com

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computational Social Science ProgramCenter for Social Complexity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUSA

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