Advertisement

Systems Modeling

  • George E. Mobus
  • Michael C. Kalton
Chapter
  • 3.6k Downloads
Part of the Understanding Complex Systems book series (UCS)

Abstract

Analysis of a system (as described in the last chapter) is not sufficient to foster understanding of that system. One must be able to show behavior and functions of the system as well. The primary tool for grasping how systems actually work is modeling the system. This is an integrative (as opposed to analytical) process in which the modeler will attempt to reconstruct the system from its components. Modern computer methods for modeling allow investigators to test their understanding of system functions by making predictions from running their models to simulate the “real” system and then observing the actual physical system. Where there is correspondence, it suggests that the model did capture some essential qualities of how the system functions. If the model fails to predict (or post-dict) the behavior of the physical system, then the modeler seeks to improve the model in various ways.We present a survey of modeling techniques and uses. Several examples of kinds of models are given.

Keywords

Operation Research Real System Memory Trace System Dynamic Model Artificial Life 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Bibliography and Further Reading 26

  1. Bagley R, Farmer DJ (1992). Spontaneous emergence of a metabolism. In: Langton et al (eds) Artificial life II, a proceedings volume in the Santa Fe Institute Studies in the sciences of complexity. Addison-Wesley Publishing Co., Redwood City. pp 93–140Google Scholar
  2. *Commons ML et al (eds) (1991) Neural network models of conditioning and action. Lawrence Erlbaum Associates, Publishers, MahwahGoogle Scholar
  3. Damasio AR (1994) Descartes’ ERROR: emotion, reason, and the human brain. G.P. Putnam’s Sons, New YorkGoogle Scholar
  4. Dennett D (1991) Consciousness explained. Little, Brown & Company, New YorkGoogle Scholar
  5. Dorigo M, Stützle T (2004) Ant colony optimization. The MIT, CambridgeGoogle Scholar
  6. Ford A (2010) Modeling the environment, 2nd edn. Island Press, WashingtonGoogle Scholar
  7. Forrester JW (1994). Learning through system dynamics as preparation for the 21st century. http://clexchange.org/ftp/documents/whyk12sd/Y_2009-02LearningThroughSD.pdf. Accessed 18 Feb 2014
  8. Gilovich T et al (eds) (2002) Heruistics and biases: the psychology of intuitive judgment. Cambridge University Press, New YorkGoogle Scholar
  9. Langton CG et al (eds) (1992) Artificial life II, a proceedings volume in the santa fe institute studies in the sciences of complexity. Addison-Wesley Publishing Co., Redwood CityGoogle Scholar
  10. *Levine DS, Aparicio IV M (1994) Neural networks for knowledge representation and inference. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  11. *Levine DS et al (eds) (2000) Oscillations in neural systems. Lawrence Erlbaum Associates, Publishers, MahwahGoogle Scholar
  12. Meadows DH (2008) Thinking in systems: a primer. Chelsea Green Publishing, White River JunctionGoogle Scholar
  13. Meadows DH et al (2004) Limits to growth: the 30-year update. Chelsea Green Publishing Company, White River JunctionGoogle Scholar
  14. *Mobus GE (1994) Toward a theory of learning and representing causal inferences in neural networks. In: Levine DS, Aparicio M (eds). Neural networks for knowledge representation and inference. Lawrence Erlbaum Associates, Mahwah. pp 339–374Google Scholar
  15. *Mobus GE (1999) Foraging search: prototypical intelligence. In: Dubois D (ed) Third international conference on computing anticipatory systems. Center for Hyperincursion and Anticipation in Ordered Systems, Institute of Mathematics, University of Liege, HEC LiegeGoogle Scholar
  16. *Mobus GE, Fisher P (1994) MAVRIC’s brain. In: Proceedings of the seventh international conference on industrial and engineering applications of artificial intelligence and expert systems, Association for Computing Machinery, 31 May to 3 June 1994, Austin, Texas. pp 315–320. http://faculty.washington.edu/gmobus/Mavric/MAVRICS_Brain_rev.html
  17. LeDoux J (2002) Synaptic self: how our brains become who we are. Viking, New YorkGoogle Scholar
  18. *Rumelhart DE et al (eds) (1986) Parallel distributed processing: explorations in the microstructure of cognition, vols. 1 and 2, The MIT, CambridgeGoogle Scholar
  19. Sawyer RK (2005) Social emergence: societies as complex systems. Cambridge University Press, New YorkGoogle Scholar
  20. *Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. The MIT, CambridgeGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • George E. Mobus
    • 1
  • Michael C. Kalton
    • 2
  1. 1.Faculty in Computer Science & Systems, Computer Engineering & Systems Institute of TechnologyUniversity of Washington TacomaTacomaUSA
  2. 2.Faculty in Interdisciplinary Arts & SciencesUniversity of Washington TacomaTacomaUSA

Personalised recommendations