Adaptive Designs for Multiresolution, Multiperspective Modeling (MRMPM)

  • P. K. Davis


This paper describes and illustrates certain principles for designing adaptive multi-resolution, multi-perspective models (MRMPM). It also demonstrates that modern interactive visual-modeling environments can be key enablers of MRMPM. The benefits are not just for the original model builder, but also for collaborators and subsequent users, who will typically need to adapt the model to their own special circumstances. The design-it-right-the-first-time ideal is a false god. The final purpose is to identify challenges for research on modeling and analysis environments.


Adaptive Design Daily Productivity Model Abstraction Subsequent User Equivalent Programmer 
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.


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  1. [1]
    Axtell, Robert Lee, Theory of Model Aggregation for Dynamical Systems with Applications to Problems of Global Change, Dissertation, Carnegie-Mellon University, UMI Dissertation Services, Ann Arbor, MI, 1992.Google Scholar
  2. [2]
    Davis, Bigelow, and McEver, Jimmie, Effects of Terrain, Maneuver, Tactics, and C4ISR on the Effectiveness of Long Range Precision Fires: a Stochastic Multiresolution Model (PEM) Calibrated to High-Resolution Simulation, RAND, Santa Monica, CA, 2000.Google Scholar
  3. [3]
    Davis, Paul K. and Bigelow, James, Experiments in Multiresolution Modeling, RAND, Santa Monica, CA, 1998.Google Scholar
  4. [4]
    Davis, Paul K. and Hillestad, Richard Families of Models that Cross Levels of Resolution: Issues for Design, Calibration, and Management, Proceedings, 1993 Winter Simulation Conference, 1993.Google Scholar
  5. [5]
    Davis, Paul K. and Hillestad, Richard, Exploratory Analysis for Strategy Problems with Massive Uncertainty, RAND, Santa Monica, CA, 2000.Google Scholar
  6. [6]
    Davis, Paul K. and Reiner Huber, Variable Resolution Modeling: Issues, Principles, and Challenges, RAND, N-3500-DARPA, Santa Monica, CA, 1992.Google Scholar
  7. [7]
    Davis, Paul K., An Introduction to Variable-Resolution Modeling and Cross-Resolution Model Connection, RAND, Santa Monica, CA, 1993. Also, J. of Naval Logistics, 42. No.2, 1995.Google Scholar
  8. [8]
    Davis, Paul K., Bigelow, James and McEver, Jimmie, Analytical Methods for Studies and Experiments on Transforming the Force, RAND, Santa Monica, CA, 1999.Google Scholar
  9. [9]
    Fishwick, Paul and Lee, Kangsung, “Dynamic Model Abstraction,” Proceedings of the 1996 Winter Simulation Conference, pp 764–771, San Diego, CA, 1996.Google Scholar
  10. [10]
    Hillestad, Richard and Juncosa, Mario, Cutting Some Trees to Save the Forest: on Aggregation and Disaggregation in Combat Models, RAND, Santa Monica, CA, 1993.Google Scholar
  11. [11]
    McEver, Jimmie, Davis, Paul K., and Bigelow, James, EXHALT: an Interdiction Model for the Halt Phase of Armored Invasions, RAND, Santa Monica, CA, 2000.Google Scholar
  12. [12]
    Morgan, M. Granger and Henrion, Max, Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis, Cambridge University Press, Cambridge, 1990; reprinted in 1998.Google Scholar
  13. [13]
    National Research Council, Modeling and Simulation, Volume 9 of Tactics and Technology for the United States Navy and Marine Corps: 2000–2035, National Academy Press, Washington, 1997.Google Scholar
  14. [14]
    Popken, Doublas, “Application of System Identification Techniques to Simulation Model Abstraction,” a paper presented at the April, 1999 SPIE conference, Proceedings of SPIE, Enabling Technology for Simulation Science III. Volume 3696. 1999.Google Scholar
  15. [15]
    Reynolds, P., Natrajan, N. and Srinivasan, S., “Consistency Maintenance in Multiresolution Simulation, ” ACM Transactions in Modeling and Computer Simulation, Vol. 7, No. 3, 368, 1997.zbMATHCrossRefGoogle Scholar
  16. [16]
    Zeigler, Bernard P., “A Framework for Modeling and Simulation,” Ch. 3 of David J. Cloud and Larry B. Rainey (editors), Applied Modeling and Simulation: An Integrated Approach to Development and Operation, McGraw Hill, New York, 1998.Google Scholar
  17. [17]
    Zeigler, Bernard P., “DEVS Representation of Dynamical Systems: Event-Based Intelligent Control,” Proceedings of IEEE, Vol. 77, No. 1, 1989, pp. 72 ff.CrossRefGoogle Scholar
  18. [18]
    Zeigler, Bernard P., Multifaceted Modelling and Discrete Event Simulation, Academic Press, New York, 1984.Google Scholar
  19. [19]
    Zeigler, Bernard P., Theory of Modeling and Simulation, Wiley, New York, 1976.Google Scholar
  20. [19a]
    The second edition is Zeigler, Bernard P., Kim, Tag Gon, and Praehofer, Herbert, Theory of Modeling and Design, Academic Press, 2000.Google Scholar
  21. [20]
    Zeigler, Bernard P., Object-Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and Endomorphic Systems, Academic Press, San Diego, 1990.zbMATHGoogle Scholar

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© Springer Science+Business Media New York 2001

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  • P. K. Davis

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