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Cognitive model exploration and optimization: a new challenge for computational science

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  • L. Richard Moore

    (Warfighter Readiness Research Division)

Abstract

Parameter space exploration is a common problem tackled on large-scale computational resources. The most common technique, a full combinatorial mesh, is robust but scales poorly to the computational demands of complex models with higher dimensional spaces. Such models are routinely found in the modeling and simulation community. To alleviate the computational requirements, I have implemented two parallelized intelligent search and exploration algorithms: one based on adaptive mesh refinement and the other on regression trees. These algorithms were chosen because there is a dual interest in approaches that allow searching a parameter space for optimal values, as well as exploring the overall space in general. Both intelligent algorithms reduce computational costs at some expense to the quality of results, yet the regression tree approach was orders of magnitude faster than the other methodologies.

Suggested Citation

  • L. Richard Moore, 2011. "Cognitive model exploration and optimization: a new challenge for computational science," Computational and Mathematical Organization Theory, Springer, vol. 17(3), pages 296-313, September.
    • Handle: RePEc:spr:comaot:v:17:y:2011:i:3:d:10.1007_s10588-011-9092-8
    DOI: 10.1007/s10588-011-9092-8
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    Cited by:

    1. Frank E. Ritter & Farnaz Tehranchi & Christopher L. Dancy & Sue E. Kase, 2020. "Some futures for cognitive modeling and architectures: design patterns for including better interaction with the world, moderators, and improved model to data fits (and so can you)," Computational and Mathematical Organization Theory, Springer, vol. 26(3), pages 278-306, September.
    2. William G. Kennedy & Frank E. Ritter & Bradley J. Best, 2011. "Behavioral representation in modeling and simulation introduction to CMOT special issue—BRiMS 2010," Computational and Mathematical Organization Theory, Springer, vol. 17(3), pages 225-228, September.
    3. Frank E. Ritter & William G. Kennedy & Bradley J. Best, 2013. "The best papers from BRIMS 2011: models of users and teams interacting," Computational and Mathematical Organization Theory, Springer, vol. 19(3), pages 283-287, September.

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