Version 1
: Received: 1 February 2023 / Approved: 2 February 2023 / Online: 2 February 2023 (01:32:18 CET)
Version 2
: Received: 17 March 2023 / Approved: 20 March 2023 / Online: 20 March 2023 (02:47:28 CET)
Thompson, J.S.; Hodson, D.D.; Grimaila, M.R.; Hanlon, N.; Dill, R. Toward a Simulation Model Complexity Measure. Information2023, 14, 202.
Thompson, J.S.; Hodson, D.D.; Grimaila, M.R.; Hanlon, N.; Dill, R. Toward a Simulation Model Complexity Measure. Information 2023, 14, 202.
Thompson, J.S.; Hodson, D.D.; Grimaila, M.R.; Hanlon, N.; Dill, R. Toward a Simulation Model Complexity Measure. Information2023, 14, 202.
Thompson, J.S.; Hodson, D.D.; Grimaila, M.R.; Hanlon, N.; Dill, R. Toward a Simulation Model Complexity Measure. Information 2023, 14, 202.
Abstract
Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. Key terms of model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes in model detail. Additional research in this area can advance the modeling and simulation body of knowledge toward the practical application of measuring simulation model complexity. Examples show that KC and NCD measurements of simulation models can detect changes in scope and detail.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.