Nonlinear Modeling: Lessons Learned and Room for Improvement in the M&S Literature
The field of Modeling and Simulation (M&S) has been growing at a rapid pace in the last few decades, particularly since the integration of growing computational capabilities, and the incorporation of more and more researchers coming from both computational as well as more traditional social, physical and biological sciences. At the same time, the ideas encapsulated in the growing paradigm of nonlinearity and Complexity have been gaining traction and have become increasingly accepted in the Literatures of all fields of study. In this sense, the rise of complexity science and of computer simulations have happened simultaneously because they have co-evolved and found inspiration from each other. This essay attempts to serve as a type of mirror in which both Complexity and Simulation can look at each other, understand each other, and compare notes. Nonetheless, the point of the article is that this is not yet a perfect mirror, and that the state-of-the-art theoretical Literature on complexity and computer simulations still do not yet see eye to eye. There are certainly lessons to be learned from the simulation side of the equation that have not been fully grasped and generalized. This work, however, focuses on the other side of the coin: On the theoretical insights that have not been appropriately modeled as of now, and about ways in which we can move the M&S field in that direction, with current and potential real-world applications of this increased rapport between theory and application.
Mauricio A. Sanchez, Leocundo Aguilar, Manuel Castañón-Puga, & Antonio Rodríguez
Dante Suarez, E. (2019). Nonlinear modeling: Lessons learned and room for improvement in the M&S literature. In M.A. Sanchez, L. Aguilar, M. Castañón-Puga, & A. Rodríguez (Eds.), Applied Decision Making: Applications in Computer Sciences and Engineering (199-215). Cham, Switzerland: Springer.
Applied Decision Making: Applications in Computer Science and Engineering