scholarly journals Introduction to special issue: “The potential contribution of computational modeling to the study of cognitive development: When, and for what topics?”

2012 ◽  
Vol 27 (4) ◽  
pp. 323-325 ◽  
Author(s):  
Stuart Marcovitch ◽  
Philip David Zelazo
Keyword(s):  
Cognitive Development ◽  
Computational Modeling ◽  
Potential Contribution ◽  
Special Issue

scholarly journals Preface for the Special Issue on Dynamical Models of Biology and Medicine

2019 ◽  
Vol 9 (11) ◽  
pp. 2380
Author(s):  
Yang Kuang ◽  
Meng Fan ◽  
Shengqiang Liu ◽  
Wanbiao Ma
Keyword(s):  
Computational Modeling ◽  
Medical Research ◽  
Rapid Growth ◽  
Special Issue ◽  
Dynamical Models ◽  
Modeling Approaches ◽  
Mathematical And Computational Modeling

Mathematical and computational modeling approaches in biological and medical research are experiencing rapid growth globally [...]

Representing qualitative social science in computational models to aid reasoning under uncertainty: National security examples

2016 ◽  
Vol 14 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Paul K Davis ◽  
Angela O’Mahony
Keyword(s):  
Social Science ◽  
Computational Modeling ◽  
National Security ◽  
Computational Models ◽  
Empirical Work ◽  
Empirical Testing ◽  
Special Issue ◽  
Reasoning Under Uncertainty ◽  
Social Science Knowledge ◽  
Qualitative Models

Representing causal social science knowledge in models is difficult: much of the best knowledge is qualitative and ambiguously conditional, unlike the knowledge in “physics models.” This paper describes a stream of RAND research that began with qualitative models providing a structured depiction of casual factors creating effects. That has subsequently been extended to an unusual kind of uncertainty sensitive computational modeling that enables exploratory reasoning and analysis. We illustrate the approach with applications to counterterrorism, detection of terrorists, and nuclear crises. We believe that the approach will complement other approaches that can reflect social science phenomena [see other papers in this special issue of JDMS] and that the approach has broad potential within and beyond the national security domain. We also believe that it has the potential to inform empirical work—encouraging a transition from the step-by-step empirical testing of simple discrete hypotheses to the testing and refinement of more comprehensive causal models.

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