Case-based modeling and the SACS Toolkit: a mathematical outline

被引:12
作者
Castellani, Brian [1 ]
Rajaram, Rajeev [2 ]
机构
[1] Kent State Univ, Dept Sociol, Ashtabula, OH 44004 USA
[2] Kent State Univ, Dept Math Sci, Ashtabula, OH 44004 USA
关键词
Complex social systems; Case-based method; Mathematical modeling; Computational modeling; Sociological method;
D O I
10.1007/s10588-012-9114-1
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Researchers in the social sciences currently employ a variety of mathematical/computational models for studying complex systems. Despite the diversity of these models, the majority can be grouped into one of three types: agent (rule-based) modeling, dynamical (equation-based) modeling and statistical (aggregate-based) modeling. The purpose of the current paper is to offer a fourth type: case-based modeling. To do so, we review the SACS Toolkit: a new method for quantitatively modeling complex social systems, based on a case-based, computational approach to data analysis. The SACS Toolkit is comprised of three main components: a theoretical blueprint of the major components of a complex system (social complexity theory); a set of case-based instructions for modeling complex systems from the ground up (assemblage); and a recommended list of case-friendly computational modeling techniques (case-based toolset). Developed as a variation on Byrne (in Sage Handbook of Case-Based Methods, pp. 260-268, 2009), the SACS Toolkit models a complex system as a set of k-dimensional vectors (cases), which it compares and contrasts, and then condenses and clusters to create a low-dimensional model (map) of a complex system's structure and dynamics over time/space. The assembled nature of the SACS Toolkit is its primary strength. While grounded in a defined mathematical framework, the SACS Toolkit is methodologically open-ended and therefore adaptable and amenable, allowing researchers to employ and bring together a wide variety of modeling techniques. Researchers can even develop and modify the SACS Toolkit for their own purposes. The other strength of the SACS Toolkit, which makes it a very effective technique for modeling large databases, is its ability to compress data matrices while preserving the most important aspects of a complex system's structure and dynamics across time/space. To date, while the SACS Toolkit has been used to study several topics, a mathematical outline of its case-based approach to quantitative analysis (along with a case study) has yet to be written-hence the purpose of the current paper.
引用
收藏
页码:153 / 174
页数:22
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