Abstract
This paper develops an automated algorithm to process input data for segmented string relative rankings (SSRRs). The purpose of the SSRR methodology is to create rankings of countries, companies, or any other units based on surveys of expert opinion. This is done without the use of grading systems, which can distort the results due to varying degrees of strictness among experts. However, the original SSRR approach relies on manual application, which is highly laborious and also carries a risk of human error. This paper seeks to solve this problem by further developing the SSRR approach by employing link analysis, which is based on network theory and is similar to the PageRank algorithm used by the Google search engine. The ranking data are treated as part of a linear, hierarchical network and each unit receives a score according to how many units are positioned below it in the network. This approach makes it possible to efficiently resolve contradictions among experts providing input for a ranking. A hypertext preprocessor (PHP) script for the algorithm is included in the article’s appendix. The proposed methodology is suitable for use across a range of social science disciplines, especially economics, sociology, and political science.
Highlights
Expert opinion can be useful for the creation of rankings when more objective data are not available [1]
segmented string relative ranking (SSRR) approach by employing link analysis, which is based on network theory and is similar to the PageRank algorithm used by the Google search engine
We propose a novel ranking algorithm based on network theory and link analysis
Summary
Expert opinion can be useful for the creation of rankings when more objective data are not available [1]. Algorithms 2019, 12, 19 experts—that is to say, professors grading student papers This helps even out some differences among professors, but it is still up to each professor to define what counts as good organization, superior grasp of the subject matter, etc. Average performance: understanding of the subject matter; ability to develop solutions to simple problems in the material; acceptable but uninspired work, not seriously faulty but lacking style and vigor; meeting the basic requirements of preparedness and regular attendance; rare participation in class discussion. The existing literature includes several techniques for addressing the weaknesses of the traditional way of dealing with criteria-based input e.g., [12,13]. There is a gap in the literature when it comes to increasing the efficiency of SSRRs, and this article seeks to fill that gap by developing an automated algorithm so that SSRRs can be produced using computers
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