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Imposing unsupervised constraints to the Benefit-of-the-Doubt (BoD) model

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  • Milica Maricic

    (University of Belgrade)

  • Veljko Jeremic

    (University of Belgrade)

Abstract

Policymakers are in growing need of metrics which will assist them in ranking and assessing entities on different topics. Composite indicators, aggregated individual indicators, have become a valuable metric to do so. One of the approaches used in the process of their creation is the benefit-of-the-doubt (BoD) model. To overcome the observed issue of full freedom of the BoD model, herein we propose the application of a novel unsupervised approach to imposing constraints: the bootstrap I-distance; a data-driven statistical method used to obtain weight intervals. The proposed variant of the BoD model is named Bootstrap I-distance benefit-of-the-doubt (B-ID-BoD). To verify the B-ID-BoD model, we employed it on the ease of doing business index (EDBI) issued by the World Bank. The obtained results indicate that the model can be solved, that all the imposed constraints have been adhered to, and that the official EDBI weighting scheme does not need alteration. The proposed approach can initiate further research on data-driven approaches to constraining the BoD model and further applications of the bootstrap I-distance.

Suggested Citation

  • Milica Maricic & Veljko Jeremic, 2023. "Imposing unsupervised constraints to the Benefit-of-the-Doubt (BoD) model," METRON, Springer;Sapienza Università di Roma, vol. 81(3), pages 259-296, December.
    • Handle: RePEc:spr:metron:v:81:y:2023:i:3:d:10.1007_s40300-023-00254-3
    DOI: 10.1007/s40300-023-00254-3
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