The potential of benchmark challenges in the social sciences

Datos Bibliográficos

ID3079735
AutoresPaulina Pankowska (0000-0001-6226-6814, Utrecht University, the Netherlands, autor de correspondencia), Adrienne Mendrik (0000-0001-6631-7068, Eyra, The Netherlands), Tepora Emery (0000-0001-6137-9577, ODISSEI & Erasmus University Rotterdam, The Netherlands), Javier Garcia-Bernardo (Utrecht University, the Netherlands), Javier Garcia‐bernardo (0000-0002-6119-1790, Utrecht University)
Año2024
Volumen63
Número4
Páginas498-519
Fecha de publicación2024-12-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSocial Science Information (JOURNAL)
Identificadores de la revistaISSN: 0539-0184 • E-ISSN: 1461-7412
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/05390184241297742
OpenAlexW4404859489
IdiomaEN
Citas recibidas2
Referencias citadas31

Social scientists aim to create explanations of the world. For each social phenomenon, scientists have proposed a myriad of theories to explain its working mechanisms. Traditionally, these theories are tested by generating hypotheses, translating them into a statistical model, and assessing the significance of the model's coefficients. Such an approach, however, often leads to the specification of a large number of (at times contradictory) models, all asserting that they capture the same theory. As things currently stand, there is no framework that allows for a comparison of these models. In this article, we argue that benchmarks can serve as a standard frame of reference that can help to determine which models fit better with empirical observations in a specific context. A benchmark is a standardized validation framework that allows for a direct comparison of the prediction accuracy of various models that address the same research problem. We outline the potential of organizing benchmark challenges in the social sciences and provide recommendations for their utilization

Data science · Empirical research · Management science · Statistics · Computer Science · Ecosystem dynamics and resilience · Innovation, Sustainability, Human-Machine Systems · Mathematics · Species Distribution and Climate Change · Artificial Intelligence