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In this PhD programme, we focus on the categorization of art museum collections using 'big data'. In particular, we study artistic and economic success in the art market by taking a quantitative meta-historical approach, which involves analyzing a novel, multimodal dataset consisting of more than 2 million images from museum-centric collections. Using categorization as our theoretical lens, combined with advanced machine learning techniques, we are the rst to study the effects of explicit and implicit art categories on dynamic mechanisms governing art appreciation, canonization and sales at art auctions. Digitally analyzing transformations in art categories, which have unfolded over time and given provenance to Western cultural heritage, enables us to systematically advance a fine-grained understanding of the art canon and the (current) economic drivers of the art market.

This PhD program, is a joint appointment of the sections Entrepreneurship & Innovation, and Operations Management of the ABS, and it is under the supervision of dr. Monika Kackovic, dr. Stevan Rudinac, prof. dr. Nachoem Wijnberg and prof. dr. Marcel Worring.