Predicting Artistic Success from Early Exhibits

Success in the art world can mean different things to different artists. While some artists work solely for the pleasure of producing art, others seek external recognition, such as being shown in prestigious galleries or museums, and selling their craft. The latter — profitability, recognition, demand — is how success is traditionally defined in the field. But out of all the emerging artists across the world, only a select few will make it to international recognition in their careers. Network physicist Albert-László Barabási believes he can predict who it’s going to be. And he doesn’t even need to look at the artist’s artwork. While talent is essential for an artist's success, understanding the networks in which their work is embedded is perhaps even more important. Access to these networks is determined by complex dependencies, with gatekeepers, such as institutions and galleries, playing a crucial role in an artist's access to the market. Through mapping out these networks, Barabási has been able to predict artistic success with impressive accuracy. With an acute understanding of the various institutions and galleries that routinely lead to the center of the network, an artist can increase their chances of success and longevity in the art world. 0:00 The key measures of success in art 0:48 Whose job is it to discover artists? 1:16 Mapping the value of art through network science 2:53 “Incredibly accurate” predictions 4:47 Talent matters 5:22 The challenge for young artists ---------------------------------------------------------------------------------- About Albert-László Barabási: Albert-László Barabási is a network scientist, fascinated with a wide range of topics, from unveiling the structure of the brain and treating diseases using network medicine to the emergence of success in art and how science really works. His research has helped unveil the hidden order behind various complex systems using the quantitative tools of network science, a research field that he pioneered, and has led to the discovery of scale-free networks, helping explain the emergence of many natural, technological, and social networks. Barabási is a Fellow of the American Physical Society. He is the author of The Formula (Little Brown), Network Science (Cambridge), Bursts (Dutton), and Linked (Penguin). He co-edited Network Medicine (Harvard, 2017) and The Structure and Dynamics of Networks (Princeton, 2005). His books have been translated into over twenty languages.

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