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The problem here is that I don’t trust the author on being able to tell who is the “best artist”. Clearly he has opinions. But for example in point 3, he says he can predict which exhibition will be great based on how easy it is to work with the artist. He predicts that some exhibition will be crap and he is right! Which sounds impressive until you notice that he is not measuring his judgement against something objective, but just against his judgement. He decides something will be crap and then he feels crap about it once he sees it. Did others, who did not know that the artist was slow to email back also feel that those exhibitions were mediocre and the others not? Who knows? All we have is this one man’s opinion. Maybe others thought differently. Even more so in his point 6. He writes “What you see in the biographies of great artists, great writers, great anything is that they are good at figuring out where the vectors align.” Which is just plainly and absolutely not true. There were plenty of people who we now recognise as “great artist” who absolutely could not figure out where the “incentive vectors align”. Thus they lived in abject poverty, or needed to support themselves from something other than their art. But if your definition of “great art” is that it is commercially succesfull then of course what you will find that the “great artist” are all like good businesman. But that doesn’t tell you about what it take to be an artist, just only what you value. |
Albert-László Barabás, a physicist, created a network map that can predict an artist's future success based on their early network connections. His work outlines two key "laws of success":
- Performance drives success, but when performance can’t be measured, networks drive success. This highlights the importance of networks when objective measures of quality are difficult to establish.
- Performance is bounded, but success is unbounded. This indicates that small differences in quality can lead to large disparities in success due to the amplifying power of social networks
Barabási's model can predict an artist's career success with surprising accuracy based on the venues of their first five exhibitions. This model underscores the importance of early connections and the venues where an artist exhibits their work, which can significantly influence their long-term success4.