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Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market

Matthew J. Salganik, Peter Sheridan Dodds, Duncan J. Watts

2006 · Science

Economics
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From the abstract

Hit songs, books, and movies are many times more successful than average, suggesting that “the best” alternatives are qualitatively different from “the rest”; yet experts routinely fail to predict which products will succeed. We investigated this paradox experimentally, by creating an artificial “music market” in which 14,341 participants downloaded previously unknown songs either with or without knowledge of previous participants' choices. Increasing the strength of social influence increased both inequality and unpredictability of success. Success was also only partly determined by quality: The best songs rarely did poorly, and the worst rarely did well, but any other result was possible.

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3 selected excerpts · 3 total mentions

pms
Neutral mention · rule
Having less inequality does not contradict meritocracy. Studies show that a fair society does not spread welfare equally [1]: a fair society still rewards merit, work, and contributions, but not to the exaggerated extent as we do nowadays most likely due to the herding and random effect of advertisements [2]. The U.S. and California lead the pack. Hopefully we will learn and adjust, but most likely it's going to be a very slow and unfortunately painful process... [1] Starmans, C., Sheskin, M., & Bloom, P. (2017). Why people prefer unequal societies. Nature Human Behaviour, 1(4), 0082. https://doi.org/10.1038/s41562-017-0082 [2] Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science (New York, N.Y.), 311(5762), 854–856. https://doi.org/10.1126/science.1121066
Original comment Bob Lee, Former CTO Square, Creator of CashApp, Stabbed to Death in SF
glifchits
Neutral mention · rule
Did I miss something or does this project include popularity measures as features? In the section on dataset features, they include "popularity" (calculated by Spotify) as well as Billboard chart stats like weeks, rank, and a custom-made "score". To me it's not clear whether these features were hidden from the train/test sets or whether the popularity features were only used in their "artist past performance" measures. If they included these popularity features, it's like asking "can we predict whether a song is a hit just by looking at how popular it is?" If it is the case that they peeked into the future and observed ex-post song popularity, obtaining just 89% accuracy…
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hints at how unpredictable song success truly is. Check out [1] for a famous study of song success which experimentally demonstrates the unpredictability of song success. [1] Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market. Science, 311(5762), 854–856. https://doi.org/10.1126/science.1121066
Original comment Using Spotify data to predict what songs will be hits

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