Experimental study of inequality and unpredictability in an artificial cultural market
Salganik, with Peter Dodds and Duncan Watts, built an artificial music market and published it in Science. In it 14,341 participants listened to and downloaded songs by bands they had never heard of. Some could see how many times each song had already been downloaded by earlier participants and some could not, and that was the whole manipulation: the same songs, the same interface, with or without a visible record of other people's choices. The market was then run as parallel worlds, the same songs and the same choice put to different populations over and over. Increasing the strength of social influence increased two things, the inequality between the hits and the rest, and the unpredictability of which songs became the hits from one world to the next. The paper's own statement of the limit is exact and is the reason it is worth citing: success was only partly determined by quality, the best songs rarely did poorly and the worst rarely did well, but any other outcome was possible. On standing this is a large and unusually clean experiment, published in a leading journal and much replicated in design by later work. The bounds matter. These were unknown songs in a laboratory market, the outcome measured was downloads rather than purchases or repeat listening, and it was run in 2006, before streaming recommendation existed.
Salganik, with Peter Dodds and Duncan Watts, built an artificial music market and published it in Science. In it 14,341 participants listened to and downloaded songs by bands they had never heard of. Some could see how many times each song had already been downloaded by earlier participants and some could not, and that was the whole manipulation: the same songs, the same interface, with or without a visible record of other people's choices. The market was then run as parallel worlds, the same songs and the same choice put to different populations over and over. Increasing the strength of social influence increased two things, the inequality between the hits and the rest, and the unpredictability of which songs became the hits from one world to the next. The paper's own statement of the limit is exact and is the reason it is worth citing: success was only partly determined by quality, the best songs rarely did poorly and the worst rarely did well, but any other outcome was possible. On standing this is a large and unusually clean experiment, published in a leading journal and much replicated in design by later work. The bounds matter. These were unknown songs in a laboratory market, the outcome measured was downloads rather than purchases or repeat listening, and it was run in 2006, before streaming recommendation existed.
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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 only partly determined by quality, with the best songs rarely doing poorly and the worst rarely doing well, but any other result possible. Verified at the published record 2026-10-05. doi:10.1126/science.1121066
Where we use it
- Why the Best Song Does Not Win, Why We Look
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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. 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 only partly determined by quality, with the best songs rarely doing poorly and the worst rarely doing well, but any other result possible. Verified at the published record 2026-10-05. doi:10.1126/science.1121066