The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
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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“Rosen's economics of superstars [21] shows formally that when the cost of serving additional consumers approaches zero, cultural markets concentrate rewards on a small number of winners regardless of marginal quality differences”
From AI as a Democratizing Force in Indie Game Development (2026) · cites this paper as [21]
In the citing paper
“The market-structure literature of §2.4 predicts what follows: superstar concentration under near-zero marginal cost [21], against which the long-tail alternative [22] is directly testable with the outcome data of Table 13, and choice-abundant markets whose winners are selected by social dynamics rather than quality gradients [23].”
From AI as a Democratizing Force in Indie Game Development (2026) · cites this paper as [21]
In the citing paper
“When considering inequality in heterogeneous populations, there are two distinct aspects to take into account-the dispersion of outcomes and the predictability of outcomes (Lynn, Podolny, & Tao, 2009;Salganik, Dodds, & Watts, 2006).”
From Relative Feedback Increases Disparities in Effort and Performance in Crowdsourcing Contests: Evidence from a Quasi-Experiment on Topcoder (2022) · cites this paper as Salganik, Dodds, & Watts, 2006
In the citing paper
“In the case of an inherent quality or virtue, more predictable outcomes imply more equality and fairness (Cook & Hegtvedt, 1983;Jasso, 1983;Salganik et al., 2006).”
From Relative Feedback Increases Disparities in Effort and Performance in Crowdsourcing Contests: Evidence from a Quasi-Experiment on Topcoder (2022) · cites this paper as Salganik et al., 2006
In the citing paper
“However, social success and quality of content are only partly correlated (Salganik et al., 2006).”
From Social Drivers and Algorithmic Mechanisms on Digital Media. (2024) · cites this paper as Salganik et al., 2006
In the citing paper
“in a classic experiment, the popularity of all but the very best and worst songs was shown to be more related to stochastic early positive reception by other users than by their inherent quality”
From Stewardship of global collective behavior (2021) · cites this paper as [87]
In the citing paper
“Moreover, the presence of a significant number of neutral individuals holding a random opinion and a low confidence level around these two attractive forces tends to increase the unpredictability of the final outcome [15].”
From Social Influence and the Collective Dynamics of Opinion Formation (2013) · cites this paper as [15]
In the citing paper
“In agreement with previous works [15], the collective outcome appears to be poorly predictable and strongly dependent on the initial conditions [36].”
From Social Influence and the Collective Dynamics of Opinion Formation (2013) · cites this paper as [15]
In the citing paper
“Although there is on average a positive relationship between quality and success [66]”
From Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2016) · cites this paper as [66]
In the citing paper
“the ultimate winners in such contests are determined not only by the intrinsic value of the work but also by more intangible social variables [66].”
From Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level (2016) · cites this paper as [66]
Citations
Cites
Riggins, F. J., & Mitra, S. (1990) · Choice Reviews Online · doi:10.5860/choice.27-6637 · Cited by 16,590
Social Influence: Compliance and Conformity
Cialdini, R. B., & Goldstein, N. J. (2004) · Annual Review of Psychology · doi:10.1146/annurev.psych.55.090902.142015 · Cited by 6,077
A simple model of global cascades on random networks
Watts, D. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082090499 · Cited by 2,673
Self-citationA Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades
Bikhchandani, S., Hirshleifer, D. A., & Welch, I. (1992) · Journal of Political Economy · doi:10.1086/261849 · Cited by 1,980
(1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511663901 · Cited by 1,774
(2009) · Harvard University Press eBooks · doi:10.2307/j.ctv1kz4h99.12 · Cited by 1,685
Culture and conformity: A meta-analysis of studies using Asch's (1952b, 1956) line judgment task.
Bond, R. A., & Smith, P. B. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.1.111 · Cited by 1,466
Processing Fads and Fashions: An Organization-Set Analysis of Cultural Industry Systems
Hirsch, P. M. (1972) · American Journal of Sociology · doi:10.1086/225192 · Cited by 1,331
Walker, D. M., Frank, R. H., & Cook, P. J. (1996) · Southern Economic Journal · doi:10.2307/1061199 · Cited by 1,180
Allison, P. D. (1978) · American Sociological Review · doi:10.2307/2094626 · Cited by 1,133
Flagged first, then by citations
1 of the 21 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- R. E. Caves Creative Industries (Harvard Univ. Press Cambridge MA 2000).
- S. Rosen, Am. Econ. Rev.71, 845 (1981).
- M. Adler, Am. Econ. Rev.75, 208 (1985).
- L. R. Anderson, C. A. Holt, Am. Econ. Rev.87, 847 (1997).
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Piper, A., & Toubia, O. (2023) · Poetics · doi:10.1016/j.poetic.2023.101793 · Cited by 20
Dahlander, L., Beretta, M., Thomas, A., Kazemi, S., Fenger, M. H. J., & Frederiksen, L. (2023) · Research Policy · doi:10.1016/j.respol.2023.104875 · Cited by 17
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Seufitelli, D. B., Oliveira, G. P., Silva, M. O., Scofield, C., & Moro, M. M. (2023) · Journal of New Music Research · doi:10.1080/09298215.2023.2282999 · Cited by 22
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Reader, S. M., Bruce, M. J., & Rebers, S. (2007) · Biology Letters · doi:10.1098/rsbl.2007.0544 · Cited by 17
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Livan, G. (2019) · Royal Society Open Science · doi:10.1098/rsos.191255 · Cited by 20
“[15] for a seminal example in an artificial cultural market).”
BackgroundAuthenticity: Meanings, targets, audiences and third parties
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Wan, Y., Kumar, V., & Bukhari, A. (2008) · IEEE Internet Computing · doi:10.1109/mic.2008.70 · Cited by 15
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Lee, Y., Kim, S., & Cha, K. C. (2012) · Journal of Global Scholars of Marketing Science · doi:10.1080/21639159.2012.721214 · Cited by 14
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“We now have broad and compelling illustrations of how the presence or absence of followers can independently shape an object or actor’s likelihood of future success (Muchnik et al. 2013; van de Rijt et al. 2014; Salganik et al. 2006).”
BackgroundInfluentialIdentity sequences and the early adoption pattern of a jazz canon, 1920–1929
Kahl, S., Kim, Y.-K., & Phillips, D. J. (2010) · Research in the sociology of organizations · doi:10.1108/s0733-558x(2010)0000031005 · Cited by 15
Uncovering and Predicting the Dynamic Process of Collective Attention with Survival Theory
Bao, P., & Zhang, X. (2017) · Scientific Reports · doi:10.1038/s41598-017-02826-6 · Cited by 16
Popularity signals in trial-offer markets with social influence and position bias
Maldonado, F., Hentenryck, P. V., Berbeglia, G., & Berbeglia, F. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2017.10.056 · Cited by 14
Sadness and happiness are amplified in solitary listening to music
Zhang, J., Yang, T., Bao, Y., Li, H., Pöppel, E., & Silveira, S. (2017) · Cognitive Processing · doi:10.1007/s10339-017-0832-7 · Cited by 15
“…participants, i.e. any form of social feedback that might influence the evaluation of stimuli, nor were they encouraged to think and feel about one another, which may increase the feeling of togetherness and render the experienced stimulus more intense (Egermann et al. 2009; Salganik et al. 2006).”
BackgroundCrosby, P. (2019) · Journal of Cultural Economics · doi:10.1007/s10824-019-09363-2 · Cited by 17
Díaz, N. B., Taño, D. G., & Armas, R. J. D. (2020) · Current Issues in Tourism · doi:10.1080/13683500.2020.1777949 · Cited by 16
“This finding is coherent with previous studies in which the role of this factor is shown within the scope of online reviews (Askalidis et al., 2017; Muchnik et al., 2013; Salganik et al., 2006).”
BackgroundResultLi, H., & Sakamoto, Y. (2015) · 2015 48th Hawaii International Conference on System Sciences · doi:10.1109/hicss.2015.240 · Cited by 22
“Another online experiment showed that the number of times a piece of music was downloaded in the past could predict its future popularity when the number of downloads was available to the users [27].”
BackgroundAudience structure and status multiplicity
Kovács, B., & Liu, C. (2015) · Social Networks · doi:10.1016/j.socnet.2015.07.008 · Cited by 18
The Visible Hand? Demand Effects of Recommendation Networks in Electronic Markets (Extended Version)Preprint
Oestreicher-Singer, G., & Sundararajan, A. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1268516 · Cited by 15
The spread of misinformation by social botsPreprint
Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017) · arXiv (Cornell University) · Cited by 18
The Diversity Gap: when diversity matters for knowledgePreprint
Sulik, J., Bahrami, B., & Deroy, O. D. (2021) · doi:10.31234/osf.io/hx7ze · Cited by 15
Generative Models for the Psychology of Art and Aesthetics
Hertzmann, A. (2024) · Empirical Studies of the Arts · doi:10.1177/02762374241288696 · Cited by 19
“Salganik et al. (2006) showed that popularity of songs in a controlled social network depended, in part, on network effects.”
The science fiction science method
Rahwan, I., Shariff, A., & Bonnefon, J. (2025) · Nature · doi:10.1038/s41586-025-09194-6 · Cited by 25
“One way to move beyond vignettes and to improve the consistency and verisimilitude of the simulated experience is to use of mock versions of futuristic apps [46, 47, 48].”
Verhaal, J. C., Hahl, O., & Fandl, K. J. (2022) · Organization Science · doi:10.1287/orsc.2022.1574 · Cited by 14
Traffic channeling under uncertain conversion rates on e‐commerce platforms
Yu, P., Zhang, Z. J., & Li, Q. (2022) · Naval Research Logistics (NRL) · doi:10.1002/nav.22079 · Cited by 14
“Consumers may interpret higher sales as a signal of higher quality, and so they are more likely to buy products with higher early sales (Salganik et al., 2006).”
Fang, J., Liu, L., Hossin, M. A., Wen, C., & Xia, G. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103329 · Cited by 14
Gligorić, K., Lifchits, G., West, R., & Anderson, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281682 · Cited by 13
Liu, Y., Dong, F.-N., Li, G., Pan, Y., Qin, C., Yang, S., & Li, J.-Y. (2023) · Energy · doi:10.1016/j.energy.2023.128448 · Cited by 14
The Multi-Scale Network Landscape of Collaboration
Bae, A., Park, D., Ahn, Y., & Park, J. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151784 · Cited by 14
Liao, Q. V., Fu, W., & Strohmaier, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858422 · Cited by 14
Metadata
- Authors
- Salganik, M. J., Dodds, P. S., & Watts, D. J.
- Affiliations
- Santa Fe Institute (US)
- Corresponding author
- Salganik, Matthew J.
- Dodds, Peter Sheridan
- Watts, Duncan J.
- Venue
- Science
- Published
- 2006-02-10
- Volume
- 311
- Issue
- 5762
- Pages
- 854-856
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2147453867
- Cited by
- 2,231
- References
- 21
- Citations by year
- 2026: 168 · 2025: 104 · 2024: 102 · 2023: 123 · 2022: 103 · 2021: 231 · 2020: 104 · 2019: 130 · 2018: 131 · 2017: 142 · 2016: 121 · 2015: 123 · 2014: 133 · 2013: 92 · 2012: 122
- FWCI
- 116.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Experimental Behavioral Economics Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- cultural market, social influence, music download, product success, consumer choice
- MeSH terms
- Consumer Behavior, Culture, Forecasting, Humans, Interpersonal Relations, Music, Research Design, Social Behavior, Sociology, Internet
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