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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In the citing paper
“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
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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BackgroundNelson, J. L. (2021) · doi:10.1093/oso/9780197542590.003.0001 · Cited by 1
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BackgroundNewman, M. E. J. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198805090.003.0002 · Cited by 2
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Pop zwischen Historismus und Geschichtslosigkeit. Kanonbildungen in der populären Musik
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Multivariate Visual Diffusion for Social GroupsPreprint
Bradlow, E. T., Berger, J. A., & McShane, B. B. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1856207 · Cited by 1
Predictive Analysis for Social Diffusion: The Role of Network CommunitiesPreprint
Colbaugh, R., & Glass, K. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0912.5242 · Cited by 1
“sciences calls into question this conventional wisdom and, indeed, indicates that there may be fundamental limits to what can be predicted about social diffusion. For example, the studies reported in [1,2] indicate that the intrinsic attributes normally considered to be important when assessing the likelihood of diffu…”
BackgroundTransnational Islamic activism and radicalization : patterns, trends, and prognosticators.
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Franck, E. P., & Nüesch, S. (2006) · RePEc: Research Papers in Economics · Cited by 1
A Topic Model Approach to Multi-Modal SimilarityPreprint
Troelsgaard, R., Jensen, B. S., & Hansen, L. K. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.6886 · Cited by 1
“Here choices are based on many different information sources including the audio/sound, meta-data like genre, and social influences [1], hence, attempts of modeling the geometry of music navigation have taken on a multi-modal perspective.”
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Borghesi, C. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0910.4661 · Cited by 1
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Hentenryck, P. V., Abeliuk, A., Berbeglia, F., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.02469 · Cited by 1
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Social Influence and Competition Among Critics
Olson, D. M., & Waguespack, D. M. (2014) · Academy of Management Proceedings · doi:10.5465/ambpp.2014.15829abstract · Cited by 2
“Salganik et al. (2006) devised an online experiment to test the effect of social influence in a constructed market for music.”
BackgroundTHE DYNAMICS OF INFORMATION DIFFUSION ON ON-LINE SOCIAL NETWORKS
Wu, S. (2013) · eCommons (Cornell University) · Cited by 1
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Prawesh, S. (2013) · Digital Commons - University of South Florida (University of South Florida) · Cited by 1
Mures : Un système de recommandation de musique
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Choi, H. S., Au, Y. A., & Liu, C. Z. (2014) · RePEc: Research Papers in Economics · Cited by 1
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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