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]
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Choi, H. S., Ko, M. S., Medlin, D., & Chen, C. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.12.005 · Cited by 94
Quantifying Biases in Online Information Exposure
Nikolov, D., Lalmas, M., Flammini, A., & Menczer, F. (2019) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24121 · Cited by 96
The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books
Xu, H., Zhang, Z., Wu, L., & Wang, C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0225385 · Cited by 114
Direct and Indirect Spillovers from Content Providers’ Switching: Evidence from Online Livestreaming
Zhao, K., Lu, Y., Hu, Y., & Hong, Y. (2019) · Information Systems Research · doi:10.1287/isre.2022.1160 · Cited by 92
“The indirect network effect is observed in previous literature on the rich-get-richer scenario in various industry contexts, including fiction books (Sorensen 2007), music (Salganik et al. 2006), crowdfunding projects (Van De Rijt et al.”
InfluentialFrom social data mining to forecasting socio-economic crises
Helbing, D., & Balietti, S. (2011) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2011-01401-8 · Cited by 95
“This includes lab experiments [17,62–65], Web experiments [18], or the study of massive multiplayer on-line games [19–21, 161].”
BackgroundDiscovering Psychological Principles by Mining Naturally Occurring Data Sets
Goldstone, R. L., & Lupyan, G. (2016) · Topics in Cognitive Science · doi:10.1111/tops.12212 · Cited by 92
Quality versus quantity of social ties in experimental cooperative networks
Shirado, H., Fu, F., Fowler, J. H., & Christakis, N. A. (2013) · Nature Communications · doi:10.1038/ncomms3814 · Cited by 88
Social Contagion: An Empirical Study of Information Spread on Digg and Twitter Follower GraphsPreprint
Lerman, K., Ghosh, R., & Surachawala, T. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.3162 · Cited by 100
“The distribution of story popularity on either site, Figure 3, shows the ‘inequality of popularity’ [23], with relatively few stories becoming very popular, accruing thousands of votes (retweets), while most are much less popular, receiving a few hundred votes (retweets).”
BackgroundEffects of Network Structure, Competition and Memory Time on Social Spreading Phenomena
Gleeson, J. P., O’Sullivan, K. P., Baños, R. A., & Moreno, Y. (2016) · Physical Review X · doi:10.1103/physrevx.6.021019 · Cited by 134
“This is consistent with random-copying models of human decision-making [36, 71] where the quality of the product—here, the “interestingness” of the meme—is less important than the social influence of peers’ decisions [72].”
Understanding and overcoming biases in online review systems
Askalidis, G., Kim, S. J., & Malthouse, E. C. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.03.002 · Cited by 97
Analytical sociology and computational social science
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Dynamics of Ranking Processes in Complex Systems
Ghoshal, G., Blumm, N., Forrò, Z., et al. (2012) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 90
User-Generated Content and Competing Firms’ Product Design
Kwark, Y., Chen, J., & Raghunathan, S. (2017) · Management Science · doi:10.1287/mnsc.2017.2839 · Cited by 90
Recruiter's perception of artificial intelligence (AI)-based tools in recruitment
Horodyski, P. (2023) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2023.100298 · Cited by 165
Out of control or right on the money? Funder self-efficacy and crowd bias in equity crowdfunding
Stevenson, R. M., Ciuchta, M. P., Letwin, C. R., Dinger, J. M., & Vancouver, J. B. (2018) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2018.05.006 · Cited by 95
Suppressing the Search Engine Manipulation Effect (SEME)
Epstein, R., Robertson, R. E., Lazer, D. M. J., & Wilson, C. (2017) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3134677 · Cited by 108
“Experiments conducted on online ranking algorithms have demonstrated their influence on users’ music preferences [111, 112], use of emotional language [69], beliefs about scientific controversy [92], and undecided voters’ preferences [30].”
BackgroundSharing Political Content in Online Social Media: A Planned and Unplanned Behaviour Approach
Hossain, M. A., Dwivedi, Y. K., Chan, C., Standing, C., & Olanrewaju, A.-S. T. (2018) · Information Systems Frontiers · doi:10.1007/s10796-017-9820-9 · Cited by 87
Information Diffusion on Social Media During Natural Disasters
Dong, R., Li, L., Zhang, Q., & Cai, G. (2018) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2017.2786545 · Cited by 85
“The seldom intersection makes the metamorphosis from grassroots activist to celebrity difficult, but it is a truth that intimate contact can make overnight success possible, though we have not found the hidden principles among this [51].”
BackgroundDesign Considerations for Online Deliberation Systems
Towne, W. B., & Herbsleb, J. D. (2012) · Journal of Information Technology & Politics · doi:10.1080/19331681.2011.637711 · Cited by 98
Reciprocity of social influence
Mahmoodi, A., Bahrami, B., & Mehring, C. (2018) · Nature Communications · doi:10.1038/s41467-018-04925-y · Cited by 102
Enabling Large-Scale Deliberation Using Attention-Mediation Metrics
Klein, M. (2012) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-012-9156-4 · Cited by 99
“they see the ideas in popularity-sorted order), then the first ideas that happen to get a rating advantage tend to become the eventual winners—they “lock in” to the winning position—even if they are worse than ideas that appeared later or started with lower ratings (Salganik et al., 2006).”
BackgroundPlatforms, Power, and Promotion: Evidence from Spotify Playlists*
Aguiar, L. L. M., & Waldfogel, J. (2021) · Journal of Industrial Economics · doi:10.1111/joie.12263 · Cited by 85
“Salganik et al. (2006) find evidence that signals of popularity such as best-seller lists lead to a “self-fulfilling prophecy.””
Background
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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