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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“Such social learning is prevalent when individuals find it difficult to evaluate an object’s true quality [28, 29] and can lead to information cascades and herding [6, 13], which might result in sub-optimal outcomes.”
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Modelling Online Comment Threads from their Start
Krohn, R., & Weninger, T. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006594 · Cited by 8
“This is an extremely difficult task - it may not be possible to make accurate predictions at all [2]–[4].”
BackgroundNetwork diffusion of competing behaviors
Hsiao, Y. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00115-x · Cited by 7
Modeling Collective Anticipation and Response on Wikipedia
Kobayashi, R., Gildersleve, P., Uno, T., & Lambiotte, R. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18063 · Cited by 8
“Take the adoption of certain hashtags, instead of others, when confronted with new social phenomena (Lin et al., 2013); or the fact that certain songs become extremely popular while most remain in the dark (Salganik, Dodds, and Watts, 2006).”
BackgroundGligorić, K., Lifchits, G., West, R., & Anderson, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257091 · Cited by 8
“More importantly, the “superstar phenomenon” [31] demonstrates that the audience for different authors can vary by orders of magnitude, while social influence can affect how an author’s content is received, independent of its quality [32].”
BackgroundGeorg Simmel’s Contribution to Social Network Research
Hollstein, B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108878296.003 · Cited by 13
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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