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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BackgroundPredictability of Popularity: Gaps between Prediction and Understanding
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“Given increasing evidence from multiple domains that content features are at best weakly connected to an item’s popularity (Salganik, Dodds, and Watts 2006; Pachet and Sony 2012; Martin et al. 2016), this seems unlikely to be the whole explanation.”
BackgroundUrban scaling laws arise from within-city inequalities
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“…rate for popularity-ranked content lists (such as Trending Stories) also effects the quality of that content, because high-quality content may not “bubble up” if the list is updated too frequently (Salganik, Dodds, and Watts, 2006; Chaney, Stew-art, and Engelhardt, 2019; Ciampaglia et al., 2018).”
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Tak, E., Correll, S. J., & Soule, S. A. (2018) · Social Forces · doi:10.1093/sf/soy125 · Cited by 58
“Sociologists have shown that status beliefs powerfully in fl uence judgments under conditions of uncertainty (Gould 2002; Podolny 1993; Salganik, Dodds, and Watts 2006).”
BackgroundBello, P., & García, D. (2021) · Humanities and Social Sciences Communications · doi:10.1057/s41599-021-00855-1 · Cited by 60
“In a pathbreaking experimental study, Salganik et al. (2006) found that information on other listener’s musical preferences results in an amplified inequality of popularity when compared to a world of independent listeners.”
BackgroundNiche as a Determinant of Word Fate in Online Groups
Altmann, E. G., Pierrehumbert, J. B., & Motter, A. E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019009 · Cited by 58
“The results support comparisons between the dynamics of the linguistic system and other social dynamics, such as the spread of opinions or the popularity of news items, videos, and music [16, 17].”
BackgroundResultSocial learning research outside the laboratory: How and why?
Kendal, R., Galef, B. G., & van Schaik, C. (2010) · Learning & Behavior · doi:10.3758/lb.38.3.187 · Cited by 48
The Climate CoLab: Large scale model-based collaborative planning
Introne, J. E., Laubacher, R., Olson, G. M., & Malone, T. W. (2011) · International Conference on Collaboration Technologies and Systems (CTS) · doi:10.1109/cts.2011.5928663 · Cited by 63
Auditory Preferences and Aesthetics
McDermott, J. H. (2011) · Elsevier eBooks · doi:10.1016/b978-0-12-381431-9.00020-6 · Cited by 59
Music Popularity: Metrics, Characteristics, and Audio-Based Prediction
Lee, J., & Lee, J. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2820903 · Cited by 59
“as in other cultural products [25], [36], [37].”
BackgroundOf songs and men: a model for multiple choice with herding
Borghesi, C., & Bouchaud, J. (2007) · Quality & Quantity · doi:10.1007/s11135-007-9074-6 · Cited by 56
“Quite a lot more data is reported in the supplementary material of [20], for example the average number of downloaded songs per participant d = C/N .”
BackgroundResultInfluentialInteracting Discovery Processes on Complex Networks
Iacopini, I., Bona, G. D., Ubaldi, E., Loreto, V., & Latora, V. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.248301 · Cited by 57
Cheliotis, G. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.02.006 · Cited by 58
“We are only now beginning to understand what perhaps talent agencies, publishers and record labels have long known by virtue of experience: success in the creative industries is as much a matter of talent as it is a matter of luck and of achieving a critical threshold of viewership [23].”
BackgroundThe economics of a good party: Social mechanics and the legitimization of art/culture
Currid, E. (2007) · Journal of Economics and Finance · doi:10.1007/bf02885728 · Cited by 58
“In a recent controlled music experiment, the subjects’ “favorite” song was random when individuals were unaware of other participants’ favorite choice, with the ranking of song popularity only exhibiting a pattern when the subjects were aware of what other listeners chose as their favorite songs ( Salganik et al., 200…”
BackgroundOpinion Formation and the Collective Dynamics of Risk Perception
Moussaïd, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0084592 · Cited by 62
“In fact, the easy access to media and social information exacerbates individuals exposition to news articles and peer opinions, which increasingly shapes their judgments in various domains, such as marketing [17,18], political science [19,20], or risk perception [21,22].”
BackgroundHow to Harvest Collective Wisdom for Complex Problems: An Introduction to the MIT Deliberatorium
Klein, M. (2007) · doi:10.13140/rg.2.2.32743.24489 · Cited by 57
The Love of Large Numbers: A Popularity Bias in Consumer Choice
Powell, D., Yu, J., DeWolf, M., & Holyoak, K. J. (2017) · Psychological Science · doi:10.1177/0956797617711291 · Cited by 64
“Furthermore, research in artificial culture markets suggests that popularity is a rather weak cue to quality (Salganik et al., 2006), and our own analysis of Amazon review data found no link between product rating and number of reviews.”
BackgroundThe Experts in the Crowd: The Role of Reputable Investors in a Crowdfunding Market
Kim, K. T., & Viswanathan, S. (2014) · Cited by 54
Testing Social Science Network Theories with Online Network Data: An Evaluation of External Validity
Bisbee, J., & Larson, J. M. (2017) · American Political Science Review · doi:10.1017/s0003055417000120 · Cited by 32
The selectivity of social learning and the tempo of cultural evolution
Bentley, R. A., & O’Brien, M. J. (2011) · Journal of Evolutionary Psychology · doi:10.1556/jep.9.2011.18.1 · Cited by 53
Estimating peer effects in longitudinal dyadic data using instrumental variables
O’Malley, A. J., Elwert, F., Rosenquist, J. N., Zaslavsky, A. M., & Christakis, N. A. (2014) · Biometrics · doi:10.1111/biom.12172 · Cited by 52
Putting the World in Orders: Plurality in Organizational Evaluation
Brandtner, C. (2017) · Sociological Theory · doi:10.1177/0735275117726104 · Cited by 56
“As Salganik et al. (2006) found in an online experiment, status inequality is strongly amplified by the visibility—or salience—of social evaluations.”
BackgroundPredicting emerging social conventions in online social networks
Kooti, F., Mason, W., Gummadi, K. P., & Cha, M. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2396820 · Cited by 54
“We also present the methods we employed to identify the retweeting convention and briefly introduce the seven retweeting variations that we analyze in this paper.”
MethodDensification and structural transitions in networks that grow by node copying
Bhat, U., Krapivsky, P. L., Lambiotte, R., et al. (2016) · Physical review. E · doi:10.1103/physreve.94.062302 · Cited by 55
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.
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