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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Waguespack, D. M., & SALOMON, R. M. (2015) · Management Science · doi:10.1287/mnsc.2014.2144 · Cited by 33
“Our goal, therefore, is to construct parallel worlds where we can compare performance persistence with and without subjective quality evaluations, similar to the Salganik et al. (2006) study that explores social influence in music markets.”
BackgroundFrom social simulation to integrative system design
Helbing, D., & Balietti, S. (2011) · The European Physical Journal Special Topics · doi:10.1140/epjst/e2011-01402-7 · Cited by 33
“Such virtual realities allow one, in principle, to explore a number of different scenarios and worlds [88].”
Solving Wicked Social Problems with Socio-computational Systems
Introne, J. E., Laubacher, R., Olson, G. M., & Malone, T. W. (2012) · Künstliche Intell. · doi:10.1007/s13218-012-0231-2 · Cited by 35
“sions of this include pairwise voting [20], preferential vot-”
BackgroundA Model of the “It” Products in Fashion
Kuksov, D., & Wang, K. (2012) · Marketing Science · doi:10.1287/mksc.1120.0742 · Cited by 33
Predicting podcast preference: An analysis framework and its application
Tsagkias, M., Larson, M., & de Rijke, M. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21259 · Cited by 32
“Salganik , Dodds , and Watts ( 2006 ) demonstrate that success in such a market is only partly determined by quality .”
BackgroundLópez-Pintado, D. (2012) · Games and Economic Behavior · doi:10.1016/j.geb.2012.01.008 · Cited by 33
“The proliferation of internet-based communication and interactivity over the past decade has led to new consumer patterns, innovative marketing approaches, and even unconventional ways of running political campaigns (e.g., Godes and Mayzlin, 2004; Salganik et al., 2006; Willimas and Gulati, 2008).”
BackgroundTucker, C. E., & Zhang, J. (2007) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 33
Ashkinaze, J., Mendelsohn, J., Qiwei, L., Budak, C., & Gilbert, E. (2025) · Proceedings of the ACM Collective Intelligence Conference · doi:10.1145/3715928.3737481 · Cited by 33
“Hence, our experimental design draws on the “multiple-world” paradigm (e.g., [68, 69]): We create multiple, parallel realizations of worlds with and without AI ideas, each evolving independently under controlled conditions.”
Cobb, N. K., Jacobs, M. A., Saul, J. E., Wileyto, E. P., & Graham, A. L. (2014) · BMJ Open · doi:10.1136/bmjopen-2013-004089 · Cited by 31
“As an application becomes more accepted and valued by a group, others are more likely to value it.(38) Since this process is unpredictable at the individual level, the end exponential effects are highly unpredictable.”
BackgroundThe political influence of peer groups: experimental evidence in the classroom
Campos, C. F., Heap, S. P. H., & de Leon, F. L. L. (2016) · Oxford Economic Papers · doi:10.1093/oep/gpw065 · Cited by 28
A topology of groups: What GitHub can tell us about online collaboration
Zöller, N., Morgan, J. H., & Schröder, T. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120291 · Cited by 40
Design of Randomized Experiments in Networks
Walker, D., & Muchnik, L. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2363674 · Cited by 30
“For example, in a sequence of experiments, Salganik et al. [38], [60] study the impact of popularity-based content ordering on the propensity to consume cultural products (music).”
Building Status in an Online Community
Smirnova, I. N., Reitzig, M. G., & Sorenson, O. (2022) · Organization Science · doi:10.1287/orsc.2021.1559 · Cited by 31
Good Night, and Good Luck: Perspectives on Luck in Management Scholarship
Liu, C., & de Rond, M. (2015) · Academy of Management Annals · doi:10.1080/19416520.2016.1120971 · Cited by 92
“ogy adoption (Arthur, 1989; David, 1985), cultural markets (Chung & Cox, 1994; Salganik et al., 2006), business competition (Lieberman & Montgomery, 1988) and even academia (Levitt & Nass, 1989; Merton, 1968).”
Statistical Evolutionary Laws in Music Styles
Nakamura, E., & Kaneko, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-52380-6 · Cited by 32
“include effect of random copying (neutral drift) in a finite population [27], interdependence among evaluators’ decision [45], active role of creators [46], indirect biases (e.”
BackgroundExposure to Foreign Media and Changes in Cultural Traits: Evidence from Naming Patterns in FrancePreprint
Disdier, A., Head, K. C., & Mayer, T. (2006) · SSRN Electronic Journal · doi:10.2139/ssrn.918431 · Cited by 32
North, A. C., & Hargreaves, D. J. (2010) · doi:10.1093/acprof:oso/9780199230143.003.0032 · Cited by 31
Shuffling Services: Current Trends in Interacting with Digital Music
Liikkanen, L. A., & Åman, P. (2015) · Interacting with Computers · doi:10.1093/iwc/iwv004 · Cited by 34
“Interaction with digital music has been the primary interest of a few studies, and a secondary one in some (e.g. Salganik et al., 2006).”
Computational Social Science of Disasters: Opportunities and Challenges
Burger, A., Oz, T., Kennedy, W. G., & Crooks, A. T. (2019) · Future Internet · doi:10.3390/fi11050103 · Cited by 33
When Personalization Is Not an Option: An In-The-Wild Study on Persuasive News Recommendation
Cristina Gena, A., Grillo, P., Lieto, A., Mattutino, C., & Vernero, F. (2019) · Information · doi:10.3390/info10100300 · Cited by 37
Nelimarkka, M., Laaksonen, S.-M., Tuokko, M., & Valkonen, T. (2020) · Social Media + Society · doi:10.1177/2056305120903856 · Cited by 48
“Tools for such research have been developed, and digital experiments have already been exploited to explore development of social norms (Salganik et al., 2006) and, more recently, to examine text-based exchanges in small groups (e.”
BackgroundMatthew effects in science and the serial diffusion of ideas: Testing old ideas with new methods
Farys, R., & Wolbring, T. (2021) · Quantitative Science Studies · doi:10.1162/qss_a_00129 · Cited by 33
Reviews and Self-Selection Bias with Operational Implications
Chen, N., Li, A., & Talluri, K. T. (2019) · Management Science · doi:10.1287/mnsc.2020.3892 · Cited by 30
“In an early well-known paper, Salganik et al. (2006) conduct an experiment on how users rate songs with and without social influence (ratings of previous consumers).”
Blockchain and art market: resistance or adoption?
Abbate, T., Vecco, M., Vermiglio, C., Zarone, V., & Perano, M. (2022) · Consumption Markets & Culture · doi:10.1080/10253866.2021.2019026 · Cited by 34
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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