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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“Investigators, for example, demonstrated that when faced with unfamiliar songs, a common strategy to find the best is to choose the most downloaded one [8].”
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“Unfortunately, we cannot rule out any potential effects of demand characteristics or social desirability (Salganik et al., 2006) that might have played a role, particularly in the case of musicology students.”
BackgroundEgermann, H., Kopiez, R., & ALTENMÜLLER, E. O. (2013) · Psychomusicology Music Mind and Brain · doi:10.1037/a0032316 · Cited by 55
“Whereas Salganik et al. (2006) did not describe the psychological mechanism that was responsible for their participants’ behavior, the present study investigates whether the emotional effects of music can be manipulated by feedback associated with the emotional ratings of others, allowing us also…”
BackgroundMethodInfluentialInferring preference correlations from social networks
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“The recent development of web-based experiments [66] and online games [7] involving economic transactions provide far larger groups than possible in laboratory settings, though with less control over user incentives and interactions.”
BackgroundQuantifying the effects of social influence
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“Let xi(t) be the guess of individual i [1, N] at round t [1, 5] for a particular question.”
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“Salganik et al. (2006) conducted two experiments similar to the one reported in Salganik and Watts (2008) with the key difference that no inversion took place.”
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“Indeed, the digital traces that we leave with all sorts of everyday activities (shopping, communicating with others, traveling) are ushering in a new kind of computational social science [5,6], which aims to shed light on human mobility [7,8], activity patterns [9], decision-making processes [10], social influence [11…”
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“His analysis of fashion in “first” or “given” names in Western countries provides what is perhaps the strongest empirical support for the importance of endogenous processes in relation to exogenous factors ðsee also Besnard 1994; Salganik, Dodds, and Watts 2006Þ.”
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Hale, S. A., John, P. C., Margetts, H. Z., & Yasseri, T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196068 · Cited by 49
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Nelson, J. L. (2020) · Media and Communication · doi:10.17645/mac.v8i2.2736 · Cited by 43
Network Dynamics and Market Structure: The Case of Art Fairs
Yogev, T., & Grund, T. (2012) · Sociological Focus · doi:10.1080/00380237.2012.630846 · Cited by 43
“In such markets, there are no precise or objective measures that determine what makes a high-quality product (Beckert and Rössel 2004; Plattner 1996; Velthuis 2005; Yogev 2010) or which artwork will be perceived as successful (Salganik, Dodds, and Watts 2006).”
BackgroundTopology and evolution of the network of western classical music composers
Park, D., Bae, A., Schich, M., & Park, J. M. (2015) · EPJ Data Science · doi:10.1140/epjds/s13688-015-0039-z · Cited by 47
Does content determine information popularity in social media?
Figueiredo, F., Almeida, J. M., Benevenuto, F., et al. (2014) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/2556288.2557285 · Cited by 43
“[9] also relied on a user study to un derstand popularity dynamics.”
BackgroundMethodNot all nonnormal distributions are created equal: Improved theoretical and measurement precision.
Joo, H., Aguinis, H., & Bradley, K. J. (2017) · Journal of Applied Psychology · doi:10.1037/apl0000214 · Cited by 46
Xiao, W., Zhao, H., Fang, T., Song, Y., Zheng, V. W., & Yang, Q. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330965 · Cited by 50
“, using popularity [27] or quality [1, 3] of the content.”
BackgroundMethodPolitical audience diversity and news reliability in algorithmic ranking
Bhadani, S., Yamaya, S., Flammini, A., Menczer, F., Ciampaglia, G. L., & Nyhan, B. J. (2022) · Nature Human Behaviour · doi:10.1038/s41562-021-01276-5 · Cited by 45
“Second, it may contribute to information cascades, amplifying differences in rankings from small variations or random fluctuations and degrading the overall quality of information consumed by users [8, 12, 24, 32, 43].”
BackgroundNummenmaa, L., Putkinen, V., & Sams, M. E. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.03.026 · Cited by 47
Liu, J., & Shi, R. (2019) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmy021 · Cited by 43
The complex link between filter bubbles and opinion polarization
Keijzer, M. A., & Mäs, M. (2022) · Data Science · doi:10.3233/ds-220054 · Cited by 49
A comparison of social, learning, and financial strategies on crowd engagement and output quality
Yu, L., André, P., Kittur, A., & Kraut, R. E. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531729 · Cited by 40
“We posted Task 2 and sent different messages to groups (two different articles were used in the task: [36] and [39].”
MethodEarly warning analysis for social diffusion events
Colbaugh, R., & Glass, K. (2012) · Security Informatics · doi:10.1186/2190-8532-1-18 · Cited by 41
“A detailed mathematical presentation of the modeling and analysis methods is provided in Appendices One and Two.”
BackgroundMethodExperimental evidence for scale-induced category convergence across populations
Guilbeault, D., Baronchelli, A., & Centola, D. M. (2021) · Nature Communications · doi:10.1038/s41467-020-20037-y · Cited by 45
Who participated in the GameStop frenzy? Evidence from brokerage accountsPreprint
Hasso, T., Müller, D., Pelster, M., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3792095 · Cited by 42
“This is in line with the notion that some mutations in narratives may lead to higher contagion rates (Shiller, 2020; Salganik et al., 2006) before new contagious narratives cause economic events (Shiller, 2020).”
ResultA survey on personalized itinerary recommendation: From optimisation to deep learning
Halder, S., Lim, K. H., Chan, J., & Zhang, X. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.111200 · Cited by 50
Behavioural economics, consumption and environmental protection
Sunstein, C. R. (2015) · Edward Elgar Publishing eBooks · doi:10.4337/9781783471270.00032 · Cited by 40
Koppman, S., & Leahey, E. (2019) · Research Policy · doi:10.1016/j.respol.2019.103807 · Cited by 39
“, prevalence of the method) as an indicator of quality (Banerjee, 1992; Salganik et al., 2006).”
MethodShifting Consumption towards Diverse Content on Music Streaming Platforms
Hansen, C., Mehrotra, R., Hansen, C., Brost, B., Maystre, L., & Lalmas, M. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441775 · Cited by 40
“This, in turn, can help counteract rich-getricher phenomena common throughout the music industry [24].”
BackgroundDeal is on! Why people buy from daily deal websites
Parsons, A. G., Ballantine, P. W., Ali, A., & Grey, H. (2013) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2013.07.003 · Cited by 41
Berger, J. A., Bradlow, E. T., Braunstein, A., & Zhang, Y. (2012) · Psychological Science · doi:10.1177/0956797612443371 · Cited by 41
Broekhuizen, T., Delre, S. A., & Torres, A. (2011) · Journal of Product Innovation Management · doi:10.1111/j.1540-5885.2011.00792.x · Cited by 42
Don't Think Twice, It's All Right: Music Piracy and Pricing in a DRM-Free Environment
Sinha, R. K., Machado, F. S., & Sellman, C. (2010) · Journal of Marketing · doi:10.1509/jm.74.2.40 · Cited by 41
“An influential study by Salganik, Dodds, and Watts (2006) demonstrates that the success of digital songs depends to a large extent on the knowledge of what other customers are downloading.”
BackgroundThe Economic World Obverse: Freedom Through Markets After Arts Education
Gerber, A., & Childress, C. (2017) · American Behavioral Scientist · doi:10.1177/0002764217734275 · Cited by 40
Product success in cultural markets: The mediating role of familiarity, peers, and experts
Keuschnigg, M. (2015) · Poetics · doi:10.1016/j.poetic.2015.03.003 · Cited by 43
“1 Valuation occurs through various channels including the reputation of creators and content (Bielby and Bielby, 1994), imitation among peers (Salganik et al., 2006), and evaluation by professional critics (Shrum, 1991).”
BackgroundInfluential
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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