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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“collective leadership networks? The general question of what mak something contagious or popular extends beyond the s research, but Salganik et al (2006) suggest that network dynamics make popularity harder to predict than previously thought.”
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“Previous research predicts that this class enrollment information makes the strategy class more attractive, as popularity tends to be self-reinforcing (see, for example, Salganik et al. 2006, Cai et al. 2009, Zhang 2010, Chen et al. 2011).”
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“Many factors differ across our laboratories—such as the description of tasks, duration, posting time, requester reputation, or even just randomness in early update of HITs (Salganik, Dodds, & Watts, 2006)— and so experimental manipulation of these factors is required to make causal claims.”
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“A large number of studies have examined the usefulness of MTurk as a platform for collecting and evaluating data for applications including machine translation [4] image databases [7], and natural language processing [22].”
BackgroundMethodComputational Social Science and Sociology
Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020) · Annual Review of Sociology · doi:10.1146/annurev-soc-121919-054621 · Cited by 305
“In an influential study, Salganik et al. (2006) created an online “music lab” to study how peer influence shapes musical preferences of new artists.”
BackgroundOpen access, readership, citations: a randomized controlled trial of scientific journal publishing
Davis, P. M. (2011) · The FASEB Journal · doi:10.1096/fj.11-183988 · Cited by 317
Using a model of social dynamics to predict popularity of news
Lerman, K., & Hogg, T. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772754 · Cited by 299
The Pulse of News in Social Media: Forecasting Popularity
Bandari, R., Asur, S., & Huberman, B. A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v6i1.14261 · Cited by 304
Salganik, M., & Watts, D. J. (2008) · Social Psychology Quarterly · doi:10.1177/019027250807100404 · Cited by 299
“The tendency for the subjects to favor the least popular songs, which at first may appear surprising, is consistent with previous work (Salganik et al. 2006) and could simply be an artifact of our experimental set-up—just as the top spot on a list is salient, so too is the bottom.”
BackgroundMethodResultInfluentialSelf-citationStewardship of global collective behavior
Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025764118 · Cited by 289
The spontaneous emergence of conventions: An experimental study of cultural evolution
Centola, D., & Baronchelli, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418838112 · Cited by 282
“Moreover, prior experimental studies have typically assumed the existence of either an explicit reward for universal coordination (15) or a mechanism that aggregates and reports the collective state of the population (17, 24), which has made it impossible to evaluate the hypothesis that global coordination is the resu…”
BackgroundA first look at online reputation on Airbnb, where every stay is above average
Zervas, G., Proserpio, D., & Byers, J. W. (2020) · Marketing Letters · doi:10.1007/s11002-020-09546-4 · Cited by 640
Music recommendation and discovery in the long tail
Herrada, Ò. C. (2009) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 283
What makes a video go viral? An analysis of emotional contagion and Internet memes
Guadagno, R. E., Rempala, D. M., Murphy, S., & Okdie, B. M. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.04.016 · Cited by 280
“In a one such study, Salganik, Dodds, and Watts (2006) created a laboratory ''music market'' online where 14,000 participants were allowed to download songs they had never been exposed to previously .”
BackgroundConsensus Decision Making by Fish
Sumpter, D. J. T., Krause, J., James, R., Couzin, I. D., & Ward, A. J. W. (2008) · Current Biology · doi:10.1016/j.cub.2008.09.064 · Cited by 276
“For example, economic bubbles and fashion trends are attributed to lack of independence between the individuals involved in these activities [23, 24].”
BackgroundThe Myth of Partisan Selective Exposure: A Portrait of the Online Political News Audience
Nelson, J. L., & Webster, J. G. (2017) · Social Media + Society · doi:10.1177/2056305117729314 · Cited by 267
Characterizing and Modeling the Dynamics of Activity and Popularity
Zhang, P., Li, M., Gao, L., Fan, Y., & Di, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089192 · Cited by 271
“It is widely believed that information can spread quickly along social links using user-to-user exchanges, also known as ‘‘word-ofmouth’’ exchanges; moreover, the users’ behaviors are strongly influenced by their neighbors [23–27].”
BackgroundWhat Makes Popular Culture Popular? Product Features and Optimal Differentiation in Music
Askin, N., & Mauskapf, M. (2017) · American Sociological Review · doi:10.1177/0003122417728662 · Cited by 271
“In a series of online experiments, Salganik and colleagues investigated how product quality and social influence affect success in an artificial music market (Salganik et al. 2006; Salganik and Watts 2008, 2009).”
BackgroundSocial contextual recommendation
Jiang, M., Cui, P., Liu, R. Z. Z., et al. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2396771 · Cited by 262
“In [25], web-based experiments are designed for music adoption prediction in an artificial music market.”
BackgroundFrom the functions of music to music preference
Schäfer, T., & Sedlmeier, P. (2009) · Psychology of Music · doi:10.1177/0305735608097247 · Cited by 252
Insiders, Outsiders, and the Struggle for Consecration in Cultural Fields
Cattani, G., Ferriani, S., & Allison, P. D. (2014) · American Sociological Review · doi:10.1177/0003122414520960 · Cited by 258
A Survey of Information Cascade Analysis
Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2021) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 282
“, is it a problem of insufficient data, or is it inherently unpredictable [38, 135, 168, 173]? Is this because the innate quality of content has a minor impact, while the social influence or other external factors govern the eventual popularity of items [144, 168]? Unfortunately, there is no explicit answer applicable…”
BackgroundSpontaneous emergence of social influence in online systems
Onnela, J.-P. “., & Reed‐Tsochas, F. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914572107 · Cited by 255
“Successful products in cultural markets have been found to be orders of magnitude more popular than the average cultural product (7).”
BackgroundChoosing experiments to accelerate collective discovery
Rzhetsky, A., Foster, J. G., Foster, I. T., & Evans, J. A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509757112 · Cited by 259
“In assuming that scientists’ research choices are influenced by concept degree, we posit that scientists are influenced by the choices of others, a well-attested choice heuristic (25, 26).”
BackgroundBalancing on the Creative Highwire
Berg, J. M. (2016) · Administrative Science Quarterly · doi:10.1177/0001839216642211 · Cited by 254
“In particular, I drew on Salganik, Dodds, and Watts (2006), who measured the quality of online content (in their case, songs) as the frequency with which audience members downloaded the songs when no other information or social encouragement was provided.”
BackgroundInfluentialCharacterizing and Modeling the Dynamics of Online Popularity
Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F., & Vespignani, A. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.158701 · Cited by 249
Crowd behaviour during high-stress evacuations in an immersive virtual environment
Moussaïd, M., Kapadia, M., Thrash, T., Sumner, R. W., Groß, M. H., Helbing, D., & Hölscher, C. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0414 · Cited by 250
“in concert with the recent development of computational methods in the social sciences that employ artificial environments to study the dynamics of large social systems (37), such as cultural markets (38), social networks (39), and collective problemsolving (40). Here, we extend this experimental principle to crowd be…”
BackgroundA survey on predicting the popularity of web content
Tatar, A., de Amorim, M. D., Serge Fdida, B., & Antoniadis, P. (2014) · Journal of Internet Services and Applications · doi:10.1186/s13174-014-0008-y · Cited by 250
“reveals that, when users are informed about the collective decisions of other individuals, the popularity of songs are driven by a “rich-get-richer” effect [87].”
BackgroundThe Simple Rules of Social Contagion
Hodas, N. O., & Lerman, K. (2014) · Scientific Reports · doi:10.1038/srep04343 · Cited by 338
“Explicit social feedback can significantly magnify user response, albeit making it less useful for popularizing high-quality content [28].”
BackgroundTALENT AND/OR POPULARITY: WHAT DOES IT TAKE TO BE A SUPERSTAR?
Franck, E. P., & Nüesch, S. (2010) · Economic Inquiry · doi:10.1111/j.1465-7295.2010.00360.x · Cited by 245
“Salganik, Dodds, and Watts (2006) use an experimental study to investigate the talent and popularity hypotheses in an artificial cultural market.”
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
Shen, H., Wang, D., Song, C., & Barabási, A.-Ĺ. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8739 · Cited by 319
The spread of fake news by social botsPreprint
Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2017) · arXiv (Cornell University) · Cited by 249
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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