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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In the citing paper
“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]
Citations
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Watts, D. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082090499 · Cited by 2,673
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Processing Fads and Fashions: An Organization-Set Analysis of Cultural Industry Systems
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Cited by
Sorted by citations
Items 2,201–2,217 of 2,217
Gada, T. N., Dave, A., Rayali, T., & Parpia, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2709309
Collective Signals and Truth Perception in AI-Generated Multimedia
Cavicchioli, R., Furini, M., & Tagliani, G. (2026) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot69657.2026.00126
“Less attention has been paid to real socio-technical systems, where users interact with interfaces that explicitly expose aggregated information, such as percentages, ratings, or popularity indicators [12].”
Liang, F., Wang, X., Fan, J., He, S., & Chen, W. (2026) · Axioms · doi:10.3390/axioms15090639
Tonooka, Y., & Nishimura, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7365626
Close to Some, Distant to OthersEvaluator-Product Distance Dispersion and Polarized ReceptionPreprint
Du, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7389430
Close to Some, Distant to OthersEvaluator-Product Distance Dispersion and Polarized ReceptionPreprint
Du, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7387975
López‐Íñiguez, G. (2026) · Journal of Social Media Research · doi:10.29329/jsomer.125
“…that algorithmic systems do not distribute visibility neutrally, but amplify particular forms of participation and engagement (Bucher, 2018; Gillespie, 2014; Prey, 2018; Salganik et al., 2006), as well as the rise and increase of kidfluencing practices (see further, Rautiainen et al., in press).”
张, 嘉. (2026) · Journalism and Communications · doi:10.12677/jc.2026.149241
McGinn, E., & Alexeev, S. (2026) · Journal of Cultural Economics · doi:10.1007/s10824-026-09607-y
Cao, Q., Zhou, Z., Wang, L., Liu, Y., & Li, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1924628
“…+ Greater delegation to common models increases exposure to convergent output and occasions to notice the resemblance (Kleinberg and Raghavan, 2021; Salganik et al., 2006) P2 PTC to AI-tool self-efficacy + Convergence makes competent output feel predictable and attainable by proxy; fluency is read…”
Social tinkering in designed environments
Burton, J. W., Lorenz-Spreen, P., & Hertwig, R. (2026) · Behavioral and Brain Sciences · doi:10.1017/s0140525x26106438
The effect of online review manipulation on post-consumption evaluations
Naini, A. J., & Safi, R. (2026) · Empirical Economics · doi:10.1007/s00181-026-02982-7
When reach and loyalty scale together: a digital music benchmark for marketing analytics
Šulik, M. (2026) · Journal of Marketing Analytics · doi:10.1057/s41270-026-00541-8
Comparability for Recommendation Model Evaluation and Selection from a Multi-stakeholder Perspective
Ionescu, S., Shilov, I., & Dörfler, F. (2026) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3773078.3831816
Algorithmic Visibility and Automated Continuation: Infrastructural Mediation in Streaming Platforms
Berumen, S. A., & Ibarra, K. A. (2026) · Platforms · doi:10.3390/platforms4040022
AI as a Democratizing Force in Indie Game Development
Madanamootoo, B., & Alla, J. (2026) · arXiv · doi:10.48550/arxiv.2608.07825
“Salganik, Dodds, and Watts demonstrated experimentally [23] that in cultural markets with abundant choice, social influence rather than intrinsic quality determines which products win, and that winners are largely unpredictable: the mechanism underlying the virality-native pattern of Table 8 and…”
Influential
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
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