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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“…and Fernandez-Mateo, 2006), securities and associated firms are assessed by investors and analysts in financial markets (Zuckerman, 1999, 2004), and books, films, and music are appraised by consumers and critics in cultural markets (Salganik, Dodds, and Watts, 2006; Kovács and Sharkey, 2014).”
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“Prior studies show that people seek out frequently-downloaded songs, popular movies, and bestselling books (Elberse and Eliashberg 2003; Salganik et al. 2006).”
BackgroundHogg, T., & Lerman, K. (2012) · EPJ Data Science · doi:10.1140/epjds5 · Cited by 151
“Predicting popularity in social media from intrinsic properties of newly submitted content is difficult [25].”
BackgroundMeasuring algorithmically infused societies
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What Sociologists Should Know About Complexity
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Online Actions with Offline Impact
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“Recent research has made great advancements towards understanding of fundamental structural properties [36, 41], growth [35], navigability [31, 37], community structure [11, 50], information diffusion [12, 19], influence maximization [30], social capital [17, 27], and social influence [47] in online social networks.”
BackgroundInformation aggregation and collective intelligence beyond the wisdom of crowds
Kameda, T., Toyokawa, W., & Tindale, R. S. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00054-y · Cited by 151
Algorithmic monoculture and social welfare
Kleinberg, J., & Raghavan, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018340118 · Cited by 147
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Model-Based Purchase Predictions for Large Assortments
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“There is also clear evidence that such personalized configurations influence behavior (Ghose et al., 2014; Pan et al., 2007; Salganik et al., 2006).”
BackgroundCultural Industries: Product–Market Characteristics, Management Challenges and Industry Dynamics
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“The highest level of support concerns the logic that sales result from information cascades through which consumers inform each other about products’ qualities (Beck 2007; De Vany and Lee 2001; De Vany and Walls 1996; Gaffeo et al. 2008; Gazley et al. 2011; Giles 2007; Godart and Mears 2009; Moul 2007; Salganik et al.…”
Estimating peer effects in networks with peer encouragement designs
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“of market success is observed (Salganik et al. 2006).”
InfluentialHow adoption speed affects the abandonment of cultural tastes
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“By more closely examining the psychological processes behind individual choice and cultural transmission, deeper insight can be gained into the relationship between individual (micro) behavior and collective (macro) outcomes such as cultural success (1, 34).”
BackgroundResultChallenges in mining social network data
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“[29] Matthew Salganik, Peter Dodds, and Duncan Watts.”
BackgroundSocial Influence, Binary Decisions and Collective Dynamics
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“…segregated (Schelling 1971), orderly crowds can erupt in a riot (Granovetter 1978), books, songs, and movies can emerge from nowhere to become hits (Salganik et al. 2006), markets can ‘lock-in’ to inferior technologies (Arthur and Lane 1993), and political regimes can collapse without warning…”
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“Recent studies show that majority formation involves a degree of chance, by which different outcomes are possible as to what choices make it to the top of popularity [31].”
BackgroundOnly 15 Minutes? The Social Stratification of Fame in Printed Media
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“Scholars have observed similar dynamics for the consecration of baseball players (Allen and Parsons 2006), song popularity (Salganik et al. 2006), author success (Sorenson 2007), and social status on the Internet (Restivo and van de Rijt 2012).”
BackgroundUnified index to quantifying heterogeneity of complex networks
Hu, H., & Wang, X. (2008) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2008.01.113 · Cited by 127
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Research Note—Music Blogging, Online Sampling, and the Long Tail
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Can personality traits predict musical style preferences? A meta-analysis
Schäfer, T., & Mehlhorn, C. (2017) · Personality and Individual Differences · doi:10.1016/j.paid.2017.04.061 · Cited by 125
Effects of network structure, competition and memory time on social spreading phenomena
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Brinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01742-2 · Cited by 132
“In sum, recommender systems influence cultural evolution by rewiring our social networks and modifying information flows such that they can substantially influence the dynamics of cultural markets 47–49 .”
BackgroundSize zero high-end ethnic: Cultural production and the reproduction of culture in fashion modeling
Mears, A. (2009) · Poetics · doi:10.1016/j.poetic.2009.10.002 · Cited by 130
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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