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Quantifying reputation and success in art

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Science  16 Nov 2018:
Vol. 362, Issue 6416, pp. 825-829
DOI: 10.1126/science.aau7224
  • Fig. 1 Coexhibition network.

    Force-directed layout of the order τ = ∞ coexhibition network, whose nodes are institutions (galleries, museums). Node size is proportional to each institution’s eigenvector centrality. Nodes are connected if they both exhibited the same artist, with link weights being equal to the number of artists’ coexhibitions. Node colors encode the region in which institutions are located. Links are of the same colors as their end nodes, or gray when end nodes have different colors. For visualization purposes, we only show the 12,238 nodes corresponding to institutions with more than 10 exhibits; we pruned the links by keeping the most statistically significant links (20) (supplementary text S2.2). We implemented community detection on the pruned network (21), identifying 122 communities (supplementary text S2.3). We highlighted five of them, the full community breakdown being shown in fig. S3. We also show the names of the most prestigious institution for each community.

  • Fig. 2 Quantifying artistic careers.

    (A) Network-based prestige ranks, captured by eigenvector centrality, for institutions that were independently assigned different grades. (B) The relationship between sales-based ranks and eigenvector centrality-based network ranks, binned in 100 intervals, showing a high Spearman’s correlation (ρS = 0.88). We report mean (black line) and standard error (gray shading) within each bin. (C) Data on top 10 institutions as predicted by the network-based ranking. Colors capture geographical location, as shown in Fig. 1. (D) Survival curves, showing the fraction of artists that continue to exhibit in the years following their first five exhibits based on the career of 99,265 artists with more than five exhibits. (E) Probability density function of average prestige during the first five exhibits for the 31,794 artists with more than 10 exhibits born between 1950 and 1990. (F) Diagram illustrating how the career high– and low–initial reputation artists evolves, showing the fraction of those artists whose final reputation (last five recorded exhibits) is either low or high. To show how the early career determines various success measures across a career, we consider as control variable the average prestige of the first five exhibits of an artist, and report (G) the total number of exhibits (left), the percentage of these exhibits outside of their home country (right), (H) the standard deviation of their exhibition prestige (left), the maximum price at which they are currently quoted in a gallery (in $, right), (I) the total number of their works that were sold in the auction market (left), and the maximum price (relative to the average market price) at which their work sold in the auction market (right). Each panel demonstrates the important role that initial reputation plays in shaping later access to institutions and financial reward.

  • Fig. 3 Modeling the emergence of reputation.

    (A) For a random sample including 30% of the 31,794 artists with more than 10 exhibits born between 1950 and 1990, we show the evolving exhibition prestige over time. (B) Evolving exhibition prestige predicted by the random walk model (memoryless), documenting its failure to capture real careers. (C) The memory model predicts the evolution of prestige. We use the first five exhibits to initialize the models. The sequence of dates at which an artist’s exhibitions occur was matched to the one we observe in the data. (D to F) Variation of the memory component with the prestige of the next exhibit π, for different ranges of values for past reputation m. π and m are reported in decile. (G) Probability density function of average prestige during the first five exhibits for the 31,794 artists, and the subset of those artists who were born in the United States, Canada, and India. (H) Final reputation versus initial reputation for artists of different country of origin.

Supplementary Materials

  • Quantifying reputation and success in art

    Samuel P. Fraiberger, Roberta Sinatra, Magnus Resch, Christoph Riedl, Albert-László Barabási

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Materials and Methods
    • Supplementary Text
    • Figs. S1 to S10
    • References

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