Evidence map›Paper›PMID 38773156›Full record

ArticleScientific reports2024

Social signals predict contemporary art prices better than visual features, particularly in emerging markets.

Kangsan Lee, Jaehyuk Park, Sam Goree, David Crandall, Yong-Yeol Ahn

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Context-aware multimodal AI navigates hidden pathways in five centuries of art evolution.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Kangsan Lee *Division of Social Science, New York University Abu Dhabi, Abu Dhabi, UAE. ks.lee@nyu.edu.
Jaehyuk Park *School of Public Policy and Management, Korea Development Institute, Sejong-si, Republic of Korea.
Sam Goree *Department of Computer Science, Stonehill College, Easton, MA, 02357, USA.
David CrandallLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
Yong-Yeol AhnLuddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.

Funding

Air Force Office of Scientific Research FA9550-19-1-0391National Research Foundation of Korea NRF-2022R1A5A7033499
6 · The paper itself

Abstract

What determines the price of an artwork? This article leverages a comprehensive and novel dataset on art auctions of contemporary artists to examine the impact of social and visual features on the valuation of artworks across global markets. Our findings indicate that social signals allow us to predict the price of artwork exceptionally well, even approaching the professionals' prediction accuracy, while the visual features play a marginal role. This pattern is especially pronounced in emerging markets, supporting the idea that social signals become more critical when it is more difficult to assess the quality. These results strongly support that the value of artwork is largely shaped by social factors, particularly in emerging markets where a stronger preference for "buying an artist" than "buying an artwork." Additionally, our study shows that it is possible to boost experts' performance, highlighting the potential benefits of human-machine models in uncertain or rapidly changing markets, where expert knowledge is limited.

Identifiers

PMID38773156
PMCPMC11109285

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.