Suppr超能文献

量化艺术中的声誉和成功。

Quantifying reputation and success in art.

机构信息

Network Science Institute, Northeastern University, Boston, MA, USA.

Harvard Institute for Quantitative Social Sciences, Cambridge, MA, USA.

出版信息

Science. 2018 Nov 16;362(6416):825-829. doi: 10.1126/science.aau7224. Epub 2018 Nov 8.

Abstract

In areas of human activity where performance is difficult to quantify in an objective fashion, reputation and networks of influence play a key role in determining access to resources and rewards. To understand the role of these factors, we reconstructed the exhibition history of half a million artists, mapping out the coexhibition network that captures the movement of art between institutions. Centrality within this network captured institutional prestige, allowing us to explore the career trajectory of individual artists in terms of access to coveted institutions. Early access to prestigious central institutions offered life-long access to high-prestige venues and reduced dropout rate. By contrast, starting at the network periphery resulted in a high dropout rate, limiting access to central institutions. A Markov model predicts the career trajectory of individual artists and documents the strong path and history dependence of valuation in art.

摘要

在难以客观量化绩效的人类活动领域,声誉和影响网络在决定资源和奖励的获取方面起着关键作用。为了了解这些因素的作用,我们重构了 50 万名艺术家的展览历史,绘制了捕捉艺术机构间流动的合作展览网络。该网络中的中心度捕捉到了机构的声望,使我们能够根据进入梦寐以求的机构的机会来探索个体艺术家的职业轨迹。早期进入享有声望的中心机构可以让艺术家一生都有机会进入高声望的场馆,并降低辍学率。相比之下,从网络边缘开始则会导致高辍学率,限制进入中心机构的机会。马尔可夫模型预测了个体艺术家的职业轨迹,并记录了艺术估值中的强路径和历史依赖性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验