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Quantifying and predicting success in show business.

Abstract：

:In certain artistic endeavours-such as acting in films and TV, where unemployment rates hover at around 90%-sustained productivity (simply making a living) is probably a better proxy for quantifying success than high impact. Drawing on a worldwide database, here we study the temporal profiles of activity of actors and actresses. We show that the dynamics of job assignment is well described by a "rich-get-richer" mechanism and we find that, while the percentage of a career spent active is unpredictable, such activity is clustered. Moreover, productivity tends to be higher towards the beginning of a career and there are signals preceding the most productive year. Accordingly, we propose a machine learning method which predicts with 85% accuracy whether this "annus mirabilis" has passed, or if better days are still to come. We analyse actors and actresses separately, also providing compelling evidence of gender bias in show business.

journal_name

Nat Commun

journal_title

Nature communications

authors

Williams OE,Lacasa L,Latora V

doi

10.1038/s41467-019-10213-0

subject

Has Abstract

pub_date

2019-06-04 00:00:00

pages

2256

issue

1

issn

2041-1723

pii

10.1038/s41467-019-10213-0

journal_volume

10

pub_type

杂志文章

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