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The Successive Alleys Test of Anxiety in Mice and Rats

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Published on: June 17, 2013

Quantifying and predicting success in show business.

Oliver E Williams¹, Lucas Lacasa², Vito Latora^3,4,5,6

¹School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK. o.e.williams@qmul.ac.uk.

Nature Communications

|June 6, 2019

Summary

This study analyzes actor and actress career activity, revealing a "rich-get-richer" job assignment dynamic. A machine learning model predicts career productivity peaks with 85% accuracy, also highlighting gender bias in show business.

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Area of Science:

Sociology of the Arts
Computational Social Science
Entertainment Industry Studies

Background:

The acting profession faces high unemployment, making sustained productivity a key success metric.
Understanding career dynamics is crucial for actors and actresses navigating the entertainment industry.
Previous research often focuses on impact rather than consistent work in artistic fields.

Purpose of the Study:

To analyze temporal activity patterns of actors and actresses globally.
To identify mechanisms governing job assignment and career progression in acting.
To develop a predictive model for peak career productivity and investigate gender bias.

Main Methods:

Utilized a worldwide database of actor and actress activity.
Applied network analysis to model job assignment dynamics, identifying a
rich-get-richer
mechanism.
Developed and validated a machine learning model for predicting career productivity peaks.

Main Results:

Career activity is clustered and follows a
rich-get-richer
pattern.
Productivity often peaks early in a career, with predictive signals identified.
The machine learning model achieved 85% accuracy in predicting the "annus mirabilis" (most productive year).
Significant gender bias was observed in show business, with separate analyses for male and female actors.

Conclusions:

Sustained productivity, not just high impact, is a critical measure of success for actors.
Career trajectories in acting are predictable to a degree, with early-career productivity being a key factor.
The findings underscore the need to address gender disparities within the entertainment industry.