Award-winning research from Professor Laura Doval tackles the “limited commitment” problem in economics, offering a model that helps governments and firms adjust rules and strategies based on new information over time.
In new research, Professors Dante Donati and Hortense Fong find that the brief TikTok outage in January benefited Meta as advertisers turned to its platforms to reach users. Small businesses, less able to switch, lost out.
Columbia Business School researchers discover that the amount of variety in a consumer’s past purchases predicts their openness to algorithm-based recommendations.
In her book Mindmasters, Professor Sandra Matz shows how big data can offer key insights into the most intimate aspects of our psyche.
Analyzing the language of social media users reveals surprising predictors of fake news sharing, offering new strategies to curb misinformation and foster healthier online communities.
Research from Columbia Business School examines the challenges posed by generative AI in hiring and entrepreneurial pitching, offering insights into when AI helps — and when it hinders.
Research from Columbia Business School Reveals How Consumers Perceive Pricing Set by Algorithms
Digital media platforms such as Netflix, Facebook, and TikTok are under increasing scrutiny regarding the ethical implications of their personalized content recommendations. To combat bias and avoid skewed content suggestions, sophisticated algorithms can perform additional layers of analysis to ensure that recommendations give space to topics such as racial equity, sexuality, and political persuasion. However, doing this in real time with the conventional algorithmic approach would greatly increase page-load times and create a frustrating user experience.