Aligning Popularity and Quality in Online Cultural Markets
By Pascal Van Hentenryck, Andrés Abeliuk, Franco Berbeglia, Felipe Maldonado, Gerardo Berbeglia. 2021
20 citations recorded by the source corpus.
Social influence is ubiquitous in cultural markets and plays an important role in recommendations for books, songs, and news articles to name only a few. Yet social influence is often presented in a bad light, often because it supposedly increases market unpredictability. Here we study a model of trial-offer markets, in which participants try products and later decide whether to purchase. We consider a simple policy which recovers product quality and ranks the products by quality when presenting them to market participants. We show that, in this setting, market efficiency always benefits from social influence. Moreover, we prove that the market converges almost surely to a monopoly for the product of highest quality, making the market both predictable and asymptotically optimal. Computational experiments confirm that the quality ranking policy quickly identifies "blockbusters", outperforms other policies, and is highly predictable.
Subfields: Management Science and Operations Research, Sociology and Political Science, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
In Sociology and Political Science
- Kristina Lerman
- Filippo Menczer
- Alessandro Flammini
- Giovanni Luca Ciampaglia
- Gerardo Berbeglia
- Andrés Abeliuk
- Pascal Van Hentenryck
- Tad Hogg
In Statistical and Nonlinear Physics
- Kristina Lerman
- Filippo Menczer
- Alessandro Flammini
- Giovanni Luca Ciampaglia
- Tad Hogg
- Azadeh Nematzadeh
- Gerardo Berbeglia
- Andrés Abeliuk
Citations in the corpus
- How algorithmic popularity bias hinders or promotes quality (2017, background): “studied a model of trial-offer markets to analyze the effect of social influence in market predictability [14].”
- Taming the Unpredictability of Cultural Markets with Social Influence (2017, methodology): “In particular, we designed the experiment to be close enough to the setting of trial-offer markets for quantitative comparisons with prior analytical results on the quality ranking [25].”