Nathanael Jo — accepted AI-conference papers

2025

Homogeneous Algorithms Can Reduce Competition in Personalized Pricing

Nathanael Jo, Ashia C. Wilson, Kathleen Creel, Manish Raghavan

NeurIPS 2025poster

Firms' algorithm development practices are often homogeneous Whether firms train algorithms on similar data or rely on similar pre-trained models, the result is correlated predictions. In the context of personalized pricing, correlated algorithms can be viewed as a means to collude among competing f…

Cited by 5 Source ↗Scholar ↗

2023

Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results

Nathanael Jo, Bill Tang, Kathryn Dullerud, Sina Aghaei, Eric Rice, Phebe Vayanos

AAAI 2023technical

We study critical systems that allocate scarce resources to satisfy basic needs, such as homeless services that provide housing. These systems often support communities disproportionately affected by systemic racial, gender, or other injustices, so it is crucial to design these systems with fairness…

Cited by 10 Source ↗PDF ↗Scholar ↗