PricingBandits
Multi-Armed Bandit Approaches to Pricing Experiments
Implements multi-armed bandit approaches for pricing experiments with an unknown demand curve, as developed in Weaver, Kumar, and Jain, "Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve" <doi:10.1287/mksc.2022.0247>. Includes Upper Confidence Bound (UCB) and Thompson Sampling (TS) baselines, Gaussian process variants ('GP-UCB', 'GP-TS'), monotonic Gaussian process variants that constrain demand to be weakly decreasing in price, and heterogeneous-noise extensions. The willingness-to-pay distribution is fully user-specified via a vector of consumer valuations, so any demand environment can be simulated or replayed.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.0 |
rolling linux/jammy R-4.5 | PricingBandits_2.0.0.tar.gz |
123.2 KiB |
2.0.0 |
rolling linux/noble R-4.5 | PricingBandits_2.0.0.tar.gz |
123.2 KiB |
2.0.0 |
rolling source/ R- | PricingBandits_2.0.0.tar.gz |
116.0 KiB |
2.0.0 |
latest linux/jammy R-4.5 | PricingBandits_2.0.0.tar.gz |
123.2 KiB |
2.0.0 |
latest linux/noble R-4.5 | PricingBandits_2.0.0.tar.gz |
123.2 KiB |
2.0.0 |
latest source/ R- | PricingBandits_2.0.0.tar.gz |
116.0 KiB |
2.0.0 |
2026-04-23 source/ R- | PricingBandits_2.0.0.tar.gz |
0 B |