PricingBandits

Multi-armed bandit approaches to pricing experiments with an unknown demand curve, from:

Weaver, I. N., Kumar, V., & Jain, L. Nonparametric Pricing Bandits Leveraging Informational Externalities to Learn the Demand Curve. Marketing Science.

This package contains the streamlined, core bandit machinery. The full replication code for the paper (WTP construction, expected-reward analysis, figures) lives in the separate replication repository; results from this package are validated seed-for-seed bit-identical against it.

Looking for the Python version? See pricingbandits-py.

Installation

# install.packages("devtools")
devtools::install_github("ian-weaver/PricingBandits")

Quick start

You supply one consumer valuation (willingness to pay) per round — drawn from any distribution you like — plus a set of candidate prices. The chosen policy prices each arriving consumer and learns from buy/no-buy feedback alone.

library(PricingBandits)

set.seed(1)
valuations <- rbeta(2500, 2, 9)          # any WTP process you like

out <- PricingBandit(valuations,
                     prices     = seq(10)/10,
                     policy     = "GP-TS-M",
                     batch_size = 10)

table(out$PricesTested)
attr(out, "diagnostics")

Policies

Name	Description
`"UCB"`	Upper Confidence Bound on independent arms
`"TS"`	Thompson Sampling with Beta posteriors per arm
`"GP-UCB"`, `"GP-TS"`	Gaussian-process variants — arms correlated through a GP demand curve (first informational externality)
`"GP-UCB-M"`, `"GP-TS-M"`	Monotonic variants — demand draws weakly decreasing everywhere by construction, via basis-function reconstruction from derivative-constrained draws (second informational externality)

Options: hetero = TRUE enables the heterogeneous-noise extension for GP variants; reset = n wipes the history every n consumers (time-varying demand); num_knots controls the monotonic variants’ constraint grid and defaults to 11 regardless of the number of arms — with many more knots the truncated sampler operates in a near-degenerate high-dimensional space and can break down, so keep it modest even for dense price grids. timeout (default 5 seconds) bounds each truncated-sampling attempt before the fallback chain advances; raise it on slow machines, lower it to fail over to cheaper approximations sooner.

Diagnostics

Every run counts the numerical fallback paths (hyperparameter-optimization failures, truncated-sampler timeouts, last-resort samplers); in normal operation these are all zero. See GetDiagnostics().