Dynamic Pricing Lab

Live dashboard: https://mohammadi.cv/dynamic-pricing-dashboard/ (also reachable via https://mohammadi-hadi.github.io/dynamic-pricing-dashboard/)

An interactive simulator for dynamic pricing with demand learning. A monopolist sells over a season, learning unknown demand by Thompson sampling, while customers may strategically wait for lower prices and a one-time advertising effort lifts willingness-to-pay before fading away.

Set your own scenario — price menu, demand, customer patience, advertising effort/efficiency/forgetting, learning policy — run it, and compare revenue, regret, price usage, and posterior learning across market conditions.

The dashboard runs entirely in your browser (Python via WebAssembly, using stlite): nothing is installed and no data leaves your machine. The first visit downloads the runtime (~40 MB), so give it half a minute.

What you can explore

• Scenario lab — compare myopic vs. forward-looking customers, with and without advertising: cumulative net revenue with confidence bands, regret against the best fixed price, how often each price is offered, and how the seller's posterior beliefs converge.
• Optimal advertising — sweep the one-time effort A and find the interior optimum A*, separately for patient and myopic customers, against the full-information benchmark A*₍FI₎ = p₁θ/(2(1−γ)).
• Patience × advertising — a heatmap of net revenue over the (patience, effort) plane with the ridge A*(λ): the optimal effort rises as customers get more patient.
• Learning policies — pit plain Thompson sampling against sliding-window, discounted, and decay-aware variants under the advertising-induced demand drift.
• Three demand models — Bernoulli purchases, Normal revenue, and a Poisson purchase-count variant with a Gamma posterior (the paper's unbounded-demand robustness check).

Model in one paragraph

Prices come from a menu p₁ < … < p_K with unknown purchase probabilities. Forward-looking customers (patience λ) buy now only when a future discount is not credible given the observed price history, and a fraction ρ of waiters returns next period. A one-time advertising effort A, paid up front at cost A², lifts every customer's willingness-to-pay by c(t) = θAγ^(t−1) — demand drifts exactly while the seller is learning, but the drift is chosen and paid for by the seller: the cumulative lift is at most θA/(1−γ) and the total variation of the demand path at most θA. Full details are on the dashboard's Model notes tab.

Install

The repository is a Python package, dynamic-pricing-lab, on PyPI:

pip install "dynamic-pricing-lab[dashboard]"
dynamic-pricing-lab   # opens the dashboard at http://localhost:8501

(or straight from GitHub: pip install "dynamic-pricing-lab[dashboard] @ git+https://github.com/mohammadi-hadi/dynamic-pricing-dashboard.git")

Leave out [dashboard] for the NumPy-only simulation core. Prebuilt wheels are attached to GitHub releases, and the dashboard is also published as a container image on GitHub Packages:

docker run --rm -p 8501:8501 ghcr.io/mohammadi-hadi/dynamic-pricing-dashboard

Use as a library

The simulation core has no dependencies beyond NumPy and can be used on its own:

from dynamic_pricing_lab import Scenario, simulate

out = simulate(Scenario(patience=0.99, ad_A=5, ad_theta=0.05, ad_gamma=0.998))
print(out["revenue"].sum(axis=1).mean())  # mean net season revenue

simulate_fixed and oracle_cum — the fixed-price benchmark behind the regret curves — are exported too.

All replicates of a scenario are simulated simultaneously (vectorized across seasons), so a 50-replicate, 2,000-period run takes about a second natively and a few seconds in the browser.

Develop

git clone https://github.com/mohammadi-hadi/dynamic-pricing-dashboard.git
cd dynamic-pricing-dashboard
pip install -e ".[dashboard]"
dynamic-pricing-lab   # or: streamlit run src/dynamic_pricing_lab/app.py
pytest                # smoke-tests for the simulation core

Repository layout

Citing

If you use this software or build on its model in academic work, please cite it (GitHub's "Cite this repository" button uses CITATION.cff):

@software{mohammadi_dynamic_pricing_lab_2026,
  author  = {Mohammadi, Hadi},
  title   = {Dynamic Pricing Lab},
  year    = {2026},
  version = {1.1.1},
  doi     = {10.5281/zenodo.21802866},
  url     = {https://github.com/mohammadi-hadi/dynamic-pricing-dashboard},
  license = {MIT}
}

Author

Hadi Mohammadi — companion tool to a research project on dynamic pricing with demand learning, forward-looking customers, and advertising. MIT-licensed: reuse is welcome, but the copyright notice must be preserved and academic use should credit the author.