# Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing

arXiv: https://arxiv.org/abs/2208.09372 · HTML: https://mlsanity.com/paper/2208.09372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [Time-uniform, nonparametric, nonasymptotic confidence sequences](https://mlsanity.com/paper/1810.08240/) (2018)
- [An Information-Theoretic Analysis of Thompson Sampling](https://mlsanity.com/paper/1403.5341/) (2014)
- [Learning to Optimize via Information-Directed Sampling](https://mlsanity.com/paper/1403.5556/) (2014)
- [Information Directed Sampling for Linear Partial Monitoring](https://mlsanity.com/paper/2002.11182/) (2020)
- [Asymptotically Optimal Information-Directed Sampling](https://mlsanity.com/paper/2011.05944/) (2020)
- [Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search](https://mlsanity.com/paper/1806.08211/) (2018)
- [Correlated Multi-armed Bandits with a Latent Random Source](https://mlsanity.com/paper/1808.05904/) (2018)
- [Multi-armed Bandit Requiring Monotone Arm Sequences](https://mlsanity.com/paper/2106.03790/) (2021)
- [A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting](https://mlsanity.com/paper/1810.08164/) (2018)

## Built on by (most cited first, 1 of 1)

- [Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data](https://mlsanity.com/paper/2406.13130/) (2024)

## Abstract (arXiv)

This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts. The agents run price experiments to learn about each product's demand curve and the profit-maximizing price, while being aware of market environment shifts to avoid high opportunity costs from offering sub-optimal prices. The proposed ACIDP extends information-directed sampling (IDS) algorithms from statistical machine learning to include microeconomic choice theory, with a novel pricing strategy auditing procedure to escape sub-optimal pricing after market environment shift. The proposed ACIDP outperforms competing bandit algorithms including Upper Confidence Bound (UCB) and Thompson sampling (TS) in a series of market environment shifts.