Yifan Wu, Ramesh Johari, Vasilis Syrgkanis, Gabriel Y. Weintraub
arXiv 18 Oct 2024 · cs.GT · 1 citations (OpenAlex)
arXiv:2410.14904 · PDF · DOI · OpenAlex · Extracted main text
We consider a retailer running a switchback experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price $p$ from a set of predefined prices that consist of a reference price and a few discounted price levels. The goal is to estimate the demand gradient at the reference price point, with the goal of adjusting the reference price to improve revenue after the experiment. In our model, in each period, a unit mass of buyers arrives on the market, with values distributed based on a time-varying process. Crucially, buyers are forward looking with a discounted utility and will choose to not purchase now if they expect to face a discounted price in the near future. We show that forward-looking demand introduces bias in naive estimators of the demand gradient, due to intertemporal interference. Furthermore, we prove that there is no estimator that uses data from price experiments with only two price points that can recover the correct demand gradient, even in the limit of an infinitely long experiment with an infinitesimal price discount. Moreover, we characterize the form of the bias of naive estimators. Finally, we show that with a simple three price level experiment, the seller can remove the bias due to strategic forward-looking behavior and construct an estimator for the demand gradient that asymptotically recovers the truth.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jun Li, Nelson Granados, and Serguei Netessine (2014) Are consumers strategic? structural estimation from the air-travel industry | 0.644 | 4 | 1 | 100% |
| 2 | Xuanming Su (2007) Intertemporal pricing with strategic customer behavior | 0.644 | 4 | 1 | 100% |
| 3 | Yiwei Chen and Vivek F Farias (2018) Robust dynamic pricing with strategic customers | 0.585 | 3 | 1 | 100% |
| 4 | Wassim Dhaouadi, Ramesh Johari, and Gabriel Y Weintraub (2023) Price experimentation and interference in online platforms self | 0.585 | 3 | 1 | 100% |
| 5 | Shipra Agrawal, Steven Yin, and Assaf Zeevi (2021) Dynamic pricing and learning under the bass model | 0.511 | 2 | 1 | 100% |
| 6 | Vivek Farias, Andrew Li, Tianyi Peng, and Andrew Zheng (2022) Markovian interference in experiments | 0.511 | 2 | 1 | 100% |
| 7 | Ramesh Johari, Hannah Li, Inessa Liskovich, and Gabriel Y Weintraub (2022) Experimental design in two-sided platforms: An analysis of bias self | 0.511 | 2 | 1 | 100% |
| 8 | N Bora Keskin and Assaf Zeevi (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies | 0.511 | 2 | 1 | 100% |
| 9 | N Bora Keskin and Assaf Zeevi (2017) Chasing demand: Learning and earning in a changing environment | 0.511 | 2 | 1 | 100% |
| 10 | Shuangning Li, Ramesh Johari, Xu Kuang, and Stefan Wager (2023) Experimenting under stochastic congestion self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 49 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Validating Causal Message Passing Against Network-Aware Methods on Real Experiments | 0.405 | 1 | 1 |