Double Auctions with Two-sided Bandit Feedback
Authors: Soumya Basu, Abishek Sankararaman
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Simulation Study We perform synthetic studies to augment our theoretical guarantees. For a fixed system of N buyers, M sellers, K participants, and gap, the rewards are Bernoulli, with means themselves chosen uniformly at random. We vary the confidence width of the buyers, b, and seller, s, in [ 1, 2]. Next we simulate the performance of the UCB( b) and LCB( s) over 100 independent sample paths with T = 50k. We report the mean, 25% and 75% value of the trajectories. We plot the cumulative regret of the buyers, Rb,i(t), and the sellers, Rs,j(t), the number of matches in the system K(t), and the price difference (p(t) p ). In Figure 2, we have a 8 8 system with K = 5. We see that K(t) converges to 5, where as (p(t) p ) converges to 0. The social regret grows as log(T). The participant and non-participant individual regret of this instance is presented in the appendix in Figure 3. |
| Researcher Affiliation | Collaboration | Soumya Basu Google Mountain View basusoumya@google.com Abishek Sankararaman AWS abishek.90@gmail.com 0Work done when AS was affiliated with UC Berkeley. |
| Pseudocode | No | The paper describes the bidding strategy and mechanism in text and equations (Equation 2) but does not provide a separate, structured pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository. |
| Open Datasets | No | The paper uses synthetic data ("rewards are Bernoulli, with means themselves chosen uniformly at random") and does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulation parameters like "100 independent sample paths with T = 50k" but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes the simulation setup (e.g., "100 independent sample paths with T = 50k") but does not provide any specific hardware details like GPU/CPU models or memory used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or libraries with version numbers (e.g., Python, PyTorch, etc.) that were used to implement the algorithms or run the simulations. |
| Experiment Setup | Yes | Next we simulate the performance of the UCB( b) and LCB( s) over 100 independent sample paths with T = 50k. We report the mean, 25% and 75% value of the trajectories. We plot the cumulative regret of the buyers, Rb,i(t), and the sellers, Rs,j(t), the number of matches in the system K(t), and the price difference (p(t) p ). In Figure 2, we have a 8 8 system with K = 5, = 0.2, 1 = 4, and 2 = 8. We vary the confidence width of the buyers, b, and seller, s, in [ 1, 2]. |