Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
Authors: Mengzi Amy Guo, Donghao Ying, Javad Lavaei, Zuo-Jun Shen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work is dedicated to the algorithm design in a competitive framework, with the primary goal of learning a stable equilibrium. ... Despite the absence of typical properties required for the convergence of online games, such as strong monotonicity and variational stability, we demonstrate that under diminishing step-sizes, the price and reference price paths generated by OPGA converge to the unique SNE, thereby achieving the no-regret learning and a stable market. Moreover, with appropriate step-sizes, we prove that this convergence exhibits a rate of O(1/t). |
| Researcher Affiliation | Academia | Mengzi Amy Guo IEOR Department UC Berkeley EMAIL Donghao Ying IEOR Department UC Berkeley EMAIL Javad Lavaei IEOR Department UC Berkeley EMAIL Zuo-Jun Max Shen IEOR Department UC Berkeley EMAIL |
| Pseudocode | Yes | Algorithm 1 Online Projected Gradient Ascent (OPGA) |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes 'numerical experiments' but does not mention the use of any publicly available datasets. The experiments are based on simulated parameters, not real-world data with public access. |
| Dataset Splits | No | The paper describes numerical experiments, but these experiments do not involve real-world datasets with typical training, validation, and test splits. The paper focuses on theoretical convergence. |
| Hardware Specification | No | The paper describes numerical experiments but does not provide any specific details about the hardware (e.g., CPU, GPU models) used to run these experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and numerical experiments but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | Figure 1: Price and reference price paths for Examples 1, 2, and 3, where the parameters are (a H, b H, c H) = (8.70, 2.00, 0.82), (a L, b L, c L) = (4.30, 1.20, 0.32), (r0 H, r0 L) = (0.10, 2.95), (p0 H, p0 L) = (4.85, 4.86), and α = 0.90. ... in Example 1 (see Figure 1a) corroborates Theorem 5.1 by demonstrating that the price and reference price trajectories converge to the unique SNE when we choose diminishing step-sizes that fulfill the criteria specified in Theorem 5.1. ... In particular, Example 1 (see Figure 1a) corroborates Theorem 5.1 by demonstrating that the price and reference price trajectories converge to the unique SNE when we choose diminishing step-sizes that fulfill the criteria specified in Theorem 5.1. By comparison, the over-large constant step-sizes employed in Example 2 (see Figure 1b) fails to ensure convergence, leading to cyclic patterns in the long run. |