A Game-Theoretic Analysis of Catalog Optimization
Authors: Joel Oren, Nina Narodytska, Craig Boutilier
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a game-theoretic model for analyzing the vendor catalog optimization problem in the face of competing vendors. We show that computing a best response is intractable in general, but can be solved by dynamic programming given certain informational or structural assumptions about consumer preferences. We also analyze conditions under which pure Nash equilibria exist and provide several price of anarchy/stability results. We first consider the algorithmic task of computing a vendor’s best response... However, we provide an efficient dynamic programming (DP) method... We then analyze the stability of the game... In contrast, under impartial culture, we show that pure equilibra exist using simple best-response dynamics, and can be computed efficiently. Finally, we provide several Price of Anarchy/Stability results... |
| Researcher Affiliation | Academia | Joel Oren and Nina Narodytska and Craig Boutilier Department of Computer Science, University of Toronto {oren,ninan,cebly}@cs.toronto.edu |
| Pseudocode | Yes | Algorithm 1: The best response finding algorithm for single-peaked, L-truncated preferences. Algorithm 2: Dynamic programming algorithm for best-response given a Mallows distribution. Algorithm 3: Best response dynamics under IC. Algorithm 4: Finding a Nash equilibrium |
| Open Source Code | No | Explanation: The paper does not contain any statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | Explanation: The paper deals with theoretical models of consumer preferences (e.g., Mallows ϕ-distribution, impartial culture) and analyzes their properties; it does not utilize or provide access information for a publicly available dataset for empirical training or evaluation. |
| Dataset Splits | No | Explanation: The paper is theoretical and does not report on experiments conducted with datasets; therefore, no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | Explanation: The paper is theoretical and focuses on mathematical modeling and algorithmic analysis, without describing any computational experiments or the specific hardware used to perform them. |
| Software Dependencies | No | Explanation: The paper is theoretical and does not describe implementation details or dependencies with specific version numbers for software components required to replicate experiments. |
| Experiment Setup | No | Explanation: The paper is theoretical and does not describe computational experiments, thus it does not provide specific details on hyperparameters, training configurations, or system-level settings for an experimental setup. |