Assessing the Robustness of Cremer-McLean with Automated Mechanism Design
Authors: Michael Albert, Vincent Conitzer, Giuseppe Lopomo
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we use an automated mechanism design approach to assess how sensitive the Cremer-Mc Lean result is to relaxing its main technical assumption. ... We perform simulations using problem instances generated under two conditions, one where the instances are deterministically specified and one where they are randomly generated. ... Figures 3 and 4 demonstrate the improvements in runtime for both calculating the full mechanism using the algorithm corresponding to Definition 5 and just calculating the revenue only as in Theorem 5 relative to solving the linear program from Definition 2. |
| Researcher Affiliation | Academia | Michael Albert The Ohio State University Fisher School of Business 2100 Neil Ave., Fisher Hall 844 Columbus, OH 43210, USA Michael.Albert@fisher.osu.edu Vincent Conitzer Department of Computer Science Duke University Durham, NC 27708, USA conitzer@cs.duke.edu Giuseppe Lopomo Duke University The Fuqua School of Business 100 Fuqua Drive Durham, NC 27708, USA glopomo@duke.edu |
| Pseudocode | No | The paper describes algorithms and definitions, but does not present them in a structured pseudocode block or clearly labeled algorithm section. |
| Open Source Code | No | The paper does not contain an explicit statement that the authors are releasing their code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper describes generating 'problem instances' for simulations, but does not refer to specific public datasets, provide links, or cite external dataset sources. |
| Dataset Splits | No | The paper mentions 'train', 'validation', and 'test' in the context of the general mechanism design problem (e.g., Definition 2 constraints) but does not describe how the data for *their* empirical simulations was split into these sets for their experiments. |
| Hardware Specification | Yes | We use a core i7 3770 CPU with 8 GB of memory. |
| Software Dependencies | Yes | The linear program in Definition 2 is solved using CPLEX with a .lp file specifying the full linear program. The algorithms corresponding to Definition 5 and calculating optimal revenue only (as in Theorem 5) are computed using Matlab R2013b. |
| Experiment Setup | No | The paper describes how problem instances are deterministically or randomly generated for simulations, but it does not specify hyperparameter values or system-level training settings for their algorithms beyond the description of the problem instances themselves. For example, it doesn't mention learning rates, batch sizes, or optimizers, as would be common for ML-based experiments. |