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 [1].
On the Interplay between Social Welfare and Tractability of Equilibria
Authors: Ioannis Anagnostides, Tuomas Sandholm
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Example A.11. ...Now, through a numerical simulation we draw the following conclusion: Although Po AG = 1, for T 1 OGD with learning rate η := 0.01 and initialization (ˆx(1) 1 , ˆx(1) 2 , ) := ((0.5, 0.25, 0.25), (0.25, 0.5, 0.25), ) satisfies NEGAP(x(t)) 0.1875 for any t JTK, where (x(t))t 1 is the sequence of iterates produced by OGD. We also note that the specific value for the learning rate specified above is used for concreteness, and the conclusion is not tied to that specific value. In particular, we conduct experiments on a set of random normal-form games. Some illustrative results for 10 random games are demonstrated in Figure 1. |
| Researcher Affiliation | Collaboration | Ioannis Anagnostides Carnegie Mellon University EMAIL Tuomas Sandholm Carnegie Mellon University Strategic Machine, Inc. Strategy Robot, Inc. Optimized Markets, Inc. EMAIL |
| Pseudocode | No | The paper provides mathematical update rules for OGD and CGD but not a structured pseudocode block or algorithm. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | No | The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year) for a publicly available or open dataset. It describes generating 'random normal-form games' and using 'Shapley’s game' as constructed examples for its simulations. |
| Dataset Splits | No | The paper describes running numerical simulations and experiments but does not specify dataset split information for training, validation, or testing subsets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Example A.11. ...for T 1 OGD with learning rate η := 0.01 and initialization (ˆx(1) 1 , ˆx(1) 2 , ) := ((0.5, 0.25, 0.25), (0.25, 0.5, 0.25), ) satisfies NEGAP(x(t)) 0.1875 for any t JTK, where (x(t))t 1 is the sequence of iterates produced by OGD. |