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..
Trembling-Hand Perfection in Extensive-Form Games with Commitment
Authors: Gabriele Farina, Alberto Marchesi, Christian Kroer, Nicola Gatti, Tuomas Sandholm
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We initiate the study of equilibrium refinements based on trembling-hand perfection in extensiveform games with commitment strategies... We also prove that determining the existence of a Stackelberg equilibrium refined or not that gives the leader expected value at least ν is NP-hard. This significantly extends prior complexity results that were specific to strong Stackelberg equilibrium. |
| Researcher Affiliation | Academia | 1 Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA 2 Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133, Milan, Italy EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured code-like blocks describing a procedure. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using datasets. The examples provided (Figure 2 and Figure 3) are conceptual game theory constructions, not empirical datasets with access information. |
| Dataset Splits | No | The paper is theoretical and does not perform empirical validation on datasets, thus no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not provide details about specific ancillary software or library versions used for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |