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..
Complexity of Computing the Shapley Value in Games with Externalities
Authors: Oskar Skibski2244-2251
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we fill a gap in the literature by determining what is the complexity of computing all the five extensions of the Shapley value in games represented as embedded and weighted MC-nets. Specifically, we show that only two out of five extensions can be computed in polynomial time for embedded MC-nets and only one can be computed in polynomial time for weighted MC-nets (unless P = NP). For all other values we show that computation is #P-complete (see Table 1). Interestingly, our results are strongly based on graph theory techniques. |
| Researcher Affiliation | Academia | Oskar Skibski Institute of Informatics, University of Warsaw, Poland EMAIL |
| Pseudocode | No | The paper describes algorithmic approaches (e.g., dynamic programming) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not use datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not discuss data splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training settings. |