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].
Tradeoffs between Incentive Mechanisms in Boolean Games
Authors: Vadim Levit, Zohar Komarovsky, Tal Grinshpoun, Amnon Meisels
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An extensive empirical evaluation addresses these two questions and uses social-network-based Boolean games which initially do not have a PNE, but for which there is at least one taxation scheme that can secure its existence. An effective distributed search algorithm for Asymmetric Distributed Constraint Optimization Problems (ADCOP) [Grinshpoun et al., 2013] is used for ο¬nding the appropriate side payments. An empirical evaluation demonstrates the properties of the two mechanisms on the family of social-network-based Boolean games. |
| Researcher Affiliation | Academia | Vadim Levit, Zohar Komarovsky Ben-Gurion University of the Negev Be er-Sheva, Israel EMAIL Tal Grinshpoun Ariel University Ariel, Israel EMAIL Amnon Meisels Ben-Gurion University of the Negev Be er-Sheva, Israel EMAIL |
| Pseudocode | No | The paper describes procedures and algorithms in prose (e.g., Section 5.3), but does not include any formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing source code, nor does it include links to a code repository for the methodology described. |
| Open Datasets | No | First, an Erd os-R enyi [1959] random network was generated. Next, the Boolean game was constructed according to the rules described in Section 5.1, where the cost of assigning was chosen from the range [100, 200) and the cost of assigning from the range [0, 100). The problems were randomly generated for each experiment, indicating they are not based on an existing public dataset with concrete access information. |
| Dataset Splits | No | The paper mentions that problems were randomly generated but does not provide specific details about training, validation, or test splits (e.g., percentages, sample counts, or cross-validation setup). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as CPU/GPU models, memory, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions the use of the "k-ary Sync ABB-1ph algorithm [Levit et al., 2013b]" but does not provide specific version numbers for any software, libraries, or dependencies used in the experiments. |
| Experiment Setup | Yes | First, an Erd os-R enyi [1959] random network was generated. Next, the Boolean game was constructed according to the rules described in Section 5.1, where the cost of assigning was chosen from the range [100, 200) and the cost of assigning from the range [0, 100). |