Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Approximately Stable Pricing for Coordinated Purchasing of Electricity
Authors: Andrew Perrault, Craig Boutilier
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,457 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,178 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show these schemes achieve a high degree of stability in practice and can be made more stable by sacrificing small amounts (< 2%) of social welfare. |
| Researcher Affiliation | Collaboration | Andrew Perrault and Craig Boutilier Department of Computer Science University of Toronto EMAIL Currently on leave at Google, Inc., Mountain View. |
| Pseudocode | No | The paper describes procedures in prose but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for its methodology. |
| Open Datasets | Yes | We use a model of the US residential energy market. Building characteristics are based on the 2011 Buildings Energy Data Book [D&R International, Ltd., 2012]. |
| Dataset Splits | No | The paper mentions running '50 trials for each experiment' and using '50 consumers, 2 producers, 4 profiles per consumer, 24 time periods', but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | We are able to solve instances with 5000 consumers, 2 producers, 4 profiles and 24 time periods in less than 15 minutes on a 12x2.6GHz, 32GB machine using CPLEX 12.51. |
| Software Dependencies | Yes | We are able to solve instances with 5000 consumers, 2 producers, 4 profiles and 24 time periods in less than 15 minutes on a 12x2.6GHz, 32GB machine using CPLEX 12.51. |
| Experiment Setup | Yes | In all experiments, we use 50 consumers, 2 producers, 4 profiles per consumer, 24 time periods, and run 50 trials for each experiment. We sample 30 random join orders, a number which was determined empirically to induce convergence. |