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.
A Statistical Decision-Theoretic Framework for Social Choice
Authors: Hossein Azari Soufiani, David C. Parkes, Lirong Xia
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 16,126 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,761 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 | We compare Bayesian estimators, which minimize Bayesian expected loss, for the Mallows model and the Condorcet model respectively, and the Kemeny rule. We consider various normative properties, in addition to computational complexity and asymptotic behavior. In particular, we show that the Bayesian estimator for the Condorcet model satisfies some desired properties such as anonymity, neutrality, and monotonicity, can be computed in polynomial time, and is asymptotically different from the other two rules when the data are generated from the Condorcet model for some ground truth parameter. |
| Researcher Affiliation | Collaboration | EMAIL, Google Research, New York, NY 10011, USA. The work was done when the author was at Harvard University. EMAIL, Harvard University, Cambridge, MA 02138, USA. EMAIL, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. |
| Pseudocode | No | The paper describes mathematical models and theorems but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of its source code. |
| Open Datasets | No | In our experiments, data are generated by M2 ϕ given W in Figure 2 (a) for m = 5, n {100, 200, . . . , 2000}, and ϕ {0.1, 0.5, 0.9}. The paper uses synthetic data which is generated, but does not provide access information (link, DOI, citation) to this generated dataset. |
| Dataset Splits | No | The paper describes the generation of synthetic data for experiments but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU, memory, specific cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software or libraries used in the experiments. |
| Experiment Setup | Yes | In our experiments, data are generated by M2 ϕ given W in Figure 2 (a) for m = 5, n {100, 200, . . . , 2000}, and ϕ {0.1, 0.5, 0.9}. For each setting we generate 3000 profiles, and calculate the fraction of trials in which g and Kemeny are different. |