Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems
Authors: Enrico Liscio, Luciano C. Siebert, Catholijn M. Jonker, Pradeep K. Murukannaiah
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed methods on a dataset of a large-scale survey on energy transition. The results show that explicitly addressing inconsistencies between choices and motivations improves the estimation of an individual s value preferences. The disambiguation strategy does not show substantial improvements when compared to similar baselines however, we discuss how the novelty of the approach can open new research avenues and propose improvements to address the current limitations. |
| Researcher Affiliation | Academia | Enrico Liscio EMAIL Luciano C. Siebert EMAIL Delft University of Technology, the Netherlands Catholijn M. Jonker EMAIL Delft University of Technology, the Netherlands and Leiden University, The Netherlands Pradeep K. Murukannaiah EMAIL Delft University of Technology, the Netherlands |
| Pseudocode | Yes | Algorithm 1: Method TB Algorithm 2: Method MC Algorithm 3: Method MO |
| Open Source Code | Yes | The code is available at https://github.com/enricoliscio/value-preferences-estimation |
| Open Datasets | Yes | We use data from a PVE conducted between April and May 2020 involving 1376 participants (Itten & Mouter, 2022) |
| Dataset Splits | Yes | As is common in AL settings, we warm up the NLP model by initializing the set of labeled participants with 10% of the available participants, and the set of labeled motivations with the motivations provided by those participants. [...] We iterate the procedure for 5 iteration steps and repeat it in a 10-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. It mentions using NLP models but no GPU/CPU models or other hardware specifications. |
| Software Dependencies | No | The paper mentions using 'Rob BERT (Delobelle et al., 2020)', 'Ro BERTa model (Liu et al., 2019)', and 'XLNet (Yang et al., 2019)' but does not provide specific version numbers for these or any other ancillary software libraries or programming languages used. |
| Experiment Setup | Yes | For all models, we used a learning rate of 1e-5, a batch size of 16, and trained for 10 epochs. We used the AdamW optimizer with a warm-up ratio of 0.1 and a weight decay of 0.01. |