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..
Incremental Decision Making Under Risk with the Weighted Expected Utility Model
Authors: Hugo Gilbert, Nawal Benabbou, Patrice Perny, Olivier Spanjaard, Paolo Viappiani
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also give experimental results showing the practical efficiency of our method. [...] Our numerical tests are given in Section 5. |
| Researcher Affiliation | Academia | Sorbonne Universit es, UPMC Univ Paris 06, CNRS, LIP6 UMR 7606, 4 place Jussieu, 75005 Paris |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The datasets used are 'randomly generated sets L of possible lotteries' and are not described as publicly available with access information. |
| Dataset Splits | No | The paper describes evaluating over '50 randomly generated sets L' and doesn't specify explicit training/validation/test dataset splits with percentages or counts. |
| Hardware Specification | Yes | Times are wall-clock times on a 2.4 GHz Intel Core i5 with 8G of memory. |
| Software Dependencies | Yes | Implementation in Java using Gurobi 5.6.3 for the LPs. |
| Experiment Setup | Yes | To model uh and wh, we use splines generated by a basis of m = 12 cubic I-spline functions as defined in Eq. 4, 5. [...] Each set L contains 1000 lotteries such that no stochastic dominance relation exist between them. The support of each lottery has a size generated uniformly in {1, . . . , 10} and consists of values generated uniformly in (0, 1). |