Incremental Decision Making Under Risk with the Weighted Expected Utility Model
Authors: Hugo Gilbert, Nawal Benabbou, Patrice Perny, Olivier Spanjaard, Paolo Viappiani
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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). |