Computing Possible and Necessary Equilibrium Actions (and Bipartisan Set Winners)
Authors: Markus Brill, Rupert Freeman, Vincent Conitzer
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude the paper by formulating and evaluating the efficacy of a mixed-integer linear programming formulation for the possible equilibrium action problem in weak tournament games. [...] We tested our MIP for the possible ES winner problem in weak tournament games containing either n 2 or n unspecified entries, where n = |A| is the number of actions available to each player. For each n, we examined the average time required to solve 100 random instances5 of size n, using CPLEX 12.6 to solve the MIP. Results are shown in Figure 3, with algorithms cut off once the average time to find a solution exceeds 10 seconds. We compared the performance of our MIP with a simple brute force algorithm. |
| Researcher Affiliation | Academia | Markus Brill, Rupert Freeman and Vincent Conitzer Department of Computer Science Duke University Durham, NC 27708, USA {brill,rupert,conitzer}@cs.duke.edu |
| Pseudocode | No | The paper presents a Mixed-Integer Programming (MIP) formulation using mathematical equations and constraints in Section 7.1, but it does not include a block of pseudocode or an algorithm labeled as such. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository for the methodology described. |
| Open Datasets | No | The paper states, 'Random instances were generated by randomly choosing each entry from { 1, 0, 1} and imposing symmetry, then randomly choosing the fixed number of entries to be unspecified.' It describes generating custom instances but does not provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not describe specific training, validation, or test dataset splits. It discusses generating random instances for evaluation, but not splitting a dataset for model training or validation. |
| Hardware Specification | No | The paper mentions using 'CPLEX 12.6 to solve the MIP' but does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud resources) on which the experiments were run. |
| Software Dependencies | Yes | We tested our MIP for the possible ES winner problem in weak tournament games [...] using CPLEX 12.6 to solve the MIP. |
| Experiment Setup | No | The paper describes the experimental conditions such as examining '100 random instances' of size 'n' and cutting off algorithms after '10 seconds'. However, it does not provide specific hyperparameter values or detailed system-level training settings, as the MIP formulation does not involve such parameters in the typical sense. |