Who Leads and Who Follows in Strategic Classification?
Authors: Tijana Zrnic, Eric Mazumdar, Shankar Sastry, Michael Jordan
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate this empirically. In Figure 1 we generate non-strategic data according to y ~ Bern(p) and x0|y ~ N(4y - 2, 1) and plot the difference in risk between the two equilibria for the decision-maker and the agents, for varying B and p. |
| Researcher Affiliation | Academia | Tijana Zrnic University of California, Berkeley tijana.zrnic@berkeley.edu Eric Mazumdar California Institute of Technology mazumdar@caltech.edu S. Shankar Sastry University of California, Berkeley sastry@eecs.berkeley.edu Michael I. Jordan University of California, Berkeley jordan@cs.berkeley.edu |
| Pseudocode | No | The paper includes mathematical equations for updates, such as "φt+1 = ΠΘ(φt − ηt d/δ L( µt, φt + δut)ut)", but these are not presented as structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | In Figure 1 we generate non-strategic data according to y ~ Bern(p) and x0|y ~ N(4y - 2, 1) and plot the difference in risk between the two equilibria for the decision-maker and the agents, for varying B and p. The paper describes data generation process rather than using a pre-existing public dataset. |
| Dataset Splits | No | The paper presents theoretical models and illustrates results with generated data in Figure 1, but it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies or their version numbers (e.g., programming languages, libraries, frameworks, solvers). |
| Experiment Setup | No | The paper describes the mathematical setups for linear and logistic regression and parameters for data generation (B and p in Figure 1), but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, optimizer) or system-level training settings. |