The Relative Value of Prediction in Algorithmic Decision Making
Authors: Juan Carlos Perdomo
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main results are theoretical in nature. We identify simple, sharp conditions determining the relative value of prediction visa-vis expanding access, within several statistical models that are popular amongst quantitative social scientists. |
| Researcher Affiliation | Academia | 1Harvard University, Center for Research on Computation and Society, Cambridge, MA. Correspondence to: Juan Carlos Perdomo <jcperdomo@g.harvard.edu>. |
| Pseudocode | No | No pseudocode or structured algorithm blocks are presented in the paper. |
| Open Source Code | No | The paper does not contain any statements about releasing source code for the described methodology or provide links to a code repository. |
| Open Datasets | No | The paper uses numerical simulations with chosen parameter values (e.g., 'we compute the cost benefit ratios with α = r2 = .01 and µ = 1, β = 10 for the linear case. For the probit case, we set b = .1 and α = r2 = 1e 3.'), not empirical datasets. Therefore, no information about public dataset access for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical model training or validation using dataset splits. |
| Hardware Specification | No | The paper is theoretical and uses numerical simulations without specifying any particular hardware used for computations. |
| Software Dependencies | No | The paper mentions 'The simulation code is included in the submission' in Appendix C but does not specify any software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | For the visuaizations in Figure 1 and Figure 2, we compute the prediction access ratios numerically. Using our closed form expressions regarding the value functions V lin and V pr from Proposition 3.4 and Proposition 4.3 we compute the cost benefit ratios with α = r2 = .01 and µ = 1, β = 10 for the linear case. For the probit case, we set b = .1 and α = r2 = 1e 3. |