Simple algorithmic rules for complex human decisions
Abstract/Contents
- Abstract
- Decision makers must often choose a course of action under limited time with limited knowledge. In this work, we formalize longstanding observations about the efficacy of improper linear models to construct accurate yet easily applied rules that can help resource-constrained practitioners make better informed decisions that are consistent with their stated objectives. We find that simple rules can help substantially improve the performance of human experts, while rivaling the accuracy of complex prediction models that base decisions on considerably more information. Policy makers, however, may be reluctant to adopt such analytical tools due to the difficulty in anticipating, prior to deployment, the impact of resulting policies. In particular, one generally cannot use historical data to directly observe what would have happened had the recommended actions been taken. To address this issue, we present two strategies for gauging the sensitivity of predicted policy outcomes to potentially unmeasured confounders. We further investigate how humans compare to systematic rules when explicitly asked to assess risk of recidivism in the context of criminal justice. Our results show that systematic rules often perform better than humans in terms of both accuracy and ranking. Finally, we introduce a simple three-step strategy---which we call risk-adjusted regression---for measuring disparities that existing policies may have across different groups defined by race, gender, and other protected attributes that may be of interest. One popular strategy is to estimate disparities after controlling for observed covariates, typically with a regression model. This approach, however, suffers from two statistical challenges: omitted- and included-variable bias. Our proposed method addresses both concerns in settings where decision makers have clearly measurable objectives.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Jung, Jongbin |
|---|---|
| Degree supervisor | Goel, Sharad, 1977- |
| Degree supervisor | Howard, Ronald |
| Thesis advisor | Goel, Sharad, 1977- |
| Thesis advisor | Howard, Ronald |
| Thesis advisor | Ugander, Johan |
| Degree committee member | Ugander, Johan |
| Associated with | Stanford University, Department of Management Science and Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Jongbin Jung. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Jongbin Jung
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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