Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908484559175680 |
|---|---|
| author | Tang, Bill Koçyiğit, Çağıl Rice, Eric Vayanos, Phebe |
| author_facet | Tang, Bill Koçyiğit, Çağıl Rice, Eric Vayanos, Phebe |
| contents | We study the problem of allocating scarce societal resources of different types (e.g., permanent housing, deceased donor kidneys for transplantation, ventilators) to heterogeneous allocatees on a waitlist (e.g., people experiencing homelessness, individuals suffering from end-stage renal disease, Covid-19 patients) based on their observed covariates. We leverage administrative data collected in deployment to design an online policy that maximizes expected outcomes while satisfying budget constraints, in the long run. Our proposed policy waitlists each individual for the resource maximizing the difference between their estimated mean treatment outcome and the estimated resource dual-price or, roughly, the opportunity cost of using the resource. Resources are then allocated as they arrive, in a first-come first-serve fashion. We demonstrate that our data-driven policy almost surely asymptotically achieves the expected outcome of the optimal out-of-sample policy under mild technical assumptions. We extend our framework to incorporate various fairness constraints. We evaluate the performance of our approach on the problem of designing policies for allocating scarce housing resources to people experiencing homelessness in Los Angeles based on data from the homeless management information system. In particular, we show that using our policies improves rates of exit from homelessness by 5.16% and that policies that are fair in either allocation or outcomes by race come at a very low price of fairness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13765 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment Tang, Bill Koçyiğit, Çağıl Rice, Eric Vayanos, Phebe Optimization and Control Machine Learning Physics and Society We study the problem of allocating scarce societal resources of different types (e.g., permanent housing, deceased donor kidneys for transplantation, ventilators) to heterogeneous allocatees on a waitlist (e.g., people experiencing homelessness, individuals suffering from end-stage renal disease, Covid-19 patients) based on their observed covariates. We leverage administrative data collected in deployment to design an online policy that maximizes expected outcomes while satisfying budget constraints, in the long run. Our proposed policy waitlists each individual for the resource maximizing the difference between their estimated mean treatment outcome and the estimated resource dual-price or, roughly, the opportunity cost of using the resource. Resources are then allocated as they arrive, in a first-come first-serve fashion. We demonstrate that our data-driven policy almost surely asymptotically achieves the expected outcome of the optimal out-of-sample policy under mild technical assumptions. We extend our framework to incorporate various fairness constraints. We evaluate the performance of our approach on the problem of designing policies for allocating scarce housing resources to people experiencing homelessness in Los Angeles based on data from the homeless management information system. In particular, we show that using our policies improves rates of exit from homelessness by 5.16% and that policies that are fair in either allocation or outcomes by race come at a very low price of fairness. |
| title | Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment |
| topic | Optimization and Control Machine Learning Physics and Society |
| url | https://arxiv.org/abs/2311.13765 |