Publication:

LOOKAHEAD AGENT ANALYSIS IN LONG-RUN FAIRNESS SIMULATION

Loading...
Thumbnail Image

Date

2021-06-04

Published Version

Published Version

Journal Title

Journal ISSN

Volume Title

Publisher

The Harvard community has made this article openly available. Please share how this access benefits you.

Research Projects

Organizational Units

Journal Issue

Citation

Balasubramanian, Gayatri. 2021. LOOKAHEAD AGENT ANALYSIS IN LONG-RUN FAIRNESS SIMULATION. Bachelor's thesis, Harvard College.

Abstract

Bank lending has been used as an exploratory scenario to understand how "fair" short term lending policies fare in the long-run. Unfortunately, policies implemented in the short-run are not necessarily fair in the long-run. In this paper, we build off previous simulation studies to propose four one step lookahead lending agents. Lookahead checks the expected outcome of a lending policy before making a lending decision. We compare these lookahead policies against each other under relative improvement and active harm scenarios after multiple time steps. We find that the advantaged group and bank always benefit most from a maximum utility agent -- that maximizes bank profit -- while the disadvantaged group always benefits most from an unbounded equality of opportunity agent -- which maximizes bank profit under lending thresholds with equal true positive rate across the groups.

Description

Other Available Sources

Research Data

Keywords

Computer science

Terms of Use

This article is made available under the terms and conditions applicable to Other Posted Material (LAA), as set forth at Terms of Service

Endorsement

Review

Supplemented By

Related Stories