We view outcome homogenization as an important class ofsystemicharms that arise when we study socialsystems, i.e.harmsthatrequire observing howindividuals aretreated bymanydecision-makers.2 In 2,we conceptually motivate outcome homogenization in the context of algorithmic hiring.

We propose Elastic Reset, a new algorithm that achieves higher reward with less drift without explicitly modifying the training objective.

In this paper, we consider whether learned optimization can help overcome these problems.

When f is convex with respect tox, many efficient algorithms can be powerful tools for solving Problem(1). One well-known algorithm is mirror descent (MD) [5]which is based on Bregman divergence.