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Robust Allocations with Diversity Constraints

Publication ,  Conference
Shen, Z; Gelauff, L; Goel, A; Korolova, A; Munagala, K
Published in: Advances in Neural Information Processing Systems
January 1, 2021

We consider the problem of allocating divisible items among multiple agents, and consider the setting where any agent is allowed to introduce diversity constraints on the items they are allocated. We motivate this via settings where the items themselves correspond to user ad slots or task workers with attributes such as race and gender on which the principal seeks to achieve demographic parity. We consider the following question: When an agent expresses diversity constraints into an allocation rule, is the allocation of other agents hurt significantly? If this happens, the cost of introducing such constraints is disproportionately borne by agents who do not benefit from diversity. We codify this via two desiderata capturing robustness. These are no negative externality – other agents are not hurt – and monotonicity –the agent enforcing the constraint does not see a large increase in value. We show in a formal sense that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced, while almost all other natural allocation rules fail this criterion. We also show that the guarantees achieved by Nash Welfare are nearly optimal within a widely studied class of allocation rules. We finally perform an empirical simulation on real-world data that models ad allocations to show that this gap between Nash Welfare and other rules persists in the wild.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

ISBN

9781713845393

Publication Date

January 1, 2021

Volume

35

Start / End Page

29684 / 29696

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

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MLA
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Shen, Z., Gelauff, L., Goel, A., Korolova, A., & Munagala, K. (2021). Robust Allocations with Diversity Constraints. In Advances in Neural Information Processing Systems (Vol. 35, pp. 29684–29696).
Shen, Z., L. Gelauff, A. Goel, A. Korolova, and K. Munagala. “Robust Allocations with Diversity Constraints.” In Advances in Neural Information Processing Systems, 35:29684–96, 2021.
Shen Z, Gelauff L, Goel A, Korolova A, Munagala K. Robust Allocations with Diversity Constraints. In: Advances in Neural Information Processing Systems. 2021. p. 29684–96.
Shen, Z., et al. “Robust Allocations with Diversity Constraints.” Advances in Neural Information Processing Systems, vol. 35, 2021, pp. 29684–96.
Shen Z, Gelauff L, Goel A, Korolova A, Munagala K. Robust Allocations with Diversity Constraints. Advances in Neural Information Processing Systems. 2021. p. 29684–29696.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

ISBN

9781713845393

Publication Date

January 1, 2021

Volume

35

Start / End Page

29684 / 29696

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology