Effective, Fair and Equitable Pandemic Rationing

Grigoryan, Aram

doi:10.2139/ssrn.3646539

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…Similarly, by maximizing the minimum expected FR, we aim to reduce the impact of an agent's place in the sequence of arrivals. Sharing similar motivation to our paper, Grigoryan (2020) and Pathak et al (2020) consider equitable COVID-19 vaccine allocation. However, the settings (e.g., offline and deterministic), models, and techniques in both papers differ drastically from those in this work.…”

Section: Related Worksupporting

confidence: 59%

Fair Dynamic Rationing

Manshadi¹,

Niazadeh²,

Rodilitz³

2021

Preprint

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We study the allocative challenges that governmental and nonprofit organizations face when tasked with equitable and efficient rationing of a social good among agents whose needs (demands) realize sequentially and are possibly correlated. As one example, early in the COVID-19 pandemic, the Federal Emergency Management Agency faced overwhelming, temporally scattered, a priori uncertain, and correlated demands for medical supplies from different states. To better achieve their dual aims of equity and efficiency in such contexts, social planners intend to maximize the minimum fill rate across agents, where each agent's fill rate must be irrevocably decided upon its arrival. For an arbitrarily correlated sequence of demands, we establish upper bounds on both the expected minimum fill rate (ex-post fairness) and the minimum expected fill rate (ex-ante fairness) achievable by any policy. Our bounds are parameterized by the number of agents and the expected demand-to-supply ratio, and they shed light on the limits of attaining equity in dynamic rationing.Further, we show that for any set of parameters, a simple adaptive policy of projected proportional allocation achieves the best possible fairness guarantee, ex post as well as ex ante. Our policy is transparent and easy to implement, as it does not rely on distributional information beyond the first conditional moments.Despite its simplicity, we demonstrate that this policy provides significant improvement over the class of non-adaptive target-fill-rate policies by characterizing the performance of the optimal such policy, which relies on full distributional knowledge. We obtain the performance guarantees of (i) our proposed adaptive policy by inductively designing lower-bound functions on its corresponding value-to-go, and (ii) the optimal target-fill-rate policy by establishing an intriguing connection to a monopoly-pricing optimization problem.Further, we extend our results to considering alternative objective functions and to rationing multiple types of resources. We complement our theoretical developments with a numerical study motivated by the rationing of COVID-19 medical supplies based on a projected-demand model used by the White House. In such a setting, our simple adaptive policy significantly outperforms its theoretical guarantee as well as the optimal target-fill-rate policy.

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Section: Related Worksupporting

confidence: 59%

Fair Dynamic Rationing

Manshadi¹,

Niazadeh²,

Rodilitz³

2021

Preprint

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“…Our axioms directly incorporate the policy objectives of reducing priority violations and choosing highest priority individual whenever possible. Like our paper, Grigoryan (2020) characterizes the regular reserves-and-quotas rule with an axiom of priority violations minimality. However, the author studies a setting with weak priorities and without quotas.…”

Section: Related Literaturementioning

confidence: 95%

“…Our work is related papers that provide axiomatic foundation for the regular reserves-andquotas rule. In a setting without quotas, such results have been provided by Echenique and Yenmez (2015), Sönmez and Yenmez (2019a), Sönmez and Yenmez (2019b) and Grigoryan (2020). The first paper uses an axiom called saturated priority compatibility, which says that an individual can cause a priority violation only if the number of individuals of her type who receive a unit is weakly smaller than the reserves.…”

Section: Related Literaturementioning

confidence: 99%

Priority-Based Assignment with Reserves and Quotas

Abdulkadiroğlu

Grigoryan

2021

SSRN Journal

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We thank Jay Sethuraman, Tayfun Sonmez and Microeconomic Theory Seminar participants at Duke for helpful comments and discussions. We gratefully acknowledge funding from the Laura and John Arnold Foundation. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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“…Bansak et al (2018) and Trapp et al (2018) introduce similar data-driven solutions for refugee resettlement to maximize employment outcomes. Grigoryan (2020) provides match quality maximization algorithm for pandemic rationing problems. Our environment is different from all the works above, and therefore, match quality maximization in our problem requires an original solution.…”

Section: Related Literaturementioning

confidence: 99%