Improving Efficiency in Allocation Markets by Balancing Learning and Strategic Incentives

Core Concepts

Designing incentive-compatible batching mechanisms can improve the efficiency of allocating scarce resources, such as organs, by balancing the planner's need to learn from agents' private information and the agents' strategic incentives.

Abstract

The paper considers the problem of offering a scarce object with a common unobserved quality to strategic agents in a priority queue. Each agent has a private signal about the quality of the object and observes the decisions made by other agents. The key insights are: Under the widely-used first-come-first-served sequential offering mechanism, herding behavior emerges: initial rejections create an information cascade resulting in inefficient waste. To address this issue, the authors introduce a class of batching mechanisms. Agents in each batch report whether they would be willing to accept or reject the object based on their private signals and prior information. If the majority opts to accept, the object is randomly allocated within that batch. The authors prove that suitable batching mechanisms are incentive-compatible and improve efficiency. A key property is the gradual increase of the batch size after each failed allocation, chosen to elicit as much information as possible without distorting the agents' incentives to report truthfully. The authors show that there always exists an incentive-compatible batching mechanism that improves correctness (the probability of a correct allocation) compared to the sequential offering mechanism, as long as the private signals are more informative than the common prior belief. Simulations illustrate that the correctness of the batching mechanisms increases with the number of batches and decreases as the prior becomes more optimistic, but improves as the signal precision increases. The results can inform policy and decision-making in critical resource allocation domains, such as deceased donor kidney allocation, where herding behavior may lead to high discard rates.

Stats

More than 25% of kidneys recovered from deceased donors were discarded in 2023. The Kidney Donor Risk Index (KDRI) is used in the US deceased donor kidney allocation process.

Quotes

"Herding occurs when refusals by preceding patients in the queue trigger a self-reinforcing chain of subsequent declines." "Failing to allocate the object in a given batch results in a more pessimistic belief about the object quality. Therefore the mechanism increases the batch size to ensure that the object is allocated only if sufficiently many agents receive positive signals."

Key Insights Distilled From

Counterbalancing Learning and Strategic Incentives in Allocation Markets

by Itai Ashlagi... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2110.14865.pdf

Deeper Inquiries

How can the proposed batching mechanisms be extended to settings with heterogeneous agents and asymmetric utilities

The extension of the proposed batching mechanisms to settings with heterogeneous agents and asymmetric utilities involves adapting the mechanism to accommodate varying preferences and information structures among agents. In the context of organ allocation, this extension would require considering different levels of risk aversion, utility functions, and information asymmetry among agents. One approach to extend the batching mechanisms is to introduce personalized batch sizes based on individual characteristics. Agents with higher risk tolerance or different utility functions may require different batch sizes to incentivize truthful reporting. By customizing the batch sizes according to agents' characteristics, the mechanism can better align with the diverse needs and incentives of heterogeneous agents. Moreover, incorporating machine learning algorithms to analyze historical data on agents' behaviors and outcomes can help in predicting individual responses and optimizing batch sizes for different agent profiles. By leveraging data-driven insights, the mechanism can adapt to the varying preferences and information structures of heterogeneous agents, enhancing efficiency and accuracy in resource allocation.

What are the potential equity implications of implementing batching mechanisms in organ allocation, and how can they be addressed

The implementation of batching mechanisms in organ allocation can have significant equity implications that need to be carefully addressed to ensure fair and just distribution of resources. Transparency and Accountability: To maintain equity, it is crucial to ensure transparency in the allocation process. Providing clear guidelines on how batch sizes are determined and how decisions are made can help build trust among stakeholders and prevent biases or favoritism. Fairness in Batch Allocation: Ensuring that batch allocation is random and unbiased is essential for equity. Randomizing the allocation within each batch and avoiding any discriminatory practices can promote fairness in resource distribution. Consideration of Patient Needs: Equity in organ allocation also involves considering the specific needs and priorities of patients. The batching mechanism should take into account factors such as medical urgency, compatibility, and waiting time to ensure that organs are allocated in a manner that prioritizes those in critical need. Continuous Evaluation and Improvement: Regular evaluation of the batching mechanism's impact on equity is essential. Monitoring outcomes, assessing any disparities, and making adjustments based on feedback can help address any inequities that may arise. By incorporating these considerations and actively addressing equity concerns, the implementation of batching mechanisms in organ allocation can promote fairness and inclusivity in the distribution of scarce resources.

What other real-world resource allocation problems beyond organ transplantation could benefit from the insights provided by this work

The insights provided by the research on batching mechanisms in organ allocation can be applied to various real-world resource allocation problems beyond organ transplantation. Some potential areas that could benefit from these insights include: Housing Allocation: Batching mechanisms can be utilized in affordable housing allocation to optimize the matching of available units with individuals' needs and preferences. By grouping housing options into batches and considering individual priorities, the allocation process can be more efficient and equitable. School Placement: In school placement systems, batching mechanisms can help streamline the assignment of students to schools based on their preferences and school capacities. By grouping students into batches and considering their educational needs, the mechanism can improve the overall allocation process. Emergency Room Triage: Batching mechanisms can be applied in emergency room triage to prioritize patient care based on severity and urgency. By categorizing patients into batches according to their medical condition, healthcare providers can allocate resources more effectively and ensure timely treatment for those in critical need. Job Matching: In job matching platforms, batching mechanisms can enhance the matching process between job seekers and employers by grouping job opportunities into batches and considering individual skills and preferences. This can lead to better job placements and improved outcomes for both parties. By adapting the principles of batching mechanisms to these diverse resource allocation scenarios, organizations and policymakers can optimize decision-making processes, improve efficiency, and promote fairness in the distribution of resources.