Who Cares More? Allocation with Diverse Preference Intensities
Goods and services -- public housing, medical appointments, schools -- are often allocated to individuals who rank them similarly but differ in their preference intensities. We characterize optimal allocation rules when individual preferences are known and when they are not. Several insights emerge. First-best allocations may involve assigning some agents "lotteries" between high- and low-ranked goods. When preference intensities are private information, second-best allocations always involve such lotteries and, crucially, may coincide with first-best allocations. Furthermore, second-best allocations may entail disposal of services. We discuss a market-based alternative and show how it differs.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Intensinist Social Welfare and Ordinal Intensity-Efficient Allocations
This paper studies social welfare and allocation efficiency in situations where, in addition to having ordinal preferences, agents also have *ordinal intensities*: they can make comparisons such as "I prefer a to b more …
CARES: Context-Aware Resolution Selector for VLMs
Large vision-language models (VLMs) commonly process images at native or high resolution to remain effective across tasks. This inflates visual tokens ofter to 97-99% of total tokens, resulting in high compute and latenc…
Context-aware Session-based Recommendation with Graph Neural Networks
Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session. While there are methods that leverage rich context information in sessions for SBR, mos…
Session-Based RecommendationsSTAR: SpatioTemporal Adaptive Reward Allocation for Text-to-Image RL Post-Training
Existing RL post-training methods for text-to-image generation usually convert the final-image reward into a single scalar advantage and apply it with the same strength to the entire generative trajectory. However, text-…
Text-to-Image GenerationImproving One-class Recommendation with Multi-tasking on Various Preference Intensities
In the one-class recommendation problem, it's required to make recommendations basing on users' implicit feedback, which is inferred from their action and inaction. Existing works obtain representations of users and item…