# Partial Strategyproofness: Relaxing Strategyproofness for the Random Assignment Problem

arXiv: https://arxiv.org/abs/1401.3675 · HTML: https://mlsanity.com/paper/1401.3675/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

## Built on by (most cited first, 3 of 3)

- [Deep Learning for Two-Sided Matching](https://mlsanity.com/paper/2107.03427/) (2021)
- [Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms](https://mlsanity.com/paper/2109.08856/) (2021)
- [No Agreement Without Loss: Learning and Social Choice in Peer Review](https://mlsanity.com/paper/2211.02144/) (2022)

## Abstract (arXiv)

We present partial strategyproofness, a new, relaxed notion of strategyproofness for studying the incentive properties of non-strategyproof assignment mechanisms. Informally, a mechanism is partially strategyproof if it makes truthful reporting a dominant strategy for those agents whose preference intensities differ sufficiently between any two objects. We demonstrate that partial strategyproofness is axiomatically motivated and yields a parametric measure for "how strategyproof" an assignment mechanism is. We apply this new concept to derive novel insights about the incentive properties of the probabilistic serial mechanism and different variants of the Boston mechanism.