Online Housing Market

Authors: Julien Lesca

IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study an online variant of the celebrated housing market problem, where each agent owns a single house and seeks to exchange it based on her preferences. In this online setting, agents may arrive and depart at any time, meaning not all agents are present in the housing market simultaneously. We extend the well-known serial dictatorship and top trading cycle mechanisms to the online scenario, aiming to retain their desirable properties, such as Pareto efficiency, individual rationality, and strategy-proofness. These extensions also seek to prevent agents from strategically delaying their arrivals or advancing their departures. We demonstrate that achieving all these properties simultaneously is impossible and present several variants that achieve different subsets of these properties.
Researcher Affiliation Academia Julien Lesca Universit e Paris-Dauphine, PSL, CNRS, LAMSADE EMAIL
Pseudocode Yes Algorithm 1 Online serial dictatorship procedure Algorithm 2 TTC-graph construction Algorithm 3 Top trading cycle algorithm Algorithm 4 Online top trading cycle procedure Algorithm 5 Departing agent excluded partition γ Algorithm 6 Scheduled departure partition θ.
Open Source Code No The paper does not provide any explicit statement about releasing code, a link to a code repository, or mention of code in supplementary materials.
Open Datasets No The paper is theoretical and focuses on mechanisms and algorithms using illustrative examples (e.g., "Example 1. Consider instance I described in Figure 1.") rather than publicly available datasets for empirical evaluation.
Dataset Splits No The paper does not conduct experiments on datasets, therefore no dataset splits are provided.
Hardware Specification No The paper is theoretical and does not describe any experimental hardware specifications.
Software Dependencies No The paper is theoretical and does not specify any software dependencies with version numbers for its own methodology.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings.