Multi-Apartment Rent Division
Authors: Ariel D. Procaccia, Benjamin Schiffer, Shirley Zhang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is a generalization of envy-freeness called negotiated envy-freeness. We show that a solution satisfying negotiated envy-freeness is guaranteed to exist and that it is possible to optimize over all negotiated envy-free solutions in polynomial time. We also define an even stronger fairness notion called universal envy-freeness and study its existence when values are drawn randomly. |
| Researcher Affiliation | Academia | Ariel D. Procaccia, Benjamin Schiffer, Shirley Zhang Harvard University |
| Pseudocode | Yes | Algorithm 1: Optimizing an objective function subject to Negotiated Envy-Freeness for j 1 to m do Aj welfare-maximizing assignment in j via max-weight bipartite matching end for A {A1, ..., Am} Choose j A such that Pn i=1 Vi(Aj A(i)) Rj A maxj Pn i=1 Vi(Aj(i)) Rj P , Q arg max P,Q Z s.t. Z fq(U1(Aj , P), ..., Un(Aj , P)) Vi(Aj (i)) P(Aj (i)) Vi(Aj(i)) P(Aj(i)) Vi(Aj(i)) Q(Aj(i)) Vi(Aj(i )) Q(Aj(i )) X j P(Aj(i)) = X i P(Aj(i)) = Rj return (A, P , j ) |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the authors have released open-source code for the methodology described. |
| Open Datasets | No | The paper focuses on theoretical models and proofs, discussing instances where "the valuation matrix V is drawn randomly, where each player s value for each room is drawn from a distribution D". This refers to a theoretical distribution for analysis rather than a specific publicly available dataset used for experiments. |
| Dataset Splits | No | The paper does not describe experiments using specific datasets and therefore does not provide any information about dataset splits. |
| Hardware Specification | No | The paper is theoretical and presents algorithms and proofs. It does not describe any computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical models and algorithms. It does not mention any specific software or library dependencies with version numbers that would be required for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical, presenting models, theorems, and algorithms. It does not describe any empirical experiments or provide details about hyperparameters, training configurations, or other system-level settings. |