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Fast TTC Computation

Irene Aldridge

arXiv 22 Mar 2024 · Econometrics

arXiv:2403.15111 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a fast Markov Matrix-based methodology for computing Top Trading Cycles (TTC) that delivers O(1) computational speed, that is speed independent of the number of agents and objects in the system. The proposed methodology is well suited for complex large-dimensional problems like housing choice. The methodology retains all the properties of TTC, namely, Pareto-efficiency, individual rationality and strategy-proofness.

Citation extraction

8
references
9
in-text mentions
8
distinct cited
1
self-citations
1,424
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Zhong Sun, Giacomo Pedretti, Elia Ambrosi, Alessandro Bricalli, and… (2020) In-memory eigenvector computation in time o(1)0.64422100%
2Atila Abdulkadiroglu and Tayfun Sönmez (1998) Random serial dictatorship and the core from random endowments in house allocation problems0.40511100%
3Atila Abdulkadiroglu and Tayfun Sönmez (2003) School choice: A mechanism design approach0.40511100%
4Anna Bogomolnaia and Hervé Moulin (2001) A new solution to the random assignment problem0.40511100%
5Irene Aldridge and Marco Avellaneda (2021) Big Data Science in Finance self0.40511100%
6Anna Bogomolnaia and Herve Moulin (2004) Random matching under dichotomous preferences0.40511100%
7Lloyd Shapley and Herbert Scarf (1974) On cores and indivisibility0.40511100%
8Daniela Saban and Jay Sethuraman (2013) School choice: A mechanism design approach0.40511100%

Showing the top 8 of 8 scored citations.