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DOI: 10.4230/LIPIcs.ITCS.2026.65

Characterizing Off-Chain Influence Proof Transaction Fee Mechanisms

Authors: Aadityan Ganesh, Clayton Thomas, and S. Matthew Weinberg

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract

Roughgarden [Roughgarden, 2020] initiates the study of Transaction Fee Mechanisms (TFMs), and posits that the on-chain game of a "good" TFM should be on-chain simple (OnC-S), i.e., incentive compatible for both the users and the miner. Recent work of Ganesh, Thomas an Weinberg [Ganesh et al., 2024] posit that they should additionally be Off-Chain Influence-Proof (OffC-IP), which means that the miner cannot achieve any additional revenue by separately conducting an off-chain auction to determine on-chain inclusion. They observe that a cryptographic second-price auction satisfies both properties, but leave open the question of whether other mechanisms (such as those not dependent on cryptography) satisfy these properties. In this paper, we characterize OffC-IP TFMs: They are those satisfying a burn identity relating the burn rule to the allocation rule. In particular, we show that auction is OffC-IP if and only if its (induced direct-revelation) allocation rule X̄(⋅) and burn rule B̅(⋅) (both of which take as input users' values v₁, … , v_n) are truthful when viewing (X̄(⋅), B̅(⋅)) as the allocation and pricing rule of a multi-item auction for a single additive buyer with values (φ(v₁),…, φ(v_n)) equal to the users' virtual values. Building on this burn identity, we characterize OffC-IP and OnC-S TFMs that are deterministic and do not use cryptography: They are posted-price mechanisms with specially-tuned burns. As a corollary, we show that such TFMs can only exist with infinite supply and prior-dependence. However, we show that for randomized TFMs, there are additional OnC-S and OffC-IP auctions that do not use cryptography (even when there is {finite} supply, under prior-dependence with a bounded prior distribution). Holistically, our results show that while OffC-IP is a fairly stringent requirement, families of OffC-IP mechanisms can be found for a variety of settings.

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Aadityan Ganesh, Clayton Thomas, and S. Matthew Weinberg. Characterizing Off-Chain Influence Proof Transaction Fee Mechanisms. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 65:1-65:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{ganesh_et_al:LIPIcs.ITCS.2026.65,
  author =	{Ganesh, Aadityan and Thomas, Clayton and Weinberg, S. Matthew},
  title =	{{Characterizing Off-Chain Influence Proof Transaction Fee Mechanisms}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{65:1--65:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.65},
  URN =		{urn:nbn:de:0030-drops-253527},
  doi =		{10.4230/LIPIcs.ITCS.2026.65},
  annote =	{Keywords: Transaction Fee Mechanism Design, Off-Chain Influence Proofness, Blockchain, Decentralized Finance, Simple Auctions}
}

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DOI: 10.4230/LIPIcs.ITCS.2021.46

Tiered Random Matching Markets: Rank Is Proportional to Popularity

Authors: Itai Ashlagi, Mark Braverman, Amin Saberi, Clayton Thomas, and Geng Zhao

Published in: LIPIcs, Volume 185, 12th Innovations in Theoretical Computer Science Conference (ITCS 2021)

Abstract

We study the stable marriage problem in two-sided markets with randomly generated preferences. Agents on each side of the market are divided into a constant number of "soft" tiers, which capture agents' qualities. Specifically, every agent within a tier has the same public score, and agents on each side have preferences independently generated proportionally to the public scores of the other side. We compute the expected average rank which agents in each tier have for their partners in the man-optimal stable matching, and prove concentration results for the average rank in asymptotically large markets. Furthermore, despite having a significant effect on ranks, public scores do not strongly influence the probability of an agent matching to a given tier of the other side. This generalizes the results by Pittel [Pittel, 1989], which analyzed markets with uniform preferences. The results quantitatively demonstrate the effect of competition due to the heterogeneous attractiveness of agents in the market.

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Itai Ashlagi, Mark Braverman, Amin Saberi, Clayton Thomas, and Geng Zhao. Tiered Random Matching Markets: Rank Is Proportional to Popularity. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 185, pp. 46:1-46:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{ashlagi_et_al:LIPIcs.ITCS.2021.46,
  author =	{Ashlagi, Itai and Braverman, Mark and Saberi, Amin and Thomas, Clayton and Zhao, Geng},
  title =	{{Tiered Random Matching Markets: Rank Is Proportional to Popularity}},
  booktitle =	{12th Innovations in Theoretical Computer Science Conference (ITCS 2021)},
  pages =	{46:1--46:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-177-1},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{185},
  editor =	{Lee, James R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2021.46},
  URN =		{urn:nbn:de:0030-drops-135851},
  doi =		{10.4230/LIPIcs.ITCS.2021.46},
  annote =	{Keywords: Stable matching, stable marriage problem, tiered random markets, deferred acceptance}
}

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Characterizing Off-Chain Influence Proof Transaction Fee Mechanisms

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Tiered Random Matching Markets: Rank Is Proportional to Popularity

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