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Documents authored by Pai, Mallesh M.


Document
Censorship Resistance in On-Chain Auctions

Authors: Elijah Fox, Mallesh M. Pai, and Max Resnick

Published in: LIPIcs, Volume 282, 5th Conference on Advances in Financial Technologies (AFT 2023)


Abstract
Modern blockchains guarantee that submitted transactions will be included eventually; a property formally known as liveness. But financial activity requires transactions to be included in a timely manner. Classical liveness does not guarantee this, particularly in the presence of a motivated adversary who benefits from censoring transactions. We define censorship resistance as the amount it would cost the adversary to censor a transaction for a fixed interval of time as a function of the associated tip. This definition has two advantages, first it captures the fact that transactions with a higher miner tip can be more costly to censor, and therefore are more likely to swiftly make their way onto the chain. Second, it applies to a finite time window, so it can be used to assess whether a blockchain is capable of hosting financial activity that relies on timely inclusion. We apply this definition in the context of auctions. Auctions are a building block for many financial applications, and censoring competing bids offers an easy-to-model motivation for our adversary. Traditional proof-of-stake blockchains have poor enough censorship resistance that it is difficult to retain the integrity of an auction when bids can only be submitted in a single block. As the number of bidders n in a single block auction increases, the probability that the winner is not the adversary, and the economic efficiency of the auction, both decrease faster than 1/n. Running the auction over multiple blocks, each with a different proposer, alleviates the problem only if the number of blocks grows faster than the number of bidders. We argue that blockchains with more than one concurrent proposer can have strong censorship resistance. We achieve this by setting up a prisoner’s dilemma among the proposers using conditional tips.

Cite as

Elijah Fox, Mallesh M. Pai, and Max Resnick. Censorship Resistance in On-Chain Auctions. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 19:1-19:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{fox_et_al:LIPIcs.AFT.2023.19,
  author =	{Fox, Elijah and Pai, Mallesh M. and Resnick, Max},
  title =	{{Censorship Resistance in On-Chain Auctions}},
  booktitle =	{5th Conference on Advances in Financial Technologies (AFT 2023)},
  pages =	{19:1--19:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-303-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{282},
  editor =	{Bonneau, Joseph and Weinberg, S. Matthew},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.19},
  URN =		{urn:nbn:de:0030-drops-192089},
  doi =		{10.4230/LIPIcs.AFT.2023.19},
  annote =	{Keywords: Censorship Resistance, Auctions, Blockchain, MEV}
}
Document
The Centralizing Effects of Private Order Flow on Proposer-Builder Separation

Authors: Tivas Gupta, Mallesh M. Pai, and Max Resnick

Published in: LIPIcs, Volume 282, 5th Conference on Advances in Financial Technologies (AFT 2023)


Abstract
The current Proposer-Builder Separation (PBS) equilibrium has several builders with different backgrounds winning blocks consistently. This paper considers how that equilibrium will shift when transactions are sold privately via order flow auctions (OFAs) rather than forwarded directly to the public mempool. We discuss a novel model that highlights the augmented value of private order flow for integrated builder searchers. We show that private order flow is complementary to top-of-block opportunities, and therefore integrated builder-searchers are more likely to participate in OFAs and outbid non integrated builders. They will then parlay access to these private transactions into an advantage in the PBS auction, winning blocks more often and extracting higher profits than non-integrated builders. To validate our main assumptions, we construct a novel dataset pairing post-merge PBS outcomes with realized 12-second volatility on a leading CEX (Binance). Our results show that integrated builder-searchers are more likely to win in the PBS auction when realized volatility is high, suggesting that indeed such builders have an advantage in extracting top-of-block opportunities. Our findings suggest that modifying PBS to disentangle the intertwined dynamics between top-of-block extraction and private order flow would pave the way for a fairer and more decentralized Ethereum.

Cite as

Tivas Gupta, Mallesh M. Pai, and Max Resnick. The Centralizing Effects of Private Order Flow on Proposer-Builder Separation. In 5th Conference on Advances in Financial Technologies (AFT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 282, pp. 20:1-20:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{gupta_et_al:LIPIcs.AFT.2023.20,
  author =	{Gupta, Tivas and Pai, Mallesh M. and Resnick, Max},
  title =	{{The Centralizing Effects of Private Order Flow on Proposer-Builder Separation}},
  booktitle =	{5th Conference on Advances in Financial Technologies (AFT 2023)},
  pages =	{20:1--20:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-303-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{282},
  editor =	{Bonneau, Joseph and Weinberg, S. Matthew},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2023.20},
  URN =		{urn:nbn:de:0030-drops-192098},
  doi =		{10.4230/LIPIcs.AFT.2023.20},
  annote =	{Keywords: Private Order Flow, PBS, OFAs, decentralization}
}
Document
Online Multivalid Learning: Means, Moments, and Prediction Intervals

Authors: Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, and Aaron Roth

Published in: LIPIcs, Volume 215, 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)


Abstract
We present a general, efficient technique for providing contextual predictions that are "multivalid" in various senses, against an online sequence of adversarially chosen examples (x,y). This means that the resulting estimates correctly predict various statistics of the labels y not just marginally - as averaged over the sequence of examples - but also conditionally on x ∈ G for any G belonging to an arbitrary intersecting collection of groups 𝒢. We provide three instantiations of this framework. The first is mean prediction, which corresponds to an online algorithm satisfying the notion of multicalibration from [Hébert-Johnson et al., 2018]. The second is variance and higher moment prediction, which corresponds to an online algorithm satisfying the notion of mean-conditioned moment multicalibration from [Jung et al., 2021]. Finally, we define a new notion of prediction interval multivalidity, and give an algorithm for finding prediction intervals which satisfy it. Because our algorithms handle adversarially chosen examples, they can equally well be used to predict statistics of the residuals of arbitrary point prediction methods, giving rise to very general techniques for quantifying the uncertainty of predictions of black box algorithms, even in an online adversarial setting. When instantiated for prediction intervals, this solves a similar problem as conformal prediction, but in an adversarial environment and with multivalidity guarantees stronger than simple marginal coverage guarantees.

Cite as

Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, and Aaron Roth. Online Multivalid Learning: Means, Moments, and Prediction Intervals. In 13th Innovations in Theoretical Computer Science Conference (ITCS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 215, pp. 82:1-82:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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@InProceedings{gupta_et_al:LIPIcs.ITCS.2022.82,
  author =	{Gupta, Varun and Jung, Christopher and Noarov, Georgy and Pai, Mallesh M. and Roth, Aaron},
  title =	{{Online Multivalid Learning: Means, Moments, and Prediction Intervals}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{82:1--82:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2022.82},
  URN =		{urn:nbn:de:0030-drops-156785},
  doi =		{10.4230/LIPIcs.ITCS.2022.82},
  annote =	{Keywords: Uncertainty Estimation, Calibration, Online Learning}
}