11 Search Results for "Pai, Mallesh M."

Document
DOI: 10.4230/LIPIcs.FORC.2026.20
Privacy, Prediction, and Allocation

Authors: Ben Jacobsen and Nitin Kohli

Published in: LIPIcs, Volume 368, 7th Symposium on Foundations of Responsible Computing (FORC 2026)

Abstract
Algorithmic predictions are increasingly used to inform the allocation of scarce resources. The promise of these methods is that, through machine learning, they can better identify the people who would benefit most from interventions. Recently, however, several works have called this assumption into question by demonstrating the existence of settings where simple, unit-level allocation strategies can meet or even exceed the performance of those based on individual-level targeting. Separately, other works have objected to individual-level targeting on privacy grounds, leading to an unusual situation where a single solution, unit-level targeting, is recommended for reasons of both privacy and utility. Motivated by the desire to fully understand the interplay of privacy and targeting levels, we initiate the study of aid allocation systems that satisfy differential privacy, synthesizing existing works on private optimization with the economic models of aid allocation used in the non-private literature. To this end, we investigate private variants of both individual and unit-level allocation strategies in both stochastic and distribution-free settings under a range of constraints on data availability. Through this analysis, we provide clean, interpretable bounds characterizing the tradeoffs between privacy, efficiency, and targeting precision in allocation.
Cite as

Ben Jacobsen and Nitin Kohli. Privacy, Prediction, and Allocation. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 20:1-20:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{jacobsen_et_al:LIPIcs.FORC.2026.20,
  author =	{Jacobsen, Ben and Kohli, Nitin},
  title =	{{Privacy, Prediction, and Allocation}},
  booktitle =	{7th Symposium on Foundations of Responsible Computing (FORC 2026)},
  pages =	{20:1--20:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-419-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{368},
  editor =	{Lin, Huijia (Rachel)},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2026.20},
  URN =		{urn:nbn:de:0030-drops-259935},
  doi =		{10.4230/LIPIcs.FORC.2026.20},
  annote =	{Keywords: Differential privacy, fair allocation, limits of prediction}
}
Document
DOI: 10.4230/LIPIcs.OPODIS.2025.36
BlindPerm: Efficient MEV Mitigation with an Encrypted Mempool and Permutation

Authors: Alireza Kavousi, Duc V. Le, Philipp Jovanovic, and George Danezis

Published in: LIPIcs, Volume 361, 29th International Conference on Principles of Distributed Systems (OPODIS 2025)

Abstract
Maximal Extractable Value (MEV) is a crucial challenge in blockchains and cryptocurrencies. A principal countermeasure is using encrypted mempools to hide the transaction payloads until they are committed in a block. However, the existing approaches based on encrypted mempools remain vulnerable to metadata leakage and may not provide sufficient mitigation against block producers due to their sole control in block preparation. In this paper, we propose techniques that utilize randomized permutation on the committed block, offering a multi-layer solution. With a focus on proof-of-stake (PoS) committee-based consensus, we then introduce BlindPerm, a framework that enhances an encrypted mempool with permutation and present various optimizations. Notably, we propose a construction where this enhancement comes at essentially no overhead by piggybacking on the encrypted mempool and without relying on any external entity such as randomness beacon. Further, we illustrate the effectiveness of our solutions by running simulations using historical Ethereum data.
Cite as

Alireza Kavousi, Duc V. Le, Philipp Jovanovic, and George Danezis. BlindPerm: Efficient MEV Mitigation with an Encrypted Mempool and Permutation. In 29th International Conference on Principles of Distributed Systems (OPODIS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 361, pp. 36:1-36:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{kavousi_et_al:LIPIcs.OPODIS.2025.36,
  author =	{Kavousi, Alireza and Le, Duc V. and Jovanovic, Philipp and Danezis, George},
  title =	{{BlindPerm: Efficient MEV Mitigation with an Encrypted Mempool and Permutation}},
  booktitle =	{29th International Conference on Principles of Distributed Systems (OPODIS 2025)},
  pages =	{36:1--36:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-409-3},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{361},
  editor =	{Arusoaie, Andrei and Onica, Emanuel and Spear, Michael and Tucci-Piergiovanni, Sara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2025.36},
  URN =		{urn:nbn:de:0030-drops-252091},
  doi =		{10.4230/LIPIcs.OPODIS.2025.36},
  annote =	{Keywords: Encrypted mempool, maximal extractable value, distributed systems}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.7
Mechanism Design for Automated Market Makers

Authors: T-H. Hubert Chan, Ke Wu, and Elaine Shi

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
Blockchains have popularized automated market makers (AMMs), applications that run on a blockchain, maintain a pool of crypto-assets, and execute trades with users governed by some pricing function. AMMs have also introduced a significant challenge known as the Miner Extractable Value (MEV). Specifically, miners who control the contents and sequencing of transactions in a block can extract value by front-running and back-running users' transactions, creating arbitrage opportunities that guarantee them risk-free returns. MEV not only harms ordinary users, but more critically, encourages miners to auction off favorable transaction placements to users and arbitragers. This has fostered a more centralized off-chain eco-system, departing from the decentralized equilibrium originally envisioned for the blockchain infrastructure layer. In this paper, we consider how to design AMM mechanisms that eliminate MEV opportunities. Specifically, we propose a new AMM mechanism that processes all transactions contained within a block according to some pre-defined rules, ensuring that some constant potential function is maintained after processing the batch. We show that our new mechanism satisfies two tiers of guarantees. First, for legacy blockchains where each block is proposed by a single (possibly rotating) miner, we prove that our mechanism satisfies arbitrage resilience, i.e., a miner cannot gain risk-free profit. Second, for blockchains where the block proposal process is decentralized and offers sequencing-fairness, we prove a strictly stronger notion called strategy proofness - roughly speaking, we guarantee that any individual user’s best response is to follow the honest strategy. Our results complement prior works on MEV resilience in the following senses. First, prior works have shown impossibilities to address MEV entirely at the consensus level. Our work demonstrates a new paradigm of mechanism design at the application (i.e., smart contract) layer to ensure provable guarantees of strategy proofness. Second, many works have attempted to augment the underlying consensus protocol with extra properties such as sequencing fairness. While most previous works heuristically argued why these extra properties help to mitigate MEV, our work demonstrates in a mathematically formal manner how to leverage such consensus-level properties to aid the design of strategy-proof mechanisms.
Cite as

T-H. Hubert Chan, Ke Wu, and Elaine Shi. Mechanism Design for Automated Market Makers. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 7:1-7:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{chan_et_al:LIPIcs.AFT.2025.7,
  author =	{Chan, T-H. Hubert and Wu, Ke and Shi, Elaine},
  title =	{{Mechanism Design for Automated Market Makers}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{7:1--7:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.7},
  URN =		{urn:nbn:de:0030-drops-247265},
  doi =		{10.4230/LIPIcs.AFT.2025.7},
  annote =	{Keywords: Mechanism design, game theory, strategy proof, blockchain}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.24
Multidimensional Blockchain Fees Are (Essentially) Optimal

Authors: Guillermo Angeris, Theo Diamandis, and Ciamac Moallemi

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
In this paper we show that, using only mild assumptions, dynamic multidimensional blockchain fee markets have strong performance guarantees, even against worst-case adversaries. In particular, we show that the average welfare gap between the following two scenarios is at most O(1/√T), where T is the length of the time horizon considered. In the first scenario, the designer knows all future actions by users and is allowed to fix the optimal prices of resources ahead of time, based on the designer’s oracular knowledge of those actions. In the second, the prices are updated by a very simple algorithm that does not have this oracular knowledge, special cases of which are EIP-4844 and EIP-1559, both fee mechanisms used by the Ethereum blockchain. Roughly speaking, this means that, on average, over a reasonable timescale, there is no difference in welfare between "correctly" fixing the prices, with oracular knowledge of the future, when compared to the proposed algorithm. We show a matching lower bound of Ω(1/√T) for any implementable algorithm and also separately consider the case where the adversary is known to be stochastic.
Cite as

Guillermo Angeris, Theo Diamandis, and Ciamac Moallemi. Multidimensional Blockchain Fees Are (Essentially) Optimal. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 24:1-24:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{angeris_et_al:LIPIcs.AFT.2025.24,
  author =	{Angeris, Guillermo and Diamandis, Theo and Moallemi, Ciamac},
  title =	{{Multidimensional Blockchain Fees Are (Essentially) Optimal}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{24:1--24:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.24},
  URN =		{urn:nbn:de:0030-drops-247433},
  doi =		{10.4230/LIPIcs.AFT.2025.24},
  annote =	{Keywords: Blockchains, transaction fees, online optimization, convex optimization}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.26
Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest

Authors: Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
This paper provides a comprehensive empirical analysis of the economics and dynamics behind arbitrages between centralized and decentralized exchanges (CEX-DEX) on Ethereum. We refine heuristics to identify arbitrage transactions from on-chain data and introduce a robust empirical framework to estimate arbitrage revenue without knowing traders' actual behaviors on CEX. Leveraging an extensive dataset spanning 19 months from August 2023 to March 2025, we estimate a total of 233.8M USD extracted by 19 major CEX-DEX searchers from 7,203,560 identified CEX-DEX arbitrages. Our analysis reveals increasing centralization trends as three searchers captured three-quarters of both volume and extracted value. We also demonstrate that searchers' profitability is tied to their integration level with block builders and uncover exclusive searcher-builder relationships and their market impact. Finally, we correct the previously underestimated profitability of block builders who vertically integrate with a searcher. These insights illuminate the darkest corner of the MEV landscape and highlight the critical implications for Ethereum’s decentralization.
Cite as

Fei Wu, Danning Sui, Thomas Thiery, and Mallesh Pai. Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 26:1-26:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wu_et_al:LIPIcs.AFT.2025.26,
  author =	{Wu, Fei and Sui, Danning and Thiery, Thomas and Pai, Mallesh},
  title =	{{Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{26:1--26:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.26},
  URN =		{urn:nbn:de:0030-drops-247450},
  doi =		{10.4230/LIPIcs.AFT.2025.26},
  annote =	{Keywords: Decentralized Finance, Maximal Extractable Value, CEX-DEX arbitrages}
}
Document
DOI: 10.4230/LIPIcs.AFT.2025.20
Selfish Mining Under General Stochastic Rewards

Authors: Maryam Bahrani, Michael Neuder, and S. Matthew Weinberg

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
Selfish miners selectively withhold blocks to earn disproportionately high revenue. The vast majority of the selfish mining literature focuses exclusively on block rewards. [Carlsten et al., 2016] is a notable exception, observing that similar strategic behavior is profitable in a zero-block-reward regime (the endgame for Bitcoin’s quadrennial halving schedule) if miners are compensated with transaction fees alone. Neither model fully captures miner incentives today. The block reward remains 3.125 BTC, yet some blocks yield significantly higher revenue. For example, congestion during the launch of the Babylon protocol in August 2024 caused transaction fees to spike from 0.14 BTC to 9.52 BTC, a 68× increase in fees within two blocks. Our results are both practical and theoretical. Of practical interest, we study selfish mining profitability under a combined reward function that more accurately models miner incentives. This analysis enables us to make quantitative claims about protocol risk (e.g., the mining power at which a selfish strategy becomes profitable is reduced by 22% when optimizing over the combined reward function versus block rewards alone) and qualitative observations (e.g., a miner considering both block rewards and transaction fees will mine more or less aggressively respectively than if they cared about either alone). These practical results follow from our novel model and methodology, which constitute our theoretical contributions. We model general, time-accruing stochastic rewards in the Nakamoto Consensus Game, which requires explicit treatment of difficult adjustment and randomness; we characterize reward function structure through a set of properties (e.g., that rewards accrue only as a function of time since the parent block). We present a new methodology to analytically calculate expected selfish miner rewards under a broad class of stochastic reward functions and validate our method numerically by comparing it with the existing literature and simulating the combined reward sources directly.
Cite as

Maryam Bahrani, Michael Neuder, and S. Matthew Weinberg. Selfish Mining Under General Stochastic Rewards. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 20:1-20:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bahrani_et_al:LIPIcs.AFT.2025.20,
  author =	{Bahrani, Maryam and Neuder, Michael and Weinberg, S. Matthew},
  title =	{{Selfish Mining Under General Stochastic Rewards}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{20:1--20:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.20},
  URN =		{urn:nbn:de:0030-drops-247396},
  doi =		{10.4230/LIPIcs.AFT.2025.20},
  annote =	{Keywords: Proof-of-Work, Selfish Mining, MEV}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2025.10
Algorithmic Collusion Without Threats

Authors: Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, Aaron Roth, and Juba Ziani

Published in: LIPIcs, Volume 325, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)

Abstract
There has been substantial recent concern that automated pricing algorithms might learn to "collude." Supra-competitive prices can emerge as a Nash equilibrium of repeated pricing games, in which sellers play strategies which threaten to punish their competitors if they ever "defect" from a set of supra-competitive prices, and these strategies can be automatically learned. But threats are anti-competitive on their face. In fact, a standard economic intuition is that supra-competitive prices emerge from either the use of threats, or a failure of one party to correctly optimize their payoff. Is this intuition correct? Would explicitly preventing threats in algorithmic decision-making prevent supra-competitive prices when sellers are optimizing for their own revenue? No. We show that supra-competitive prices can robustly emerge even when both players are using algorithms which do not explicitly encode threats, and which optimize for their own revenue. Since deploying an algorithm is a form of commitment, we study sequential Bertrand pricing games (and a continuous variant) in which a first mover deploys an algorithm and then a second mover optimizes within the resulting environment. We show that if the first mover deploys any algorithm with a no-regret guarantee, and then the second mover even approximately optimizes within this now static environment, monopoly-like prices arise. The result holds for any no-regret learning algorithm deployed by the first mover and for any pricing policy of the second mover that obtains them profit at least as high as a random pricing would - and hence the result applies even when the second mover is optimizing only within a space of non-responsive pricing distributions which are incapable of encoding threats. In fact, there exists a set of strategies, neither of which explicitly encode threats that form a Nash equilibrium of the simultaneous pricing game in algorithm space, and lead to near monopoly prices. This suggests that the definition of "algorithmic collusion" may need to be expanded, to include strategies without explicitly encoded threats.
Cite as

Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, Aaron Roth, and Juba Ziani. Algorithmic Collusion Without Threats. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 10:1-10:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{arunachaleswaran_et_al:LIPIcs.ITCS.2025.10,
  author =	{Arunachaleswaran, Eshwar Ram and Collina, Natalie and Kannan, Sampath and Roth, Aaron and Ziani, Juba},
  title =	{{Algorithmic Collusion Without Threats}},
  booktitle =	{16th Innovations in Theoretical Computer Science Conference (ITCS 2025)},
  pages =	{10:1--10:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-361-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{325},
  editor =	{Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.10},
  URN =		{urn:nbn:de:0030-drops-226386},
  doi =		{10.4230/LIPIcs.ITCS.2025.10},
  annote =	{Keywords: Algorithmic Game Theory, Algorithmic Collusion, No-Regret Dynamics}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2025.48
Robust Restaking Networks

Authors: Naveen Durvasula and Tim Roughgarden

Published in: LIPIcs, Volume 325, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)

Abstract
We study the risks of validator reuse across multiple services in a restaking protocol. We characterize the robust security of a restaking network as a function of the buffer between the costs and profits from attacks. For example, our results imply that if attack costs always exceed attack profits by 10%, then a sudden loss of .1% of the overall stake (e.g., due to a software error) cannot result in the ultimate loss of more than 1.1% of the overall stake. We also provide local analogs of these overcollateralization conditions and robust security guarantees that apply specifically for a target service or coalition of services. All of our bounds on worst-case stake loss are the best possible. Finally, we bound the maximum-possible length of a cascade of attacks. Our results suggest measures of robustness that could be exposed to the participants in a restaking protocol. We also suggest polynomial-time computable sufficient conditions that can proxy for these measures.
Cite as

Naveen Durvasula and Tim Roughgarden. Robust Restaking Networks. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 48:1-48:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{durvasula_et_al:LIPIcs.ITCS.2025.48,
  author =	{Durvasula, Naveen and Roughgarden, Tim},
  title =	{{Robust Restaking Networks}},
  booktitle =	{16th Innovations in Theoretical Computer Science Conference (ITCS 2025)},
  pages =	{48:1--48:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-361-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{325},
  editor =	{Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.48},
  URN =		{urn:nbn:de:0030-drops-226769},
  doi =		{10.4230/LIPIcs.ITCS.2025.48},
  annote =	{Keywords: Proof of stake, Restaking, Staking Risks}
}
Document
DOI: 10.4230/LIPIcs.AFT.2023.19
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
DOI: 10.4230/LIPIcs.AFT.2023.20
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)

Copy BibTex To Clipboard

@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
DOI: 10.4230/LIPIcs.ITCS.2022.82
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)

Copy BibTex To Clipboard

@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}
}
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