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1. Transaction Fee Mechanism Design in a Post-MEV World

   https://doi.org/10.4230/LIPIcs.AFT.2024.29  

   Bahrani, Maryam; Garimidi, Pranav; Roughgarden, Tim (January 2024, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Böhme, Rainer; Kiffer, Lucianna (Ed.)

   The incentive-compatibility properties of blockchain transaction fee mechanisms have been investigated with passive block producers that are motivated purely by the net rewards earned at the consensus layer. This paper introduces a model of active block producers that have their own private valuations for blocks (representing, for example, additional value derived from the application layer). The block producer surplus in our model can be interpreted as one of the more common colloquial meanings of the phrase "maximal extractable value (MEV)." We first prove that transaction fee mechanism design is fundamentally more difficult with active block producers than with passive ones: With active block producers, no non-trivial or approximately welfare-maximizing transaction fee mechanism can be incentive-compatible for both users and block producers. These results can be interpreted as a mathematical justification for augmenting transaction fee mechanisms with additional components such as order flow auctions, block producer competition, trusted hardware, or cryptographic techniques. We then consider a more fine-grained model of block production that more accurately reflects current practice, in which we distinguish the roles of "searchers" (who actively identify opportunities for value extraction from the application layer and compete for the right to take advantage of them) and "proposers" (who participate directly in the blockchain protocol and make the final choice of the published block). Searchers can effectively act as an "MEV oracle" for a transaction fee mechanism, thereby enlarging the design space. Here, we first consider a TFM that is inspired by how searchers have traditionally been incorporated into the block production process, with each transaction effectively sold off to a searcher through a first-price auction. We then explore the TFM design space with searchers more generally, and design a mechanism that circumvents our impossibility results for TFMs without searchers. Our mechanism (the "SAKA" mechanism) is incentive-compatible (for users, searchers, and the block producer), sybil-proof, and guarantees roughly 50% of the maximum-possible welfare when transaction sizes are small relative to block sizes. We conclude with a matching negative result: even when transaction sizes are small, no DSIC and sybil-proof deterministic TFM can guarantee more than 50% of the maximum-possible welfare. 

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2. Selfish Mining Under General Stochastic Rewards

   https://doi.org/10.4230/lipics.aft.2025.20  

   Bahrani, Maryam; Neuder, Michael; Weinberg, S Matthew (January 2025, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Avarikioti, Zeta; Christin, Nicolas (Ed.)

   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. 

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3. Public-Key Cryptography in the Fine-Grained Setting

   https://doi.org/10.1007/978-3-030-26954-8_20  

   LaVigne, R.; Lincoln, A.; Vassilevska Williams, V. (January 2019, Advances in Cryptology {\textendash} {CRYPTO} 2019)

   Cryptography is largely based on unproven assumptions, which, while believable, might fail. Notably if P=NP, or if we live in Pessiland, then all current cryptographic assumptions will be broken. A compelling question is if any interesting cryptography might exist in Pessiland. A natural approach to tackle this question is to base cryptography on an assumption from fine-grained complexity. Ball, Rosen, Sabin, and Vasudevan [BRSV’17] attempted this, starting from popular hardness assumptions, such as the Orthogonal Vectors (OV) Conjecture. They obtained problems that are hard on average, assuming that OV and other problems are hard in the worst case. They obtained proofs of work, and hoped to use their average-case hard problems to build a fine-grained one-way function. Unfortunately, they proved that constructing one using their approach would violate a popular hardness hypothesis. This motivates the search for other fine-grained average-case hard problems. The main goal of this paper is to identify sufficient properties for a fine-grained average-case assumption that imply cryptographic primitives such as fine-grained public key cryptography (PKC). Our main contribution is a novel construction of a cryptographic key exchange, together with the definition of a small number of relatively weak structural properties, such that if a computational problem satisfies them, our key exchange has provable fine-grained security guarantees, based on the hardness of this problem. We then show that a natural and plausible average-case assumption for the key problem Zero-k-Clique from fine-grained complexity satisfies our properties. We also develop fine-grained one-way functions and hardcore bits even under these weaker assumptions. Where previous works had to assume random oracles or the existence of strong one-way functions to get a key-exchange computable in O(n) time secure against O(n^2) time adversaries (see [Merkle’78] and [BGI’08]), our assumptions seem much weaker. Our key exchange has a similar gap between the computation of the honest party and the adversary as prior work, while being non-interactive, implying fine-grained PKC. 

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4. Prism: Deconstructing the Blockchain to Approach Physical Limits

   https://doi.org/10.1145/3319535.3363213  

   Bagaria, Vivek; Kannan, Sreeram; Tse, David; Fanti, Giulia; Viswanath, Pramod (November 2019, ACM SIGSAC Conference on Computer and Communications Security)

   The concept of a blockchain was invented by Satoshi Nakamoto to maintain a distributed ledger. In addition to its security, important performance measures of a blockchain protocol are its transaction throughput and confirmation latency. In a decentralized setting, these measures are limited by two underlying physical network attributes: communication capacity and speed-of-light propagation delay. In this work we introduce Prism, a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in the bandwidth-delay product CD; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing Nakamoto’s blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits. 

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5. Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs

   https://doi.org/10.24963/ijcai.2020/47  

   Balcan, Maria-Florina; Prasad, Siddharth; Sandholm, Tuomas (July 2020, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, {IJCAI-20})

   null (Ed.)

   A two-part tariff is a pricing scheme that consists of an up-front lump sum fee and a per unit fee. Various products in the real world are sold via a menu, or list, of two-part tariffs---for example gym memberships, cell phone data plans, etc. We study learning high-revenue menus of two-part tariffs from buyer valuation data, in the setting where the mechanism designer has access to samples from the distribution over buyers' values rather than an explicit description thereof. Our algorithms have clear direct uses, and provide the missing piece for the recent generalization theory of two-part tariffs. We present a polynomial time algorithm for optimizing one two-part tariff. We also present an algorithm for optimizing a length-L menu of two-part tariffs with run time exponential in L but polynomial in all other problem parameters. We then generalize the problem to multiple markets. We prove how many samples suffice to guarantee that a two-part tariff scheme that is feasible on the samples is also feasible on a new problem instance with high probability. We then show that computing revenue-maximizing feasible prices is hard even for buyers with additive valuations. Then, for buyers with identical valuation distributions, we present a condition that is sufficient for the two-part tariff scheme from the unsegmented setting to be optimal for the market-segmented setting. Finally, we prove a generalization result that states how many samples suffice so that we can compute the unsegmented solution on the samples and still be guaranteed that we get a near-optimal solution for the market-segmented setting with high probability. 

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