Sealed-bid auctions play a crucial role in blockchain ecosystems. Previous works introduced viable blockchain sealed-bid auction protocols, leveraging timed commitments for bid encryption. However, a crucial challenge remains unresolved in these works: Who should bear the cost of decrypting these timed commitments? This work introduces a timed commitment outsourcing market as a solution to the aforementioned challenge. We first introduce an aggregation scheme for timed commitments, which combines all bidders' timed commitments into one while ensuring security and correctness and allowing a varying number of bidders. Next, we remodel the utility of auctioneers and timed commitment solvers, developing a new timed commitment competition mechanism and combining it with the sealed-bid auction to form a two-sided market. The protocol includes bid commitment collection, timed commitment solving, and payment. Through game-theoretical analysis, we prove that our protocol satisfies Dominant Strategy Incentive Compatibility (DSIC) for bidders, Bayesian Incentive Compatibility (BIC) for solvers, and achieves optimal revenue for the auctioneer among a large class of mechanisms. Finally, we prove that no mechanism can achieve positive expected revenue for the auctioneer while satisfying DSIC and Individual Rationality (IR) for both bidders and solvers.
Aadityan Ganesh, Clayton Thomas, S. Matthew Weinberg
Transaction Fee Mechanism Design studies auctions run by untrusted miners for transaction inclusion in a blockchain. Under previously-considered desiderata, an auction is considered `good' if, informally-speaking, each party (i.e., the miner, the users, and coalitions of both miners and users) has no incentive to deviate from the fixed and pre-determined protocol. In this paper, we propose a novel desideratum for transaction fee mechanisms. We say that a TFM is off-chain influence proof when the miner cannot achieve additional revenue by running a separate auction off-chain. While the previously-highlighted EIP-1559 is the gold-standard according to prior desiderata, we show that it does not satisfy off-chain influence proofness. Intuitively, this holds because a Bayesian revenue-maximizing miner can strictly increase profits by persuasively threatening to censor any bids that do not transfer a tip directly to the miner off-chain. On the other hand, we reconsider the Cryptographic (multi-party computation assisted) Second Price Auction mechanism, which is technically not `simple for miners' according to previous desiderata (since miners may wish to set a reserve by fabricating bids). We show that, in a slightly different model where the miner is allowed to set the reserve directly, this auction satisfies simplicity for users and miners, and off-chain influence proofness. Finally, we prove a strong impossibility result: no mechanism satisfies all previously-considered properties along with off-chain influence proofness, even with unlimited supply, and even after soliciting input from the miner.
It is well-known that RANDAO manipulation is possible in Ethereum if an adversary controls the proposers assigned to the last slots in an epoch. We provide a methodology to compute, for any fraction $α$ of stake owned by an adversary, the maximum fraction $f(α)$ of rounds that a strategic adversary can propose. We further implement our methodology and compute $f(\cdot)$ for all $α$. For example, we conclude that an optimal strategic participant with $5\%$ of the stake can propose a $5.048\%$ fraction of rounds, $10\%$ of the stake can propose a $10.19\%$ fraction of rounds, and $20\%$ of the stake can propose a $20.68\%$ fraction of rounds.
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Target Tracking and Data Fusion in Sensor Networks
We establish a Stackelberg attack by Liquidity Providers against Governance of an AMM, leveraging forking and commitments through a Grim Forker smart contract. We produce a dynamic, block-by-block model of AMM reserves and trading volume in the presence of competing forks, derive equilibrium conditions in the presence of protocol fees, and analyze Stackelberg equilibria with smart contract moves.
Bitcoin demonstrated the possibility of a financial ledger that operates without the need for a trusted central authority. However, concerns persist regarding its security and considerable energy consumption. We assess the consensus protocols that underpin Bitcoin's functionality, questioning whether they can ensure economically meaningful security while maintaining a permissionless design that allows free entry of operators. We answer this affirmatively by constructing a protocol that guarantees economic security and preserves Bitcoin's permissionless design. This protocol's security does not depend on monetary payments to miners or immense electricity consumption, which our analysis suggests are ineffective. Our framework integrates economic theory with distributed systems theory, and formalizes the role of the protocol's user community.
Trade-intent auctions intermediate around USD~9~billion in monthly trading volume. In these auctions, specialized intermediaries called solvers compete for the right to execute orders across fragmented blockchain-based financial markets. These auctions are combinatorial because executing multiple trade intents jointly generates additional efficiencies. However, there is no best-execution benchmark to determine how to share those efficiencies: the best possible execution of a trade is solvers' private information and must be elicited. We study theoretically the two main mechanisms: batch auctions, in which a group of trades is auctioned off jointly, and independent trade-by-trade auctions. Batch auctions return more total value to traders, but their outcome may be unfair, in the sense of leaving one trader worse off than under independent auctions. We propose a fair combinatorial auction: solvers bid on individual trades and on batches of trades, but a batched bid is filtered out if any trader earns less than an execution benchmark constructed from the bids on individual trades and a counterfactual mechanism. Whether fairness guarantees arise in equilibrium depends on the counterfactual mechanism: independent first-price auctions generate such guarantees; independent second-price auctions do not. These fairness guarantees come at a cost: a lower total value returned to traders.
We provide an economic model of Execution Tickets and use it to study the ability of the Ethereum protocol to capture MEV from block construction. We demonstrate that Execution Tickets extract all MEV when all buyers are homogeneous, risk neutral and face no capital costs. We also show that MEV capture decreases with risk aversion and capital costs. Moreover, when buyers are heterogeneous, MEV capture can be especially low and a single dominant buyer can extract much of the MEV. This adverse effect can be partially mitigated by the presence of a Proposer Builder Separation (PBS) mechanism, which gives ET buyers access to a market of specialized builders, but in practice centralization vectors still persist. With PBS, ETs are concentrated among those with the highest ex-ante MEV extraction ability and lowest cost of capital. We show how it is possible that large investors that are not builders but have substantial advantage in capital cost can come to dominate the ET market.
Behkish Nassirzadeh, Albert Heinle, Stefanos Leonardos, Anwar Hasan · 5 authors
Blockchain integration in industries like online advertising is hindered by its connectivity limitations to off-chain data. These industries heavily rely on precise counting systems for collecting and analyzing off-chain data. This requires mechanisms, often called oracles, to feed off-chain data into smart contracts. However, current oracle solutions are ill-suited for counting systems since the oracles do not know when to expect the data, posing a significant challenge. To address this, we present CountChain, a decentralized oracle network for counting systems. In CountChain, data is received by all oracle nodes, and any node can submit a proposition request. Each proposition contains enough data to evaluate the occurrence of an event. Only randomly selected nodes participate in a game to evaluate the truthfulness of each proposition by providing proof and some stake. Finally, the propositions with the outcome of True increment the counter in a smart contract. Thus, instead of a contract calling oracles for data, in CountChain, the oracles call a smart contract when the data is available. Furthermore, we present a formal analysis and experimental evaluation of the system's parameters on over half a million data points to obtain optimal system parameters. In such conditions, our game-theoretical analysis demonstrates that a Nash equilibrium exists wherein all rational parties participate with honesty.
Perpetual voting studies fair collective decision-making in settings where many decisions are to be made, and is a natural framework for settings such as parliaments and the running of blockchain Decentralized Autonomous Organizations (DAOs). We focus our attention on the binary case (YES/NO decisions) and \textit{individual} guarantees for each of the participating agents. We introduce a novel notion, inspired by the popular maxi-min-share (MMS) for fair allocation. The agent expects to get as many decisions as if they were to optimally partition the decisions among the agents, with an adversary deciding which of the agents decides on what bundle. We show an online algorithm that guarantees the MMS notion for $n=3$ agents, an offline algorithm for $n=4$ agents, and show that no online algorithm can guarantee the $MMS^{adapt}$ for $n\geq 7$ agents. We also show that the Maximum Nash Welfare (MNW) outcome can only guarantee $O(\frac{1}{n})$ of the MMS notion in the worst case.
In the era of Web3, decentralized technologies have emerged as the cornerstone of a new digital paradigm. Backed by a decentralized blockchain architecture, the Web3 space aims to democratize all aspects of the web. From data-sharing to learning models, outsourcing computation is an established, prevalent practice. Verifiable computation makes this practice trustworthy as clients/users can now efficiently validate the integrity of a computation. As verifiable computation gets considered for applications in the Web3 space, decentralization is crucial for system reliability, ensuring that no single entity can suppress clients. At the same time, however, decentralization needs to be balanced with efficiency: clients want their computations done as quickly as possible. Motivated by these issues, we study the trade-off between decentralization and efficiency when outsourcing computational tasks to strategic, rational solution providers. Specifically, we examine this trade-off when the client employs (1) revelation mechanisms, i.e. auctions, where solution providers bid their desired reward for completing the task by a specific deadline and then the client selects which of them will do the task and how much they will be rewarded, and (2) simple, non-revelation mechanisms, where the client commits to the set of rules she will use to map solutions at specific times to rewards and then solution providers decide whether they want to do the task or not. We completely characterize the power and limitations of revelation and non-revelation mechanisms in our model.
This paper investigates which alternative benefits from vote delegation in binary collective decisions within blockchains. We begin by examining two extreme cases of voting weight distributions: Equal-Weight (EW), where each voter has equal voting weight, and Dominant-Weight (DW), where a single voter holds a majority of the voting weights before any delegation occurs. We show that vote delegation tends to benefit the ex-ante minority under EW, i.e., the alternative with a lower initial probability of winning. The converse holds under DW distribution. Through numerical simulations, we extend our findings to arbitrary voting weight distributions, showing that vote delegation benefits the ex-ante majority when it leads to a more balanced distribution of voting weights. Finally, in large communities where all agents have equal voting weight, vote delegation has a negligible impact on the outcome. As a practical consequence, vote delegation can be beneficial for blockchains with highly unbalanced voting rights, but not for those with balanced rights. In decentralized finance (DeFi), vote delegation is widely adopted to streamline governance and increase participation. However, it remains unclear when delegation actually aligns outcomes with community preferences.
Rob McLaughlin, Nir Chemaya, Dingyue Liu, Dahlia Malkhi
This paper introduces a trade ordering rule that aims to reduce intra-block price volatility in Automated Market Maker (AMM) powered decentralized exchanges. The ordering rule introduced here, Clever Look-ahead Volatility Reduction (CLVR), operates under the (common) framework in decentralized finance that allows some entities to observe trade requests before they are settled, assemble them into "blocks", and order them as they like. On AMM exchanges, asset prices are continuously and transparently updated as a result of each trade and therefore, transaction order has high financial value. CLVR aims to order transactions for traders' benefit. Our primary focus is intra-block price stability (minimizing volatility), which has two main benefits for traders: it reduces transaction failure rate and allows traders to receive closer prices to the reference price at which they submit their transactions accordingly. We show that CLVR constructs an ordering which approximately minimizes price volatility with a small computation cost and can be trivially verified externally.
Announcement games, where information is disseminated by announcers and challenged by validators, are prevalent in real-world scenarios. Validators take effort to verify the validity of the announcements, gaining rewards for successfully challenging invalid ones, while receiving nothing for valid ones. Optimistic Rollup, a Layer 2 blockchain scaling solution, exemplifies such games, offering significant improvements in transaction throughput and cost efficiency. We present a game-theoretic model of announcement games to analyze the potential behaviors of announcers and validators. We identify all Nash equilibria and study the corresponding system losses for different Nash equilibria. Additionally, we analyze the impact of various system parameters on system loss under the Nash equilibrium. Finally, we provide suggestions for mechanism optimization to reduce system losses.
Linda Cai, Jingyi Liu, S. Matthew Weinberg, Chenghan Zhou
Cryptographic Self-Selection is a common primitive underlying leader-selection for Proof-of-Stake blockchain protocols. The concept was first popularized in Algorand [CM19], who also observed that the protocol might be manipulable. [FHWY22] provide a concrete manipulation that is strictly profitable for a staker of any size (and also prove upper bounds on the gains from manipulation). Separately, [YSZ23, BM24] initiate the study of undetectable profitable manipulations of consensus protocols with a focus on the seminal Selfish Mining strategy [ES14] for Bitcoin's Proof-of-Work longest-chain protocol. They design a Selfish Mining variant that, for sufficiently large miners, is strictly profitable yet also indistinguishable to an onlooker from routine latency (that is, a sufficiently large profit-maximizing miner could use their strategy to strictly profit over being honest in a way that still appears to the rest of the network as though everyone is honest but experiencing mildly higher latency. This avoids any risk of negatively impacting the value of the underlying cryptocurrency due to attack detection). We investigate the detectability of profitable manipulations of the canonical cryptographic self-selection leader selection protocol introduced in [CM19] and studied in [FHWY22], and establish that for any player with $α< \frac{3-\sqrt{5}}{2} \approx 0.38$ fraction of the total stake, every strictly profitable manipulation is statistically detectable. Specifically, we consider an onlooker who sees only the random seed of each round (and does not need to see any other broadcasts by any other players). We show that the distribution of the sequence of random seeds when any player is profitably manipulating the protocol is inconsistent with any distribution that could arise by honest stakers being offline or timing out (for a natural stylized model of honest timeouts).
This paper surveys products and studies on cryptoeconomics and tokenomics from an economic perspective, as these terms are still (i) ill-defined and (ii) disconnected from economic disciplines. We first suggest that they can be novel when integrated; we then conduct a literature review and case study following consensus-building for decentralization and token value for autonomy. Integration requires simultaneous consideration of strategic behavior, spamming, Sybil attacks, free-riding, marginal cost, marginal utility and stabilizers. This survey is the first systematization of knowledge on cryptoeconomics and tokenomics, aiming to bridge the contexts of economics and blockchain.
The first generation of cryptocurrencies introduced revolutionary concepts, yet faced challenges in privacy and regulatory compliance. While subsequent cryptocurrencies aimed to address privacy concerns (like Zcash and Monero), they often conflicted with regulatory frameworks, hindering broader adoption. In response, inspired by recent researches about privacy and accountability and incentive techniques in Blockchain, we propose IdentityChain as a novel framework that integrates privacy and accountability principles, leading to a robust system equipped with adaptable rules. IdentityChain is a KYC (Know Your Customer) service on top of a public Blockchain (e.g., Ethereum, Ton, Polygon). The goal is to maintain privacy while ensuring compliance with existing regulations. Privacy is one of the key characteristics of IdentityChain, it's crucial for preventing conflicts of interests further discussed how. Accountability is also one of the main characteristics of IdentityChain and prevents from misbehave of users. Privacy and accountability together wouldn't be possible unless advancements in cryptography.
Blockchain platforms attempt to expand their user base by awarding tokens to users, a practice known as issuing airdrops. Empirical data and related work implies that previous airdrops fall short of their stated aim of attracting long-term users, partially due to adversarial farmers who game airdrop mechanisms and receive an outsize share of rewards. In this work, we argue that given the futility of fighting farmers, the airdrop business model should be reconsidered: farmers should be harnessed to generate activity that attracts real users, i.e., strengthens network effects. To understand the impact of farmers on airdrops, we analyze their performance in a market inhabited by two competing platforms and two tiers of users: real users and farmers. We show that counterintuitively, farmers sometimes represent a necessary evil-it can be revenue-optimal for airdrop issuers to give some tokens to farmers, even in the hypothetical case where platforms could costlessly detect and banish all farmers. Although we focus on airdrops, our results generally apply to activity-based incentive schemes.
Kun Li, Guangpeng Qi, Guangyong Shang, Wanli Deng · 6 authors
With the widespread adoption of blockchain technology, the transaction fee mechanism (TFM) in blockchain systems has become a prominent research topic. An ideal TFM should satisfy user incentive compatibility (UIC), miner incentive compatibility (MIC), and miner-user side contract proofness ($c$-SCP). However, state-of-the-art works either fail to meet these three properties simultaneously or only satisfy them under certain conditions. In this paper, we propose a burning $N$-price auction TFM named BNP. This mechanism divides the transaction fee into a base fee, which is burned, and a priority fee, which is allocated to miners. Theoretical proofs and experimental analyses demonstrate that, even under conditions of significant transaction congestion, this mechanism satisfies UIC, MIC, and $c$-SCP simultaneously. Furthermore, the BNP mechanism is not constrained by the type of blockchain consensus, making it widely applicable.
Matheus V. X. Ferreira, Aadityan Ganesh, Jack Hourigan, Hannah Huh · 6 authors
Cryptographic Self-Selection is a paradigm employed by modern Proof-of-Stake consensus protocols to select a block-proposing "leader." Algorand [Chen and Micali, 2019] proposes a canonical protocol, and Ferreira et al. [2022] establish bounds $f(α,β)$ on the maximum fraction of rounds a strategic player can lead as a function of their stake $α$ and a network connectivity parameter $β$. While both their lower and upper bounds are non-trivial, there is a substantial gap between them (for example, they establish $f(10\%,1) \in [10.08\%, 21.12\%]$), leaving open the question of how significant of a concern these manipulations are. We develop computational methods to provably nail $f(α,β)$ for any desired $(α,β)$ up to arbitrary precision, and implement our method on a wide range of parameters (for example, we confirm $f(10\%,1) \in [10.08\%, 10.15\%]$). Methodologically, estimating $f(α,β)$ can be phrased as estimating to high precision the value of a Markov Decision Process whose states are countably-long lists of real numbers. Our methodological contributions involve (a) reformulating the question instead as computing to high precision the expected value of a distribution that is a fixed-point of a non-linear sampling operator, and (b) provably bounding the error induced by various truncations and sampling estimations of this distribution (which appears intractable to solve in closed form). One technical challenge, for example, is that natural sampling-based estimates of the mean of our target distribution are \emph{not} unbiased estimators, and therefore our methods necessarily go beyond claiming sufficiently-many samples to be close to the mean.
Georgios Birmpas, Philip Lazos, Evangelos Markakis, Paolo Penna
In this paper, we investigate the impact of reward schemes and committee sizes motivated by governance systems over blockchain communities. We introduce a model for elections with a binary outcome space where there is a ground truth (i.e., a "correct" outcome), and where stakeholders can only choose to delegate their voting power to a set of delegation representatives (DReps). Moreover, the effort (cost) invested by each DRep positively influences both (i) her ability to vote correctly and (ii) the total delegation that she attracts, thereby increasing her voting power. This model constitutes the natural counterpart of delegated proof-of-stake (PoS) protocols, where delegated stakes are used to elect the block builders. As a way to motivate the representatives to exert effort, a reward scheme can be used based on the delegation attracted by each DRep. We analyze both the game-theoretic aspects and the optimization counterpart of this model. Our primary focus is on selecting a committee that maximizes the probability of reaching the correct outcome, given a fixed monetary budget allocated for rewarding the delegates. Our findings provide insights into the design of effective reward mechanisms and optimal committee structures (i.e., how many DReps are enough) in these PoS-like governance systems.
The security of blockchain systems is fundamentally based on the decentralized consensus in which the majority of parties behave honestly, and the content verification process is essential to maintaining the robustness of blockchain systems. However, the phenomenon that a rational verifier may not have the incentive to honestly perform the costly verification, referred to as the Verifier's Dilemma, could incentivize lazy reporting and undermine the fundamental security of blockchain systems, particularly for verification-expensive decentralized AI applications. In this paper, we initiate the research with the development of a Byzantine-robust peer prediction framework towards the design of one-phase Bayesian truthful mechanisms for the decentralized verification games among multiple verifiers, incentivizing all verifiers to perform honest verification without access to the ground truth even in the presence of noisy observations, malicious players and inaccurate priors in the verification process, proposing the compactness criteria that ensures such robustness guarantees. With robust incentive guarantees and budget efficiency, our study provides a framework of incentive design for decentralized verification protocols that enhances the security and robustness of the blockchain, decentralized AI, and potentially other decentralized systems.
In the face of limited block size, miners (e.g., in Bitcoin) prioritize high-bid transactions, forming a large part of their revenue. If the block size were to expand significantly, meeting all transaction demand due to infrastructure or protocol improvements, bids could drop to zero or to a minimum fee, reducing mining incentives and potentially affecting security. To address this, Lavi et al. (2022) introduced a monopolistic pricing mechanism where miners only include transactions paying a minimum price, ensuring some revenue but resulting in an unbounded loss in welfare. Nisan (2023) expands this by modeling bidders as patient, who wait indefinitely long for lower prices, causing price fluctuations even with stable demand. In order to capture users' diminishing interest in having their transactions added to the ledger over time, we consider a more realistic setting with quasi-patient users, where only a fraction $δ\in [0,1]$ of pending transactions remains in the next round. This richer model encompasses both Lavi et al.'s impatient users ($δ=0$) and Nisan's patient users ($δ=1$) as special cases. We demonstrate that Nisan's fluctuating dynamics persist for $δ$ close to 1, while for $δ$ close to 0, the dynamics resemble the impatient case. For $δ\in (0,1)$, we establish new bounds on price dynamics, revealing unexpected effects. Unlike the fully patient case, the bounds of the dynamics for $δ<1$ depend on the demand curve and undergo a "transition phase". For some $δ$, the model mirrors the fully patient setting, and for smaller $δ' < δ$, it stabilizes at the highest monopolist price, thus collapsing to the impatient case. We provide quantitative bounds and analytical results, showing that the bounds for $δ=1$ are generally not tight for $δ<1$, and we give guarantees on the minimum ("admission") price for transactions.
Distributed consensus protocols reach agreement among $n$ players in the presence of $f$ adversaries; different protocols support different values of $f$. Existing works study this problem for different adversary types (captured by threat models). There are three primary threat models: (i) Crash fault tolerance (CFT), (ii) Byzantine fault tolerance (BFT), and (iii) Rational fault tolerance (RFT), each more general than the previous. Agreement in repeated rounds on both (1) the proposed value in each round and (2) the ordering among agreed-upon values across multiple rounds is called Atomic BroadCast (ABC). ABC is more generalized than consensus and is employed in blockchains. This work studies ABC under the RFT threat model. We consider $t$ byzantine and $k$ rational adversaries among $n$ players. We also study different types of rational players based on their utility towards (1) liveness attack, (2) censorship or (3) disagreement (forking attack). We study the problem of ABC under this general threat model in partially-synchronous networks. We show (1) ABC is impossible for $n/3< (t+k) <n/2$ if rational players prefer liveness or censorship attacks and (2) the consensus protocol proposed by Ranchal-Pedrosa and Gramoli cannot be generalized to solve ABC due to insecure Nash equilibrium (resulting in disagreement). For ABC in partially synchronous network settings, we propose a novel protocol \textsf{pRFT}(practical Rational Fault Tolerance). We show \textsf{pRFT} achieves ABC if (a) rational players prefer only disagreement attacks and (b) $t < \frac{n}{4}$ and $(t + k) < \frac{n}{2}$. In \textsf{pRFT}, we incorporate accountability (capturing deviating players) within the protocol by leveraging honest players. We also show that the message complexity of \textsf{pRFT} is at par with the best consensus protocols that guarantee accountability.
Ethereum 2.0 is the second-largest cryptocurrency by market capitalization and a widely used smart contract platform. Therefore, examining the reliability of Ethereum 2.0's incentive mechanism is crucial, particularly its effectiveness in encouraging validators to adhere to the Ethereum 2.0's protocol. This paper studies the incentive mechanism of Ethereum 2.0 and evaluates its robustness by analyzing the interaction between block proposers and attesters in a single slot. To this end, we use Bayesian games to model the strategies of block proposers and attesters and calculate their expected utilities. Our results demonstrate that the Ethereum 2.0 incentive mechanism is incentive-compatible and promotes cooperation among validators. We prove that a Bayesian Nash equilibrium and an ex ante dominant strategy exist between the block proposer and attesters in a single slot. Our research provides a solid foundation for further analysis of Ethereum 2.0's incentive mechanism and insights for individuals considering participation as a validator in Ethereum 2.0.