This paper introduces the Proof of Sampling (PoSP) protocol, a Nash Equilibrium-based verification mechanism, and its application to decentralized machine learning inference through spML. Our protocol has a pure strategy Nash Equilibrium, compelling rational participants to act honestly. It economically disincentivizes dishonest behavior, making it costly for participants to compromise the network's integrity. In our spML protocol, we apply PoSP to decentralized inference for AI applications via a novel cryptographic protocol. The resulting protocol is much more efficient than zero knowledge proof based approaches. Moreover, we anticipate that the PoSP protocol could be effectively utilized for designing verification mechanisms within Actively Validated Services (AVS) in restaking solutions. We further expect that the PoSP protocol could be applied to a variety of other decentralized applications. Our approach enhances the reliability and efficiency of decentralized systems, paving the way for a new generation of decentralized applications.
Peer review is a laborious, yet essential, part of academic publishing with crucial impact on the scientific endeavor. The current lack of incentives and transparency harms the credibility of this process. Researchers are neither rewarded for superior nor penalized for bad reviews. Additionally, confidential reports cause a loss of insights and make the review process vulnerable to scientific misconduct. We propose a community-owned and -governed system that 1) remunerates reviewers for their efforts, 2) publishes the (anonymized) reports for scrutiny by the community, 3) tracks reputation of reviewers and 4) provides digital certificates. Automated by transparent smart-contract blockchain technology, the system aims to increase quality and speed of peer review while lowering the chance and impact of erroneous judgements.
Proof-of-stake (PoS) has emerged as a natural alternative to the resource-intensive Proof-of-Work (PoW) blockchain, as was recently seen with the Ethereum Merge. PoS-based blockchains require an initial stake distribution among the participants. Typically, this initial stake distribution is called bootstrapping. This paper argues that existing bootstrapping protocols are prone to centralization. To address centralization due to bootstrapping, we propose a novel game $Γ_\textsf{bootstrap}$. Next, we define three conditions: (i) Individual Rationality (IR), (ii) Incentive Compatibility (IC), and (iii) $(τ,δ,ε)-$ Decentralization that an \emph{ideal} bootstrapping protocol must satisfy. $(τ,δ,ε)$ are certain parameters to quantify decentralization. Towards this, we propose a novel centralization metric, C-NORM, to measure centralization in a PoS System. We define a centralization game -- $Γ_\textsf{cent}$, to analyze the efficacy of centralization metrics. We show that C-NORM effectively captures centralization in the presence of strategic players capable of launching Sybil attacks. With C-NORM, we analyze popular bootstrapping protocols such as Airdrop and Proof-of-Burn (PoB) and prove that they do not satisfy IC and IR, respectively. Motivated by the Ethereum Merge, we study W2SB (a PoW-based bootstrapping protocol) and prove it is ideal. In addition, we conduct synthetic simulations to empirically validate that W2SB bootstrapped PoS is decentralized.
Most concurrent blockchain systems rely heavily on the Proof-of-Work (PoW) or Proof-of-Stake (PoS) mechanisms for decentralized consensus and security assurance. However, the substantial energy expenditure stemming from computationally intensive yet meaningless tasks has raised considerable concerns surrounding traditional PoW approaches, The PoS mechanism, while free of energy consumption, is subject to security and economic issues. Addressing these issues, the paradigm of Proof-of-Useful-Work (PoUW) seeks to employ challenges of practical significance as PoW, thereby imbuing energy consumption with tangible value. While previous efforts in Proof of Learning (PoL) explored the utilization of deep learning model training SGD tasks as PoUW challenges, recent research has revealed its vulnerabilities to adversarial attacks and the theoretical hardness in crafting a byzantine-secure PoL mechanism. In this paper, we introduce the concept of incentive-security that incentivizes rational provers to behave honestly for their best interest, bypassing the existing hardness to design a PoL mechanism with computational efficiency, a provable incentive-security guarantee and controllable difficulty. Particularly, our work is secure against two attacks, and also improves the computational overhead from $Θ(1)$ to $O(\frac{\log E}{E})$. Furthermore, while most recent research assumes trusted problem providers and verifiers, our design also guarantees frontend incentive-security even when problem providers are untrusted, and verifier incentive-security that bypasses the Verifier's Dilemma. By incorporating ML training into blockchain consensus mechanisms with provable guarantees, our research not only proposes an eco-friendly solution to blockchain systems, but also provides a proposal for a completely decentralized computing power market in the new AI age.
Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market model that captures the interactions between users submitting ZKP tasks and provers competing to generate proofs. Building on this model, we introduce $Proo\varphi$, an auction-based ZKP market mechanism. We prove that $Proo\varphi$ is incentive compatible for users and provers, and budget balanced. We augment $Proo\varphi$ with system-level designs to address the practical challenges of our setting, such as Sybil attacks, misreporting of prover capacity, and collusion. We analyze our system-level designs and show how they can mitigate the various security concerns.
Artificial Intelligence-Generated Content (AIGC) is a rapidly evolving field that utilizes advanced AI algorithms to generate content. Through integration with mobile edge networks, mobile AIGC networks have gained significant attention, which can provide real-time customized and personalized AIGC services and products. Since blockchains can facilitate decentralized and transparent data management, AIGC products can be securely managed by blockchain to avoid tampering and plagiarization. However, the evolution of blockchain-empowered mobile AIGC is still in its nascent phase, grappling with challenges such as improving information propagation efficiency to enable blockchain-empowered mobile AIGC. In this paper, we design a Graph Attention Network (GAT)-based information propagation optimization framework for blockchain-empowered mobile AIGC. We first innovatively apply age of information as a data-freshness metric to measure information propagation efficiency in public blockchains. Considering that GATs possess the excellent ability to process graph-structured data, we utilize the GAT to obtain the optimal information propagation trajectory. Numerical results demonstrate that the proposed scheme exhibits the most outstanding information propagation efficiency compared with traditional routing mechanisms.
The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction, involves high-performance models. In this dynamic environment, users' tasks frequently shift, requiring fast model personalization despite limited data. This evolution consumes extensive resources and requires vast data volumes. To address this, meta-learning emerges as an invaluable tool for metaverse users, with federated meta-learning (FML), offering even more tailored solutions owing to its adaptive capabilities. However, the metaverse is characterized by users heterogeneity with diverse data structures, varied tasks, and uneven sample sizes, potentially undermining global training outcomes due to statistical difference. Given this, an urgent need arises for smart coalition formation that accounts for these disparities. This paper introduces a dual game-theoretic framework for metaverse services involving meta-learners as workers to manage FML. A blockchain-based cooperative coalition formation game is crafted, grounded on a reputation metric, user similarity, and incentives. We also introduce a novel reputation system based on users' historical contributions and potential contributions to present tasks, leveraging correlations between past and new tasks. Finally, a Stackelberg game-based incentive mechanism is presented to attract reliable workers to participate in meta-learning, minimizing users' energy costs, increasing payoffs, boosting FML efficacy, and improving metaverse utility. Results show that our dual game framework outperforms best-effort, random, and non-uniform clustering schemes - improving training performance by up to 10%, cutting completion times by as much as 30%, enhancing metaverse utility by more than 25%, and offering up to 5% boost in training efficiency over non-blockchain systems, effectively countering misbehaving users.
Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, Web 3.0 based on blockchains is still in its infancy, such as ensuring block freshness and optimizing block propagation for improving blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block Information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI.
Sophie Rain, Lea Salome Brugger, Anja Petkovic Komel, Laura Kovacs · 5 authors
We present the CheckMate tool for automated verification of game-theoretic security properties, with application to blockchain protocols. CheckMate applies automated reasoning techniques to determine whether a game-theoretic protocol model is game-theoretically secure, that is, Byzantine fault tolerant and incentive compatible. We describe CheckMate's input format and its various components, modes, and output. CheckMate is evaluated on 15 benchmarks, including models of decentralized protocols, board games, and game-theoretic examples.
We study long-term equilibria that arise in the token monetary policy, or tokenomics, design of proof-of-stake (PoS) blockchain systems that engage utility maximizing users and validators. Validators are system maintainers who get rewarded with tokens for performing the work necessary for the system to function properly, while users compete and pay with such tokens for getting a desired portion of the system service. <br/><br/>We study how the system service provision and suitable rewards schemes together can lead to equilibria with the following desirable characteristics (1) viability: the system keeps parties engaged, (2) decentralization and skin-in-the-game: multiple sufficiently invested validators are participating, (3) stability: the price path of the underlying token used to transact with the system does not change widely over time, and (4) feasibility: the mechanism is easy to implement as a smart contract, e.g., it does not require a fiat reserve on-chain to perform token buybacks or to perform bookkeeping of exponentially growing token holdings.<br/><br/>Our analysis enables us to put forward a novel generic mechanism for blockchain monetary policy that we call quantitative rewarding (QR). We investigate how to implement QR in single-token and two-token proof of stake (PoS) blockchain systems. The latter are systems that utilize one token for the users to pay the transaction fees and a different token for the validators to participate in the PoS protocol and get rewarded. Our approach demonstrates a concrete advantage of the two-token setting in terms of the ability of the QR mechanism to be realized effectively and provide good equilibria. Our analysis also reveals an inherent limitation of the single token setting in terms of implementing an effective blockchain monetary policy - a distinction that is, to the best of our knowledge, highlighted for the first time.licy - a distinction that is, to the best of our knowledge, highlighted for the first time.
Austin Adams, Ciamac C. Moallemi, Sara Reynolds, Dan Robinson
Automated market makers (AMMs) have emerged as the dominant market mechanism for trading on decentralized exchanges implemented on blockchains. This paper presents a single mechanism that targets two important unsolved problems for AMMs: reducing losses to informed orderflow, and maximizing revenue from uninformed orderflow. The ``auction-managed AMM'' works by running a censorship-resistant onchain auction for the right to temporarily act as ``pool manager'' for a constant-product AMM. The pool manager sets the swap fee rate on the pool, and also receives the accrued fees from swaps. The pool manager can exclusively capture some arbitrage by trading against the pool in response to small price movements, and also can set swap fees incorporating price sensitivity of retail orderflow and adapting to changing market conditions, with the benefits from both ultimately accruing to liquidity providers. Liquidity providers can enter and exit the pool freely in response to changing rent, though they must pay a small fee on withdrawal. We prove that under certain assumptions, this AMM should have higher liquidity in equilibrium than any standard, fixed-fee AMM.
This paper analyzes the Execution Tickets proposal on Ethereum Research, unveiling its potential to revolutionize the Ethereum blockchain's economic model. At the core of this proposal lies a novel ticketing mechanism poised to redefine how the Ethereum protocol distributes the value associated with proposing execution payloads. This innovative approach enables the Ethereum protocol to directly broker Maximal Extractable Value (MEV), traditionally an external revenue stream for validators. The implementation of Execution Tickets goes beyond optimizing validator compensation; it also introduces a new Ethereum native asset with a market capitalization expected to correlate closely with the present value of all value associated with future block production. The analysis demonstrates that the Execution Ticket system can facilitate a more equitable distribution of value within the Ethereum ecosystem, and pave the way for a more secure and economically robust blockchain network.
Tarun Chitra, Kshitij Kulkarni, Mallesh Pai, Theo Diamandis
Mechanisms for decentralized finance on blockchains suffer from various problems, including suboptimal price execution for users, latency, and a worse user experience compared to their centralized counterparts. Recently, off-chain marketplaces, colloquially called `intent markets,' have been proposed as a solution to these problems. In these markets, agents called \emph{solvers} compete to satisfy user orders, which may include complicated user-specified conditions. We provide two formal models of solvers' strategic behavior: one probabilistic and another deterministic. In our first model, solvers initially pay upfront costs to enter a Dutch auction to fill the user's order and then exert congestive, costly effort to search for prices for the user. Our results show that the costs incurred by solvers result in restricted entry in the market. Further, in the presence of costly effort and congestion, our results counter-intuitively show that a planner who aims to maximize user welfare may actually prefer to restrict entry, resulting in limited oligopoly. We then introduce an alternative, optimization-based deterministic model which corroborates these results. We conclude with extensions of our model to other auctions within blockchains and non-cryptocurrency applications, such as the US SEC's Proposal 615.
Motivated by the challenges inherent in implementing trusted services in the Society 5.0 initiative, we propose a novel trust and reputation service for a decentralized marketplace. We assume that a Smart Contract is associated with each transaction and that the Smart Contract is responsible for providing automatic feedback, replacing notoriously unreliable buyer feedback by a more objective assessment of how well the parties have fulfilled their obligations. Our trust and reputation service was inspired by Laplace Law of Succession, where trust in a seller is defined as the probability that she will fulfill her obligations on the next transaction. We offer three applications. First, we discuss an application to a multi-segment marketplace, where a malicious seller may establish a stellar reputation by selling cheap items, only to use their excellent reputation to defraud buyers in a different market segment. Next, we demonstrate how our trust and reputation service works in the context of sellers with time-varying performance by providing two discounting schemes wherein older reputation scores are given less weight than more recent ones. Finally, we show how to predict trust and reputation far in the future, based on incomplete information. Extensive simulations have confirmed our analytical results.
Aadityan Ganesh, Jason Hartline, Atanu R Sinha, Matthew vonAllmen
This paper studies the fundamental limits of availability and throughput for independent and heterogeneous demands of a limited resource. Availability is the probability that the demands are below the capacity of the resource. Throughput is the expected fraction of the resource that is utilized by the demands. We offer a concentration inequality generator that gives lower bounds on feasible availability and throughput pairs with a given capacity and independent but not necessarily identical distributions of up-to-unit demands. We show that availability and throughput cannot both be poor. These bounds are analogous to tail inequalities on sums of independent random variables, but hold throughout the support of the demand distribution. This analysis gives analytically tractable bounds supporting the unit-demand characterization of Chawla, Devanur, and Lykouris (2023) and generalizes to up-to-unit demands. Our bounds also provide an approach towards improved multi-unit prophet inequalities (Hajiaghayi, Kleinberg, and Sandholm, 2007). They have applications to transaction fee mechanism design (for blockchains) where high availability limits the probability of profitable user-miner coalitions (Chung and Shi, 2023).
Leader Election (LE) is crucial in distributed systems and blockchain technology, ensuring one participant acts as the leader. Traditional LE methods often depend on distributed random number generation (RNG), facing issues like vulnerability to manipulation, lack of fairness, and the need for complex procedures such as verifiable delay functions (VDFs) and publicly-verifiable secret sharing (PVSS). This Bachelor's thesis presents a novel approach to randomized LE, leveraging a game-theoretic assumption that participants, aiming to be chosen as leaders, will naturally avoid actions that diminish their chances. This perspective simplifies LE by eliminating the need for decentralized RNG. Introducing PureLottery, inspired by single-elimination sports tournaments, this method offers a fair, bias-resistant, and efficient LE solution for blockchain environments. It operates on the principle of two participants competing in each match, rendering collusion efforts useless. PureLottery stands out for its low computational and communication complexity, suitable for smart contract implementation. It provides strong game-theoretic incentives for honesty and is robust against adversaries, ensuring no increase in election chances through dishonesty. The protocol guarantees that each honest player has at least a 1/n chance of winning, irrespective of adversary manipulation among the other n-1 participants. PureLottery can also address related problems like participant ranking, electing multiple leaders, and leader aversion, showcasing its versatility across various applications, including lotteries and blockchain protocols. An open-source implementation is made available for public use.
Pedro Braga, Georgios Chionas, Piotr Krysta, Stefanos Leonardos · 6 authors
Maximal Extractable Value (MEV) has emerged as a new frontier in the design of blockchain systems. In this paper, we propose making the MEV extraction rate as part of the protocol design space. Our aim is to leverage this parameter to maintain a healthy balance between block producers (who need to be compensated) and users (who need to feel encouraged to transact). We follow the approach introduced by EIP-1559 and design a similar mechanism to dynamically update the MEV extraction rate with the goal of stabilizing it at a target value. We study the properties of this dynamic mechanism and show that, while convergence to the target can be guaranteed for certain parameters, instability, and even chaos, can occur in other cases. Despite these complexities, under general conditions, the system concentrates in a neighborhood of the target equilibrium implying high long-term performance. Our work establishes, the first to our knowledge, dynamic framework for the integral problem of MEV sharing between extractors and users.
Abhimanyu Nag, Samrat Gupta, Sudipan Sinha, Arka Datta
Decentralized Finance (DeFi) governance models have become increasingly complex due to the involvement of numerous independent agents, each with their own incentives and strategies. To effectively analyze these systems, we propose using Multi Agent Influence Diagrams (MAIDs) as a powerful tool for modeling and studying the strategic interactions within DeFi governance. MAIDs allow for a comprehensive representation of the decision-making processes of various agents, capturing the influence of their actions on one another and on the overall governance outcomes. In this paper, we study a simple governance game that approximates real governance protocols and compute the Nash equilibria using MAIDs. We further outline the structure of a MAID in MakerDAO.
Major cryptocurrency networks have relied on random peering choice rules for making connections in their peer-to-peer networks. Generally, these choices have good properties, particularly for open, permissionless networks. Random peering choices however do not take into account that some actors may choose to optimize who they connect to such that they are quicker to hear about information being propagated in the network. In this paper, we explore the dynamics of such greedy strategies. We study a model in which nodes select peers with the objective of minimizing their average distance to a designated subset of nodes in the network, and consider the impact of several factors including the peer selection process, degree constraints, and the size of the designated subset. The latter is particularly interesting in the context of blockchain networks as generally only a subset of nodes are the propagation source for content. We first analyze an idealized version of the game where each node has full knowledge of the current network and aims to select the $d$ best connections, and prove the existence of equilibria under various model assumptions. Since in reality nodes only have local knowledge based on their peers' behavior, we also study a greedy protocol which runs in rounds, with each node replacing its worst-performing edge with a new random edge. We exactly characterize stability properties of networks that evolve with this peering rule and derive regimes where stability is possible and even inevitable. We also run extensive simulations with this peering rule examining both how the network evolves and how different network parameters affect the stability properties of the network. Our findings generally show that the only stable networks that arise from greedy peering choices are low-diameter and result in disparate performance for nodes in the network.
The secondary market for Ethereum non-fungible tokens (NFTs) has resulted in over $1.8bn being paid to creators in the form of a sales tax commonly called creator royalties. This was despite royalty payments being enforced by no more than social contract alone. Predictably, such an incentive structure led to zero-royalty alternatives becoming abundant and payments dwindled. A purely programmatic solution to royalty enforcement is hampered by the prevailing NFT standard, ERC-721, which is ignorant of sale values and royalty enforcement therefore relies on (potentially dishonest) third parties. We thus introduce an incentive-compatible mechanism for which there is a single rationalisable solution, in which royalties are paid in full, while maintaining full ERC-721 compatibility. The mechanism constitutes the core of ERC-7526.
Johann Lussange, Stefano Vrizzi, Stefano Palminteri, Boris Gutkin
Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with reinforcement learning (RL) techniques in order to model crypto markets. This integration is designed to emulate, with a bottom-up approach to complexity inference, both individual and collective agents, ensuring robustness in the recent volatile conditions of such markets and during the COVID-19 era. A key feature of our model also lies in the fact that its autonomous agents perform asset price valuation based on two sources of information: the market prices themselves, and the approximation of the crypto assets fundamental values beyond what those market prices are. Our MAS calibration against real market data allows for an accurate emulation of crypto markets microstructure and probing key market behaviors, in both the bearish and bullish regimes of that particular time period.
We study the effect of public tallies on online elections, in a setting where voting is costly and voters are allowed to strategically time their votes. The strategic importance of choosing \emph{when} to vote arises when votes are public, such as in online event scheduling polls (e.g., Doodle), or in blockchain governance mechanisms. In particular, there is a tension between voting early to influence future votes and waiting to observe interim results and avoid voting costs if the outcome has already been decided. Our study draws on empirical findings showing that "temporal" bandwagon effects occur when interim results are revealed to the electorate: late voters are more likely to vote for leading candidates. To capture this phenomenon, we analyze a novel model where the electorate consists of informed voters who have a preferred candidate, and uninformed swing voters who can be swayed according to the interim outcome at the time of voting. In our main results, we prove the existence of equilibria where both early and late voting occur with a positive probability, and we characterize conditions that lead to the appearance of "last minute" voting behavior, where all informed voters vote late.
Blockchains have popularized automated market makers (AMMs). An AMM exchange is an application running on a blockchain which maintains a pool of crypto-assets and automatically trades assets with users governed by some pricing function that prices the assets based on their relative demand/supply. AMMs have created an important challenge commonly known as the Miner Extractable Value (MEV). In particular, the miners who control the contents and ordering of transactions in a block can extract value by front-running and back-running users' transactions, leading to arbitrage opportunities that guarantee them risk-free returns. 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 in a 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. Moreover, we also guarantee fair treatment among all transactions within the same block, such that the miner is unable to sell off favorable positions in the block to users or arbitragers. Second, for blockchains where the block proposal process is decentralized and offers sequencing-fairness, we prove a stronger notion called incentive compatibility -- roughly speaking, we guarantee that any individual user's best response is to follow the honest strategy.
Users bid in a transaction fee mechanism (TFM) to get their transactions included and confirmed by a blockchain protocol. Roughgarden (EC'21) initiated the formal treatment of TFMs and proposed three requirements: user incentive compatibility (UIC), miner incentive compatibility (MIC), and a form of collusion-resilience called OCA-proofness. Ethereum's EIP-1559 mechanism satisfies all three properties simultaneously when there is no contention between transactions, but loses the UIC property when there are too many eligible transactions to fit in a single block. Chung and Shi (SODA'23) considered an alternative notion of collusion-resilience, called $c$-side-contract-proofness ($c$-SCP), and showed that, when there is contention between transactions, no TFM can satisfy UIC, MIC, and $c$-SCP for any $c\geq 1$. OCA-proofness asserts that the users and a miner should not be able to "steal from the protocol." On the other hand, the $c$-SCP condition requires that a coalition of a miner and a subset of users should not be able to profit through strategic deviations (whether at the expense of the protocol or of the users outside the coalition). Our main result is the first proof that, when there is contention between transactions, no (possibly randomized) TFM in which users are expected to bid truthfully satisfies UIC, MIC, and OCA-proofness.This result resolves the main open question in Roughgarden (EC'21). We also suggest several relaxations of the basic model that allow our impossibility result to be circumvented.