Oceanic Games: Centralization Risks and Incentives in Blockchain Mining

Leonardos, Nikos; Leonardos, Stefanos; Piliouras, Georgios

doi:10.1007/978-3-030-37110-4_13

Cited by 23 publications

(10 citation statements)

References 36 publications

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“…A reward scheme that incentivizes honest protocol execution and decentralization is one of the most important parts of a blockchain system. Despite a certain amount of research in this area (e.g., [1], [17], [8]), there are still many open questions on how to build a robust incentive scheme that would facilitate honest behavior of participants.…”

Section: Related Workmentioning

confidence: 99%

Latus Incentive Scheme: Enabling Decentralization in Blockchains based on Recursive SNARKs

Garoffolo,

Kaidalov,

Oliynykov

2021

Preprint

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In [11] we introduced a novel SNARK-based construction, called Zendoo, that allows Bitcoin-like blockchains to create and communicate with sidechains of different types without knowing their internal structure. We also introduced a specific construction, called Latus, allowing creation of fully verifiable sidechains. But in [11] we omitted a detailed description of an incentive scheme for Latus that is an essential element of a real decentralized system. This paper fills the gap by introducing details of the incentive scheme for the Latus sidechain. Represented ideas can also be adopted by other SNARK-based blockchains to incentivize decentralized proofs creation.

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Section: Related Workmentioning

confidence: 99%

Latus Incentive Scheme: Enabling Decentralization in Blockchains based on Recursive SNARKs

Garoffolo,

Kaidalov,

Oliynykov

2021

Preprint

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“…The study of strategic and selfish mining is a very active area; see e.g. [7,22,18,9,12,3,14]. Recently, there have been efforts in studying variations of the blockchain protocol and to analyze the impact in terms of mining incentives [5,20].…”

Section: Related Literaturementioning

confidence: 99%

The Convergence Rates of Blockchain Mining Games: A Markovian Approach

Jofré¹,

Pardo²,

Salas³

et al. 2021

Preprint

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Understanding the strategic behavior of miners in a blockchain is of great importance for its proper operation. A common model for mining games considers an infinite time horizon, with players optimizing asymptotic average objectives. Implicitly, this assumes that the asymptotic behaviors are realized at human-scale times, otherwise invalidating current models. We study the mining game utilizing Markov Decision Processes. Our approach allows us to describe the asymptotic behavior of the game in terms of the stationary distribution of the induced Markov chain. We focus on a model with two players under immediate release, assuming two different objectives: the (asymptotic) average reward per turn and the (asymptotic) percentage of obtained blocks.Using tools from Markov chain analysis, we show the existence of a strategy achieving slow mixing times, exponential in the policy parameters. This result emphasizes the imperative need to understand convergence rates in mining games, validating the standard models. Towards this end, we provide upper bounds for the mixing time of certain meaningful classes of strategies. This result yields criteria for establishing that long-term averaged functions are coherent as payoff functions. Moreover, by studying hitting times, we provide a criterion to validate the common simplification of considering finite states models. For both considered objectives functions, we provide explicit formulae depending on the stationary distribution of the underlying Markov chain. In particular, this shows that both mentioned objectives are not equivalent. Finally, we perform a market share case study in a particular regime of the game. More precisely, we show that an strategic player with a sufficiently large processing power can impose negative revenue on honest players.

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“…Among these, Sapirshtein et al (2015) [42] encapsulates some features of the mining game into a Markov decision process, and solves for the optimal selfish strategy using value iteration. Related to selfish mining, Arnosti and Weinberg (2018) [3] and Leonardos et al (2019) [29] provide theoretical analyses that indicate miners may profit from concentrating their power, suggesting that PoW systems will grow increasingly centralized. Lastly, Fiat et al (2019) [19] and Goran and Spiegelman (2019) [21] propose and analyze another profitable deviation from the honest PoW protocol: occasionally turning mining equipment off in order to reduce the difficulty threshold, making mining more profitable later.…”

Section: Game-theoretic Analyses Of Pow Systemsmentioning

confidence: 99%

Double-Spend Counterattacks: Threat of Retaliation in Proof-of-Work Systems

Moroz¹,

J.²,

Narula³

et al. 2020

Preprint

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Proof-of-Work mining is intended to provide blockchains with robustness against double-spend attacks. However, an economic analysis that follows from Budish (2018), which considers free entry conditions together with the ability to rent sufficient hashrate to conduct an attack, suggests that the resulting block rewards can make an attack cheap. We formalize a defense to double-spend attacks. We show that when the victim can counterattack in the same way as the attacker, this leads to a variation on the classic game-theoretic War of Attrition model. The threat of this kind of counterattack induces a subgame perfect equilibrium in which no attack occurs in the first place.

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Oceanic Games: Centralization Risks and Incentives in Blockchain Mining

Cited by 23 publications

References 36 publications

Latus Incentive Scheme: Enabling Decentralization in Blockchains based on Recursive SNARKs

Latus Incentive Scheme: Enabling Decentralization in Blockchains based on Recursive SNARKs

The Convergence Rates of Blockchain Mining Games: A Markovian Approach

Double-Spend Counterattacks: Threat of Retaliation in Proof-of-Work Systems

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