Game theory is the study of strategic decision-making where the outcome for each participant is contingent upon the choices made by all involved parties. In the context of digital assets and blockchain, game theory is instrumental in designing incentive structures, consensus mechanisms, and economic models that encourage rational actors to behave in ways beneficial to the network. It aids in predicting participant conduct and ensuring the integrity of decentralized systems.
Context
Game theory plays a pivotal role in the design and analysis of blockchain protocols, particularly in aligning incentives for network participants such as miners, validators, and users. Current discussions frequently address the refinement of consensus algorithms, like Proof-of-Stake, to ensure individual incentives align with collective network security and efficiency, thereby addressing potential game-theoretic vulnerabilities.
This research establishes a formal theory of Maximal Extractable Value (MEV) through an abstract blockchain model, enabling rigorous security proofs against economic attacks.
This research formalizes Maximal Extractable Value, providing a rigorous framework for understanding and mitigating systemic blockchain vulnerabilities.
This research establishes a universal, game-theoretic definition for Maximal Extractable Value, fundamentally reframing economic attacks within public blockchains for systematic mitigation.
This research formalizes Maximal Extractable Value dynamics through a multi-stage game, revealing systemic inefficiencies and quantifying mitigation strategies.
This research introduces revelation mechanisms within Proof-of-Stake protocols, fundamentally addressing consensus disputes by incentivizing truthful block proposals.
This theory formally defines Maximal Extractable Value, offering a robust framework for proving smart contract security and clarifying adversarial extraction in blockchains.
This research formally models MEV as a multi-stage game, revealing competitive dynamics that degrade welfare and quantifies mitigation through commit-reveal schemes.
This research establishes a foundational framework for analyzing the economic security of blockchain consensus protocols, quantifying attack costs to enable more robust designs.
This paper establishes a rigorous, abstract framework for Maximal Extractable Value, enabling systematic analysis and robust defenses against economic exploits in decentralized systems.
This research formalizes MEV extraction as a multi-stage game, revealing systemic welfare losses and proposing cryptographic mechanisms to restore market fairness.
This research establishes a rigorous theoretical framework for Maximal Extractable Value, enabling provably secure mitigation strategies for blockchain vulnerabilities.
This research leverages game theory to model Maximal Extractable Value dynamics, proposing commit-reveal and threshold encryption mechanisms to enhance DeFi fairness.
This research establishes a formal theory for Maximal Extractable Value (MEV), providing a foundational framework to analyze and mitigate economic attacks on public blockchains.
This research introduces a novel transaction fee mechanism, overcoming a foundational impossibility theorem to ensure miner incentives and user truthfulness in blockchain networks.
This research establishes a rigorous theoretical framework for Maximal Extractable Value (MEV), enabling systematic analysis and the development of provably secure blockchain protocols.
This research establishes a foundational mathematical framework to rigorously assess the economic security of permissionless blockchain consensus, enabling the design of more resilient protocols.
A rigorous model of Maximal Extractable Value provides a foundational framework for proving contract security and mitigating adversarial value extraction.
This research introduces revelation mechanisms to ensure truthful blockchain consensus, leveraging economic incentives to prevent malicious forks and enhance network reliability.
This research introduces a novel auction mechanism for leaderless blockchains, ensuring block producer incentive alignment and substantial welfare guarantees.
A novel game-theoretic model analyzes voter behavior in liquid democracy, offering incentive structures to boost participation and decentralize governance.
This research introduces a novel game-theoretic framework to incentivize participation and optimize resource pricing in blockchain-enabled federated edge learning, unlocking efficient decentralized AI.
This research introduces a novel reward mechanism to prevent Sybil-like mirroring attacks in decentralized oracles, ensuring data integrity and fair compensation.
A new formal theory of MEV provides provable security against economic attacks, differentiating beneficial from malicious value extraction in blockchain protocols.
Introducing "off-chain influence proofness" reveals fundamental trade-offs in blockchain transaction fee mechanism design, critical for equitable value distribution.
This research introduces uncertainty principles to model the fundamental trade-off between transaction reordering flexibility and user economic outcomes, revealing limits of universal MEV mitigation.
A novel framework leverages zero-knowledge proofs to allow mechanism designers to commit to hidden rules, proving incentive properties and outcome correctness without disclosing the mechanism itself, thereby eliminating trusted intermediaries.
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