Rational agent behavior describes the actions of an entity that consistently chooses the optimal strategy to maximize its own utility or achieve its objectives, given its available information and constraints. In economic and game-theoretic contexts, this assumption is used to model how participants will react to incentives within a system. It posits that actors will always make choices that serve their best interests. This behavior drives system dynamics.
Context
The assumption of rational agent behavior is fundamental to the design of incentive mechanisms in blockchain consensus protocols, such as proof-of-stake. Protocol designers engineer rewards and penalties to align these self-interested actions with the overall security of the network. Ongoing research examines how deviations from strict rationality might impact system stability and security under various market conditions.
A novel Proof-of-Learning mechanism replaces Byzantine security with incentive-security, provably aligning rational agents to build a decentralized AI compute market.
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