Game theory is deeply connected to both WEB3 and LLM
1Game theory is deeply connected to both WEB3 and LLM. Concepts such as the prisoner's dilemma, Nash equilibrium, auction theory, and Markov decision processes play crucial roles. In WEB3, mining and staking competitions can be modeled as a prisoner's dilemma, where Nash equilibrium helps analyze risks like 51% attacks and transaction fee optimization. Auction theory applies to Ethereum’s fee mechanism and NFT market pricing, guiding players in determining optimal pricing strategies. On the other hand, in LLM, game theory is used for model optimization through reinforcement learning, balancing competition and cooperation among agents via Markov decision processes. For example, when an LLM learns to generate optimal text, it employs reinforcement learning frameworks, selecting the next word while considering long-term rewards. The Bellman equation is applied to calculate the expected cumulative reward for each state. At the intersection of WEB3 and LLM, incentive design for decentralized AI training becomes critical, ensuring that contributors are fairly rewarded through mechanism design. When data providers strategize to maximize their rewards, concepts like Nash equilibrium and cooperative games come into play. In this way, game theory is indispensable in WEB3 and LLM design, optimizing incentives and ensuring system stability.