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Collective wisdom is the ability of a group to perform more effectively than any individual alone. Through an evolutionary game-theoretic model of collective prediction, we investigate the role that reinforcement learning stimulus may play the role in enhancing collective voting accuracy. And collective voting bias can be dismissed through self-reinforcing global cooperative learning. Numeric simulations suggest that the provided method can increase collective voting accuracy. We conclude that real-world systems might seek reward-based incentive mechanism as an alternative to surmount group decision error. [ABSTRACT FROM AUTHOR]