Zhenpeng Li, Xijin Tang
School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000 Zhejiang China.
Academy of Mathematics and Systems Sciences Chinese Academy of Sciences, Beijing, 100190 China.
Eur Phys J B. 2021;94(12):248. doi: 10.1140/epjb/s10051-021-00259-9. Epub 2021 Dec 23.
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.
集体智慧是指一个群体比任何个体单独行动时更有效地执行任务的能力。通过一个集体预测的进化博弈论模型,我们研究了强化学习刺激在提高集体投票准确性方面可能发挥的作用。并且,集体投票偏差可以通过自我强化的全局合作学习来消除。数值模拟表明,所提出的方法可以提高集体投票的准确性。我们得出结论,现实世界中的系统可能会寻求基于奖励的激励机制,作为克服群体决策错误的一种替代方法。