首页|人工智能算法决策与企业研发"合谋"

人工智能算法决策与企业研发"合谋"

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在一个企业研发博弈环境下,运用强化学习Q-learning算法对多智能体进行联合训练,探讨人工智能决策下企业研发竞争的可能结果.主要结论:第一,与纳什均衡结果相比,人工智能决策下企业研发投入更低而利润水平更高的情况是普遍存在的;第二,智能体数量的增加能够降低"合谋"的实现概率;第三,研发的外溢度会促进智能体"合谋",且在智能体数量较多时更易实现;第四,学习率的提高能够降低"合谋"的实现概率,且在智能体数量较多且研发外溢度较低时作用更显著.基于以上研究结果,提出加强人工智能算法在企业决策层面应用管理的政策建议,为数字经济时代的算法监管政策和反垄断规制的完善提供了理论支持.
Algorithms decision-making and corporate R&D"collusion"
This paper uses Q-learning algorithm to train agents in a R&D competition environment,and discusses the possible results caused by artificial intelligence decision.The results show that:first,it is common for lower R&D investment and higher profit levels than Nash equilibrium results.Second,the increase in the number of agents can reduce the probability of achieving"collusion".Third,the spillover degree of R&D will promote the"collusion"of agents,and it is easier to achieve when the number of agents is large.Fourth,the increase in the learning rate can reduce the probability of achieving"collusion",and it is more obvious when the number of agents is large and the spillover of R&D is low.Based on the above research results,this paper puts forward three suggestions to strengthen the management of artificial intelligence application at the decision-making level of enterprises,which provides theoretical support for the continuous improvement of anti-monopoly regulation in the era of digital economy.

artificial intelligenceQ-learning algorithmR&D gameenterprise innovation

徐雷、李政、郭晓玲

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辽宁大学经济学部经济学院,辽宁 沈阳 110036

人工智能 Q-learning算法 研发博弈 企业创新

国家社会科学基金重点项目国家社会科学基金青年项目

22AZD03221CJY043

2024

中国软科学
中国软科学研究会

中国软科学

CSTPCDCSSCICHSSCD北大核心
影响因子:2.793
ISSN:1002-9753
年,卷(期):2024.(6)
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