Department of Computer Science, Aalto University School of Science, 00076, Espoo, Finland.
Department of Industrial Engineering and Management, Aalto University School of Science, 00076, Espoo, Finland.
Sci Rep. 2021 May 24;11(1):10744. doi: 10.1038/s41598-021-90123-8.
Coordination and cooperation between humans and autonomous agents in cooperative games raise interesting questions on human decision making and behaviour changes. Here we report our findings from a group formation game in a small-world network of different mixes of human and agent players, aiming to achieve connected clusters of the same colour by swapping places with neighbouring players using non-overlapping information. In the experiments the human players are incentivized by rewarding to prioritize their own cluster while the model of agents' decision making is derived from our previous experiment of purely cooperative game between human players. The experiments were performed by grouping the players in three different setups to investigate the overall effect of having cooperative autonomous agents within teams. We observe that the human subjects adjust to autonomous agents by being less risk averse, while keeping the overall performance efficient by splitting the behaviour into selfish and cooperative actions performed during the rounds of the game. Moreover, results from two hybrid human-agent setups suggest that the group composition affects the evolution of clusters. Our findings indicate that in purely or lesser cooperative settings, providing more control to humans could help in maximizing the overall performance of hybrid systems.
人类与自主代理在合作游戏中的协调与合作,引发了关于人类决策和行为改变的有趣问题。在这里,我们报告了在一个具有不同人类和代理玩家混合的小世界网络中的群体形成游戏中的发现,目的是通过与相邻玩家交换位置,使用非重叠信息来实现相同颜色的连接集群。在实验中,人类玩家通过奖励来激励他们优先考虑自己的集群,而代理决策模型则源自我们之前关于纯粹的人类玩家合作游戏的实验。实验通过将玩家分组在三种不同的设置中进行,以研究在团队中拥有合作自主代理的整体效果。我们观察到,人类参与者通过降低风险规避来适应自主代理,同时通过在游戏回合中进行自私和合作行为的划分,保持整体表现的高效。此外,来自两个混合人类-代理设置的结果表明,群体组成会影响集群的演变。我们的研究结果表明,在纯粹或合作程度较低的情况下,为人类提供更多控制可能有助于最大限度地提高混合系统的整体性能。