Jones Matthew I, Pauls Scott D, Fu Feng
Department of Mathematics, Dartmouth College, 27 N. Main Street, 6188 Kemeny Hall, Hanover, NH 03755, USA.
Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA.
iScience. 2021 Mar 24;24(4):102340. doi: 10.1016/j.isci.2021.102340. eCollection 2021 Apr 23.
Global coordination is required to solve a wide variety of challenging collective action problems from network colorings to the tragedy of the commons. Recent empirical study shows that the presence of a few noisy autonomous agents can greatly improve collective performance of humans in solving networked color coordination games. To provide analytical insights into the role of behavioral randomness, here we study myopic artificial agents attempting to solve similar network coloring problems using decision update rules that are only based on local information but allow random choices at various stages of their heuristic reasonings. We show that the resulting efficacy of resolving color conflicts is dependent on the implementation of random behavior of agents and specific population characteristics. Our work demonstrates that distributed greedy optimization algorithms exploiting local information should be deployed in combination with occasional exploration via random choices in order to overcome local minima and achieve global coordination.
解决从网络着色到公地悲剧等各种各样具有挑战性的集体行动问题需要全球协调。最近的实证研究表明，少数有噪声的自主智能体的存在可以极大地提高人类在解决网络颜色协调博弈中的集体表现。为了深入分析行为随机性的作用，我们在此研究近视人工智能体，它们试图使用仅基于局部信息但在启发式推理的各个阶段允许随机选择的决策更新规则来解决类似的网络着色问题。我们表明，解决颜色冲突的最终效果取决于智能体随机行为的实施和特定的群体特征。我们的工作表明，利用局部信息的分布式贪婪优化算法应与通过随机选择进行的偶尔探索相结合，以克服局部最小值并实现全球协调。
