Luo Yu, Iyengar Garud, Venkatasubramanian Venkat
Department of Chemical Engineering, Columbia University, New York, NY, United States of America.
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, United States of America.
PLoS One. 2016 Mar 15;11(3):e0150343. doi: 10.1371/journal.pone.0150343. eCollection 2016.
Regulating emerging industries is challenging, even controversial at times. Under-regulation can result in safety threats to plant personnel, surrounding communities, and the environment. Over-regulation may hinder innovation, progress, and economic growth. Since one typically has limited understanding of, and experience with, the novel technology in practice, it is difficult to accomplish a properly balanced regulation. In this work, we propose a control and coordination policy called soft regulation that attempts to strike the right balance and create a collective learning environment. In soft regulation mechanism, individual agents can accept, reject, or partially accept the regulator's recommendation. This non-intrusive coordination does not interrupt normal operations. The extent to which an agent accepts the recommendation is mediated by a confidence level (from 0 to 100%). Among all possible recommendation methods, we investigate two in particular: the best recommendation wherein the regulator is completely informed and the crowd recommendation wherein the regulator collects the crowd's average and recommends that value. We show by analysis and simulations that soft regulation with crowd recommendation performs well. It converges to optimum, and is as good as the best recommendation for a wide range of confidence levels. This work sheds a new theoretical perspective on the concept of the wisdom of crowds.
对新兴产业进行监管具有挑战性，有时甚至存在争议。监管不足可能会对工厂员工、周边社区和环境造成安全威胁。监管过度则可能阻碍创新、进步和经济增长。由于人们通常对实践中的新技术了解有限且经验不足，因此很难实现恰当平衡的监管。在这项工作中，我们提出了一种名为软监管的控制与协调政策，旨在实现恰当的平衡并营造一个集体学习的环境。在软监管机制中，个体主体可以接受、拒绝或部分接受监管者的建议。这种非侵入性的协调不会干扰正常运营。主体接受建议的程度由一个置信水平（从0到100%）来调节。在所有可能的建议方法中，我们特别研究了两种：一种是最佳建议，即监管者完全了解情况；另一种是群体建议，即监管者收集群体的平均值并推荐该值。我们通过分析和模拟表明，采用群体建议的软监管表现良好。它能收敛到最优状态，并且在广泛的置信水平范围内与最佳建议效果相当。这项工作为群体智慧的概念提供了一个新的理论视角。
