ScienceAtHome, Department of Physics and Astronomy, Aarhus University, 8000 Aarhus C, Denmark.
Department of Management, School of Business and Social Sciences, Aarhus University, 8000 Aarhus C, Denmark.
Proc Natl Acad Sci U S A. 2018 Nov 27;115(48):E11231-E11237. doi: 10.1073/pnas.1716869115. Epub 2018 Nov 9.
We introduce a remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians used a remote version of their dressed chopped random basis optimization algorithm (RedCRAB), and second, a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global searches. This form of multiagent adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This is encouraging because a much wider range of challenges could potentially be opened to gamification in the future.
我们引入了一个远程接口来控制和优化玻色-爱因斯坦凝聚（BEC）的实验生产，并使用两种不同的实现找到了改进的解决方案。首先，一组理论学家使用他们的远程 dressed chopped random basis optimization algorithm（RedCRAB）版本，其次，一个游戏化界面允许来自世界各地的 600 名公民科学家参与实时优化。对玩家搜索行为的定量研究表明，他们共同进行了局部和全局搜索的组合。这种多智能体自适应搜索形式通过低绩效玩家的探索性行为防止了过早收敛，而高绩效玩家则对其解决方案进行局部优化。此外，许多成功的公民科学游戏都依赖于直接吸引玩家视觉或经验直觉的问题表示。在这里，我们证明公民科学家也可以在完全抽象的问题可视化中取得成功。这是令人鼓舞的，因为未来可能会有更多的挑战可以被游戏化。
