算法 / Algorithm

「释义」

算法是为了解决一个问题而设计的一种策略。解决不同的问题，需要不同的算法。

计算机算法，是用计算机解决问题的方法、步骤，常用于计算、数据处理和自动推理。

作为一个有效方法，算法包含了一系列定义清晰的指令，并在有限步骤中清楚地表述出来。

「应用场景」

对于大多数管理者，做预测是工作的一部分：HR决定聘用人选，是预测谁工作最出色；营销人员选择分销渠道，是预测产品在哪里最好卖；风险投资人决定是否投资某家初创企业，是预测它能否成功。为做好种种商业预测，越来越多企业现在求助于计算机算法——这种技术能以惊人速度完成超大规模分析过程。

Most managers’ jobs involve making predictions. When HR specialists decide whom to hire, they’re predicting who will be most effective. When marketers choose which distribution channels to use, they’re predicting where a product will sell best. When VCs determine whether to fund a start-up, they’re predicting whether it will succeed. To make these and myriad other business predictions, companies today are turning more and more to computer algorithms, which perform step-by-step analytical operations at incredible speed and scale.

算法能让预测更准确，但也会带来风险，尤其是在我们不理解这些算法的情况下。诸如此类广为人知的例子不胜枚举。Netflix为了更精确地了解用户看电影的口味，曾拿出100万美元征集内容推荐算法，很多数据科学家组队参赛。然而这种算法只在用户挑选DVD时能较为准确地推荐，随着Netflix的用户转向在线电影，其偏好与算法的预测结果就会出现偏离。

Algorithms make predictions more accurate—but they also create risks of their own, especially if we do not understand them. High-profile examples abound. When Netflix ran a million-dollar competition to develop an algorithm that could identify which movies a given user would like, teams of data scientists joined forces and produced a winner. But it was one that applied to DVDs—and as Netflix’s viewers transitioned to streaming movies, their preferences shifted in ways that didn’t match the algorithm’s predictions.

《你要管理你的算法》

迈克尔·卢卡，乔恩·克莱因伯格，森迪尔·穆莱纳坦

2016年8月刊

“Algorithms Need Managers, Too”

by Michael Luca , Jon Kleinberg and Sendhil Mullainathan

编辑：马冰仑