大数据的局限性

    阿贝斯曼的分析单位是事实，希尔则聚焦于“预测的有效性”。希尔拥有良好的风度和自我认知，他承认人性的弱点是一种设计约束。“但我认为，我们的信念永远不能达到完美的客观性，合理性和准确性，”希尔写道。“相反，我们可以力争少一点主观性、少一点不合理性、少犯一点错误。根据我们的信念作出预测，是进行自我测试的最佳（或许也是唯一的）方式。如果客观性关系到一个更大的超越我们自身条件的真理，那么预测就是审视我们个人看法与那个更大真理之间的联系究竟有多么密切的最佳方式，最客观的往往是那些做出最准确预测的人。”

    然而，我想知道的是，希尔是否充分意识到，他将警示故事与令人震惊的失败混合在一起的做法，可能会对将其报道铭记于心的读者产生累积效应。他提供了一个又一个例子来说明，带有缺陷和偏见的人，使用带有缺陷和偏见的方式，构建出带有缺陷和偏见的模型。他非常出色地反复阐述了“过度拟合的”统计模型。希尔解释称，为了适应数据，统计学家们竭力调试自己的模型，最终往往大大降低了这些模型的准确性，进而无法用其进行可靠的预测。

    希尔的故事为现在的预测模型构建者提供了一个公平的样本。就这一点而言，这本书预测称，未来的新世界将充斥着许多由统计数据驱动的成功案例，既不快乐，也不勇敢。在这个世界中，平均表现距离世界级水准或许相差好几个标准差。

    希尔引用了菲利浦•泰洛克对专家意见所进行的经典研究。这项研究显示，数量多得令人不安的专业领域的“专家”在预测可能结果方面的表现往往差得离谱。此外，专家们往往对其预测的质量过度自信，简言之，专家意见时常获得两个世界的最差结果：以妄自尊大的态度给出了错误答案。这不是成功的秘诀。

    从IBM的超级电脑Watson，谷歌（Google）的搜索算法，到亚马逊网站（Amazon）的推荐引擎，数据驱动的计算系统无疑能够获得非凡的成功，特别是当它们专注于现实生活测试，而不是抽象理论的时候。“真正‘懂得’大数据的公司，比如谷歌，并没有将大量时间花在构建模型上，”希尔写道。“这些公司每年从事数十万次实验，在真实的顾客身上测试自己的想法。”

    然而，读完这两部著作，我们可以得出一个颇具讽刺意味的结论：一个人获得的数据和事实越多，预测就越有意义，人的判断也就显得愈发重要。人类、数据集和算法的协同进化将最终决定“大数据”究竟是会创造新财富，还是会摧毁旧价值。

    本文作者迈克尔•施拉格是麻省理工学院斯隆管理学院数字商务研究中心（MIT Sloan School's Center for Digital Business）研究员，曾经担任《财富》杂志（Fortune ）专栏作家，著有《你想让你的客户变成什么样的人？》（Who Do You Want Your Customers To Become？）一书。

    译者：任文科

    Where Arbesman's unit of analysis is the fact, Silver focuses on "predictive validity." He has the good grace and self-awareness to accept human frailty as a design constraint. "But I'm of the view we can never achieve perfect objectivity, rationality or accuracy in our beliefs," Silver writes.

    "Instead we can strive to be less subjective, less irrational and less wrong. [emphasis in original] Making predictions based on our beliefs is the best (and perhaps only) way to test ourselves. If objectivity is the concern for a greater truth beyond our personal circumstances, and prediction is the best way to examine how closely aligned our personal perceptions are with that greater truth, the most objective among us are those who make the most accurate predictions."

    I wonder, however, if Silver is fully aware of the cumulative effect his mix of cautionary tales and shocking failures might have on readers who take his reporting to heart and mind. He provides example after example of flawed and biased human beings building flawed and biased models and using them flawed and biased ways. He provides a superb riff on "overfitting" in statistical models. By trying to get their models to fit the data a little too well, Silver explains, statisticians all too frequently end up making them far less accurate and reliable for prediction.

    To the extent that Silver's stories present a fair sampling of today's predictive modelers, this book predicts neither a happy nor brave new world of statistics-driven success. In this world, average performance may prove to be quite a few standard deviations away from world class.

    Silver cites Philip Tetlock's classic study of expertise, which shows that "experts" in a disconcerting number of disciplines are disproportionately worse than chance at predicting likely outcomes. Experts also tend to be disproportionately overconfident about the quality of their predictions. In short, expertise frequently yields the worst of both worlds: wrong answers stewed in arrogance. This is not a recipe for success.

    Between IBM's Jeopardy-winning Watson, Google's search algorithms and Amazon's recommendation engines, there's no doubt that data-driven computational systems can enjoy remarkable success, particularly when they focus on real-life testing rather than abstract theory. "Companies that really 'get' Big Data, like Google, aren't spending a lot of time in model land," Silver writes. "They're running hundreds of thousands of experiments every year and testing their ideas on real customers."

    The ironic takeaway from both these fine books, however, is that the more data and facts one has, and the more predictions matter, the more important human judgment becomes. The co-evolution of human beings, datasets, and algorithms will ultimately determine whether Big Data creates new wealth or destroys old value.

    A research fellow at MIT Sloan School's Center for Digital Business, Michael Schrage is a former Fortune columnist and the author of Who Do You Want Your Customers To Become?