来源：《华尔街日报》
原文刊登日期：2020年10月1日
Where do good ideas come from? How can we find new solutions to difficult, urgent problems? A zillion consulting firms and business books may claim that they know the answer, but there’s been remarkably little empirical data.
好的想法从哪里来？我们如何才能找到解决困难和紧迫问题的新办法？无数的咨询公司和商业书籍可能声称他们知道答案，但实证数据却少得可怜。
Elena Miu and Luke Rendell at the University of St. Andrews argue that “cultural evolution” is one of the secrets. Human beings gradually accumulate new ideas and solutions. New technologies, from stone axes to smartphones, almost always come from the interaction of many individual problem-solvers rather than a lone genius. But how could such a complicated process be studied?
圣安德鲁斯大学的Miu和Rendell认为“文化进化”是秘密之一。人类逐渐积累新的想法和解决方案。新技术，从石斧到智能手机，几乎总是来自许多解决问题的个体的互动，而不是某一个孤独的天才。但是，如何研究如此复杂的过程呢？
In two studies, Prof. Miu and colleagues cleverly took advantage of data from coding competitions. From 1998 to 2012, Mathworks Software held a series of 19 public competitions to find the best coding solutions to computer-science problems. Nearly 2,000 participants submitted more than 45,000 entries. There was no single correct solution to the problems; instead, the contestants tried to produce code that would be simpler and work better, with judges assigning a score to each entry.
在两项研究中，Miu教授及其同事巧妙地利用了编程比赛的数据。从1998年到2012年，Mathworks软件公司举办了一系列19场公开竞赛，以寻找计算机科学问题的最佳编程解决方案。近2000名参与者提交了45000多份参赛作品。这些问题没有单一的正确解决方案；相反，参赛者试图开发更简单、更有效的代码，评委为每个参赛作品打分。
All the solutions and scores were open for public viewing, so new contestants could see how earlier ones approached the problems. Researchers were able to measure how similar each new solution was to earlier attempts, allowing them to witness cultural evolution in action.
所有的答案和分数都对公众开放，这样新选手就可以看到以前的选手是如何解决问题的。研究人员能够测量每个新解决方案与早期尝试的相似程度，从而使他们能够见证实际的文化进化。
One of the central challenges of cultural evolution is how to balance imitation and innovation. Imitation lets us take advantage of all the ideas that our ancestors have discovered before us. But innovation is also crucial, since if everybody just copied everybody else, we’d never make any progress.
文化进化的核心挑战之一是如何平衡模仿和创新。模仿使我们能够利用先人在我们之前发现的所有思想。但创新也是至关重要的，因为如果每个人都只是模仿别人，我们永远不会取得任何进步。
Prof. Miu and her colleagues found that the coding contestants fell into three groups. There were “copiers” who consistently imitated the successful solutions, making only the smallest changes. There were also “mavericks” who didn’t copy the entries that were already out there but tried something new, more like the stereotypical lone genius. And then there were “pragmatists” who flexibly switched back and forth between copying and innovating.
Miu教授和她的同事们发现，编程选手分为三组。有些“模仿者”总是模仿成功的解决方案，只做最小的改变。也有“特立独行者”，他们不模仿已有的参赛作品，而是尝试新的东西，更像是刻板印象中的孤独天才。然后是“实用主义者”，他们灵活地在模仿和创新之间来回切换。
The researchers found that pragmatists were by far the most likely to receive high scores. They built on the work that had already been done, but unlike the simple copiers, they substantially altered and improved the code, too.
研究人员发现，到目前为止，实用主义者最有可能得到高分。他们建立在已经完成的工作的基础上，但与简单的复制不同，他们也对代码进行了实质性的修改和改进。
About 75% of entries were “tweaks,” making small changes to solutions that other people had already suggested. But there were also “leaps,” solutions that were very different from the ones already out there.
大约75%的参赛作品是“调整”，对其他人已经提出的解决方案进行了小改动。但也有“飞跃式”的解决方案，与现有的解决方案非常不同。
Overall, the leaps were much less likely to be successful than the tweaks; most of them went nowhere. But when leaps were successful, they led to much better solutions and opened up whole new sets of ideas. In fact, there was a consistent pattern: Someone would introduce a fabulous new leap and then the next generation of contestants would refine it with tweaks.
总的来说，飞跃比调整成功的可能性要小得多；它们中的大多数都没获得更好的结果。但当飞跃成功时，它们带来了更好的解决方案，并开辟了全新的思路。事实上，有一个一贯的模式：有人会引入一个惊人的新飞跃，然后下一代参赛者会通过调整来完善它。
What can these studies tell us about how to solve real-life problems? Diversity is key. Rather than the lone genius, it’s the combination of different kinds of knowledge and temperament, humble tweaks and bold leaps, that produces new solutions.
这些研究能告诉我们如何解决现实生活中的问题吗？多样性是关键。产生新解决方案靠的不是某个孤独的天才，而是各种知识和气质、不大的调整和大胆的飞跃的结合。