转载自: HBR。
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导读：探讨企业成长规律和人生规律的好文。 对我发展自己的成长企业投资模型和对未来的规划帮助很大。 （终于找到中文版本的了）
中文版本：
我们的世界观是由我们的个人准则为支撑的：观察构成我们个人的社交体系的各要素（包括人）间的相互作用，并寻找接下来将会发生的事情的预测方法。当体系间体现为线性行为关系且会立即作出反应时，我们的预测会相当的准确。学走步的小孩之所以善于发现灯的开关，是因为开与关的因果关系很直接。小孩一按开关，灯就会立即亮起来。然而，在存在时间递延或非线性关系时，我们的预测力就会骤然下降。例如，股价下跌时却实现了比预期还要高的收益，CEO一定会觉得很奇怪。
了解一下我的合著人，在麻省理工学院受过战略工程师培训、作为新兴企业及财富500强企业咨询师的蒙德斯格雷西亚（Juan Carlos Méndez-García）。据蒙德斯格雷西亚称，理解非线性世界的最好模型是S型曲线（S-Curve）。我们已经应用该模型来理解颠覆性创新的扩散，而且我们预测可用它来理解人格分裂——我们职业路径中的必经点。
在像企业或人脑这样的复杂体系中，因果关系通常不会像开关与灯泡之间的关系那样明显。由于存在时间的延迟关系和依赖关系，即使是大量的投入在近期内所产生的收益抑或微乎甚微，或今天的高产出也许就是长期以来的行动结果。S型曲线通过沿途路标来解释这样的系统。我们的前提是，那些能够成功地以这些逐级学习循环与该S型学习曲线为指导甚至加以应用的人，他们将在这种人格分裂（亦称“自我颠覆”）的时代里茁壮成长。
在我们面临新领域专业技能的培养、提升个人学习曲线时，起初的进步会很慢。但我们可通过刻苦训练而获得一种牵引力，牵引我们进入一个良性循环，从而推动我们进入能力提升和自信心提升的最有效点。然而，当我们到达精通阶段时，恶性循环开始出现：我们做的事情越平常，我们对学习成果“感觉良好”所持的欣赏态度就越低。这两种循环（良性与恶性循环）构成了上述的S型曲线。
有关S型曲线如何才能帮助我们更好地预测未来，高尔夫运动员丹.麦可劳林（Dan McLaughlin）的经历就是一个铁铮铮的例子。2010年4月，从未打过18洞高尔夫球的麦可劳林辞掉了他商业摄影师的工作。经过1万个小时的刻苦训练后，终于实现了做一名顶级职业高尔夫球员的目标。在最初的18个月训练中，他放球、切球、发球的进步很慢。后来，他将各个环节整合、连贯在一起，训练速度得到了提高并很快进入高速增长期。不过对于他是如何迅速克服训练障碍的，他未做任何记录，为此我们很难对他的训练过程做出相应的S型曲线。他仅仅花了28个月就实现他的计划。而据美国高尔夫协会数据统计，近2600万球员在训练时都会遇到过类似障碍，而麦克劳林克服训练障碍的能力却超出了其中91%的球员。
正如我们在学习新知识时，掌握S型曲线可能会让我们陷入灰心的困境，但它也可帮助我们明白为什么一旦达到某个高度、处于停滞阶段时我们会感到厌倦。当我们达到精通阶段时，我们的学习速度开始减慢，而当做事得心应手就意味着有能力实现时，这还意味着我们大脑里感觉良好的神经介质在减少，兴奋的动因已经结束。
当我们的学习到达一定的顶峰时，我们应该不会跳到新的学习曲线，而实际上也许是在迅速下降。但这未必就是指经济衰落，而是指我们的情志与社交健康将会受到冲击。企业创新工厂（Business Innovation Factory）的主要推动人索尔.卡普兰（Saul Kaplan）曾说：“我这一生一直在追求陡峭的学习型曲线，因为只有这样我才能全力以赴的去工作。当我全力以赴的去工作时，金钱和地位通常也就成了水到渠成的事了”。或者用詹姆斯.歐沃斯（James Allworth）的理解就是，“史蒂夫.乔布斯解决了创新者进退两难的窘境，因为他关心的不是利润，而是产品的越来越好”。那么，请忘记追求利润的巅峰吧：追求和放大学习型曲线的范畴。
S型曲线构思模型是针对个人分裂而提出的前所未有的实例。面对线性问题时我们也许是预测未来的数学专家，但问题是商业和生活问题都不是线性问题，而我们人脑最终需要的甚至是必需的东西是不可预知的多巴胺。更重要的是，由于我们是生活在一个日益曲折多变的世界里，那么能够甩开竞争的最佳曲线就是你从某一曲线跃迁至下一曲线的能力。
英文原文：
Our view of the world is powered by personal algorithms: observing how all of the component pieces (and people) that make up our personal social system interact, and looking for patterns to predict what will happen next. When systems behave linearly and react immediately, we tend to be fairly accurate with our forecasts. This is why toddlers love discovering light switches: cause and effect are immediate. The child flips the switch, and on goes the light. But our predictive power plummets when there is a time delay or non-linearity, as in the case of a CEO who delivers better-than-expected earnings only to wonder at a drop in the stock price.
Enter my co-author, MIT-trained strategist and engineer Juan Carlos Méndez-García, who consults with both start-ups and Fortune 500 companies. According to Méndez-García, one of the best models for making sense of a non-linear world is the S-curve, the model we have used to understand the diffusion of disruptive innovations, and which he and I speculate can be used to understand personal disruption — the necessary pivots in our own career paths.
In complex systems like a business (or a brain), cause and effect may not always be as clear as the relationship between the light switch and the light bulb. There are time-delayed and time-dependent relationships in which huge effort may yield little in the near-term, or in which high output today may be the result of actions taken a long time ago. The S-curve decodes these systems by providing signposts along a path that, while frequently trod, is not always evident. Our hypothesis is that those who can successfully navigate, even harness, the successive cycles of learning and maxing out that resemble the S-curve will thrive in this era of personal disruption.
Let's do a quick review. According to the theory of the diffusion of innovations — an attempt to understand how, why and at what rate ideas and technology spread throughout cultures — diffusion or adoption is relatively slow at the outset until a tipping point is reached. Then you enter hypergrowth, which typically happens somewhere between 10-15% of market penetration. Saturation is reached at 90%+.
With Facebook for example, assuming an estimated market opportunity of one billion, it took roughly 4 years to reach penetration of 10%. Once Facebook reached a critical mass of a hundred million users, hypergrowth kicked in due to the network effect (i.e. friends and family were now on Facebook), as well as virality (email updates, photo albums for friends of friends, etc.). Though we could quibble, depending on our inputs, over when Facebook will reach saturation, there is no question the rate of growth has begun to slow and is now limited, if for no other reason, by the number of people who can access the service. (Here's some more on Méndez-García's Facebook and S-curve math.)
As we look to develop competence within a new domain of expertise, moving up a personal learning curve, initially progress is slow. But through deliberate practice, we gain traction, entering into a virtuous cycle that propels us into a sweet spot of accelerating competence and confidence. Then, as we approach mastery, the vicious cycle commences: the more habitual what we are doing becomes, the less we enjoy the "feel good" effects of learning: these two cycles constitute the S-curve.
One anecdotal example of how the S-curve model can help us better predict the future is the experience of golfer Dan McLaughlin. Never having played 18 holes of golf, in April 2010, McLaughlin quit his job as a commercial photographer to pursue a goal of becoming a top professional golfer through 10,000 hours of deliberate practice. During the first 18 months, improvement was slow as McLaughlin first practiced his putting, chipping, and his drive. Then, as he began to put the various pieces together, improvement accelerated, consistent with hypergrowth behavior. While he didn't track how quickly his handicap decreased, making it impossible for us to build an S-curve, 28 months into the project, he has surpassed 91% of the 26 million golfers who register a handicap with the US Golf Association (USGA) database. Not surprisingly, his rate of improvement (if measured as handicap) is now slowing as he faces competition from the top 10% amateur golfers.
Just as understanding the S-curve can keep discouragement at bay as we build new knowledge, it can also help us understand why ennui kicks in once we reach the plateau. As we approach mastery, our learning rate decelerates, and while the ability to do something automatically implies competence, it also means our brains are now producing less of the feel-good neurotransmitters — the thrill ride is over.
As our learning crests, should we fail to jump to new curves, we may actually precipitate our own decline. That doesn't necessarily mean a financial downfall, but our emotional and social well-being will take a hit. Saul Kaplan, Chief Catalyst at Business Innovation Factory, shares: "My life has been about searching for the steep learning curve because that's where I do my best work. When I do my best work, money and stature have always followed." Or paraphrasing James Allworth, "Steve Jobs solved the innovator's dilemma because his focus was never on profit, but better and better products." Forget the plateau of profits: seek and scale a learning curve.
The S-curve mental model makes a compelling case for personal disruption. We may be quite adept at doing the math around our future when things are linear, but neither business nor life is linear, and ultimately what our brain needs, even requires, is the dopamine of the unpredictable. More importantly, as we inhabit an increasingly zig-zag world, the best curve you can throw the competition is your ability to leap from one learning curve to the next.