唐纳德·诺曼（Donald Arthur Norman，1935年12月25日－）为美国认知心理学家、计算机工程师、工业设计家，认知科学学会的发起人之一，关注人类社会学、行为学的研究。1999年，他被Upside杂志提名为世界100精英之一。Norman博士出版了大量的书籍和研究报告。他的作品有13本之多，并被翻译成12种语言。其中最有名的要数《设计心理学》、 《情感化设计》 以及2009年出版的 《未来产品的设计》。
Don Norman声称他的目标在生活中做出了显著的差异，但这样做有乐趣。他是商人（在苹果公司，惠普执行副总裁和启动）和学术（哈佛大学，加州大学圣迭戈分校，西北大学，KAIST）。由于诺曼尼尔森集团的创始人之一，他担任公司董事会，并帮助企业使产品更愉快，称心，和盈利。他是一个IDEO的研究院和国家工程学院的成员。
第一次受到优秀产品设计的震撼，令我终身难忘。那时我刚加入Apple，还在熟悉业务。工业设计团队的一名成员来访，他给我看了一个产品提案的仿制模型。“哇哦，”我说，“我想要一个！这啥东西呀？”
那次经历让我感受到了设计的力量：我还不知道那是什么东西，它就令我为之激动和狂热！这种让人不禁叫出“哇哦”的设计，只来自有创意的设计师。这是很主观的、很个人的事情。你瞧瞧，工程师可不喜欢听这个——没办法量化？那就不重要！如此一来，消灭设计师的趋势就出现了。我们工程师只靠测试就可以走向成功，简单得很，谁还需要设计师！有震撼力、俘获人心的设计所带来的激动心情，被认为是无关紧要的。更糟糕的是，设计的本质正在被忽视和践踏，设计正在面临危机。
两种创新：渐进式改善和新概念
谈到设计，和几乎所有的创新，至少有两种截然不同的实践形式。其一是渐进式改善（incremental improvement）。这意味着，一家企业在产品制造过程中的单位成本随着对产品持续、渐进的改善而逐渐降低。由此形成的稳定的渐进式创新链条，有助于运营、部件的供货以及供应链管理。持续对产品设计进行修补：调整界面、追加新功能、在各处做小修订等等。在既有平台的基础上，采用不同的功能搭配组合，简单做些微修改，就可做到每年都发布新产品。既减少部分功能，以便推出低端产品线，也可以对部分功能进行强化或追加全新的功能。采用渐进式改善，基本的平台底子总是不变的。渐进式设计和创新不如开创新概念、新想法来的有魅力，但比后者常见得多，也重要得多。这样的创新都是小创新，但其中大部分都非常成功。此即所谓的企业“摇钱树（cash cows）”：这样的一条产品线，只需追加很少的开发成本，就能实现常年获利颇丰。
第二种形式的设计，则是教授“突破性产品创新”时所谈的那种设计，广泛见之于设计、工程和MBA课程当中。这种设计即发明新概念、定义新产品、开创新商机，是创新中有趣的那一部分，因而也是大部分设计师、发明家希望盘踞的领地。然而这种设计的风险是很大的：大部分创新会失败。成功的创新可能经历数十年才会被广泛接受——所以创新者并不一定就是获益者。
在开头我提到的那个Apple产品模型事例中，设计师就是在发明新概念。相较之下，Google和Amazon实践的就是渐进式改善。这是两种不同的实践活动。和大部分创新一样，那个Apple产品最终失败了。为什么会失败呢？我过一会儿再来说明。
两种形式的设计都是必要的。围绕“数据驱动（data-driven）”型设计的争论是有误导性的，因为其无非是用一种设计的优势来否定另一种设计的重要性。对于改善既有产品而言，数据驱动型设计确实行之有效。然而，产品本身又是从哪儿来的呢？当然是来自某个有创意的脑袋瓜。测试有助于强化一个既有想法，前提是需要有创意的设计师和发明家来给出这个想法。
为什么测试很重要却又不完善
数据驱动型设计正好比一种知名的优化算法——“爬山（hill-climbing）”法。设想你身在一座不熟悉的山丘上，一片漆黑伸手不见五指。如果你看不见，要如何爬到山顶呢？你可以测试自己周围的地形，哪个方向是最陡且往上的，就向哪个方向迈一步。重复探寻，直到你周围任一方向都往下行为止。
但如果这片地区有很多山丘怎么办呢？如何能知道你是否处于整片山丘的最高处呢？答案是：你不能知道。此即“local maximum（局部最大值）”问题：你无法判定你是在最高的山丘顶上（即全局最大值，global maximum）上，还是在一个小山丘顶上。
在数学空间中，计算机可尝试从空间中多个不同的部分同时施行“爬山”算法，并选取所有尝试结果中的最大值，从而避免“局部最大值”问题。这种做法仍然无法保证能取到真正的最大值，但能避免被局限在单一的局部最大值上。这种策略对设计师而言鲜能凑效。确定一个起点就已经很不容易了，更不用说确定多个不同的起点。如此一来，通过测试来进行改进的设计只可能达到一个局部上限。测试永远不可能告诉我们，是否存在好得多的方案（也许另一个山丘要高得多）。
于是就需要有创意的人来参与。当这个人重新构造问题，认识到之前探索的局限性，突破就会出现。设计和发明需要创意的一面。渐进式的设计无法做到这一点。
伟大创新的障碍
激动人心的创新所具备的一些根本特征，使创新本身不适合通过测试来进行决断。人们对新颖设计有抵触情绪，采取的态度会趋于保守。做事情的新技术、新方法往往要历经数十甚至上百年才会被接受。与此不同的是，各种基于测试的设计方式都假设，做出一个改动之后，能够立刻测试、得到反馈，并立刻决定改动后是否比改动前更好。
我们没有办法判别激进的新想法最终是否能成功。我们还需要伟大的领头者和勇气。历史告诉我们，有许多人面对一次又一次的拒绝和抵触，坚持了很长时间，其想法才终获接受。这些成功者经常指出，在产品获得成功后，人们就无法想象以前没有这个产品的时候是怎么过的了。历史也告诉我们，有许多人坚持过，最终也未获得成功。对激进的新想法持怀疑态度并不为过。
一个初成的想法不被接受，因素很多：可能是因为技术还不成熟，可能是因为还有很多东西有待优化，可能是因为受众群体还没有做好接受它的准备，也可能是因为这是个糟糕的想法。判定其中的主导因素是很困难的——是在确立想法很久之后，才会得到的后见之明。
一个激动人心的想法，从想法形成并初步实现，到最终认定其在市场中的成功或失败，历时长久。 有些人想以证据作为标准，对新发展方向进行定夺，却被这漫长的时间差所击败。 更好的方案 即使曾经被提出过 ，也可能会被自动化测试否决掉——这并不是因为它不好，而是因为它等不了数十年的时间来获得认可。只看测试结果的人注定会错过巨大的回报。
当然，有很多合理的商业考虑能够解释，为什么忽略有可能更好的方案是明智的。毕竟，如果受众没有做好接受新想法的准备，这个新想法一开始就是会在市场中失败。短期看来确实如此。但若要想在未来获得成功，最佳的方案是先发展新想法并将其商业化，投入市场以获取经验，并不断地进行优化，发展客户基础。同时，公司还要做好准备，应对现有方案之不测。既要保持把现有的做好，还要准备随时迎接新的。如果公司没能洞察到新趋势，其竞争对手就会迎头赶上，接手市场。这些竞争对手往往是被现有公司忽略的小创业团队。之所以被忽略，是因为这些新来者的所作所为还不太为市场所接受，无论如何都不像是老公司现有业务的有力挑战者。请参见“创新者的困境（The Innovator's Dilemma） ”，以了解这种公司的运营困境。
用于屏幕驱动（screen-driven）型设备和电子游戏的势控（gestural）界面和多点触控界面，正是 两个久经蹉跎才成功的创新例子。 它们难道不是杰出的创新吗？当然是。它们难道不杰出吗？当然杰出。但是它们新吗？绝对不新！多点触控设备在研究实验室里等待了近30年，才首次迎来大规模量产的成功产品。20年前我就见过势控界面演示。新想法要花上相当可观的时间，才会在市场上获得成功。过快地把想法商业化，往往以失败（以及大笔的资金损失）而告终。
当年那位给我看模型的Apple设计师同事也未能幸免。他给我看的是一台为小学生设计的便携设备，其外形设计不同于我之前所见的任何东西。那真是绝妙的设计——即便是在我这通常很挑剔的眼里，其设计也完美切合了其用途和受众。可惜的是，最终产品成了Apple公司部门间内讧的牺牲品。尽管产品最终被投放到了市场中，但部门间的不合导致了糟糕的实施、糟糕的产品支持和糟糕的市场推广，破坏了产品的整体性。
公司抵触完全地创新，也有根有据。在不能确定赢利潜力的情况下开发新产品线，代价是很高的。而且现有产品的责任部门也会担心新产品打压了现有产品的销售（这叫做“同类相食”）。这些担忧一般都是合理的。这种形势也属经典案例，即有益于公司的好事情对现有产品部门来说却是坏事情，因为那意味着现有产品部门职员得到升迁和奖励的机会不容乐观。如此想来，公司会抵触创新也就不足为奇了。统计数据清楚地表明，尽管极少数创新取得了非凡的成功，但绝大部分创新都失败了并付出惨重代价。无论公司的新闻稿和年度报告里怎么说，公司都会犹豫甚至抵触创新，这都不足为奇，因为持保守态度是明智的。
展望未来
数据驱动的自动化流程会慢慢侵占如今人类设计师所掌握的地盘。诸如基因算法、知识密集型系统等等这些依靠计算机生成创意的新方法会开始接管设计的创意空间。医疗诊断或工程设计等其他领域也正在发生相同的变化。
我们将面对更多无需设计师的设计，但主要只限于在对既有概念的强化、精化和优化方面。即使到了以后，神经网络、基因算法，抑或其他某种尚未被发现的方法都能被用来开发新的、有创意的人工系统了，任何新概念也还是须要面对同样的困难，经历漫长的接受周期，？？人类在心理上的、社会上的和政治上的复杂需求。要做到这一点，我们需要有创意的设计师、有创意的商业人士和有冒险精神的人来突破极限。会有新想法遭到抵触。许多伟大的创新将以更多巨大的失败为代价。
无需设计师的设计？有些人讨厌人类判断的含糊性和不确定性，讨厌人类不靠谱的过往表现和自相矛盾的论调。这些人会尝试剥离设计中的人为因素，转投数字和数据和怀抱，只因为数字和数据看起来似乎能提供确定性。还有一些人希望借助创意来得到巨大收获，他们会遵循自己的原则来做。前者会带来持续的小改进，显著提高生产力并降低成本。后者会面对巨大的失败，并迎接偶然发生的巨大成功——这些巨大成功会改变世界。
————以下是英文原文：
I will always remember my first introduction to the power of good product design. I was newly arrived at Apple, still learning the ways of business, when I was visited by a member of Apple's Industrial Design team. He showed me a foam mockup of a proposed product. "Wow," I said, "I want one! What is it?"
That experience brought home the power of design: I was excited and enthusiastic even before I knew what it was. This type of visceral "wow" response requires creative designers. It is subjective, personal. Uh oh, this is not what engineers like to hear. If you can't put a number to it, it's not important. As a result, there is a trend to eliminate designers. Who needs them when we can simply test our way to success? The excitement of powerful, captivating design is defined as irrelevant. Worse, the nature of design is in danger.
Don't believe me? Consider Google. In a well-publicized move, a senior designer at Google recently quit, stating that Google had no interest in or understanding of design. Google, it seems, relies primarily upon test results, not human skill or judgment. Want to know whether a design is effective? Try it out. Google can quickly submit samples to millions of people in well-controlled trials, pitting one design against another, selecting the winner based upon number of clicks, or sales, or whatever objective measure they wish. Which color of blue is best? Test. Item placement? Test. Web page layout? Test.
This procedure is hardly unique to Google. Amazon.com has long followed this practice. Years ago I was proudly informed that they no longer have debates about which design is best: they simply test them and use the data to decide. And this, of course, is the approach used by the human-centered iterative design approach: prototype, test, revise.
Is this the future of design? Certainly there are many who believe so. This is a hot topic on the talk and seminar circuit. After all, the proponents ask reasonably, who could object to making decisions based upon data?
Two Types of Innovation: Incremental Improvements and New Concepts
In design—and almost all innovation, for that matter—there are at least two distinct forms. One is incremental improvement. In the manufacturing of products, companies assume that unit costs will continually decrease through continual, incremental improvements. A steady chain of incremental innovation enhances operations, the sourcing of parts and supply-chain management. The product design is continually tinkered with, adjusting the interface, adding new features, changing small things here and there. New products are announced yearly that are simply small modifications to the existing platform by a different constellation of features. Sometimes features are removed to enable a new, low-cost line. Sometimes features are enhanced or added. In incremental improvement, the basic platform is unchanged. Incremental design and innovation is less glamorous than the development of new concepts and ideas, but it is both far more frequent and far more important. Most of these innovations are small, but most are quite successful. This is what companies call "their cash cow": a product line that requires very little new development cost while being profitable year after year.
The second form of design is what is generally taught in design, engineering and MBA courses on "breakthrough product innovation." Here is where new concepts get invented, new products defined, and new businesses formed. This is the fun part of innovation. As a result, it is the arena that most designers and inventors wish to inhabit. But the risks are great: most new innovations fail. Successful innovations can take decades to become accepted. As a result, the people who create the innovation are not necessarily the people who profit from it.
In my Apple example, the designers were devising a new conception. In the case of Google and Amazon, the companies are practicing incremental enhancement. They are two different activities. Note that the Apple product, like most new innovations, failed. Why? I return to this example later.
Both forms of innovation are necessary. The fight over data-driven design is misleading in that it uses the power of one method to deny the importance of the second. Data-driven design through testing is indeed effective at improving existing products. But where did the idea for the product come from in the first place? From someone's creative mind. Testing is effective at enhancing an idea, but creative designers and inventors are required to come up with the idea.
Why Testing Is Both Essential and Incomplete
Data-driven design is "hill-climbing," a well-known algorithm for optimization. Imagine standing in the dark in an unknown, hilly terrain. How do you get to the top of the hill when you can't see? Test the immediate surroundings to determine which direction goes up the most steeply and take a step that way. Repeat until every direction leads to a lower level.
But what if the terrain has many hills? How would you know whether you are on the highest? Answer: you can't know. This is called the "local maximum" problem: you can't tell if you are on highest hill (a global maximum) or just at the top of a small one.
When a computer does hill climbing on a mathematical space, it tries to avoid the problem of local maxima by initiating climbs from numerous, different parts of the space being explored, selecting the highest of the separate attempts. This doesn't guarantee the very highest peak, but it can avoid being stuck on a low-ranking one. This strategy is seldom available to a designer: it is difficult enough to come up with a single starting point, let alone multiple, different ones. So, refinement through testing in the world of design is usually only capable of reaching the local maximum. Is there a far better solution (that is, is there a different hill which yields far superior results)? Testing will never tell us.
Here is where creative people come in. Breakthroughs occur when a person restructures the problem, thereby recognizing that one is exploring the wrong space. This is the creative side of design and invention. Incremental enhancements will not get us there.
Barriers to Great Innovation
Dramatic new innovation has some fundamental characteristics that make it inappropriate for judgment through testing. People resist novelty. Behavior tends to be conservative. New technologies and new methods of doing things usually take decades to be accepted – sometimes multiple decades. But the testing methods all assume that one can make a change, try it out, and immediately determine if it is better than what is currently available.
There is no known way to tell if a radical new idea will eventually be successful. Here is where great leadership and courage is required. History tells us of many people who persevered for long periods in the face of repeated rejection before their idea was accepted, often to the point that after success, people could not imagine how they got along without it before. History also tells us of many people who persevered yet never were able to succeed. It is proper to be skeptical of radical new ideas.
In the early years of an idea, it might not be accepted because the technology isn't ready, or because there is a lot more optimization still to be done, or because the audience isn't ready. or because it is a bad idea. It is difficult to determine which of those reasons dominates. The task only becomes easy in hindsight, long after it becomes established.
These long periods between formation and initial implementation of a novel idea and its eventual determination of success or failure in the marketplace is what defeats those who wish to use evidence as a decision criterion for following a new direction. Even if a superior way of doing something has been found, the automated test process will probably reject it, not because the idea is inferior, but because it cannot wait decades for the answer. Those who look only at test results will miss the large payoff.
Of course there are sound business reasons why ignoring potentially superior approaches might be a wise decision. After all, if the audience is not ready for the new approach, it would initially fail in the marketplace. That is true, in the short run. But to prosper in the future, the best approach would be to develop and commercialize the new idea to get marketplace experience, to begin the optimization process, and to develop the customer base. At the same time one is preparing the company for the day when the method takes off. Sure, keep doing the old, but get ready for the new. If the company fails to recognize the newly emerging method, its competitors will take over. Quite often these competitors will be a startup that existing companies ignored because what they were doing was not well accepted, and in any event did not appear to challenge the existing business: see "The innovator's dilemma."
Gestural, multi-touch interfaces for screen-driven devices and computer games are good examples. Are these a brilliant new innovation? Brilliant? Yes. New? Absolutely not. Multi-touch devices were in research labs for almost three decades before the first successful mass-produced products. I saw gestures demonstrated over two decades ago. New ideas take considerable time to reach success in the marketplace. If an idea is commercialized too soon, the result is usually failure (and a large loss of money).
This is precisely what the Apple designer of my opening paragraph had done. What I was shown was a portable computer designed for schoolchildren with a form factor unlike anything I had ever seen before. It was wonderful, and even to my normally critical eye, it looked like a perfect fit for the purpose and audience. Alas, the product got caught in a political fight between warring Apple divisions. Although it was eventually released into the marketplace, the fight crippled its integrity and it was badly executed, badly supported, and badly marketed.
The resistance of a company to new innovations is well founded. It is expensive to develop a new product line with unknown profitability. Moreover, existing product divisions will be concerned that the new product will disrupt existing sales (this is called "cannibalization"). These fears are often correct. This is a classic case of what is good for the company being bad for an existing division, which means bad for the promotion and reward opportunities for the existing division. Is it a wonder companies resist? The data clearly show that although a few new innovations are dramatically successful, most fail, often at great expense. It is no wonder that companies are hesitant – resistant – to innovation no matter what their press releases and annual reports claim. To be conservative is to be sensible.
The Future
Automated data-driven processes will slowly make more and more inroads into the space now occupied by human designers. New approaches to computer-generated creativity such as genetic algorithms, knowledge-intensive systems, and others will start taking over the creative aspect of design. This is happening in many other fields, whether it be medical diagnosis or engineering design.
We will get more design without designers, but primarily of the enhancement, refinement, and optimization of existing concepts. Even where new creative artificial systems are developed, whether by neural networks, genetic algorithms, or some yet undiscovered method, any new concept will still face the hurdle of overcoming the slow adoption rate of people and of overcoming the complex psychological, social, and political needs of people. To do this, we need creative designers, creative business people, and risk takers willing to push the boundaries. New ideas will be resisted. Great innovations will come at the cost of multiple great failures.
Design without designers? Those who dislike the ambiguity and uncertainty of human judgments, with its uncertain track record and contradictory statements will try to abolish the human element in favor of the certainty that numbers and data appear to offer. But those who want the big gains that creative judgment can produce will follow their own judgment. The first case will bring about the small, continual improvements that have contributed greatly to the increased productivity and lowering of costs of our technologies. The second case will be rewarded with great failures and occasional great success. But those great successes will transform the world.
