Theories, models and the future of science

科学的未来是模型还是理论？

Ashutosh Jogalekar/文果壳网·Ent/译

Last year's Nobel Prize for physics was awarded to Saul Perlmutter, Brian Schmidt and Adam Riess for their discovery of an accelerating universe, a finding leading to the startling postulate that 75% of our universe contains a hitherto unknown entity called dark energy. This is an important discovery which is predated by brilliant minds and an exciting history. It continues a grand narrative that starts from Henrietta Swan Leavitt (who established a standard reference for calculating astronomical distances) through Albert Einstein (whose despised cosmological constant was resurrected by these findings) and Edwin Hubble, continuing through George Lemaitre and George Gamow (with their ideas about the Big Bang) and finally culminating in our current sophisticated understanding of the expanding universe.

去年的诺贝尔物理学奖颁发给了索尔·佩尔穆特（Saul Perlmutter）、布赖恩·施密特（Brian P. Schmidt）和亚当·里斯（Adam G. Riess），以表彰他们发现宇宙在加速膨胀。这项发现带来了一个惊人的猜想：我们宇宙 75% 的成分可能是迄今为止未知的一种东西——暗能量。这是一个重要的发现，背后有许多聪明的头脑和精彩的历史。它延续了一个宏大叙事的传统，从亨丽爱塔·勒维特（他建立了计算天体间距离的标准参考物）到爱因斯坦（被他鄙弃的宇宙常数因为这些发现而复活了）到埃德温·哈勃，继之以乔治·勒梅特和乔治·伽莫夫（的大爆炸理论），最终汇聚于我们当今对膨胀宇宙的繁复领悟。

But what is equally interesting is the ignorance that the prizewinning discovery reveals. The prize was awarded for the observation of an accelerating universe, not the explanation. Nobody really knows why the universe is accelerating. The current explanation for the acceleration consists of a set of different models incorporating entities like dark energy, none of which has been definitively proven to explain the facts well enough. And this makes me wonder if such a proliferation of models without accompanying concrete theories is going to embody science in the future.

但是和这些知识同样有趣的，是获奖的发现所揭示出来的我们的无知程度。这项奖颁给了一项证明宇宙在加速膨胀的观测，而不是对这个现象的解释。没人真正知道宇宙为什么在加速膨胀。当下对加速的解释包含一系列的不同模型，其中用到了像 “暗能量” 这样的东西，但是这些东西是否存在都没有得到确定的证明，不能良好地解释我们观测到的事实。这让我很好奇，会不会这样一连串的模型就会在没有相应坚实理论的情况下成为未来科学的主体。

The twentieth century saw theoretical advances in physics that agreed with experiment to an astonishing degree of accuracy. This progress culminated in the development of quantum electrodynamics, whose accuracy in Richard Feynman's words is equivalent to calculating the distance between New York and Los Angeles within a hairsbreadth. Since then we have had some successes in quantitatively correlating theory to experiment, most notably in the work on validating the Big Bang and the development of the standard model of particle physics. But dark energy- there's no theory for it as of now that remotely approaches the rigor of QED when it comes to comparison with experiment.

20 世纪见证了许多物理学理论的进展，这些理论和实验相符到了惊人的准确度。这一进步的顶峰是在量子电动力学，其精确程度用费曼的话说等同于测量纽约和洛杉矶之间的距离，误差不超过一根头发。自那时起，我们令理论和实验在数值上相符这件事又取得了一些成功，最著名的是验证大爆炸理论和粒子物理标准模型的工作。但是暗能量这东西——要论和实验的符合程度，迄今为止关于它的理论里还没有一个能赶得上量子电动力学的精确程度，事实上是差十万八千里。

Of course it's unfair to criticize dark energy since we are just getting started on tackling its mysteries. Maybe someday a comprehensive theory will be found, but given the complexity of what we are trying to achieve (essentially explain the nature of all the matter and energy in the universe) it seems likely that we may always be stuck with models, not actual theories. And this may be the case not just with cosmology but with other sciences. The fact is that the kinds of phenomena that science has been dealing with recently have been multifactorial, complex and emergent. The kind of mechanical, reductionist approaches that worked so well for atomic physics and molecular biology may turn out to be too impoverished for taking these phenomena apart. Take biology for instance. Do you think we could have a complete "theory" for the human brain that can quantitatively calculate all brain states leading to consciousness and our reaction to the external world? How about trying to build a "theory" for signal transduction that would allow us to not just predict but truly understand (in a holistic way) all the interactions with drugs and biomolecules that living organisms undergo? And then there's other complex phenomena like the economy, the weather and social networks. It seems wise to say that we don't anticipate real overarching theories for these phenomena anytime soon.

当然，就这么批评暗能量是不公正的，毕竟我们只是刚开始探索它的奥秘而已。也许有一天我们能找到一个综合全面的理论，但考虑到这件事情的复杂程度（我们这可是等于要解释宇宙中一切物质和能量的本质啊），有可能我们将不得不与模型为伍，永远得不到真正的理论。而且这可能不只限于宇宙学，对其他学科也一样。事实是，近来科学试图处理的那些现象全都是多因素的、复杂的、涌现的。那种机械的、还原论的研究方法，虽然曾经在原子物理和分子生物学领域大显身手，但可能要想把这些复杂现象拆开就力不从心了。就以生物学为例，你真的认为我们可以得到人类大脑的完整 “理论”，能够定量计算出任何一个大脑状态，进而导出我们的意识和我们对外部世界的回应吗？那么试着建立一个信号传导的 “理论”，允许我们不但能预测、还能（在整体论的意义上）真正理解生物体内药物和生物分子的所有相互作用，又如何呢？然后还有其它的复杂现象，比如经济学、天气和社交网络呢。看起来，我们不太能指望在短期内为这些现象搭建真正高屋建瓴的理论。

On the other hand, I think it's a sign of things to come that most of these fields are rife with explanatory models of varying accuracy and validity. Most importantly, modeling and simulation are starting to be considered as a respectable "third leg" of science, in addition to theory and experiment. One simple reason for this is the recognition that many of science's greatest current challenges may not be amenable to rigorous theorizing, and we may have to treat models of phenomena as independent, authoritative explanatory entities in their own right. We are already seeing this happen in chemistry, biology, climate science and social science, and I have been told that even cosmologists are now extensively relying on computational models of the universe. My own field of drug discovery is a great example of the success and failure of models. Here models are used not just in computationally simulating the interactions of drugs with diseased proteins at a molecular level but in fitting pharmacological data and x-ray diffraction data, in constructing gene and protein networks and even in running and analyzing clinical trials. Models permeate drug discovery and development at every stage, and it's hard to imagine a time when we will have an overarching "theory" encompassing the various stages of the process.

另一方面，大部分这类领域都充斥着准确度和有效性各异的解释性模型，这在我看来也许预兆了未来的方向。最重要的是，数学建模和模拟会逐渐被承认为是科学的 “第三条腿”——另两条是理论和实验。很多人已经逐渐意识到，当前科学面临的诸多巨大挑战可能不适合建成严谨的理论，我们可能不得不将描述现象的模型也看作是独立的、权威的、解释性的实体，拥有单独存在的权利。其实在化学、生物、气候科学和社会科学领域，我们已经见证了这一现象的发生，我也听说现在就连宇宙学家也严重依赖于宇宙的计算模型。我自己研究的制药领域就是模型成败的绝好案例。这个领域里，模型不但用于计算模拟药物和疾病相关蛋白之间在分子层面的相互作用，还用来整合药理学数据和X射线衍射数据，构建基因和蛋白质网络，甚至执行和分析临床试验。药物发现和开发的每一步都要以模型为先驱，很难想象我们什么时候能获得一个整体的 “理论” 来包容药物研制全过程的每一个阶段。

Admittedly these and other models are still far behind theory and experiment which have had head starts of about a thousand years. But there can be little doubt that such models can only become more accurate with increasing computational firepower and more comprehensive inclusion of data. How accurate remains to be seen, but it's worth noting that there are already books that make a case for an independent, study-worthy philosophy of modeling and simulation; a recent book by the University of South Florida philosopher Eric Winsberg for instance extols philosophers of science to treat models not just as convenient applications and representations of theories (which are then the only fundamental things worth studying) but as ultimate independent explanatory devices in themselves that deserve separate philosophical consideration.

诚然，这些模型和所有其他模型都落后理论和实验很远，毕竟后两者已经有上千年的历史了。但是毋庸置疑，这些模型随着计算能力的增长、数据的愈发完整，只会变得越来越准确。具体的准确程度我们还不知道，但是值得注意的是已经有著作认为，建模和模拟应当拥有其独立的、值得研究的哲学。比如说，最近南佛罗里达大学的哲学家埃里克·温斯堡（Eric Winsberg）出版的一本著作就表扬了一部分科学哲学家，他们不把模型看成是纯粹为了方便的应用或者理论的外在表象（按照传统观点，理论是基本的、是唯一值得研究的），而是认为模型本身就是终极的独立的解释，值得从哲学上单独考量。

Could this then be at least part of the future of science? A future where robust experimental observations are encompassed not by beautifully rigorous and complete theories like general relativity or QED but only by different models which are patched together through a combination of rigor, empirical data, fudge factors and plain old intuition? This would be a new kind of science, as useful in its applications as its old counterpart but rooting itself only in models and not in complete theories. Given the history of theoretical science, such a future may seem dark and depressing. That is because as the statistician George Box famously quipped, although some models are useful, all models are in some sense wrong. What Box meant was that models often feature unrealistic assumptions about the details of a system, and yet allow us to reproduce the essential features of reality. They are subject to fudge factors and to the whims of their creators. Thus they can never provide the certain connection to "reality" that theories seem to. This is especially a problem when disparate models give the same answer to a question. In the absence of discriminating ideas, which model is then the "correct" one? The usual, convenient answer is "none of them", since they all do an equally good job of explaining the facts. But this view of science, where models that can be judged only on the basis of their utility are the ultimate arbiters of reality and where there is thus no sense of a unified theoretical framework, feels deeply unsettling. In this universe the "real" theory will always remain hidden behind a facade of models, much as reality is always hidden behind the event horizon of a black hole. Such a universe can hardly warm the cockles of the heart of those who are used to crafting grand narratives for life and the cosmos. However it may be the price we pay for more comprehensive understanding. In the future, Nobel Prizes may be frequently awarded for important observations for which there are no real theories, only models. The discovery of dark matter and energy and our current attempts to understand the brain and signal transduction could well be the harbingers of this new kind of science.

这是否会是科学的未来——至少是一部分未来？在这个未来里，强劲有力的实验观测最后不是被严谨而完备、具有美感的理论——就像量子电动力学或者广义相对论——所囊括，而只是化入许多个不同的模型，用逻辑一致、实验数据、经验系数还有老一套的 “直觉” 给缝缝补补到一起。这将会是一种新式的科学，从应用上讲和旧式科学一样，但是扎根在模型而不是完整的理论之中。考虑到理论科学的历史，这个未来看起来大概会很黯淡、很让人沮丧。因为，正如统计学家乔治·博克斯（George Box）的名言，虽然有些模型是有用的，但所有的模型某种意义上都是错的。博克斯的意思是，模型经常要为系统的细节设下不符合实际的假定，但是却允许我们重现出现实中最为关键的一些特征。这些模型完全受制于各种 “经验拟合常数” 和模型创立者的个人灵感，因此它们永远无法提供和 “现实” 的那种连接，而理论却可以。当不同的模型对一个问题给出同样的解答时，这个问题尤其严重。如果没有其他办法进行区分，那么哪一个模型是 “正确” 的呢？传统的标准答案是 “以上皆非”，因为它们都有相同的解释事实的能力。而在这个新科学观里，模型可以仅仅以它们的有效性来裁断，在这里模型才是 “现实” 的终极裁判官，统一的理论框架不复存在；但是这个科学观让人感到深深的不安。在这个宇宙里，“真正” 的理论将永远隐藏在模型的高墙背后，正如真实永远隐藏在黑洞事件视界背后一样。对于那些毕生编织关于生命、宇宙和一切的宏大叙事者来说，这样的宇宙恐怕无法温暖他们的心房。然而，这也许是我们为了追求更完整的理解而不得不付出的代价。未来，诺贝尔奖可能更多被授予重要的发现，而这些发现背后并无真正的理论，唯有模型。暗物质和暗能量的发现，还有我们当下试图理解大脑和细胞信号传导的努力，很可能就是这种新科学的预表。

Should we worry about such a world rife with models and devoid of theories? Not necessarily. If there's one thing about science that we know, it's that it evolves. Grand explanatory theories have traditionally been supposed to be a key part- probably the key part- of the scientific enterprise. But this is mostly because of historical precedent as well a psychological urge for seeking elegance and unification. And even historically sciences have progressed much without complete theories, as chemistry did for hundreds of years before the emergence of the atomic and structural theories. The belief that a grand theory is essential for the true development of a discipline has been resoundingly validated in the past but it's utility may well have plateaued. I am not advocating some "end of science" scenario here - far from it - but as the recent history of string theory and theoretical physics in general demonstrates, even the most mathematically elegant and psychologically pleasing theories may have scant connection to reality. Because of the sheer scale and complexity of what we are trying to currently explain, we may have hit a roadblock in the application of the largely reductionist traditional scientific thinking which has served us so well for half a millennium

面对这个充斥了模型却找不到理论的世界，我们应该担心吗？也未必。如果说我们对于科学是怎么回事还算知道一点的话，那就是它在不断演化。宏大的解释性理论曾经被认为是科学事业的一个关键——甚至是唯一的关键。但这主要是出于历史先例、还有人们追求优雅和统一的心理渴望。何况就算是历史上，科学也在没有完整理论的情况下有过重大进步，就像化学在原子和结构理论出现之前的那几百年里一样。 “学科欲真正发展，必先有宏大理论” ，这个信念过去曾被反反复复地证实，但是它的有用性如今可能也到头了。我不是在宣扬什么 “科学的终结”，远远不是；但是近来弦理论的发展史、事实上是整个理论物理的发展都表明，就连数学上最优雅、心理上最招人喜欢的理论，可能也和现实几乎没有联系。鉴于我们现在正试图解释的东西的规模是如此之大、如此之复杂，我们继续运用传统的、以还原论为主的科学思考方式可能已经撞上了南墙，哪怕它在过去 500 年效果是那样好。

Ultimately what matters though is whether our constructs- theories, models, rules of thumb or heuristic pattern recognition- are up to the task of constructing consistent explanations of complex phenomena. The business of science is explanation, whether through unified narratives or piecemeal explanation is secondary. Although the former sounds more psychologically satisfying, science does not really care about stoking our egos. What is out there exists, and we do whatever's necessary and sufficient to unravel it.

说到底，真正至关重要的是我们建构的东西——理论、模型、经验规则或者启发式模式识别——能否完成它们的使命：为复杂现象建构出一致的解释。科学的任务就是解释，而到底是用统一的叙事还是支离破碎的解释则是次要的。虽然前者听起来让人心理上更加满意，但是科学其实不在乎如何满足我们的自我。事物存在，我们尽一切必要和充分的努力，去解读它们。就这么简单。

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