大卫·克拉考尔谈21世纪科学图景

在高原沙漠的山巅，有一群特立独行的人，正在知识领域做着极其奇特的事情。

There is a world of individuals on a mountain in the high deserts doing very strange things in the world of knowledge.

三百年来，科学界的梦想就是通过分解世界来理解它。

For three hundred years, the dream of science was to understand the world by chopping it up into pieces.

但将万物简化为基本组成部分，并不能告诉我们这些部分如何协同作用。

But boiling everything down to basic parts does not tell us about the way those parts behave together.

物理学家发现了原子，继而发现了夸克，然而这些伟大发现仍未能解答关于生命、智能、语言、创新、生态系统或经济等古老问题。

Physicists found the atom, then the quark, and yet these great discoveries don't answer age old questions about life, intelligence, or language, innovation, ecosystems, or economies.

于是人们学会了新方法——不仅拆解事物，更要研究事物如何在无计划的情况下自组织成不可预测的形态。

So people learned a new trick, not just taking things apart, but studying how things organize themselves without a plan in ways that cannot be predicted.

一个新兴领域——复杂适应系统科学应运而生，用以解释和驾驭这个超出控制的世界。

A new field, complex adaptive systems science, sprang up to explain and navigate a world beyond control.

与此同时，计算机处理能力的提升为探索不可简化的复杂性提供了新途径。

At the same time, improvements in computer processing enabled yet another method for exploring irreducible complexity.

我们学会了将进化过程工具化，锻造出能够处理海量数据的机器智能。

We learned to instrumental ize the evolutionary process, forging machine intelligences that can correlate unthinkable amounts of data.

互联网的爆炸性增长使得科学能够以前所未有的规模和网络资源进行发展，这在20世纪是难以想象的。

The Internet's explosive growth empowered science at scale in networks and with resources we could not have imagined in the nineteen hundreds.

世纪。

Hundreds.

如今存在不同类型的科学或不同种类的问题，但它们都无法提供我们曾期待的那种简单答案。

Now there are different kinds of science or different kinds of problems, and none of them give us the kind of easy answers we were hoping for.

对于任何准备好抛弃长期依赖的舒适分类的人来说，这是一场大胆的探索新冒险。

This is a daring new adventure of discovery for anyone prepared to jettison the comfortable category that served us for so long.

我们最重大的问题和最棘手的挑战，呼唤着一个独特的全球性思想家社区，他们愿意在生物学与经济学、化学与社会科学、物理学与认知神经科学的交叉领域，研究那些庞大而综合的难题。

Our biggest questions and most wicked problems call for a unique and planet wide community of thinkers willing to work on massive and synthetic puzzles at the intersection of biology and economics, chemistry and social science, physics and cognitive neuroscience.

欢迎收听《复杂性》，圣塔菲研究所的官方播客，该研究所是全球顶尖的复杂系统科学研究机构。

Welcome to Complexity, official podcast of the Santa Fe Institute, the world's foremost complex systems science research organization.

我是主持人迈克尔·加菲尔德，每周我们将带您参与与全球研究网络的深入对话，包括严谨的科学家、数学家、哲学家和艺术家，他们正在开发新框架、工具和理论来解释宇宙最深邃的奥秘。

I'm your host, Michael Garfield, and each week, we'll bring you with us for far reaching conversations with our worldwide network of researchers, rigorous scientists and mathematicians, philosophers, and artists developing new frameworks, tools, and theories to explain the deepest mysteries of the universe.

本节目讲述的是关于您的世界，以及那些毕生致力于探索和解释其涌现秩序的人们的故事、研究和洞见。

This is a show about your world and the people who have dedicated their lives to exploring and explaining its emergent order, their stories, research, and insights.

加入我们，一起探索复杂性的奇妙之旅。

Join us for an adventure into complexity.

我们的首位嘉宾是圣塔菲研究所所长大卫·克拉考尔，他获得牛津大学进化理论博士学位，并被《连线》杂志评为2012年将改变世界的50人之一。

Our first guest is the president of Santa Fe Institute, David Krakauer, who received his doctorate in evolutionary theory from Oxford University and was named by Wired magazine in twenty twelve one of the 50 people who would change the world.

大卫的研究主要关注生物学与文化中信息处理机制的进化历程。

David's research focuses on the evolutionary history of information processing mechanisms in biology and culture.

在本期节目中，我们将探讨21世纪科学的发展格局，以及圣塔菲研究所的独特之处。

In this episode, we discuss the landscape of science in the twenty first century and what sets the Santa Fe Institute apart.

那么大卫，这期节目我们要向听众说明他们能从这个节目中获得什么。

So, David, this is the episode where we lay out what people can expect for this show.

成为一名复杂性探索者意味着什么？圣塔菲研究所打算如何为人们提供这方面的支持？

What does it mean to be a complexity explorer, and how is SFI intending to facilitate this for people?

是的，我认为我们想让人们接触一种不同的科学形式，一种他们可能不熟悉的科学，一种不同于传统刻板科学家形象的人格特质——愿意承担风险，提出人们前所未闻的观点，并坦诚面对我们所从事的这项充满雄心的事业：试图为我们所处的复杂世界寻找宏大的统一理论。

Yeah, I think we want to expose people to a different kind of science, one that they might not be familiar with, a different kind of personality from the usual, rather dowel representation of scientists, someone willing to take some risks, present ideas that they haven't people haven't heard before, and be honest about the hubristic enterprise that we're engaged in, which is trying to find big unifying theories for the complexity that we live in.

那我们就从这里开始吧。

So let's start there, actually.

我想，当大多数人思考科学事业时，他们想象的是我们正在将整个人类知识体系倾斜向某个单一的、包罗万象的万物理论。

I think, you know, when most people think about the project of science, they're imagining that we're we're sort of tilting the whole human knowledge enterprise towards this single, all embracing theory of everything.

而从我们的讨论中我明白，这不仅是现实中并不唯一存在的科学类型，也未必是我们在此关注的那种科学。

And I know from our discussions that that's not only not the only kind of science that happens, but that's not necessarily the kind of science that we're focusing on here.

根据过去三十五年研究复杂系统的经验，你如何理解这类科学事业？它与大众认知中的科学进程有何不同？

How do you understand that sort of enterprise in light of what we've learned over the last thirty five years studying complex systems and how that project differs from the kind of popular perception of scientific process.

是的。

Yeah.

在八十年代和九十年代初，确实曾流行谈论那些宏大统一理论、某些万物理论。

So, yeah, there was a time in the eighties and early nineties when it was very popular to talk about guts, grand unified theories, some theories of everything.

这些理论在物理学领域尤为突出。

And these were prominent in physics.

我常说这些万物理论其实是'除了理论化本身之外万物的理论'。

I like to say that these theories of everything were theories of everything except those things that theorize.

对吧？

Right?

因为它们对地球上的生命无话可说。

Because they had nothing to say about life on the planet.

你知道吗？

You know?

所以即便是最宏大、最大胆的大统一理论，如果仔细审视，其适用范围其实非常有限。

And so even the grand unified theory, the most ambitious, the boldest, were really very limited in their scope, if you actually looked carefully.

它们试图解释原子结构、引力如何产生——这至今仍是个未解之谜。

They were trying to explain the structure of the atom, the way in which gravity emerges, which remains an open question.

因此，即便是大统一理论，也没有你想象的那么宏大。

So even grand unified theories are not as grand as you might imagine.

从某种意义上说，我们在这里所做的更加雄心勃勃。

In a sense, what we do here is more ambitious.

我们宣称，在跨越生物学、文化、文明和技术的广阔适应性现象领域中，存在着可以用统一的数学计算语言表达的规律与法则。

We declare that in the vast space of adaptive phenomena that span biology, culture, civilization and technology, there are rules and regularities that can be expressed in a common mathematical computational language.

因此，目前在不同建筑、不同院系由不同人员教授和研究的事物，实际上比你想象的有着更多共同之处。

And so things that are currently taught and investigated in very different buildings and different departments by very different people actually have much more in common than you might think.

例如，在生物系研究新陈代谢的人，可能对经济领域也有所见解。

And so for example someone studying metabolism, say in a biology department, might have something to say about the economy.

而研究计算机网络的人，显然对大脑研究有话可说。

And someone studying computer networks has clearly something to say about the brain.

因此，我们对这些跨领域联系很感兴趣，对那些表面上看似毫无共同点，实则可能存在深层联系的理论很感兴趣。

So we're interested in those, and we're interested in theories that can, in some sense, span fields that look as if on the surface, when you superficially investigate them, have very little in common.

那么，这些会最终融合成一个庞大的、包罗万象的'万物理论'吗？

Now, will that coalesce into one giant amorphous theory of everything?

我希望不会，但我也不确定。

I hope not, but I don't know.

举个例子，如果我们超越这种定量研究的框架，就会面临不同知识领域之间不可通约性的问题，比如科学与艺术之间。

So one example here would be if we scale up this even past the sort of framing of this as different quantitative approaches, there is a question about the incommensurability of different fields of knowledge with respect to, say, the sciences and the arts.

我很好奇，你认为美学研究和实证研究之间的差异究竟有多大？你如何看待这些差异？

And I'm curious just how divergent you think an aesthetic or an empirical inquiry really are from one another and how you see those differences?

是的。

Yeah.

我认为表达复杂性的一种方式是：它是现实领域中随机性与规律性的平衡点。

So I think so one way to express complexity, it's that domain of reality that balances the random and the regular.

这两个概念本身都很难理解。

And both of those are difficult concepts to understand in themselves.

但这有助于回答你的问题。

But it helps answer your question.

科学对具有大量随机性或我们称之为偶然性的系统几乎无话可说，用专业术语来说就是具有大量自由参数的系统。

Science has almost nothing to say about systems with lots of randomness or lots of what we would call contingencies or in the formal language lots of free parameters.

科学更青睐紧凑、小巧、优雅的编码方式，这些在规律性光谱的一端表现极佳。

It really likes compact, small, elegant encodings which do very well at the regular end of the spectrum.

现在你可以把艺术视为那个无法被压缩的经验领域。

Now you can actually think about the arts as being that domain of experience which are incompressible.

它们包含太多特定元素、太多特质性、太多个人表达，以至于无法被压缩。

There are so many specific elements in them, so much idiosyncrasy, so much individual expression that they don't lend themselves to compression.

而这正是它们永远无法用物理学的数学语言来表达的原因。

And that's precisely why they will never be expressed in the mathematical language of physics.

因此，复杂性实际上部分解释了为什么这些研究领域看似不可通约。

So complexity actually provides a partial answer to why these domains of inquiry appear to be incommensurable.

但它们并非如此。

But they're not.

只是它们存在于从随机到规律的连续谱上，适合用不同的表达语言来描述。

It's just that they live on this spectrum of the random to the regular that lend themselves to different languages of expression.

我认为小说是理论化现实的近乎完美平台——当你描述的现实充满独特性时。相比之下，微积分或动力系统理论则擅长表达和编码几乎不含随机性的现实（说实话几乎为零），存在于我们所谓的低维空间中。

And I think the novel is an almost perfect platform for theorizing about reality when the reality you're describing has a ton of idiosyncrasy in it, as opposed to calculus or the theory of dynamical systems, which is really good at expressing and encoding a reality that has very little randomness in it, almost none, to be honest, and lives in a what we would call low dimensional space.

这是理解问题的一种方式：复杂性实际上帮助我们理解为何存在这些不同的表达形式。

So that's one way of getting at it, that complexity actually helps us understand why these different forms of expression exist.

另一个让我深感有趣的观点是：我们从事的是不同的本体论和认识论实践。

The other one that I find really intriguing is we're engaged in different ontological and epistemological exercises.

艺术家本质上从事的是世界创造的工作。

Artists are basically in the business of world creation.

而科学家（实际上人文学者也是）从事的是世界解释的工作。

And scientists are in the business of world explanation, as are humanists actually.

但区别在于科学家致力于解释经验现实的体验，而人文学者则阐释艺术现实的创造。

But the difference is scientists are in the business of explaining empirical reality as experienced and humanists explaining artistic reality as created.

创造世界与解释世界的游戏规则是不同的。

And the rules of the game in creating worlds and explaining worlds are different.

一个非常个人化且独特，另一个则非常集体化。

One is very individual and idiosyncratic and one is very collective.

所以我认为两者的议程略有不同，但你可以看到它们如何关联，因为实际上科学中也存在世界创造的成分。

So again, I think the agenda is a little different, but it but you can see how they relate because actually there's an element of science which can be about world creation.

你可以思考反事实假设。

You can think about counterfactuals.

如果没有重力会怎样？

What if there was no gravity?

对吧？

Right?

如果原始人类有九种性别而非两种会怎样？

What if there were nine sexes instead of two among hominids?

这些都是科学中反事实的本体论世界创造。

These are all counterfactual ontological world creations in science.

同样地，艺术家也可以说，我要画这幅画，但我只有有限的颜料，而且还得在可见光谱范围内创作。

And an artist similarly can say, you know, I'm gonna paint this painting, but I only have so many pigments, and I still have to work within the visible spectrum of color.

因此他们必须处理经验现实，其程度与我们创造现实相当。

So they have to deal with empirical reality to the same extent that we create realities.

这也是一种压缩。

That's also a kind of a compression.

我们工具的局限性以及技术环境提供的可能性限制，这某种程度上引出了工程师在这个光谱中的角色问题。

The limitations of our tools and the limitations of the affordances of our technical environments, which sort of segues into the question of the role of the engineer on that spectrum.

你会把这类角色放在社会功能和模态生态中的什么位置？

Like, where would you place that sort of role in this ecology of social functions and modalities?

是的，工程师这个角色很有意思。

Yeah, the engineer is interesting.

伟大的工程师两者兼具，对吧？

The great engineers combine the both, right?

他们创造世界，然后对其进行理论化。

They create worlds which they then theorize about.

当然，这些都是简化模型。

And of course, these are simplifications.

伟大的科学家都是艺术家，因为他们大部分时间都在创造不存在的世界，然后将其推翻。

Great scientists are artists because they spend most of their time creating worlds that don't exist to demolish them.

伟大的艺术家对现实工具的限制非常熟悉，因为这能让他们成为更好的世界创造者。

Great artists are very, very familiar with the constraints of their tools in empirical reality because it allows them to be better world creators.

所以有趣的是，在极限处，有些区别是值得注意的。

And so it's interesting, in the end, at the limits, there are distinctions that are worth making.

但能让你理解它们存在于这种本体连续体上的元理论很有用，因为它打破了壁垒，因为我们始终在相互联系。

But the meta theory that allows you to understand them as living on this kind of ontological continuum is useful because it breaks down barriers, because we're constantly in contact.

我的艺术家朋友们对他们的工具、仪器以及光学理论或声学理论非常感兴趣。

I mean, my artistic friends are very interested in their tools and instruments and the theory of optics or the theory of acoustics.

而科学家们常常着迷于如何更有效地探索反事实现实。

And scientists are often fascinated by how do I more effectively explore a counterfactual reality?

我该如何从学科的严格束缚中解放自己，从而有所发现？

How should I liberate myself from the rigors of my discipline to make a discovery?

我认为这就是为什么我们在Sphi将两者结合，因为我们相信最好的想法——无论是科学还是艺术——都源于这些不同感受力的碰撞融合。

And I think this is why here at Sphi we put them together, because we believe that the best ideas, I don't care if they're scientific or artistic, come from a collision fusion of those different sensibilities.

所以我经常思考这个问题，尤其是关于诱惑行为如何被描述为本质上具有风险的事业。

So I think a lot about this in terms of the way that the act of seduction is posed as an inherently risky enterprise.

你知道，你正在放松自己的个人边界以邀请对方进入。

You know, you're releasing or relaxing your own personal boundaries to invite in the other.

同样地，艺术和科学以及其他人类知识事业，常常被用探索和风险来描述——跨越已知的边界。

And, similarly, both art and science, as well as other human knowledge enterprises, are often communicated, self described in terms of exploration and risk, stepping out beyond the known.

那么，你如何理解科学和圣塔菲研究所的工作是一种冒险事业？

So I mean, How do you understand science and the Santa Fe Institute's work as a risky endeavor?

这又是如何...

And how does that?

这里所承担的风险与其他机构所承担的风险有何不同？

How does the kind of risk that's assumed here differ from the kind of risk that is being taken on by other institutions?

是的。

Yeah.

我是说，我们讨论过这个，我认为我们对风险的理解还不够深入。

I mean, we've talked about this and I think we're not sophisticated enough about risk.

人们倾向于把它量化，比如问'你的风险系数是多少？'

So people think of it as a number, say how risky are you?

我可能会说大概是0.7之类的数值。

I'm like a point seven or something.

但这并不是一个数字。

But it's not a number.

实际上可能是一个列表。

It's actually maybe a list.

人们在风险承受上各不相同。

And people differ in their risks.

有些人不会拿自己的钱去冒险。

Some people will not take risks with their money.

其他人则不愿拿自己的声誉冒险。

Other people won't take risks with their reputation.

还有些人不会拿自己的生命冒险。

Other people won't take risks with their lives.

我认为SFI在这个风险向量空间理论中，包含所有这些不同维度，非常愿意在声誉上冒险，并跨出学科舒适区。

And I think SFI in that vector space theory of risk that has all these different components is very willing to take risks with its reputation and stepping out of the comfort zone of a discipline.

我们在这方面非常擅长。

We're very good at that.

对我们来说，是否存在一个声称'你是我们一员'的群体并不重要。

It doesn't matter to us whether or not there is a community that says you are of us.

在这一点上，我们反而享受探索过程中的孤独感。

We kind of like the solitude of discovery in that respect.

由于我们不是大数据、大基建的研究所，我们极少在财务方面冒险——因为对圣塔菲研究所来说，重大的财务投资不过是一支铅笔。

And because we are not a big data, big infrastructure institute, we're very rarely taking risks that are financial because a significant financial investment for the Santa Fe Institute is a pencil.

当你写下错误理论时，你其实并没有在用铅笔芯冒险。

You're not really taking risks with lead when you write down a wrong theory.

因此我们的风险与大型实验室不同，比如说，大型实验室进行可能被证明错误的大规模实验时，在某种意义上就是在冒险。

So our risks are different from a large lab, for example, which is in some sense taking a risk by performing a massive experiment that might prove to be wrong.

我是说，他们在研究生、博士后、机械设备、实体设施上的巨额投入。

I mean, a huge investment in grad students, in post docs, in machinery, in physical plant.

他们承担的是那种风险。

They are taking that kind of risk.

他们实际上并没有承担思想上的风险，因为通常那些实验都是非常渐进式的。

They're not really taking a risk of ideas because usually those experiments are very incremental.

我们讨论过LIGO，这个为探测引力波以验证我们已知正确的理论而进行的不可思议的巧妙工程。

We talked about LIGO, this incredible ingenious engineering endeavor to detect gravitational waves to test a theory that we already knew was true.

所以那个广义相对论实验根本不是思想领域上的冒险。

So that experiment in general relativity was not an it wasn't a risk in the world of ideas in any way.

它的风险在于仪器可能失效，投入巨资却未能探测到引力波。

It was a risk in that the instruments might not work, you spent so much money and you didn't detect a gravitational wave.

如果没探测到，他们会宣布广义相对论错了吗？

If you hadn't, would they have declared generativity wrong?

不会。

No.

所以从这个意义上说，这根本不是对理论的冒险，因为理论本身依然成立。

So in that sense it exactly wasn't a risk of an idea because the idea would have held.

他们只会说，探测器的灵敏度太高了，环境中的微小地震扰动干扰了结果。

They simply would have said, you know, detectors were too sensitive, too small seismic perturbations in the environment.

所以SFI的做法与此完全相反。

So SFI is the opposite of that.

我们不会投入大量资金购买大型设备来验证已被接受的理论。

We don't invest tons of money in big machines to test accepted theories.

我们将有限的资金投入到富有创造力的人才身上，用于创立那些未知的、尚不存在的、甚至可能挑战现有知识体系的新理论。

We invest the small amounts of money that we have in creative minds to create theories that are not known and that do not exist and that might offend existing bodies of knowledge.

听你这么说，这里几乎有一种种群生物地理学的考量，即思想的发展方式和智力风险的承担取决于实际参与其中的群体规模。

Listening to this, there's almost a kind of population biogeography consideration here, and that the way that ideas develop and the way that intellectual risks are taken is dependent on the scale of the population that's actually engaged in that.

有一种观点认为，基因漂变在某种程度上会掩盖大陆上的新突变。

There's the notion that genetic drift sort of buries novel mutations on the mainland.

那么你将SFI置于连续体的哪个位置？

So where do you place SFI on a continuum?

就像想象一下，一边是独自在山上探索的怪人，另一边是这些庞大的机构企业。

Like if you were to think of sort of the lone weirdo explorer out on the mountain versus sort of these massive institutional enterprises.

你如何规划寻找这些不同类型行动和探索的地理布局？

How do you lay out the geography of where to look for these different types of action and exploration?

是的。

Yeah.

不。

No.

是的。

Yeah.

再次强调，你知道，我对山脉、修道院和大都市这个比喻有点着迷。

And again, know, as you know, I'm a little obsessed with this metaphor of mountains, monasteries, the metropolis.

你在2000英尺高的悬崖上悬挂时对自我和世界的发现，与在纽约街头行走时可能获得的截然不同。

The things that you will discover, let's say, about yourself and the world when you're hanging from a ledge at 2,000 feet that you probably won't walking across the street in New York City.

但在纽约街头行走时学到的东西，你在山上也学不到。

But there are things you'll learn walking across the street in New York City that you won't learn on a mountain.

而在高山寺院中通过群体生活学到的东西，是另外两种环境无法提供的。

And there are things you'll learn on a monastery in the high mountains through community that you couldn't learn in the other two cases.

所以我的观点是，每个富有创造力的人都必须经历这三种环境。

So, essentially, my view is every creative mind needs to pass through all three environments.

你需要一段独处时光来最大化熵率，最大化创造性思维的探索面。

There is a period when you need solitude to maximize the entropy rate, maximize the exploratory side of creative thought.

然后你需要把这些想法——其中大部分可能都很疯狂——交给支持你的社群严格检验。

There is a time when you need to then subject those ideas, most of which are probably insane, to the rigors of your community who are on your side.

经过验证后，再把它们带到都市的广阔天地中。

And having tested them, bring them to the world in the metropolis.

SFI就是山中的寺院，从某种意义上说，我的职责就是给予个人攀登的自由，让他们参与寺院的各种仪式，将想法打磨到足以呈现给都市世界的程度。

SFI is a monastery in the mountains and in some sense it's my job to allow individuals the freedom to climb and to engage in the sort of ceremonies of the monastery that hone their ideas to a point that they can be presented to the world in the metropolis.

看起来山寺都市的划分也对应着第一人称、第二人称和第三人称的方法论。

Seems like the mountain monastery metropolis also maps onto first person, second person, and third person methodologies.

比如德普雷斯、瓦雷拉、维门提翁在《论觉知》中谈到的，知识始于某种直觉感知，一种第一人称现象学或认识论的东西，你知道的，又绕回我们刚才讨论的内容，从那个个人领域进入主体间性和诠释学话语的领域，再超越出去。

Have like Depress, Varela, Vermention, on becoming aware, talked about knowledge starting in a sort of intuitive perception, a first person phenomenological or epistemological thing, you know, looping back to what we were just talking about a moment ago, moving out of that personal domain into a domain of intersubjective and hermeneutical discourse and then beyond.

我想问，你是否认为科学在某种程度上确实是将本体论还原为这些其他形式？

I guess do you regard science as in some way honestly a reduction of ontology to these other forms?

或者说，你如何看待这种关系？

Or like how do you relate that?

是的，这很有趣。

Yeah, it's interesting.

我认为作为科学家，我们并不擅长思考那些最有效的环境和过程，我们大多数人从小在小组或实验室中成长，学习专业技能，然后简单地重复我们所经历的模式。

I think as scientists we're not very good at thinking about the environments and the process that would be most effective, where most of us grow up in small groups or in labs and we learn our trade and then we simply repeat what we've experienced.

从某种意义上说，我认为SFI代表着对科学人才培养需求的一次高度自觉的实验。

And I think SFI represents, in some sense, a very self conscious experiment in the needs of the scientific pipeline.

经过三十多年的探索，我们已经了解了创造性个体真正需要什么，所以我认为你是完全正确的，科学过程有时确实会呈现出艺术般的形态。

And I think we've discovered over thirty years what creative individuals want and so I think you're absolutely right, I think there simply is a time where the scientific process looks like art.

这就是自我意识的第一人称表达，对吧？

It's that first person assertion of the ego, right?

这就是我需要的，这就是我认为真实的东西。

It's this is what I need, this is what I think to be true.

然后它就会与你所知的东西发生冲突或接触。

And then it comes into conflict or contact with the ego, which is what you know.

在某种程度上，你是抵御自己最糟糕想法的第一道防线。

And in a way you're the first line of defense against your own worst ideas.

但那些幸存下来的想法会直面超我，对吧，就是社会结构、我们的社群，更广泛地说就是你所说的第三方视角。

But then those ideas that survive confront the superego, right, the social structure, our communities, more largely what you're calling the third person.

所以我认为你完全正确。

So I think you're absolutely right.

我认为这里有一个非常有趣的发现：个人探索轨迹与我们栖身的机构之间存在复现关系。

Think there is this very interesting recapitulation of the individual arc of discovery and the institutions that we inhabit.

而且我认为，我们本可以更深入地思考这两者应该如何协调一致。

And we haven't been, I think, as thoughtful as we might be about how those should align.

而圣塔菲研究所的魅力就在于，我们正在为这个机构生态补充那些早期阶段——我认为这些阶段在大规模科学生产中常常缺失。

And the appeal of SFI is that we're adding, if you like, to the ecology of institutions some of those earlier phases that I think are often missing in the large scale production of science.

如果我们从这个角度审视，借用圣经的类比，宗教机构的发展往往始于某位沙漠族长或某种孤独的神秘体验，随后经历同样的检验过程并逐渐固化。

There's another piece in this if we look at this pardon, sort of biblical analogy, but it seems as though often there's an evolution of religious institution that starts with some sort of desert patriarch or some sort of lone mystical experience that is then tested in the same way, congealed.

在某个阶段，它会从神秘体验转变为试图以有意义的方式探索世界，或者按其逻辑延伸，最终演变成实际控制世界的企图。

And at some point it moves from a kind of a mystical experience to an attempt to navigate in a meaningful way or in its sort of extension, its logical conclusion, actually like control the world.

是的。

Yeah.

你看，我们从施洗者圣约翰的时期，逐渐发展到讨论天主教统治权的阶段。

You know, that we move from Saint John the Baptist to conversations about Catholic dominion.

哲学家威廉·欧文·汤普森将这种现象描述为科学领域的两种形态：对超验持开放态度的毕达哥拉斯学派，和专注于系统控制的阿基米德学派。

And philosopher William Irwin Thompson described this as actually appearing in two different forms of science, the Pythagorean approach that is open to the transcendent and an Archimedean approach that's focused on the system control.

那么，你认为这两种方法中哪一种在学术上更为诚实？

So like, do you see one of these approaches as more intellectually honest than the other?

还是说科学界整体已经发生变化，随着时间推移在这两种观点之间取得了平衡？

Or do you think that in general the scientific community has changed and it's sort of balanced between these points over time?

不。

No.

这确实有点意思。

Again, it's sort of interesting.

这些概念看似松散，但我觉得我们对它们的思考还不够深入。

These seem like these loose concepts, but I think we don't reflect on them enough.

我认为这些原型是真实存在的。

I think these archetypes are real.

当然，按照你的定义，SFI更偏向毕达哥拉斯而非阿基米德。

Certainly, SFI, by your definition, is much more on the the side of Pythagoras than Archimedes.

但没有阿基米德，就不会有毕达哥拉斯。

But without Archimedes, you wouldn't have Pythagoras.

换句话说，必须建造工具，必须存在能放大我们推理能力的机器。

In other words, tools have to be built, machines that amplify our ability to reason need to exist.

所以它们其实是完全兼容的。

And so they're completely compatible.

不过我认为，大多数学者会承认我们在工业化路线上走得有点太远了。

I think it is true though that most academics I think would say that we've moved a little bit too far the industrialization route.

我是说阿基米德式的冲动有点太强烈了。

I mean the Archimedean impulse is a little bit too strong.

可能过于强烈了，现在有点本末倒置的感觉。

It's maybe much too strong, a bit like the tail wagging the dog at the moment.

考虑到你需要那种突变概念的培养皿不断涌现，其中一些确实非常有用。

And given that you need that the that sort of petri dish of mutant concepts to be emerging all the time, some of which are really useful.

如果你把所有时间都花在生产上，而没有足够的时间用于创造，最终我们的灵感源泉和想法都会枯竭。

If you spend all your time in production and not enough time in creation, eventually the sources of our inspiration, our ideas will run out.

我认为，在这个领域，Esify就是要成为那个培养皿。

And I I think, again, on the landscape, Esify is there to be that Petri dish.

它的存在就是为了支持毕达哥拉斯式的冲动。

It's there to support the Pythagorean impulse.

我确实以非常互补的视角看待这些事情。

And I do view these things in very complementary terms.

我不认为每个地方都需要像我们一样，但你们需要我们。

I don't think you want every place to be like us, but you need us.

我们的一位创始人Marigel Mann提出的古典原型让我深受触动。

I was very struck by one of our founders, Marigel Mann's classical archetypes.

他对所谓的'狄俄尼索斯派'非常感兴趣，这些人本质上追求对现实的直接洞察，亲身体验它。

He was very interested in what he called the Dionysians, who are essentially seeking immediate insight into reality, to experience it directly.

他将这些人对比于'阿波罗派'，后者将这些洞见加以抽象、提炼，并追求一种更为精炼的产物。

He contrasted those to the Apollonians who took those insights and abstracted them and distilled them and were interested in a much more rarefied product.

但介于两者之间的是'奥德修斯派'，这些探索者既享受当下的感官愉悦，又与神明保持着沟通。

But in between the two were the Odysseans, the explorers who enjoyed the sensual pleasures of the immediate but were in communication with the gods.

而这某种程度上正是我们所追求的，对吧？

And that's sort of what we're after, right?

就是奥德修斯派。

The Odysseans.

我觉得这是个很有用的古典参照。

That's a classical reference I find useful.

这很有趣，因为某种程度上这可以回归到复杂系统的定义——多种模型在进行某种集体计算，当我们回溯到古代，似乎真正的进化行动发生在所有这些不同现实进路之间的某种结合中，比如庙堂宗教与荒野神秘主义等等。

It's interesting because, again, to bring this back to in a way that is a complex systems definition of diverse models that are performing a kind of collective computation where we look back even into antiquity and it seems as though the real action going on evolutionarily is in some combination between all of these different approaches to reality, you know, the temple religion versus the wilderness mystics, etcetera.

这感觉就像在复杂系统科学与机器学习之间的关系中，有一个现代的实例化体现？

And it feels as though there's a modern instantiation of this in the relationship between complex systems science and machine learning?

我是说，似乎这些——我听你描述过——就像是姐妹学科。

I mean, it seems as though those are I've heard you describe this as these are like sibling disciplines.

是的，这里有两个问题。

Yeah, so there's two issues here.

要正确回答这个问题，我们必须先理解什么是复杂性。

So right, to answer this properly we have to understand what complexity is.

复杂性是现实中的一个领域，横跨极规律与随机之间。

And complexity is this domain of reality that straddles the very regular and the random.

科学在这两个极限方面做得非常好。

And science has been really good at those two limits.

对吧？

Right?

所以一个极限是经典力学，另一个极限是统计力学。

And so one limit, classical mechanics, and the other limit, statistical mechanics.

这两者都是非常强大的理论，一个处理的是晶体这类高度有序的物质，另一个则处理气体这类高度无序的物质。

And both are very powerful theories, one dealing with if you like, crystals and the other one dealing with gases, you know, the the perfectly ordered and the very disordered.

而介于两者之间的就是复杂系统领域，这正是我们圣塔菲研究所(SFI)所研究的核心。

And in the middle is where it all gets complicated and complex, and that's where we live at at SFI.

由于历史上科学在这个中间地带的研究并不深入，这催生了两种可能的研究路径。

Now what that's done, because science is not very good there historically, is generated two possible approaches.

一种是复杂性科学，另一种则是机器学习和人工智能。

One of them is complexity science and one of them is machine learning and AI.

它们的功能各不相同。

And they do different things.

机器学习和人工智能会吸收所有这些复杂性，将其编码到深度神经网络等大型模型中，并做出预测。

Machine learning and AI takes all that complexity in, encodes it in big models like deep neural networks, and makes predictions.

但这些预测完全是不透明的，无法让人理解其推理过程。

But those predictions are completely opaque and don't give anyone an understanding as to how they were reached.

另一方面，复杂性科学则试图——用Murray的话来说——'对整体进行粗略把握'。

On the other hand you have complexity science which tries to, in Murray's language, take a crude look at the whole.

它试图找到适当的尺度，以便对这些自适应系统进行理论研究——如果你愿意的话——其核心目标不在于做出完美预测，而是产生真正的洞见，解释它们为何存在。

It tries to find the right scale at which you can do theory of these adaptive systems, if you like, in the center with a view to not producing perfect predictions but generating real insight, explanation for why they exist.

我认为我们正进入二十一世纪的一种新型科学分裂状态：我们将同时采用两种截然不同的现实研究方法——一种是基于机器的、高维度的、极其精确但如同黑箱的预测框架；另一种则是更符合科学史传统的框架（如果你愿意这么说的话），它忠实于我们所研究系统的复杂性，虽不擅长预测，却能让我们理解产生这些现象的基本机制。

And I think we're now entering in the twenty first century a new kind of scientific schism where we're going to live with two very different ways of engaging with reality: A machine based, high dimensional, very precise, predictive framework that is a black box, and ours, which is a more familiar framework from the history of science, if you like, but that is faithful to the complexity of the systems we study, which doesn't predict so well, but does allow us to understand the basic mechanisms generating the phenomena of interest.

我认为这就是复杂性科学的存在之处，它必须学会与机器学习和人工智能共存。

And that's where I think complexity lives and it's going to have to come to terms with living with machine learning and AI.

这几乎就像我们回到了——用你的圣经隐喻来说——该隐与亚伯的时代，这两个兄弟必须学会和睦相处，而不是一个杀死另一个。

It's almost as if we've returned, to use your biblical metaphors, to the Cain and Abel, and those two brothers are going to have to get on as opposed to one killing the other.

有趣的是，这与我们在90年代SFI计算资源应用方面所观察到的、某种程度上带有机构自传性质的情况截然不同，包括该组织在细胞自动机等相关领域产生的流行文本遗产。

It's funny because it's a very different situation than to make this somewhat institutionally autobiographical than we were looking at in terms of the application of computational resources at SFI in the 90s, know, and the legacy of this organization in terms of popular texts that have emerged around cellular automata and that kind of thing.

因此，似乎那里存在着一个非常清晰的共同起源故事。

So it seems as though there's a very clear shared origin story there.

你知道，我不断被吸引的一个问题是：我们的知识统一性能否像市场情绪分析那样呈现出脉冲式的波动。

You know, one thing that I'm constantly drawn to is this question of whether there are pulses in almost like a market sentiment analysis of just how unified our knowledge can be.

你知道，这有点像把我们的话题带回到最初的起点。

You know, it's like to bring this somewhat back to where we started.

我们正处于历史上的一个时刻，也许我们正处在一个被新方法和新数据淹没的脉冲期，处于分化而非整合的阶段，或者你认为这种状况已经结束了吗？

And that we're at a moment in history now where maybe we're at a we're in one of these pulses where we're overwhelmed by new methodologies and new data, and we're at a point of differentiation rather than integration, or do you think that's over Well,

我不知道。

don't know.

我认为洞见的历史就是构建物理或认知工具，让我们能够推理复杂性的过程。

I mean, think the history of insight is building physical or cognitive artifacts to allow us to reason through complexity.

如果你回顾早期萨菲派的观点，他们说核心问题是涌现性，对吧？

If you go back to the earlier Sapphites said the big problem was emergence, right?

所以科学的历史就是还原论的历史。

So the history of science is the history of reduction.

理解就是将事物拆解并观察其组成部分。

To understand is to take something apart and look at its constituents.

你看，我们都是孩子，对吧？

So, you know, we're kids, right?

所以当有人给我们收音机或汽车时，我们就会直接拆开它。

So someone gives us a radio or a car and we just take it apart.

通过观察事物的组成部分确实能获得一些洞见。

And there is some insight to be had by looking at what makes something up.

但更困难的是如何将其重新组装起来。

But the harder problem is to put it together again.

对吧？

Right?

这就是涌现现象，对吗？

And that's emergence, right?

这是问题的另一面。

That's the other side.

这是科学中构建性的一面。

That's the construction side of science.

科学的历史是还原论，而科学的未来是涌现论。

And the history of science is reduction, the future of science is emergence.

早期圣塔菲研究所（SFI）试图理解简单系统如何自发产生结构，因为我们觉得这能帮助我们重新组装收音机。

And SFI, in its early days, if you like, was trying to come to terms with how simple systems spontaneously generate structure, because we felt it would help us reassemble the radio.

我认为确实如此，过去三十年间我们开发了越来越好的工具，让我们能深入理解涌现现象——如果你愿意的话，这其实就是理解适应性形态起源与构建的智力实践。

And I think it has, and what's happened over the last thirty years is we've developed better and better tools to give us deep insights into emergence, which, if you like, is the practice, the intellectual practice of understanding the origin and construction of adaptive form.

而且变得更加实证化。

It's And become more empirical.

对吧？

Right?

我们现在通过实证研究获得了更多知识，可以与早期的玩具模型相结合。

We now know more from empirical work to couple with those early toy models.

我认为这非常关键。

And I think that's really key.

我的意思是，如果你思考其中一种科学，那就是NHC（国家飓风中心）。

I mean, if you think about the one kind of science, it's the NHC.

大型粒子对撞机将物质分解成最基本的组成部分。

Massive particle colliders that break things into the most elementary constituents.

而我们则处于光谱的完全相反一端。

And we're at the absolute opposite end of the spectrum.

我们探讨的是，哪些基本要素与适当的规则相结合，能产生全新类型的结构。

We're saying, what constituents, when combined with appropriate rules, produce completely novel kinds of structure.

而哪些是正确的理论能让我们理解这种现象？

And what are the right theories to allow us to understand that?

这是一场智慧的博弈。

And that's a Sophia's game.

从这个意义上说，我们之前讨论过区分模型与理论的问题。

So in that sense, we've spoken about this before, about differentiating models and theories.

你也提到过预测与理解之间的区别，这点你已经有所涉及。

And then also that you know the difference between say prediction and understanding, which you've touched on already here.

显然这些要素存在于某种生态平衡中。

So obviously these exist in some sort of ecological balance.

我很好奇，你认为我们是否正在转向一种新的科学实践，其中模型更重要而理论相对次要？我是这么认为的，

And I'm curious, do you think that we're shifting into a new practice of science in which models matter more, theories matter less, or I mean I do think so,

这归根结底...

and it comes down to utility and prediction, so let's try and make that distinction clearer.

假设有一张台球桌，或者如果你在英国长大，那就是斯诺克球桌。

So let's imagine a billiard table or a snooker table if you're raised in Britain.

你可以建立一个模型，把它变成手机游戏，而无需理解能量守恒和质量守恒的基本理论。

You can build a model of that and turn it into a game that you can play on your phone without understanding the fundamental theory of the conservation of energy and conservation of mass.

对吧？

Right?

你在这些模型里输入牛顿定律，设置一个有摩擦力的表面，近乎完美的弹性碰撞，所有这些东西。

You put into those models Newton's laws, and you have a frictional surface, and you have near perfect elastic collisions, all the things you put in.

但要理解这些定律的来源——那些让你能开发游戏的定律——需要理解热力学第二定律。

But to understand where that comes from, where those laws come from that allow you to make your game, needs understanding the second law of thermodynamics.

什么是熵？

What is entropy?

什么是摩擦力？

What is friction?

为什么球最终会自己停下来，对吧？

What's happening there that the ball eventually stops on its own, right?

而这并不显而易见。

And that's not obvious.

这并非经典力学自然推导得出的结论。

That doesn't fall out of classical mechanics.

能量和质量守恒定律从何而来？

Where does the conservation of energy and mass come in?

为什么球不会自行蒸发？

Why doesn't the ball just spontaneously evaporate?

要理解这些，你必须回溯到数学家艾米·诺特提出的基本对称性原则。

And well, to understand them you have to go back to fundamental principles of symmetry that were worked out by the mathematician Emmy Nota.

因此从某种意义上说，理论能让你更深入地理解你所使用的规则为何能够成立。

So theory in a sense is giving you the bigger insight into why the rules that you're using are possible in the first place.

但这并不一定能打造出更好的游戏。

But that doesn't necessarily make a better game.

这也不会造出更好的台球桌。

That doesn't make a better billiard table.

这一直都是个挑战。

And that's always been the challenge.

某种程度上，这种调和体现在数字电路史上从真空管到晶体管的创造过程中，量子力学的理解、原理和理论确实起到了帮助作用。

And where that was reconciled in a sense was in the creation of the transistor out of the vacuum tube in the history of digital circuitry, where an understanding of quantum mechanics, the principles and theory, actually helped.

这种张力一直存在，鉴于社会对即时满足的痴迷，模型思维者、那些构建即时实用或愉悦事物（如游戏）的人，往往比探究模型存在原因的人更受重视。

And that's always been a tension, and so given society's obsession with immediate gratification, the model mind, the modeler, the person who builds things that are immediately of utility or pleasure in the case of a game, tends to be emphasized over the reason why the model can exist.

这似乎也符合一种关于模型算法复杂性降低趋势的进化理论。

That also seems to play into an evolutionary theory about a trend towards reduced algorithmic complexity in our models.

在科学中表现为简约即美，你知道，这最终又回到了某种根本性的审美关切。

It shows up in science as parsimonious as aesthetic, you know, and that ultimately there it is again that sort of bedrock into, in some sense, an ultimately aesthetic concern.

正如萨宾·霍斯皮特勒所探讨的，美是否正在误导科学实践。

Know Sabine Hospilder has talked about this, you know, whether or not beauty is leading the practice of science astray.

我是说，要担心这个吗？

I mean, worry about that?

其实我认为，这又回到了我们这个系列的主题——我称之为复杂美学。

Well, I actually think, again, it's sort of a topic for this series, what I call complexity aesthetics.

有一种标准的科学美学，某种意义上，在现代主义和抽象主义中得到了最好的体现。

There's a standard scientific aesthetic, which is really, in some sense, best represented in modernism and abstraction.

非常简约。

Very minimal.

想想蒙德里安。

Think Mondrian.

对吧？

Right?

想想分析立体主义或综合立体主义。

Think, analytical cubism or synthetic cubism.

但在艺术中，还有其他传统，比如巴洛克风格，就并非如此。

But in art, there are other traditions too, like the Baroque, which are not like that.

那也是美学。

That's also aesthetic.

而且有一个非常有趣的问题，在数学理论科学的发展过程中，是否可能存在另一种不如此依赖现代主义严苛性的美学。

And there's a very interesting question, moving forward in the mathematical, theoretical sciences, whether or not there might not be another kind of aesthetic that isn't so beholden to the austerities of modernism.

对此我感到无比兴奋。

And I'm extremely excited about this.

事实上我认为我们可以探索的方向之一就是这种新的美学——关于复杂性的新美学，它依然是一种美学。

I actually think one of the things that we might explore is this new aesthetic, this new aesthetic of complexity, which is still an aesthetic.

有时候让我恼火的是，数学物理学劫持了美学概念，只为单一艺术传统服务。

And sometimes it irritates me that mathematical physics hijacked aesthetics in the service of only one artistic tradition.

对我们大多数人而言，这种极简主义、稀疏的美学观念是否适合复杂世界，仍是一个开放性问题。

And it's an open question, I think, to most of us here, whether the sort of minimal notion, the sparse notion of beauty, is the correct one for the complex world.

这又回到了那个问题，即需要足够的复杂性才能准确映射现象，对吧？

Again, gets back to that issue of, you know, sufficient complexity in order to adequately map the phenomenon, right?

就像当我观察那条曲线时，那种介于纯粹描述与纯粹探究之间的抛物线形态，让我想起SFI历来将生命本身描述为恰好发生在临界阈值上的现象。

Like, there's something about that when I look at the curve there, you know, the sort of parabolic arc between something that is all description, something that is all investigation, and you know, like open ended inquiry, that reminds me of the way that SFI has historically talked about life itself as a phenomenon that occurs right there at the threshold of criticality.

这么说或许粗俗，但就物理学对生物学和神经科学的某种挪用而言，你是否认为这里进行的工作具有某种征兆性或递归性——仿佛我们已不再掌控方向盘，而是朝着优化不确定世界导航策略的方向发展？

It is perhaps crass, but to the degree that we can describe this as a sort of appropriation by physics of biology and of neuroscience, do you see the work that's going on here as symptomatic or recursive in some kind of way that we not at the wheel anymore, that we're moving towards an attempt to optimize strategies for navigating an uncertain world?

嗯，我是说，让我们回到这个概念上来。

Well, I mean, let's get back to this notion.

在Sefazos早期的辩论中，我记得读过一份记录，斯坦尼斯拉夫·乌拉姆——某种意义上可以说是研究所的守护天才，至少是他的精神——提出了以下观察。

There was in the early days of Sefazos debate, and I remember reading a transcript where Stanislav Ulam, one of the tutelary geniuses of the institute in a sense, at least his spirit, made the following observation.

他说：不要问物理学能为生物学做什么，而要问生物学能为物理学做什么。

He said, ask not what physics can do for biology, but what biology can do for physics.

我认为这在某种意义上概括了一切，因为我们非常习惯于认为那些数学上更严谨的学科是描述性学科的保姆。

And I think that's really the in some sense that sums it up because we're very used to the idea that the more, if you like, mathematically rigorous disciplines are nurse maids to the more descriptive ones.

但实际上，在复杂性的世界里，我们已进入了一个可能恰恰相反的世界。

But actually now we've entered a world, in the world of complexity, where the opposite might be true.

例如进化论的洞见，尤其是其数学形式，可能是物理学进步的必要条件。

Where the insights, for example, of evolutionary theory, and in particular its mathematical form, might be necessary for physics to advance.

这确实是我们不习惯的事情。

And it's just not something we're used to.

它可能会改变物理学的实践方式。

And it might change the practice of physics.

这很可能会彻底.

It'll probably change the whole discipline of physics.

我认为我们对这种颠覆非常感兴趣。

And I think we're very interested in that disruption.

所以当你谈到探索和不确定性时，我们作为一个群体必须接受的其中一个不确定性是：我们熟悉、热爱并运用的这些领域可能会被彻底解构。

So when you talk about exploration and uncertainty, one of the uncertainties that we as a community have to embrace is the possibility that the fields that we've come to know and love and deploy are going to be taken to pieces.

而这将会非常美妙。

And that would be wonderful.

事实上，这正是科学的本质所在。

In fact, that's what science is all about.

我是说，正如我所说，这是人类文化史上创造出的最不敬的行为。

I mean, is the most, as I said, disrespectful activity created in the history of human culture.

它从根本上对之前的一切都不敬。

It's fundamentally disrespectful of everything that came before it.

如果它在某种深层意义上对之前的事物保持敬意并受其历史束缚，它就不会进步。

If it is respectful of what came before it in some deep sense and beholden to its history, it will not advance.

我是说，这几乎像是人文学科与科学之间的另一个不同之处。

I mean, that's almost like another different difference between the humanities and the sciences.

科学就像一种非常任性、近乎孩子气的反叛，而这种反叛对发现来说是必要的。

The sciences is kind of very petulant, almost childish rebellion that's necessary for discovery.

有时，正如你所知，科学家们的个性必须反映这种认知需求。

And sometimes, as you know, the personalities have to reflect that cognitive requirement.

因此我对这个理念非常感兴趣。

And so I'm very interested in that idea.

我对不敬和挑战权威很感兴趣。

I'm interested in disrespect and challenging authority.

我记得理查德·费曼说过类似的话：科学就是相信专家也会犯错。

And, you know, I mean, I remember I think Richard Feynman who said something like, you know, science is the belief in the fallibility of experts.

这种理念认为，除非你自己验证过，否则别人告诉你的都不是终极真理。

You know, that sort of idea that nothing you're told is really ultimately true until you've proved it yourself.

我非常喜欢这个理念。

And I love that idea.

我认为有时我们被塑造成学者、权威、真理守护者或知识传承者的形象，这是非常令人遗憾的。

I think sometimes the way we're presented as academics, as authorities, as, you know, holders of the truth, of perpetuating bodies of knowledge is extremely regrettable.

我希望营造这样一种氛围：每个人都时刻保持警觉，随时准备以某种文明的方式为自己的观点而战。

I would like this to be an environment where everyone's constantly kind of feeling a little itchy and ready to go into combat for their own idea in a, you know, somewhat civilized fashion.

奥德修斯。

Odysseus.

是啊。

Yeah.

奥德修斯式的人物。

The Odysseans.

然而归根结底，要做出真正能挑战现有信仰体系的全新发现。

And, but at the end of it, make genuinely new discoveries that challenge the existing bodies of belief.

这里有个实实在在的现实案例可以说明这一点。

So here's a real brick and mortar example of this.

这似乎反映了一种更宏大的文化转向——正如詹姆斯·P·卡斯所言

It seems like this is symptomatic of a larger cultural movement from an emphasis on, as James P.

从积累功绩的有限游戏（本质上是一种面向过去的历史叙事，你们将其归入人文学科），转向无限的文化复兴游戏，这就像又一次相变——我们站在二十一世纪的起点，正从固态的知识模式迈向液态模式。

Carse would put it, the finite game of accumulated merit, largely a narrative past oriented historical enterprise that you're putting in the humanities, and an infinite game of endless cultural renewal, a sort of phase shift again where we stand here at the beginning of the twenty first century on the precipice of moving out of a sort of solid model of knowledge into a, like a fluid model.