卡洛斯·格申森谈平衡、临界性、反脆弱性与复杂系统哲学

如果我们只看化学反应，从所有可能的化学反应中，大多数在我们的宇宙中永远不会发生。

If we just look at chemical reactions, from all possible chemical reactions, most of them will never take place in our universe.

可能性太多了，根本不可能全部发生。

There are just so many that there's no possibility that they will happen.

然而，我们依然拥有如此丰富的化学，最终催生了生物学；同样，所有可能的生物相互作用、所有可能的社会互动、所有可能的想法，也都永远不会实现。

And still, we have such a rich chemistry that led to biology and the same, all possible biological interactions will never take place, all possible social interactions will never take place, all possible ideas will never materialize.

但即便如此，那个在宇宙历史中哪怕只被实现过一次的、可能性空间中极其微小的区域，也必须在某种程度上是可行的，哪怕只是短暂的一瞬。

But still, that minimum infinitesimal region of the space of the possible that actually gets instantiated even just once in the history of a universe needs to be viable in in some way, even for a short moment.

当然，这也决定了哪些会存活下来、哪些会持续存在、哪些会演化。

And, of course, that constraints which ones will survive or which ones will endure, which ones will evolve.

我们该如何把握复杂系统思维？

How do we get a handle on complex systems thinking?

这一科学对哲学意味着什么？哲学传统又如何预示了科学前沿的发现？

What is the implications of this science for philosophy, and where does philosophical tradition foreshadow findings from the scientific frontier?

欢迎收听《复杂性》，圣塔菲研究所的官方播客。

Welcome to Complexity, the official podcast of the Santa Fe Institute.

我是您的主持人，迈克尔·加菲尔德。

I'm your host, Michael Garfield.

每隔一周，我们将带您参与我们全球网络中严谨研究者的广泛对话，他们正在开发新的框架，以解释宇宙最深层的奥秘。

And every other week, we'll bring you with us for far ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

在本期节目中，我们与卡洛斯·格申森交谈，他是圣塔菲研究所的访问学者，也是墨西哥国立自治大学的计算机科学教授，他领导着自我组织系统实验室，更多头衔可在我们的节目说明页面中找到。

In this episode, we speak with Carlos Gershenson, SFI sabbatical visitor and professor of computer science at the Universidad Nacional Autonoma de Mexico, where he leads the Self Organizing Systems Lab among many other titles you can find on his site in our show notes.

在未来一小时里，我们将讨论他数十年来在广泛的核心复杂系统概念上的研究与著述，以及这些概念与西方和东方哲学传统的交汇，这在本播客中尚属首次。

For the next hour, we'll discuss his decades of research and writing on a vast array of core complex systems concepts and their intersections with both Western and Eastern philosophical traditions, a first for this podcast.

如果您重视我们的研究与传播工作，请在Apple Podcasts或Spotify上订阅、评分并评论我们，并考虑通过santafe.edu/engage进行捐赠或以其他方式参与我们的活动。

If you value our research and communication efforts, please subscribe, rate, and review us at Apple Podcasts or Spotify, and consider making a donation or finding other ways to engage with us at santafe.edu/engage.

为了获取圣塔菲研究所校园的高清虚拟背景用于视频通话，以及有机会赢取一本由圣塔菲出版社签名的书籍，请通过完成节目说明中链接的调查来帮助我们改进科学传播，或直接购买最新重新出版的圣塔菲出版社档案卷《复杂性、熵与信息的物理》。

For HD virtual backgrounds at the SFI campus to use on video calls and a chance to win a signed copy of one of our books from the SFI Press, please help us improve our scicom by completing a survey linked in the show notes, or just buy a copy of the recently resurfaced SFI Press archival volume, Complexity, Entropy, and the Physics of Information.

现在仍有机会申请面向博士生的Complexity GAINS英国项目。

There's still time to apply for the Complexity GAINS UK program for PhD students.

申请截止日期为3月15日。

Apps close March 15.

或者来我们这里工作。

Or come work for us.

我们正在寻找一位新的数字媒体专家、可持续发展领域的应用复杂性研究员、新兴政治经济领域的研究助理，以及一名应付应收账款专员。

We're on the lookout for a new digital media specialist, an applied complexity fellow in sustainability, a research assistant in emerging political economies, and a payroll accounts payable and receivable specialist.

你也可以加入我们的Facebook讨论组，结识志同道合的人，讨论每一集内容。

You can also join our Facebook discussion group to meet like minds and talk about each episode.

感谢收听。

Thank you for listening.

卡洛斯·格申森，很高兴您做客《复杂性播客》。

Carlos Gershenson, it's a pleasure to have you on Complexity Podcast.

很高兴能来这里。

Pleasure to be here.

是的。

Yeah.

过去几个月里，我一直在观看您在我们研讨会系列中所做的主题演讲，我很期待看到整个内容如何与前五场串联起来。

It's been great watching you give these thematic talks in our seminar series over the last few months, and I'm excited to see the whole thing will link to the first five.

现在我们将在节目中稍微深入一下这部分内容，但你也写了很多关于复杂系统科学与哲学交叉领域的有趣作品，我想了解一下。

Now we're gonna we're gonna dip into a little bit of that in the show, but you've also written a lot of interesting stuff at the intersection of complex systems science and philosophy that I wanna get to.

所以，无论如何，我们先从给你自己做一个简单的背景介绍开始吧——你是谁，又是如何走上这条探索和研究之路的呢？

So, anyway, let's just start by giving people a little bit of background about who you are as a person and how it is that you came to devote your life to the exploration and the inquiry around all of this stuff?

嗯，我想从小时候起我就充满好奇心。

Well, I guess I was curious since I was a child.

我的父母一直激发着我的这种好奇心。

My parents stimulated that curiosity.

比如，我六岁的时候，我爸爸带我去参加了一个由墨西哥科学院组织的项目，教孩子们用Logo语言编程。

For example, when I was six, my dad took me to a program that was organized by the Mexican Academy of Sciences to teach kids how to program with logo.

在墨西哥城，还有许多老师以不同的方式激发了我的兴趣，我后来学习了计算机工程，也涉猎了一些哲学。

I had many teachers that also stimulated that in different ways back in Mexico City, and I studied their computer engineering and also a bit of philosophy.

后来，我去了英国苏塞克斯大学，攻读进化适应系统硕士学位，那是大约二十年前的事了。

Later, I went for a master's in evolutionary adaptive systems at Sussex University in The UK, So that's about twenty years ago.

之后，我在布鲁塞尔的自由大学完成了博士学业，然后又在新英格兰复杂系统研究所跟随Apostol学习。

Then I did my PhD in Brussels at the Free University, and then Apostol at the New England Complex Systems Institute.

然后我回到墨西哥城，在国立大学担任教职。

Then I joined the faculty back in Mexico City at the National University.

所以，就像我在我们开始这次通话前跟你说的，你的研究引用列表非常丰富，尤其是像你这样看起来如此年轻的人。

So you've like I was telling you before we started this call, your list of research citations is extensive, especially someone who seems as young as you are.

所以我跟戴维·克拉克考尔讨论过这个问题。

So I've talked with David Krakauer about this.

他说，我们的研究网络中似乎有两种人：一种是对非常具体狭窄的领域感兴趣，但想把整个复杂系统工具包应用到其中。

He says there's two kinds of people that seem to populate our research network, and some of them are people that are interested in very specific narrow domains, but they wanna bring this whole palette of complex systems tools to it.

另一种人则对一切事物都感兴趣，而你似乎属于第二种。

And then there are these people that are just interested in everything and you seem like one of those second categories.

因此，我很好奇，基于这一大堆丰富的兴趣，是什么样的问题在驱动着你，至今仍在驱动着你？你是如何界定或解释自己最初通过计算机科学进入这个领域的呢？

So I'm curious what kinds of questions based on this enormous bouquet of stuff animated you, continue to animate you, like, how would you delimit or explain what it is it seems like you came in through computer science.

所以，你真正追求的是什么？在你的职业生涯中，你真正想挑战的是什么？

So, yeah, what are you really what windmill are you really tilting after in your career here?

是的。

Yeah.

我选择在大学攻读计算机工程，因为那是我所能找到的最广泛的课程。

I chose computer engineering at the university I studied because it was, like, the broadest program I could find.

所以它不仅仅是计算机科学。

So it was not only computer science.

它还包括数学、物理，甚至经济学、哲学和历史，因为我对这些都感兴趣。

It was also math and physics and even economy and philosophy and history because I was interested in all of that.

所以你可以用复杂系统的视角来观察任何现象，并且它能惠及任何学科。

So think that you can use the lens of complex systems to observe any phenomena, and it can benefit any discipline.

因此，我在理论层面、工程层面，甚至哲学层面和艺术层面都尝试过这样做。

So in a way, I've tried to do that at the theoretical level, at the engineering level, even the philosophical level, and also at the arts level.

所以在我看来，我一直试图将这些全部结合起来，因为例如，如果你能用实际应用来检验理论，理论就会更完善，当然，你的应用也会建立在更扎实的理论基础上。

So in my case, it has been, I think, to combine all of these because, for example, you will have better theory if you manage to test it with applications, and of course, your applications will be better grounded in solid theory.

而且，当然，如果你已经有过解决现实问题的经验，你就能提出更有说服力的哲学论点。

And, of course, you can have better arguments to philosophize about it if you already have experience with trying to solve real world problems.

我想在艺术中，你可以探索创造力，而这种创造力对其他三个领域也大有裨益。

And I guess in art, you can explore creativity that can be beneficial also for the other three.

嗯，我其实想做的是，我们节目上通常不会这么做，但因为你既撰写过自己的定量研究，也写过文献综述，还写过一些关于复杂性科学如何改变哲学经典思维的哲学性文章。

Well, what I'd like to do, I think, is so we don't typically do this on the show, but because you've written both your own quantitative research as well as lit reviews, as well as more like philosophical pieces about the way that complexity science changes classical thinking in philosophy.

我想让这个思路完整地循环一圈，是的。

I'd like to run that circuit actually kind of in a circle Yep.

所以首先邀请你为我们拆解一下这篇论文。

And start by inviting you to break down for us this paper.

弗朗西斯·海利根，是的。

Francis Heylighen Yep.

他是你的博士导师吗？

Who was that your PhD advisor?

是的。

Yes.

你早期和这位学者合作写了大量论文。

You've written a lot of papers with this guy, especially early on.

而你这篇论文特别值得一提，我们该如何理解复杂性呢？是的。

And you got this one in particular, how can we think the complex Yes.

那是一本书的章节。

That was a book chapter.

我希望你能详细解析一下，因为你在这篇文章中明确区分了复杂性思维与经典或笛卡尔思维之间的大约六组关键差异。

And I'd like for you to break this down because you make about half a dozen key distinctions between complexity thinking and classical or Cartesian thinking.

即使有人已经听了本节目101期，我发现你在这里提出的区分依然非常清晰。

And even if somebody's been listening to the show for a 101 episodes, I find that the distinctions that you've made here are really clear.

所以，请为我们逐一讲解一下。

So take us through, please.

是的。

Yep.

事实上，在我攻读博士期间，我的论文题目是《自组织系统的设计与控制》。

So indeed, during my PhD, the title of my thesis is Design and Control of Self Organizing Systems.

但当时我也想借此更好地理解什么是复杂性、什么是自组织、什么是涌现等等。

But then it was also something I felt like doing to understand better what was Complexity, what was Self Organization, what was Emergence, and so on.

不过，我在博士期间所做的研究并没有就此停止，我至今仍在继续写作和思考这些问题。

But the fact that I did during my PhD hasn't stopped, so I still write and think about these issues.

我们还与来自南非的保罗·西利耶斯合作，他几年前去世了。

We also collaborated with Paul Cilliers from South Africa, who passed away a few years ago.

我曾有机会与盖尔什·莫兰有过几次交流。

I had the opportunity to interact with Gersh Moran in a couple of occasions.

所以我想试着总结一下迄今为止所做的所有工作，因为仅这个话题我们就足以聊上好几期。

So trying to summarize everything that has been done because we could spend several episodes just on this topic.

比如传统哲学，我们可以称之为还原主义，它试图简化现象以进行预测，这种方法取得了极大的成功。

Like, traditional philosophy, which we could call reductionists, tries to simplify phenomena in order to predict, that has been extremely successful.

我们的预期寿命之所以翻倍，正是得益于这一点。

We have duplicated life expectancy because of this.

汽车、航空旅行、互联网，一切成就都归功于还原主义。

Cars, air travel, Internet, everything is thanks to reductionism.

但有一句话说得好：还原主义是正确的，但不完整。

But there's this phrase of reductionism is correct but incomplete.

那么还原主义缺少了什么？

So what is missing from reductionism?

当你进行简化时，往往会忽略相互作用，而这正是复杂性的特征。

When you simplify, you tend to ignore interactions, and that's what characterizes complexity.

那么，当你关注相互作用，或将其视为真实存在而非额外附加物时，会发生什么？

So what happens when you put attention to interactions or you consider them as something real, not something that's just something extra?

然后，你最相关的观点是，你的预测能力是有限的。

And then you perhaps most relevant aspect is that your prediction is limited.

还原论试图通过简化来实现预测，这很有价值，因为这样你就能在问题恶化之前尝试解决它。

Reductionism tries to simplify in order to predict which is desirable because then you can try to solve problems before it's too late.

然而，如果你面对的是复杂性，即相互作用，它会产生新的信息。我们在高中学到，如果你掌握了初始条件、边界条件以及支配系统的规律，你就能预测未来，这源于拉普拉斯妖的概念。

However, if you have complexity, meaning interactions, this will generate novel information, and we are taught in high school that if you have, let's say, initial and boundary conditions and the laws that rule the system, you'll be able to predict the future, and it has its roots in Laplace's daemon.

但我们知道，由于这些相互作用会产生新信息，因此仅有初始条件、边界条件和支配系统的规律是不够的。

But we know that because of these interactions, they generate new information, so it's not enough to have initial and boundary conditions and the laws that govern a system.

你实际上需要运行这个系统，技术术语称之为计算不可约性。

You need actually to run the system, and the technical term for this is computational irreducibility.

那么，当你无法预测时，该怎么办？

So then what to do when you can't predict?

我们一直在探索的是适应，而自组织是适应的一种方式。

What we have been exploring is to adapt, and self organization is one way of adapting.

这样一来，许多源自我们还原主义科学视角的推论开始瓦解，这些推论曾被哲学界视为真理。

And like that, many pieces start to fall down that were implications of our reductionist scientific perspective that have been taken as true in philosophy.

因此，我们持续探索这一切：考虑互动意味着什么，面对有限的预测能力我们该怎么做，以及总体上该如何应对复杂性。

So we explore all of that, and we keep on exploring all of that, and what are the implications of considering interactions, what should we do with our limited predictability, and in general, how to face complexity.

你和迪尔克·哈尔宾在‘慢即是快’这一研究中也提到过一点，是的。

So one of the things that you mentioned also in the work you did with Dirk Halbing on the slower is faster Yep.

效应。

Effect.

我们最近刚邀请了艾莉森·戈普尼克做节目，她谈到了她所谓的‘探索与利用’之间的张力，是的。

And you talk about We just had Alison Gopnik on the show, and Gopnik talks about what she calls the explore exploit tension Yep.

你曾在多个地方提到，由于决策所面临的要求，你可能因信息太少而行动，也可能因信息太多而行动；而经典思维往往像你刚才说的那样，假设拥有更多信息就能做出更好决策——即便这种假设本身是否成立都值得怀疑，而我们知道它并不成立。

Which is, you mentioned this in a couple places that due to demands made on decision making that there you can act with too little information or you can act with too much, and classical thinking tends to, like you just said, tends to assume that you're gonna make better decisions if you have all of the is, like, assuming that it's even possible, and we know that it's not.

但我想听听你对这一点的看法，因为当我们和西蒙·德德奥讨论时，他也提到了类似的观点：西蒙认为科学之所以成功，很大程度上是因为不同人对什么是令人满意的解释有着不同的关注点或审美偏好。

But, yeah, I'd love to hear this piece because this also came up when we were talking with Simon De Deo about Simon sees science as successful in large part because there's a tension between the ways that different people care or, like, the aesthetics they have about what constitutes a satisfying explanation.

有些人想要一个包罗万象、统一的解释，而有些人则希望一个非常有限、简洁到能写在T恤上的解释。

Some people want a an all embracing, consilient explanation, and some people want something that's very limited and parsimonious and can fit on a t shirt.

这就是差异所在，这也与Alison Gopnik所谈的儿童与成人解决问题方式的不同密切相关。

And that's that's the difference, and that's kind of related to the way that Alison Gopnik talks about the way that children kind of relate to problem solving and and the way that adults do.

我再补充一点：当我们讨论这一点以及其他一些研究时，你实际上跳过了很多内容，但你在与Fernanda Sancas Puig团队合著的论文中谈到了异质性，以及系统如何随着时间自我调整以实现异质性。

And so, just one more thing I'll stack on this is when we're when we talk about in in this and and other work actually, you talk about it kind of skipping ahead to all of these things, but you talk about it in the piece that you coauthored with a team led by Fernanda Sancas Puig on heterogeneity and how systems will tune themselves to have heterogeneity across time.

你还提到了像Andre Derrousse这样的学者的研究，他探讨了生态学如何通过将生物视为具有不同生命阶段的个体而受益——例如幼虫的食物来源可能与成体完全不同。

And so you talk about some of the work of somebody like Andre Derrouse, who looks at the way that ecology benefits from understanding organisms as having these diff distinct life stages where the larva might be feeding on something else from the adult.

所以，是的。

And so yeah.

所以，这里面有很多内容，我不知道该怎么说。

So there's I don't know.

这里面的内容太多了。

There's a lot there.

对。

Yep.

但我非常希望你能谈谈异质性和多样性的价值，以及它如何与你和迪尔探讨的问题相关——当我们从系统适应的角度出发，通过分裂并探索多种不同策略时，为什么会得到反直觉的结果。

But I'd love to hear you talk a little bit about the value of heterogeneity and diversity and how it also relates to these questions that you pursued with Dirk about when we get counterintuitive results in how systems adapt by splitting up and exploring multiple different strategies.

是的。

Yeah.

所以我认为，所有这些都可以被视为平衡的特定案例，比如探索与利用之间的平衡，我们希望在这两者之间取得平衡，以使搜索达到最优。

So I I think all of these can be seen as particular cases of balance in the sense that, let's say, exploration exploitation, we want to have a balance between both that will, let's say, make search optimum.

当然，我们从大卫·沃尔珀特和北依赖定理的研究中知道，根据搜索空间的不同，基本上需要采用不同的策略来搜索该空间。

And, of course, we know from work of David Walpert, and North Reliance theorems that depending on the search space, basically, you need different strategy to search that search space.

因此，这种平衡是动态变化的，这与‘慢即是快’效应中发生的情况相同。

So this balance is moving, which is the same what happens in the slower is faster effect.

此外，西蒙在某些情况下可能希望了解现象的所有细节，并预测所有相关信息。

And also, the case of Simon, in some cases, might want to know all the details and to predict all the information of a phenomenon.

但这样做当然成本极高，需要大量的信息。

But then that, of course, has a very high cost, you need lots of information.

而在另一个极端，你可能希望拥有一个非常通用的理论，但这样又会缺乏实用性，因为它只会说一些像‘事物就是如此’这样的空洞结论，而这总是对的，但你却无法从中获得太多实际帮助。

And on the other extreme, you might want to have a very general theory, then that won't be very practical because it will say something like stuff is, and that's always true, but then there's not much you can do with that.

所以，再次强调，这种平衡取决于你应用理论的背景，以及你的兴趣或该理论的目的——你想实现什么，然后所需的具体细节程度也将由此决定。

So, again, the balance lies depending on the context that you're trying to apply a theory and what are your interests or the purpose of that theory, what are you trying to achieve, and then the, let's say, how much detail is needed will depend on that.

所以，再次强调，这是一种平衡，但你无法事先预设最优的平衡点。

So, again, a balance, but you cannot pre specify what's the optimum balance beforehand.

因此，这是一种动态变化的东西。

So it's something that is shifting.

将这一点与异质性联系起来，当你事先不知道哪种最优平衡最有效时，如果你拥有多种元素——无论是空间上、时间上还是功能上的——你就无需精确地找到参数，无需微调参数，因为其中一些元素很可能已经接近最优状态，你可以利用这些元素。

Relating it to heterogeneity, it seems that when you don't know beforehand what's the optimum balance that will be best, if you have a variety of elements, either spatial or temporal or functional, then you don't really need to find the precise parameters, don't need to find tune parameters because some of those elements probably will be close to the optimum, so then you can exploit those.

这似乎是自然界普遍在做的一件事。

It seems it is something general that nature does.

我不清楚这是否是有特定目的，还是因为异质性更容易实现，实际上在许多情况下，你需要付出相当努力才能实现同质性。

And I don't know whether with a specific purpose or it's just easier to have heterogeneity because actually, you will need to put some effort into having homogeneity in many cases.

所以，我们以前在节目中从未真正讨论过这一点，但既然你也不是校园里唯一的佛教徒，而我们现在在弗雷德·库珀的办公室里。

So this we've never actually talked about this on the show before, but since you're not the only Buddhist on campus here, we're here we're in Fred Cooper's office.

我和他关于这个特定的交叉点有过一些非常深入的对话。

I've had some great conversations with him about this particular intersection.

你写过一些关于佛教哲学与复杂系统科学之间关系的有趣著作。

You've written some interesting work on the relationship between Buddhist philosophy and complex system science.

关于如何用海利根的思路思考复杂性，你提到在更传统的亚里士多德逻辑中，现象只能属于类别A或非A。

And something that came in, again, on this how can we think the complex with Heylighen is you talk about how in more classical thinking Aristotelian logic, phenomenon begin belongs as you write to either to category a or not a.

它不能同时是两者，也不能处于中间状态，也不能视情况而定。

It cannot be both neither in between or it depends.

然而，佛教的四重逻辑还包括：它可以同时是A和B，或既非A也非B，或因为这类原因。

And yet Buddhist tetratic logic also includes it can be both a and b or neither a nor b or because these kinds of things.

随后，在关于不确定性的章节中，你引用了2002年一篇论文，区分了绝对存在与相对存在——尽管你2009年才接触藏传佛教，但这一观点我此前只在更隐秘的哲学文献中见过。

And then later on in the section on indeterminacy, you make a distinction from a paper you wrote in 2002 between absolute being and relative being, which is even say you picked up Tibetan Buddhism in 2009, but this is something I've really only ever encountered in more esoteric philosophical tracts.

是的。

Yep.

比如蒂莫西·莫顿的著作中，他在物导向本体论里谈到，你永远无法真正认识事物的全部。

Where Or in the writing of Timothy Morton, who talks a lot about in object oriented ontology, how you can never actually know the whole thing.

因此，我很想听听你谈谈这个问题，它对科学和哲学都至关重要：观察者本质上是有限的，无法获取关于某事物的完整信息。

So I'd love to hear you talk about this issue, which is fundamental to both the science and the philosophy of this whole thing about how observers are inherently finite and they can't gather complete information about something.

所以我们经常谈论这样一个事实，正如你刚才所说，这些事物本质上是多元的，因此不存在唯一正确的方式。

And so there is We talk a lot about the just the the fact that, like you said a moment ago, that, these things are only pluralistic, and therefore, there isn't like a right way.

只有更有效或较无效的理解方式，是的。

There are only more or less functional ways Yep.

理解的方式。

Of understanding.

我很想听听你进一步谈谈这在实践中是如何运作的，以及你是如何在自己的研究中拆解这一观点的，是的。

And I'd love to hear you talk a little bit more about how this works in practice and how you've unpacked this in your own Yep.

研究。

Research.

我认为圆形或球体的形象有助于说明这一点。

Think that that figure of the circle sphere helps illustrate this point.

想象一下，你有一个半白半黑的球体，但你只能从一个视角去感知它。

So imagine you have a sphere and it's half white, half black, but actually you can just perceive it one perspective.

所以有些人会看到一个黑圆，有些人会看到一个白圆，还有一些人会看到一半一半。

So some people will see a black circle, some people will see a white circle, some people will see half and half.

然后我们可能会为了争论或说服彼此圆圈是白色还是黑色而爆发圣战。

And then we can fight holy wars trying to decide or convince each other that the color is that the circle is white or black.

但当然，这并不会改变球体本身。

But, of course, that will not change the sphere.

我们也不能进行民主数学的运算，简单地取平均值，因为多数人可能只是从某个特定视角来看待这个球体。

And we cannot make an exercise of democratic mathematics and just take the average because it could be that the majority is perceiving the sphere from a particular perspective.

因此，我们可以说这些不同的视角是不同人所处的情境，或者同一个人在不同条件下也可能拥有不同的情境。

So we can say that these different perspectives are contexts that different people have or the same person can have different contexts in different conditions.

所以你不能真正地说圆圈是纯粹的白色或纯粹的黑色，因为你无法从所有视角去观察它。

So you cannot really say that the circle is really white or really black because you cannot really observe it from perspectives.

真实的现象可能具有无限多个维度，因为你总能从全新的视角去描述它。

And real phenomena can have, let's say, an infinite number of dimensions because you can always describe it from novel perspectives.

因此，与其试图断言‘它就是这样’或‘它不是那样’，不如更有效地表达：‘从这个视角看，它是这个颜色’。

So instead of trying to say, okay, it is this way or it's not that way, It can be more productive to say, Okay, from this perspective, it's this color.

从那个视角看，它是那个颜色。

From that perspective, it is that color.

当然，这并不意味着一切都可以，所有颜色都成立，因为从特定视角出发，你确实可以验证：从这个角度看，它是白色的，我们都可以认同这一点。

And of course, that doesn't mean that anything goes and then all the colors are valid because from a specific perspective, you can really check, Okay, from from this perspective, it's white, and we can all agree on that.

那么，这如何影响你实际开展研究的方式呢？

So so how does this affect the the way that you actually conduct research?

比如，当理论落实到实践中，你在谈论模型选择时，情况是怎样的？

Like, how does this when the rubber hits the road, you're talking about model selection.

是的。

Yeah.

比如，我们在圣塔菲研究所这里有个内部笑话。

Like, we we have this kind of an inside joke here at SFI.

有几年时间，每个人都想把一切看作网络，或者每个人都想把一切看作是标度律的结果。

There were a few years where everyone wanted to see everything as a network or everyone wanted to see everything as the outcome of a scaling law.

随着这门科学日益成熟，我们身后留下的，是越来越多不同偏好框架的痕迹。

And the longer that this science matures, the longer a trail of these different sort of preferred framing stretches out behind us.

那么在你的实际工作中，你如何根据具体情境来决定正确的研究方法呢？

So how do you in in practice in your work, how do you actually decide the correct approach for the appropriate context?

是的。

Yeah.

所以我倾向于务实。

So I'm I tend to be pragmatic.

当然，存在一些学科内的笑话，比如数学家嘲笑物理学家不够严谨，而物理学家又嘲笑工程师不够严谨。

And, of course, there are these discipline jokes of mathematicians making fun of physicists because they're not rigorous enough, and then physicists making fun of engineers because we are not rigorous enough.

当然，我们也可以嘲笑医生或心理学家，因为按照我们的标准，他们也不够严谨。

And, of course, we can make fun of, I don't know, doctors or psychologists because they're not rigorous enough according to our standards.

所以，既然我作为工程师有这个‘特权’，在很多情况下，我 simply 会先让事情运转起来，之后再寻找解释，这可能不是最优雅的做法。

So, since I have permission as an engineer, then in many cases I just try to make things work and afterwards look for explanations and it might not be the most elegant thing.

但某种程度上，我可以借助维特根斯坦的观点来为自己辩护：好吧，我们就务实一点吧。

But in a way, I could kind of guard myself behind Wittgenstein and say, Okay, let's be pragmatic about it.

因为，当然，我们可以花所有精力去证明某件事，但如果它能完成它该完成的任务，那至少它方向没错，应该也不算太糟。

Because, of course, we can spend all efforts trying to justify something, but if it does what what it should do, then I guess it shouldn't be too bad, at least on the on the right direction.

嗯，这个问题确实很容易陷入无休止的循环中。

Well, this just because it's kind of easy to go in circles around this kind of a question.

在你的工作和相关研究中，反复出现的一个问题是，组织在适应过程中需要在记忆与遗忘之间保持某种平衡。

One of the things that comes up again and again in your work and related work is the way that as you you talk about adaptation in an organization, and adaptation requires some kind of balance between memory and forgetting.

为了适应，你必须能够遗忘。

You need to be able to forget in order to adapt.

是的。

Yep.

前几天我其实和迈克尔·洛克曼讨论过这个问题，当你提出像‘如何调整像圣塔菲研究所这样的研究机构以产生新颖性’这样的问题时。

So I was actually talking about this with Michael Lockman the other day when you ask a question such as, well, wanna tune a research organization like SFI to produce novelty.

是的。

Yep.

为了做到这一点，你必须知道在这个领域中哪些坑你已经挖过了。

In order to do that, you have to know which holes you've already dug in this kind of a terrain.

因此，如何在组织中引导科学研究或技术创新，也涉及复杂性经济学的问题——你必须清楚自己究竟在衡量什么，或者说，像那个……

And so the question of how do you even orient, say, scientific research or technological innovation in an organization also requires that it's a complexity economics question where it's like, you have to know what you're actually measuring for, or like what the Yeah.

也就是你真正要让这个系统去实现的目标。

Like what you're actually tuning this thing to do.

此外，你也不希望失去你已经拥有的东西，因为我们虽然追求新颖性，但仍希望保留那些行之有效的良好部分。

Also, you don't want to lose what you already have in the sense that, okay, we want novelty, but we still want to keep the good things that are working.

实际上，进化也必须解决同样的问题。

And, actually, evolution has to solve the same problem.

这一点已经被斯图尔特·考夫曼、安德烈亚斯·瓦格纳以及许多其他人研究过。

This has been studied by Stuart Kauffman, by Andreas Wagner and many others.

因为无论是基因层面、文化层面还是经济层面，进化的运作方式都是通过探索，但生物体要存活，就必须持续保持功能。

Because how evolution works, the genetic level, at the cultural level, at the economic level, is by exploring, but organisms in order to be viable, they need to keep on functioning.

因此，这里再次出现了稳健性与适应性之间的平衡。

So there's, again, this balance between robustness and adaptability.

稳健性基本上能确保你的功能保持正常。

Robustness basically keeps your functions as they should.

而适应性则让你能够探索新事物。

Then adaptability allows you to explore new things.

实际上，异质性似乎也有利于这两者。

And actually, heterogeneity seems to favor both as well.

正是这种异质性的想法的由来，因为我们当时正与墨西哥物理研究所的同事们研究等级动态，结果发现，各种复杂系统中最重要的元素变化速度比不那么重要的元素要慢。

That's how the idea of heterogeneity arose, because we were studying rank dynamics with colleagues from the Physics Institute in Mexico, and it turns out that the most important elements of a wide variety of complex systems change slower than not so important elements.

所以，既然我熟悉进化领域的相关文献，我认为关键元素不应该轻易改变，这能带来稳健性。

So, let's say, since I knew this literature from evolution, it made sense to me that you don't want your relevant elements to change, so that gives you robustness.

但那些最不重要的元素则有自由进行探索，而不会导致整个系统崩溃。

But then the least relevant elements have the freedom to explore without breaking everything down.

因此，这种异质性也为你提供了以可持续方式探索和可能创新的机会。

So that heterogeneity also gives you the opportunity to explore and possibly innovate in a sustainable way.

因为如果一切都在改变，那就太糟糕了。

Because if everything is changed, it's too bad.

而如果什么都不变，那也同样糟糕。

And if nothing changes, that's also bad.

是的。

So Yeah.

然后这就回到了我们之前的问题：要确定哪些元素是值得保留的重要元素。

And that and then it's like we're and then we're back at the question of, well, in order to know which elements are important to conserve Yeah.

你需要在不同的时间尺度上具备适应性。

You have to be adaptable at a different time scale.

这就引出了一个问题：我们现在的经济是否就像一台纸夹机，过度优化了错误的目标，把整个世界都变成了纸夹。

And so that's to the sort of question about whether the economy as we have it now is that paperclip machine that's optimized for the wrong thing and is turning the whole world into paperclips.

所以我再次提到我和海利根合写的那篇论文。

So I talk about this again to go back to the paper with Haylighen.

你提到过，我认为这对任何听众来说都会感到熟悉。

You mentioned, and I think this is gonna be familiar to anyone listening.

你说，组织中根深蒂固的文化往往很难改变，因为新的措施会遭到主动或被动的抵制、忽视或回避。

You say, an entrenched culture in an organization can be very difficult to change as new measures are actively or passively resisted, ignored or deflected.

这样的系统会抹平差异，因为不同的原因会导致相同的结果。

Such a system destroys distinctions as distinct causes will lead to the same outcomes.

这是一个稳健的系统，但未必能实现最佳效果。

That's a robust system, but it's a system that's not necessarily tuned to achieve the best results.

那么，基于这项研究，你认为组织如何才能在不破坏自身稳定性的前提下，更好地将这种理解应用于提升适应性？

And so how do you based on this work, how do you imagine that organizations can better implement this understanding into accommodating adaptability without destabilizing themselves?

是的

Yeah.

对

Yeah.

所以阿什比，这位二十世纪中期的英国神经科学家，他的贡献之一是必要多样性定律。

So Ashby, who was a British supernetician from middle of the twentieth century, one of his contributions was the law of requisite variety.

这基本上意味着，在控制理论中，控制器必须至少拥有与它所要控制的对象相同的多样性。

So this basically says that in terms of control theory that a controller should have at least the same variety as that which it's trying to control.

而所谓多样性，我们可以简单理解为状态的数量。

And by variety, we can just think number of states.

想象一下，你有一个工厂里的机器人，你想让它操作六种物品，那么它至少需要能够区分这六种不同的物品才能正常运作。

So imagine you have a robot in a factory, you want the robot to manipulate six objects, so it should distinguish at least those six different objects in order to function.

但当你把这种低多样性的原则应用到组织和政府时，我们会发现它们缺乏足够的多样性来应对经济、社会以及其他许多复杂事物。

But then when you take that low requisite variety to organizations, to governments, we see that they don't have a requisite variety to deal with economy, with societies, and with many other things.

这就解释了为什么它们经常失败。

So that kind of explains why they fail so often.

要增加这些组织的多样性，或者减少它们试图控制的事物的多样性，都不是一件简单的事；就我们的目的而言，可以把多样性看作是复杂性的同义词。

And it's not trivial to either increase the variety of these organizations or decrease the variety of that which they are trying to control, which for our purposes we could use variety as a synonym of complexity.

但你可以像使用温度计一样，把它应用到组织上，问一问：我们到底要应对多少事情？

But then it's like a thermometer that you can apply to organizations and say, Okay, how many things we have to deal with?

然后，我们是否有能力应对所有这些事情？

And then do we have the capacity to deal with all those things?

是或否？

Yes or no?

比如说，每个月我们得应对多少意外情况？

Let's say, how many unexpected things we have to deal with every month?

我们应对这些情况的效果如何？

How well are we dealing with those?

它们是否会打乱我们原本计划的其他所有事情？

Does it break everything else that we had planned or not?

因此，这类问题有助于将组织调整到适当的适应水平，因为基本上，那些持续发生且可以提前规划的事情，就不需要为此做出改变。

So these sort of questions can help adjust the organisation to an appropriate level of adaptability because basically the things that are constantly occurring and you can plan for them, then you don't need to change for them.

然后，时不时会有一些新情况出现，这时你就需要投入时间和精力来应对这些问题。

Then there are other things that every now and then it's something new, and then you need to dedicate time and effort to address those things.

当然，你希望所有事情都能并行处理。

And, of course, you want to be able to do everything in parallel.

是的。

Yeah.

从这里出发，我们可以采取很多种方式，但我还想回到‘慢就是快’这个观点，因为这确实如此。

There are a lot of ways that we could move from there, but I wanna bring it back to when slower is faster just because this is Yep.

这是一份充满工作成果的回顾，其中一些是你自己的成果，比如关于自组织交通信号灯控制的研究，我认为这为人们提供了许多切实的现实案例，展示了系统如何实际实现或可能实现这种设计上的灵活性。

This is a review full of work, some of which is your own, like the work on self organizing traffic light control that I think gives people a lot of really tangible real world examples of how systems either do or could accommodate this kind of flexibility in their design.

所以，是的，你在本次演讲中提供了大量这些例子，我们会在节目笔记中附上链接。

So, yeah, you gave a lot of these examples in the talk that you gave here that will link from the show notes.

但另外，让我们谈谈物流、供应链和交通基础设施这类实际运作中的问题。

But, yeah, let's talk about stuff like logistics and supply chains and transportation infrastructure and how this stuff is actually working in practice.

是的。

Yeah.

传统上，这些问题大多通过线性代数或其他形式化方法解决。

So traditionally, most of these were solved with linear algebra or some other formal approach.

同样，你基本上试图预测或优化，然后将问题理想化，以抽象的方式解决它，然后你就完成了。

And there again, you basically try to predict or optimize, and then you idealize the problem, and in an abstract way, you solve it, and supposedly you're it.

你完成了。

You're done.

这些问题的特点是它们在不断变化。

The thing with these problems is that they're changing constantly.

技术术语叫做非平稳，意思是问题本身在变化。

The technical term is non stationary, and it's basically that the problem itself is changing.

所以，如果你以为自己找到了解决方案，一旦实施，它就已经过时了。

So if you thought you had a solution, the moment you implement it is already obsolete.

这些问题之所以是非平稳的，正是由于复杂性。

And they are non stationary precisely because of complexity.

如果你有大量元素相互作用并生成新信息，而这些信息又改变了问题，那么你的解决方案就必须与问题变化的速度同步调整。

If you have lots of elements interacting and they generate new information, that information changes the problem, then your solution needs to adapt at the same time scales at which the problems change.

因此，在许多情况下，处理这些问题的工程师更倾向于忽视这些事实，只是试图勉强应对，并留出一些余地。

So it seems that in many cases, engineers or engineers that deal with these problems prefer to ignore these facts and then just try to, say, more or less cope with it and leave some margins.

但当你采取另一种方法，不再试图预测那些你明知会变化且无法真正预测的事物，而是转向适应性策略时，你就能实现更好的性能，有时甚至达到最优，有时还会超越最优。

But then when you take a different approach and instead of trying to predict something that you know will change and that you cannot really predict, you shift your approach to adaptation, then you can achieve much better performance, in some cases optimal, in some cases even beyond optimal.

所以这篇论文是关于公共交通系统的超最优性，也许我没发给你，但没有。

So that's this paper for public transportation systems about supra optimality, We Perhaps I didn't send it to you, but No.

但我确实看过，是的。

But I did see it Yeah.

在谷歌学术的页面上。

On the Google Scholar page.

然后，同样有趣的是，我们刚刚在通讯办公室讨论过危机管控，以及何时需要立即响应、何时不需要，当时我们提到了丹尼尔·卡尼曼。

And then, again So it's funny because actually, this was a conversation we were just having in the comms office about crisis control and when things We're cited Daniel Kahneman, and when things require an immediate response versus when they don't.

当我第一次邀请拉吉夫·塞蒂做客节目时，我们讨论了在一个变化极快的世界中，这些类别容易被混淆的问题。

And when I had the first time I had Rajiv Sethi on the show, and we were talking about the confusion of those categories in a world that moves extremely fast.

对。

Yep.

他谈到了警察暴力问题，以及在缺乏足够时间了解他人的情况下，刻板印象如何悄然渗透到执法人员与公民的互动中，导致人们做出仓促判断，而人的生命却岌岌可危。

And so he was talking about the problem of police violence and the way that stereotypes sneak into these interactions between authorities and citizens when you don't have a lot of time to get to know somebody and you end up making a snap judgment and someone's life is on the line.

这些类型的体验，或者说我们用错误的时间尺度去应对问题的情境，在技术基础设施越来越快的社会中正变得越来越普遍。

It's it seems those kinds of experiences or those kinds of situations where we're we're thinking on the wrong time scale to address the issue are proliferating in a society where the technological infrastructure is going faster and faster all the time.

因此，当你再次谈到这篇论文时，你提到的是——这其实是这种现象中一个我特别喜欢的悖论：当你谈到控制论中的‘必要多样性’时，系统所面临的扰动种类越多，它为保持控制所需的应对行动种类也必须越多。

And so when you talk about, again, in in this piece, you're talking about how and this really this is one of those kind of paradoxes that's inherent to this that I love when you mention in talking about requisite variety in cybernetics, that the greater the variety of perturbations a system may be subjected to, the larger the variety of actions it needs to remain in control.

对吧？

Right?

所以这里出现了一个分岔点，你可以选择任何一种方式，或者两种都选，或者都不选。

So there's kind of a fork here, and I'm you can take it either way you want or both or neither.

对吧？

Right?

但有一篇著名的预印本论文，我记不清是谁写的了，是关于人工智能的，讲的是最优策略往往会寻求权力。

But there's that famous preprint from, I can't remember who read it, the artificial intelligence piece on how optimal policies tend to seek power.

嗯。

Mhmm.

对吧？

Right?

而智力的定义，正是关于如何应对这种不确定性，能够在不同时间尺度上做出决策，并调整到合适的多样性水平。

And, like, the definition of intelligence is about navigating that kind of uncertainty and being able to make decisions across time scales and tuning for the appropriate level of variety.

对我来说，这听起来就像一个坐在扶手椅上空谈的人。

And then to me, this sounds a lot like to just be a kind of an armchair guy about this.

你就是一个普通人。

You're a man on the street.

这听起来很像佛教老师在谈论现代生活节奏迅猛、压力巨大时，强调心智训练的重要性。

This sounds a lot like what you hear Buddhist teachers saying about the importance of mind training practices in the intensity and the pace of modern life.

奇怪的是，控制越多，反而越无执著，或者说，越能放松，允许事情自然酝酿后再做决定。

That in a weird way, there's a kind of identity between more control and more unattachment or, like, the the ability to relax and allow things to percolate before making a decision.

所以，我这是在试图切入你之前关于佛教哲学的那部分内容，但我很想知道你对此有什么见解。

So this is me trying to edge into the other piece that you've done on Buddhist philosophy, but I'd love to hear you riff So on

一般来说，在一个网络中，互动越多，就会产生越多的连锁变化，这些变化会通过网络传播开来。

in general, in a network when you have more and more interactions, that creates more and more change that propagates through the network.

所以你很快就会陷入混沌动力学。

So you end up with chaotic dynamics very quickly.

当然，这也会有负面作用。

Of course, this can have negative sides.

再次强调，我们还可以深入探讨秩序与混沌之间的平衡。

Again, there is this balance between order and chaos we could dive into.

但当然，我们往往更关注连接性增加和互动加速带来的负面影响，比如十年前电子交易在股市中引发的闪电崩盘，如今你可以在各个地方开始看到类似现象。

But of course, we tend to notice the negative effects of increasing connectivity, of accelerating interactions, and in the stock market with electronic trading, this was very clear with flash crashes a decade ago and you can start seeing it everywhere.

而当你处于混沌状态时，你能控制的东西就非常少，因为变化是持续不断的。

And of course, if you are in a chaotic regime, there's very little you can control because changing is constant.

当然，我们可以尝试运用高明的不作为，但这假设了事物会趋向最优状态，因此我们其实无需干预——确实有时会出现这种情况：你对某个情境的反应反而让它变得更糟，那么什么都不做，让事情顺其自然，反而更好。

And of course, we could try to apply masterful inactivity, but, of course, that would assume that things tend to their optimal state and then we don't really need to intervene because it does happen that sometimes, let's say, there's a situation and your response to the situation makes it worse, so it would be better just not to do anything and let things run its course.

但在大多数情况下，情况并非如此。

But in most cases, that's not the case.

但究竟该怎么做，仍然是一个开放性问题，尤其是在我们从未遇到过的全新情境中。

But, of course, what to do is an open question, especially in situations that are novel, that we've never encountered before.

当没有先例可循时，你如何做决定？

How do you make decisions when there's no previous example?

有太多事情可能出错，或者不一定是出错，而是以意想不到的方式发生。

And there are so many things that could go wrong, or not necessarily wrong, but let's say in unexpected ways.

这种趋势似乎正在增强。

And that has kind of a tendency of increasing.

当然，也有人主张放慢变化速度，以便更好地掌控它。

And, of course, there's arguments for trying to slow down the change in order to make it more manageable.

是的。

Yep.

所以关于这两者之间的平衡问题，我想简要回到《前沿》那篇文章，异质性扩展了临界性。

So this question of the balance between these two things, and I wanna go back just briefly to the frontiers piece, heterogeneity extends criticality.

因为你知道，在圣塔菲研究所经常提到的是，尽管可能性的范围极其广泛，但复杂系统实际上只占据着非常狭窄的可能性通道。

Because, you know, something that comes up a lot at SFI is the way that given the enormous breadth of the possible, what we find in complex systems is that they actually inhabit very narrow channels of possibility.

你提到克里斯·坎皮斯和杰夫·韦斯特展示过，所有生物物理的标度规律都将树木的可能形态限制在这样一个非常狭窄的范围内。

You talk about, like, the way that Chris Campus and Jeff West have shown that all like, biophysical scaling puts all of the possible forms of a tree within this sort of very narrow scope.

因此，鉴于我们可以将这一点更牢固地建立在某种更严谨的理论框架之上，我想听听你进一步谈谈临界性，结合我们在现实中看到的各种表现形式。

And so, yeah, I'd like just given that we could anchor this in a bit more of a sort of a rigorous formalization, I'd like to hear you talk a little bit more about criticality in light of all of the different ways that we see this manifesting in the world.

你在这里提供了一些非常有趣的例子，谈到了任意复杂性。

And you give some really interesting examples here talking about arbitrary complexity.

也许我们可以花更多时间深入探讨这一点。

Maybe that's something that we can spend a little more time on.

是的。

Yep.

嗯，我确实想进一步谈谈。

Well, I would like to Yeah.

我想更详细地展开一下临界性：简单来说，临界性是一种介于有序相和混沌相之间的状态，或者说介于两个相之间；但一般来说，我们可以将它们描述为有序或混沌，比如在流体动力学中，它可以是湍流与层流；在交通动力学中，它可以是自由流动相——所有车辆都以相同或期望的速度行驶，或者拥堵相——由于密度太高而必须停下。

Expand a bit on on criticality, which brought way of saying what criticality is simply a regime that's between order phase and chaotic phase, or between two phases, but in general we could describe them as chaotic or order, but it could be also turbulent and laminar flow in fluid dynamics, or in traffic dynamics it could be free flow phase where all cars are driving at the same speed or at the desired speed, and jammed phase where you have to stop because of high density.

因此，正是这种标度现象、幂律分布以及许多其他现象，都在临界性附近被发现。

So precisely this scaling phenomenon, power loss and many things have been found close to criticality.

你也可以将临界性看作一种平衡，而这是一种理想的平衡，因为它能同时获得两个相的优势。

And you can see criticality also as a balance, and it's a desirable balance because then you get the benefits of both phases.

当然，在进化过程中，我们观察到许多系统都处于临界点附近，正是因为它们能从这种平衡中获益。

Of course, throughout evolution, we observe that many systems are poised near criticality precisely because they benefit from finding this balance.

正如你所提到的，这在所有可能的参数空间中只是一个非常狭窄的区域。

And as you mentioned, this is like a very narrow region from all possible parameter spaces.

对此，我想稍作澄清，然后回到主题。

And there, I would like to make a small deterrent then to return.

是的，是的。

Yeah, yeah.

如果我们只看所有可能的化学反应，其中绝大多数在我们的宇宙中永远不会发生。

If we just look at chemical reactions from all possible chemical reactions, most of them will never take place in our universe.

可能性实在太多了，根本不可能全部发生。

There just so many that there is no possibility that they will happen.

但我们依然拥有如此丰富的化学反应，最终催生了生命；同样，所有可能的生物相互作用、社会相互作用、创意构想也永远不会全部实现。

And still we have such a rich chemistry that led to biology and the same, all possible biological interactions will never take place, all possible social interactions will never take place, all possible ideas will never materialize.

但即便如此，那个在宇宙历史中哪怕只被实现过一次的、微乎其微的可能区域，也必须在某种程度上是可行的，哪怕只是短暂的一刻。

But still, that minimum infinitesimal region of the space of the possible that actually gets instantiated even just once in the history of a universe needs to be viable in some way, even for a short moment.

而这决定了哪些会生存下来，哪些会持续存在，哪些会演化。

And, of course, that constraints which ones will survive or which ones will endure, which ones will evolve.

因此，回到临界态的问题，传统复杂系统模型往往是同质的，因为将模型中的所有元素以相同方式处理更为简便，比如细胞自动机。

So returning into criticality, traditional modes of complex systems were homogenous because simply it's convenient to treat all the elements of a model in in the same way, like in a cellular automaton.

因此你可以看到，它们都遵循相同的规则、相同的时间步长，可能具有相同的初始状态，或只是随机状态，然后观察会发生什么。

So you can see they're all all with the same rule, all with the same time, all with the same initial states perhaps, or just with random states, and then you observe what happens.

但当我们观察真实现象时，就会发现多样性以及各种异质性；而当我们把这些异质性纳入模型后，我们意识到，原本需要精细调节参数才能达到的临界态特性，现在在更宽泛的参数范围内就能出现——异质性越强，这种特性越明显。

But then what we began seeing, of course, you look at the real phenomenon, you diversity, different types of heterogeneity, but then when you include that heterogeneity in your models, we realize that the properties that we usually found in criticality that was difficult to get because you need to fine tune the parameter to reach this phase transition, These same properties you would find for a broader region of the parameter space, the more criticality you add.

这同样适用于结构临界性，即网络拓扑结构，这一点在网络科学中已被深入研究；同时也适用于不同时间点上数据元素的时间异质性。

And this applies for structural criticality, which basically the network topology, that has been studied thoroughly in network science, but then also the temporal heterogeneity of different elements of data at different times.

事实上，这种现象早在20世纪60年代的物理学中就已经被发现了。

It turns out that this was known since the sixties in physics.

它们被称为格里菲斯相，但似乎这是一个被保守的秘密，至少我认识的人中没人了解它们。

They're called Griffith's phases, but it seems it's a well kept secret, at least nobody I know about them.

而我们发现，如果在函数层面引入异质性，也会产生类似的效果。

And what we're finding is that also if you introduce heterogeneity in the functions, that also has a similar effect.

不仅如此，如果将它们结合起来，还会产生叠加效应。

Not only that, but if you combine them, then they have an additive effect.

因此，当你引入这些异质性时，就使得进化或任何其他过程更容易具备因临界性而 desirable 的特性。

So, basically, when you include these heterogeneities, you make it easy for evolution or whatever process to have these properties that are desirable because of criticality.

所以搜索也就变得更加容易了。

So then search becomes even easier.

所以节目中经常会出现一个问题，我喜欢从多个角度来深入探讨它。

So there's a question that comes up on the show a lot, and I love interrogating this from as many angles as possible.

我们在这里，已经稍微提到了这一点，那就是我将和杰夫·韦斯特以及曼弗雷德·劳布利亨讨论这个话题。

And we're here, and we've already kind of teased it, which is And I'm gonna talk about this with Jeff West and Manfred Laublighen.

我邀请他们上节目，是因为曼弗雷德一直在撰写关于人类世的内容——这个地质历史时期以人类活动的主导及其在地质记录中的体现为特征。

I have them on the show because Manfred's been writing about the Anthropocene, this era of geological history on this planet defined by the predominance and geological record of human activity.

是的。

Yes.

这是一个前所未有的时代。

This is an unprecedented time.

也许你可以把20亿年前现代大气的形成看作是一种前兆。

Maybe you can look at the formation of a modern atmosphere 2,000,000,000 ago as a kind of a precursor.

不。

No.

但你正处于这样一个时期：正如你所描述的，网络拓扑结构使得地球上这些短暂易逝的生命体，能够在一个极长的时间尺度上，调整如此多不同事物的结果，而我们自己却并没有真正适应——。

But you have this time where it's the network topology, as you're talking about it, is one where suddenly, these ephemeral fleeting creatures on the surface of the planet are capable of adjusting the outcomes of so many different things over such a large time scale, and yet we're not actually we're not actually adapting at Yeah.

这些时间尺度。

Those timescales.

我们并没有真正适应，所以你面临一种奇特的情况：比如，一位亿万富翁决定要创办一个新产业，然后开始开采所有稀土矿物，铺设数以吨计的混凝土，用火箭燃料改变大气——这一切都与我之前和杜恩·法默讨论的问题相关，杜恩在市场生态学中的研究曾提到，你刚才提到了闪崩。

We're not actually so you've got these this weird situation where you can you have a, like, a multibillionaire who decides that they wanna launch a new industry, and then, you know, they're gonna be and then pulling up all the rare earth minerals and, like, laying out tons of concrete and changing the atmosphere with rocket fuels, like all and so this is a very this is related to the problem that I talked about with Duane Farmer, where Duane's work in market ecology showed that you mentioned flash crashes a moment ago.

杜恩的研究表明，像Robinhood这样允许散户免费交易的系统，会导致巨大的市场不稳定。

And Duane showed that systems like Robinhood, which allow for fee free trading by retail investors, lead to enormous market instability.

因此，一方面，这对人们来说是一种机遇。

And so on the one hand, it's an opportunity for people.

但另一方面，也可以说，受这套系统伤害的人比受益的人更多。

And on the other hand, it's like arguable that more people are getting wrecked by this system than are benefiting from it.

所以，是的，我总觉得我又回到了这个问题上，但当谈到‘慢即是快’时，关于复杂经济学的一个关键问题经常出现：我们该如何放慢速度？

And so, yeah, I feel like I just keep going back to this, but, like, when it comes to when slower is faster, one of the big questions seems to come up a lot with respect to complexity economics is how do we slow down?

对吧？

Right?

我们该如何降低这个系统的‘温度’，以便能够在我们有能力做决策的时间尺度上有效整合信息？

Like, how do we lower the temperature of this system enough that we can actually aggregate information at the time scale where we're capable of making decisions?

是的。

And Yeah.

市场激励机制实际上如何实现这一点？

And how do market incentives actually achieve this?

我们如何在实践中更贴近这一点？

How can we bring this closer in practice?

我很想知道你对这一切的看法。

And I'm curious what your thoughts are on all of it.

有两种方式。

There are two ways.

所谓‘慢即是快’的问题在于，一旦你超越了相变点，你的性能就会下降。

The problem of, let's say, when slower is faster is that if you go beyond the phase transition, then your performance decreases.

因此，你希望最大化你的性能。

So you want to maximize your performance.

而实现这一点有两种方式。

And there are two ways of achieving that.

一种当然是放慢速度，另一种是改变系统，使其能够以你希望的任何速度运行。

One, of course, it's slowing down, but another is changing the system so it can go as fast as you want it to go.

但这样一来，自然会增强追求更快的动机，于是你又面临同样的问题。

But then, of course, that increases the incentive of going even faster, and then you have the same problem again.

而且，你提到的许多问题，可以说正在破坏我们的星球、经济和生活方式。

And also, many of the issues you mentioned, it could be argued that it's unbalancing our planet, economy, our way of life.

但另一方面，确实这些变化会使系统偏离其自然状态或理想状态，但最终，不稳定的配置是不可持续的。

But on the other hand, it is true that these changes kind of drive systems out of their natural states or preferred states, but at the end, unstable configurations are not sustainable.

迟早，它们会形成一个新的平衡状态。

Sooner or later, they will create a new balanced state.

就像大氧化事件改变了大气，实际上为更复杂、更丰富的生命创造了生态位。

Just like the Great Oxidation Event transformed the atmosphere, actually created a niche for more complex and richer life.

当然，在我们有生之年，问题是：我们能预防多少痛苦？

The question, of course, within our lifetimes is, okay, how much suffering can we prevent?

我们能避免多少损害？

How much damage can we avoid?

在许多情况下，即使我们已经拥有足够的信息或工具，能够预见到某种轨迹的后果，我们仍往往等到为时已晚才行动，而即使为时已晚，我们可能也只是说：唉，真糟糕。

And in many cases, even when we have enough information or enough tools to be able to say, Okay, this will be the consequences of this trajectory, it's like we won't do anything until it's too late, and even after it's too late, probably we will just say, oh, too bad.

太晚了。

It's too late.

我们什么都做不了了。

Then we cannot do anything.

关于疫情，我在想，也许它会带来一个额外的好处，就是让我们能更好地为应对全球性挑战做好准备。

So with the pandemic, I was thinking, well, maybe it will have the added benefit that we'll be able to be better prepared to kind of face global challenges.

但我认为它表明我们缺乏应对全球性挑战的能力。

But I think it showed that we are incapable of dealing with global challenges.