293 | 多因·法默谈混沌、崩盘与经济复杂性

大家好，欢迎收听《心智景观》播客。我是主持人肖恩·卡罗尔。通常在节目开场时，我会先提出一个宏观问题，逐步聚焦，最后介绍本期对谈嘉宾。我们始终秉持'观点优先，讲者次之'的原则。今天这期节目也不例外，不过我要倒叙开场——因为我们请到了一位非常特别的嘉宾：多恩·法默。

Hello, everyone, and welcome to the Mindscape Podcast. I'm your host, Sean Carroll. Usually in these intros to the episodes, I will start with some big picture kind of question, narrow it down a little bit, and then eventually introduce the speaker who we're going to talk to during the course of the episode. It's ideas first and speaker second. And the episode that we have here today is also that, but let me do the intro in the opposite direction because we have a very special speaker here today, Doan Farmer.

他的名字发音类似'多恩'。他说就像人名'Owen'前面加个D，尽管拼写是D-O-Y-N-E。这显然是爱尔兰与美国东南部发音的混合体。他在1980年代因《尤德摩尼之饼》一书声名鹊起——在座年长的听众可能还记得这本书。

His name is pronounced Doan. He says it's like the word the name Owen, but with a D in front of it, even though it's spelled like D O Y N E. Apparently, some amalgamation of Irish and Southeastern US pronunciations. But he came to fame in some sense back in the 1980s, when a book was written called The Eudemonic Pie. Those of you who are of a certain age will remember this book.

这本书讲述了多恩和研究生院伙伴们在1970年代横扫拉斯维加斯的故事。你们都听过各种赌场博弈传说，有人靠算牌，有人靠高科技设备。而多恩团队是首创者——至少在高科技领域是开山鼻祖。多恩亲自编程了我们如今称为'可穿戴数字计算机'的设备，堪称史上首例，它能藏在鞋子里不被发现。

It was about something that Don and his friends from graduate school pulled off in the nineteen seventies to win in Vegas. You've all heard of different stories of people trying to beat the house, beat the casinos in Las Vegas, either through counting cards or through high-tech apparatuses. And, Don and his friends, they were first. They did it first, at least the first in the, high-tech world. Dohen himself programmed, what we would now call a wearable digital computer, arguably the first of its kind that fit into a shoe so that you couldn't see it.

他们玩的不是二十一点而是轮盘赌，运用了一点物理学原理。玩过轮盘的人知道，荷官抛出小球后，轮盘会旋转多圈，小球也会持续滚动一段时间。

And they were not counting cards or anything like that. They were playing roulette. They were using a little bit of physics. And if you're a roulette player, you know that the roulette cupier throws the ball around the roulette wheel and it spins several times. It spends some time spinning.

理论上你可以测算小球速度与轮盘转速，虽然无法次次精准，但至少能获得高于平均值的预测准确率。他们通过这套机制显著提高了胜率，也确实赢了不少——不过坦白说，最终赢得的金额并不算特别巨大。

So you could actually, in principle, time how fast that ball is moving and time the motion of the wheel and at least probabilistically, even though you wouldn't get it exactly right every time, have a better than average chance of or better than typical chance, I should say, of getting the right answer to where the ball was going to fall. And they figured out that through their mechanism, they could get a substantial increase in the odds of winning. And in fact, they did win. The winnings were not very large. I have to I have to confess to that.

硬件故障和软件问题接踵而至，有段时间他们甚至担心赌场会把他们拖进暗巷痛揍。但这个原理验证开启了他的事业：他重返物理研究生院研究混沌理论——轮盘赌运用的是经典牛顿力学，而混沌理论正是当时的新兴领域。

Hardware problems, software problems kept coming in the way, and at some point, they got a they got worried that the casinos were gonna, you know, throw them in a dark alley and beat them up or something like that. But proof of principle was there. And it launched Don on a career. He went back to grad school in physics, actually, started thinking about chaos theory. You know, it was sort of straightforward Newtonian mechanics for the roulette wheel, but chaos theory was the new thing at the time.

作为'混沌与动力系统联盟'创始成员，他为混沌理论奠定了物理基础。后来他越发专注于如何运用混沌理论预测未来——毕竟世界充满混乱复杂的变量。他创立公司征战金融市场，再次取得了'高于平均水平'的佳绩。后来与圣塔菲研究所及复杂性科学结缘，几乎可说是必然的归宿。

He was a founding member of the Chaos and Dynamical Systems Collective, which helped really put the physics of chaos theory on a firm footing. And he then became more and more interested in thinking about how to apply insights from chaos theory to predicting the future because the world is a messy place and there's all sorts of chaotic, complex things going on. He founded a company to play the financial markets and did very, well at that. Better than average, let's put it that way once again. Of course, it almost is inevitable that he became involved with the Santa Fe Institute and the sciences of complexity.

鉴于他先前的兴趣，董辉所关注的那种复杂性特指复杂经济学。现在我们终于切入今天播客的主题。董即将出版一本新书《理解更美好的经济学，创造更美好的世界》。正如我们与其他嘉宾讨论时他所主张的——与传统经济模型相比，用复杂系统科学家的思维方式来思考经济问题要合适得多。我之前采访过许多经济学家和复杂理论学者，经过这次对话后，我更加理解这个论断为何成立，为何说用复杂系统科学家的思维模式对理解经济现象特别有帮助。

And given his previous interest, the particular kind of complexity that Donghue became interested in is complexity economics. So now we finally get to the topic of today's podcast. Don has a new book coming out called Making Sense of A Better Economics for a Better World. And he's arguing, as we've talked to other speakers about, that thinking like a complex systems scientist is just so much more suited to thinking about the economy than traditional economic models. And I've talked to a bunch of economists, a bunch of complexity theorists before, I think that after this conversation, I have a much better understanding of why that statement is true, of why thinking like a complex systems scientist is very very helpful in understanding the economy in particular.

这绝非象牙塔里的空想。英国政府曾邀请董及其牛津大学研究团队的成员协助评估新冠大流行的经济影响。他们运用小型复杂理论模型进行推演，结果再次证明该方法非常有效，尤其与传统模型相比。可以说在复杂系统科学领域，我们正站在真正突破的门槛上。

And this is not just ivory tower theorizing. The British government asked Doan and his collaborators in his group at Oxford University to help understand what the economic impact of the COVID pandemic would be. And they did their little complexity theory models. And again, it worked really well, especially compared to traditional models. So, know, in as in many ways, in the world of complex systems science, I think that we're on the cusp of really getting better at this.

我认为重大突破已近在眼前。这是场科学革命，我们正处在范式转换的进程中——通过以复杂系统思考者的视角，不仅审视具体现象，更进行全局思考，世界各类难题都将迎来新的曙光。与斯通·法默的这次对话更让我确信这种转变即将发生。那么让我们开始吧。

I think that we have breakthroughs right ahead of us. This is revolutionary science. This is the middle of a paradigm shift where all sorts of problems in the world are going to have light shed on them by thinking about not just the specific phenomenon under consideration, but more broadly as a complex systems thinker. And I think that, this conversation with Stone Farmer helps convince us that that's what's gonna be happening. So let's go.

董·法默，欢迎来到《心智景观》播客。

Don Farmer, welcome to the Mindscape Podcast.

谢谢，很高兴来做客。

Welcome. I'm very happy to be here.

你在新书中探讨复杂经济学。我要提醒各位听众，不妨回顾你之前那些精彩有趣的学术探索。但今天我们聚焦复杂经济学，这让我不禁要问：是否存在简单经济学？难道经济学从诞生之初不就充满复杂性吗？

So you're writing about complexity economics in your latest book. I will encourage all the readers, all the listeners rather to, go back and and check out all of your previous adventures, which are colorful and fun. But today, we're gonna think about complexity economics, which leads me to ask, is there simplicity economics? Isn't all of economics pretty complex right off the bat?

确实，我有时担心这个术语不够准确。使用这个名称是为了表明它源自复杂系统科学，我们采用的方法论甚至科学认识论都脱胎于复杂系统——尽管经济学家总喜欢援引亚当·斯密等人的理论，但我们的研究路径与他们截然不同。我认为我们的方式更鲜明地体现了复杂系统思维的特质。

Yeah, well, I sometimes worry it's not the best term. The term is there to indicate that it's coming from the science of complex systems and that we're using methods and maybe even a scientific philosophy of epistemology that's coming out of complex systems, which, although, you know, economists like to point to Adam Smith, and so on, it's very different than the way they do things. And I think it's much more explicitly complex systems than the way they do things.

好的。当然，现实中存在许多复杂性。或许我们可以这样提问：传统的非复杂性经济学是否通过过度简化事物而取得任何进展？换句话说，你们是否通过认真对待复杂性，在我们已知的基础上有所补充？

Great. So of course, there is a lot of complexity out there. I mean, maybe let's ask it the question this way. Can conventional non complexity economics get anywhere by oversimplifying things? So are you, in other words, adding to something that we already know by taking the complexity seriously?

嗯，既是也不是。首先，我竭力对经济学家保持友善——希望他们能体会到这点。所以我刻意在书中尽可能减少对经济学的批评，多位编辑协助我做到了这一点。

Well, yes and no. That is, you know, first of all, I'm betting over backwards to be nice to the economists. I hope I hope they appreciate that. And so I, you know, intentionally took as much criticism of economics as I possibly could out of the book. I had several editors help me do that.

我认为传统经济学在某些方面确实表现不错。在需要理解战略互动或战略思维起重要作用的简单情境中，它能很好发挥作用。但当情况变得更复杂、需要加入更多制度结构，或个体无法理性分析局势时，它就会失效。这时我们不得不依赖启发式和更简单的推理。

Now I think there are some things that conventional economics is pretty good at. In a simple situation where you need to understand strategic interaction, and that plays an important role, or strategic thinking plays a role. But in a simple context where you can understand, then I think it can work pretty well. I think where it where it fails is when things get more complicated, when you need to put in more, institutional structure, or when individual agents can't reason well about what's going on. And so you really have to fall back to heuristics and more simple reasoning.

或许需要稍微展开说明第一点，因为这是核心问题。我在书中引用经济学家对问题的阐述时指出——可能需要先说明主流经济学的工作原理及两种方法的差异，这样会更清晰。主流经济学的简化版本是：先为所有决策者分配效用函数（即偏好评分表），然后赋予他们某种理解世界的推理方式。

And maybe to amplify a little bit on that first point, because I think it's a central one. And, in my book, I quote economists, laying the problem out. And the problem is and and maybe we need to digress to how mainstream economics works and what the difference between the two approaches are, and then I think this will become more apparent. But in mainstream economics, in the capsule version, is that you begin by assigning all the agents, all the decision makers, utility functions, scorecards that say what they like better and what they don't like as much. And then you give them some way of reasoning about the world.

传统上这是理性预期，意味着决策者像《星际迷航》中的史波克先生——能理性分析一切，逻辑严密，处理所有信息并得出正确结论。在此基础上还假设均衡状态，标准经济模型中即供需平衡。但在博弈论等战略情境中，这意味着我们都采取了在他人策略不变前提下对自己最有利的策略。

Traditionally, that's rational expectations, meaning they're like Mr. Spock in Star Trek. They can reason about everything, and they're very logical, and they can process all the information and arrive at the correct conclusions. And so you you give them those things, and then you and you furthermore assume equilibrium, which in a standard economic model means supply equals demand. But sometimes you're in a strategic setting where it means you're you're doing something like game theory, where it's a strategic equilibrium, meaning we've all arrived at, strategies that make decisions that are as good as we can do, given that everybody else is not changing what they're doing, and everybody does that.

这就是标准做法：理性预期。最后把这些都写成方程式，求解经济学家所谓的一阶条件——也就是令导数等于零。

So that'd be the standard thing, rational expectations. Now and then just to finish, you you write all that down in equations. You solve what economists call the first order conditions, meaning, you know, you set the derivative to zero.

嗯。

Mhmm.

然后你计算那些能为所有主体带来最大效用的决策，并评估这些决策的经济后果。而在复杂性经济学中，我们的做法完全不同。这实际上摒弃了自十九世纪以来经济学中的传统内容。我们主张：假设存在若干行为主体，赋予他们一些决策方式——这些方式可能非常简单或较为复杂，但我们并不预设最优性。

And and you compute the the decisions that maximize utility for all the agents, and then you calculate the economic consequences of those decisions. Now so in complexity economics, we do things completely differently. So it's really throwing out stuff that's been in economics since the nineteenth century. And we say, well, let's assume we have some agents. Let's give them some ways of making decisions that could be very simple or more complicated, but they're not we're not assuming optimality.

所以

So

信息不断流入，主体们运用各自的规则做出决策——这些规则可能是学习算法，也可能是简单的启发式方法，比如买入被低估的资产，或是模仿最优者——观察周围表现最佳者并效仿，也可能是试错法。

so information flows in. The agents use their rules to make decisions, which might be learning algorithms, or they might just be simple heuristics like buy undervalued assets or imitate, the best. Look around and see who's doing the best. Imitate them. Or it might be trial and error.

尝试某种方法，若有效就持续采用，无效则换其他方式。都是些简单逻辑。他们就这样做出决策。

Try something. If it works, keep doing it. Doesn't work, try something else. Simple stuff. And so they make their decisions.

接着我们计算这些决策的经济影响，由此产生新信息。同时外部也可能输入新信息，然后我们重复这个过程，如此循环往复。最终可能达到供需平衡或主体决策固化的均衡状态。

We then calculate the economic consequences of the decisions. That generates new information. In addition, new information may flow in from the outside, and then we repeat the process. And we just go around and around that loop. Now we may arrive at an equilibrium where supply equals demand or agents' decisions get locked in.

若出现这种情况，我们复杂系统科学家会将其视为涌现特性。当然也可能不会出现——事实上往往不会。我认为这正是该理论框架的重要优势：我们能更自然地捕捉动态变化，特别是内生动态——即系统内部产生的动态。

If so, that's what we would regard as complex system scientists as an emergent property. Yeah. Or we might not. And actually, oftentimes, we don't. And I think that's one of the important strengths of this formalism, that we we more naturally capture dynamics and endogenous dynamics, that is dynamics that arises from within.

以商业周期为例，拿2008年所谓'大金融危机'来说，很明显这是内生性危机。并非流星撞击地球或人类突然改变所致，而是因为我们引入了新型金融工具（抵押贷款证券），房地产市场过度膨胀后崩盘——所有这些都源自经济体系内部。在主流模型中，这类情况根本无法再现。

Things like business cycles, where if you take, say, the, financial crisis of two thousand eight, the so called great financial crisis, I think it's pretty clear it's an endogenous crisis. It wasn't like a meteor hit the earth and that caused the crisis or that people suddenly changed. It was that we introduced new types of financial instruments, mortgage backed securities, housing market got overpriced, it crashed. All these things happened from within the economy. In a mainstream model, you can't get that to happen.

因此，你无法仅凭内生动力就获得动态变化，必须将经济推向极端，必须做出看似不合理的假设才能实现。实际上有个'收费公路定理'指出：除非满足某些条件（比如极度短视的决策等），否则系统只会收敛到固定点。但在正常合理条件下，这就像一条收费公路——你顺着路望去就能预见走向，可以提前做好修正，因此不会出现剧烈转向。在书中我展示了多个案例，说明行为偏差、有限理性和人们难免会犯的错误如何导致内生动态变化。

And so you just don't get you can get endogenous dynamics, but you have to push the economy into extreme you have to make what seem like unreasonable assumptions in order to get there. There's actually something called a turnpike theorem that says things are just gonna settle into a fixed point unless certain conditions are satisfied, like very myopic reasoning, etcetera. But but if under normal reasonable conditions, you know, it's like a turnpike. You look down the road, you see where things are going, you make corrections as needed because you can see everything well ahead, and so you're not, steering wildly. Now in the book, I make the well, I show several examples where, behavioral errors, bounded rationality, making mistakes we should expect people should make, lead to endogenous dynamics.

我用一个比喻：经济更像醉汉在山路上开车，不断偏离车道，无法始终按规矩行驶。再补充一点——这让我回到最初离题的原因。经济学家们应该都认同：当计算每个主体的最优策略时，你其实是在演绎这些策略。

And, you know, the analogy I make is that the economy is more like a drunk driver on a mountain road, you know, swerving and not quite always doing what he or she is supposed to do. So maybe one more thing. Sure. So it gets me back to what it was what caused the whole digression. The part that economists, I think, will all agree on is when you're computing optimal strategies for each agent, you're deducing those strategies.

你无法把事情搞得太复杂。一旦系统非线性化，一旦主体超过十几个，方程就无解了。所以必须简化模型，这是被迫的选择。你还在用方程描述一切。

You can't make things very complicated. Once the system gets nonlinear, once you have more than a dozen agents, you can't solve the equations anymore. And so you have to keep the model simple. You're just forced to do that. You're also writing down equations.

在基于主体的模型中这些限制都不存在。我们运行过数百万主体同时决策的模拟，有充足空间纳入制度结构、异质性和现实世界要素。

You have to write down everything in equations. In an agent based model, those constraints don't exist. We have models we've run simulations with millions of agents making decisions. And and there's just a lot more room to put in institutional structure, heterogeneity, real world stuff.

我相信多数听众已经明白了——外生性指外部输入的影响，内生性则是系统内部的动态变化。

I'm sure that most audience members have figured this out. So exogenous is some influence coming in from outside. Endogenous is the dynamics within the system.

是的，非常感谢。完全正确。

Yes. Thank you. Definitely.

对。你是说古典经济理论如果只考虑内生影响，由于它寻求均衡点，理论上系统应该永不随时间改变？虽然能处理外生冲击（比如陨石撞击），但它强烈预测系统终将稳定。是这样吗？

Yeah. And you're saying that classical economic theory, if you restrict yourself to the endogenous influences, because it's looking for equilibrium points, nothing should ever change with time. Right? I mean, it can deal maybe with exogenous things, the meteor hitting, but it predicts pretty strongly that things settle down. Is that right?

或者说

Or is

这是否有些夸张了？

that an exaggeration?

听着，让我在这里打个星号。确实存在特殊情况——那些会产生动态变化的特例，但它们终究是特例。如果你观察美联储或任何实际运用模型做经济决策的人所采用的核心模型，他们实际上仍在沿用理性预期理论。这些模型在没有外部刺激时会趋于稳定状态。

Look. Let me just put an asterisk by this. There are special cases where that doesn't where you get dynamics, but they're really special cases. If you look at the workhorse models used by the Federal Reserve or any of the people who are actually making using models to make decisions about the economy, they're still actually using rational expectations. And there's and those models settle into fixed points absent external stimuli.

但存在商业周期理论不是吗？

But there are business cycle theories. No?

嗯，既是也不是。它们甚至有诸如'真实商业周期'这样的名称。但当你深入研究时，会发现除非受到外力冲击，否则这些模型并不会自发产生商业周期。所以我认为这个名称存在误导性。

Yeah. Yes and no. They even have names like real business cycles. But when you look at them, they don't make business cycles unless they're getting kicked. So I find the name a misnomer.

明白了。要知道

Got it. You know

这些并非内生性商业周期。它们是

They're not endogenous business cycles. They're

啊，好的。好的。这非常...我是说，这让我很着迷。我们之前在播客里讨论过复杂性和复杂性经济学，但我之前从未真正理解偏离均衡状态的核心意义。

Ah, okay. Okay. That's very I mean, it's fascinating to me. I never I I we talked on the podcast about complexity and complexity economics before. The centrality of departures from equilibrium hadn't quite sunk into me before.

这让我想起过去二十年物理学的发展。当然我们有统计力学和热力学，所有经典理论都围绕均衡状态展开，你知道，系统会很快稳定下来。虽然有人尝试过非均衡统计力学，但直到最近几十年人们才开始认真对待这些动态过程，讨论波动、罕见事件、肥尾现象等。所以这是否可以理解为在问题中引入了时间尺度？我的意思是，系统最终可能会达到均衡，但可能需要很长时间。

And it reminds me of things going on in physics over the past twenty years, because of course we have statistical mechanics and thermodynamics, and all the classical theory is about equilibria, and you know, you settle down fairly quickly. There have been some attempts to do non equilibrium STATMEC, but it's really only in the past couple decades that people have taken those dynamical processes seriously and talked about fluctuations and, you know, unlikely events and and and fat tails and things like that. And so is it could I think of it as adding time scales into the problem? I mean, maybe I reach equilibrium, but maybe it just takes me a long time.

是的，这是其中一个方面。但与物理学不同的是，你提到的研究是基于经典理论发展而来的。使用的方法与传统均衡统计力学相似。

Yeah. Well, that's one of the things. But but unlike in in physics where, you know, the work you're talking about builds out of the classical work. Yeah. The methods used are similar to those used in traditional equilibrium statistical mechanics.

而在这里，我们完全抛弃了旧有框架重新开始。这正是学术界内部产生分歧的原因，也是主流经济学家强烈反对这种研究方法的原因。

Here, we throw the whole thing out and start over. And that's actually the source of the tension within the academic community and why this of this way of doing things is so strongly opposed by the mainstream academic economists.

这对我很有帮助。2008年金融危机显然是经济学家们反复讨论的话题，我一直无法确定是大家一致认为预测失败（甚至预警失败），还是不清楚到底是具体模型的问题还是整个方法论的问题。而你似乎认为整个方法论都存在缺陷。

This is very helpful to me because the two thousand and eight financial crisis is obviously something that many economists have talked about to death, and I could never quite decide whether the everyone agrees that people did a bad job of predicting it, you know, anticipating it even. But it was unclear whether or not it was the specific models being used were inadequate or the whole approach was inadequate. And you seem to be coming down on the very approach was inadequate.

是的。我尽量对经济学家们保持善意，他们当时已经尽力了。但确实有两个关键问题：首先我认为2008年危机本质上是一个非均衡事件。

Yeah. Again, I'm trying to be nice to the economists. They were doing the best they could. But, you know, yeah, I think there were two key things that happened. One is, I think, the two thousand eight crisis was substantially a nonequilibrium event.

事情失控了。我们并不处于原有的均衡状态。为什么？因为我们当时在操作自己并不真正理解的金融工具，它们产生的副作用我们完全没意识到。当然也有少数人像罗伯特·席勒那样预见到了房地产泡沫会破裂。

Things got out of whack. We weren't sitting at the equilibrium we've been at. Why? Because we were dealing with, financial instruments we didn't properly understand, and they had side effects that we really didn't understand. I mean, there were a few people like Robert Chiller who anticipated the housing bubble, that the housing bubble would pop.

但几乎没人预料到的是，对实体经济的副作用会如此巨大。这是因为抵押贷款支持证券几乎被全球所有机构持有。外国机构持有美国抵押贷款支持证券，因为它们看似是热门的新事物，投资回报高、风险低。欢呼吧，欢呼吧。但他们没意识到的是，当房地产泡沫破裂时，所有这些抵押贷款支持证券的估值暴跌，导致持有它们的金融机构陷入困境，无法继续放贷。这意味着实体经济中需要资金建设新建筑、开展正常经济活动的企业借不到钱，实体经济因此遭受重创。

But almost nobody anticipated was how enormous the side effects on the real economy would be. And that was because mortgage backed securities, almost everybody globally was holding them. Foreign institutions were holding US mortgage backed securities because they seemed like they were the hot new thing, great investment, low risk, high return. Hooray, hooray. But what they didn't realize is when the housing bubble popped, that meant that all those mortgage backed securities got enormously, their their valuations dropped very low, which meant that all these financial institutions holding them were stressed, which meant that they were not in shape to lend money anymore, which meant nobody was lending money to the businesses in the real economy that needed it to, you know, construct new buildings and do all the things that we do in the real economy, and the real economy got whacked really hard.

所以我认为这完全是一场内生驱动的非均衡事件。

So I would argue it was all an out of equilibrium event, endogenously driven.

我注意到一件事——我想是在你书里看到的表格——如果查看历史上股市出现较大幅度波动的日期，会发现几乎全是下跌。涨跌波动并非对称分布。你觉得这个现象合理吗？

One thing that I noticed, I I think I saw this table in your book. If you look at the historical dates when let's say the stock market changed by a relatively large fraction, one thing I can't help but notice is they're almost all downward. It's not an equal distribution of fluctuations upward and downward. Is that something that makes sense to you?

哦，这完全合理。确实如此。作为物理学家你会理解这一点，因为它表明经济似乎不具有时间可逆性。

Oh, that totally makes sense. It's true. As a physicist, you will appreciate it because it says that the economy is doesn't seem to be time reversible.

正是如此。

Exactly.

观察时间序列时，仅凭数据我就能判断时间流向。当然另一个重要现象是经济总体呈增长趋势——虽然也可能收缩，但你知道，自独立战争以来美国经济多数时间保持年均2%的增长。所以这是另一种时间不对称性。不过没错，市场下跌确实比上涨更容易发生。

If I look at time series, just from looking at the time series, I can tell which way time is flowing. Now, of course, the other significant thing that happens is the economy tends to grow. It's not that it can't shrink, but, you know, The US economy has been going up at 2% per year on average most of the time since, the revolutionary war. So, so that's another time asymmetry. But, yeah, markets go down easier than they go up.

是否存在一个简单的...我猜可能是类似相变的过程？比如经济总体增长，但在增长过程中也在探索新形态，突然发现一个更低能量的最小值，于是暴跌，然后重新开始增长？

Is there a simple I mean, I I can guess at a sort of phase transition kind of thing. Like, the the economy generally grows, but as it grows, it's also exploring new configurations, and suddenly, you know, it finds a lower energy minimum, and it sort of crashes down and then starts growing again.

对，对。既然我们讨论内生性与外生性的区别，我必须指出当冲击来自外部时，非均衡模型特别有用。新冠疫情就是个典型案例——那显然是外部冲击。

Right. Right. You know, since we're talking about the endogenous exogenous distinction, I should say disequilibrium models are also really useful when the shocks come from outside. And the good example of that would be COVID. Now that was clearly outside.

对吧？从经济学角度看，病毒突然导致人们无法工作就是典型的外部冲击。但这个冲击来得异常迅猛剧烈，我们在疫情初期就紧急构建了模型，并实际用于为英国政府评估不同封锁形式的经济影响。这个模型非常明确地采用了非均衡框架，其运作原理其实很简单。

Right? I mean, at least from the point of view of the economy, having a virus suddenly cause people to not go to work is an outside shock. But that was a very sharp and sudden outside shock, and we built a model in a crash program as the pandemic was starting and actually used it to advise the British government about the economic consequences of different forms of lockdowns. And that model was very, very explicitly disequilibrium. I mean, you know, it works in a very simple way.

我们的基本假设是：一个行业若面临产品需求不足、缺乏生产所需原材料或劳动力短缺，就无法正常生产。基于这个简单观察（具体如何预判冲击规模和受影响行业的细节就不展开了），我们结合职业劳动知识体系和美国劳工统计局提供的优质数据集——其中包含不同职业工作场所的密集程度等关键信息。

We said, an industry can't make its product if there's no demand for the product, if it doesn't have the inputs it needs to the prod for the product, and if it doesn't have the labor it needs. And so just using that basic observation, longer story for how we managed to guess how big the shocks were gonna be and which industries would get shocked, that had to do with our knowledge of occupational labor and beautiful dataset put together by the Bureau of Labor Statistics, but that contained information like how close together do people work in different occupations.

嗯。

Yeah.

由此我们能推断出哪些人群可以继续工作。模型初始状态设定为疫情前的稳态，然后施加冲击。运用前述规则，我们能看到冲击在经济体中层层传导——因为每天都会更新模型数据：这个行业有劳动力吗？有原材料供应吗？

So from that, we can infer who would be able to go to work and who wouldn't. But we initialized the model in the steady state it was in before the pandemic started, and then we hit it with the shocks. And using the rule I said, we could see those shocks reverberating around the economy because every day we'd update the model. We'd say, oh, does this industry have labor? Does it have inputs?

有市场需求吗？只要缺其中一项，我们就调降其产出。所以这是个随时间变化的动态输出过程。例如某些行业在封锁开始一两个月后就出现了原材料枯竭，因此...

Does it have demand? And if it didn't have some of those, we would reduce its output. So it was a very dynamic output that changed through time. For example, some of the industries were running out of inputs a month or two after the lockdown started. So it didn't.

影响并非即时显现。当上游产业（如自然资源或基础材料生产商）减产时，会像他们说的那样产生'下游传导'，波及其他行业。这种复杂的动态效应是主流模型无法捕捉的。

The consequences weren't necessarily felt immediately. Yeah. And then once they ran out of once they reduced production, if they were upstream in the economy, meaning producing natural resources or stuff that lots of other industries use, then that would propagate, as they say, downstream and hit the other industries. There were complicated dynamic effects. And mainstream models don't work that way.

他们从一开始就假设均衡状态。所以我认为他们就像被束缚在紧身衣里，因为那确实是一个非常明显的非均衡事件。顺便说一句，注意到我们刚提到的近代史上两件大事，它们都是非均衡事件。

They assume equilibrium from the get go. And so I think they were just their hands were they were in a straight jacket because you really had it was a very explicitly disequilibrium event. And by the way, note that the two big events that we just happened, you know, in recent history, they're both disequilibrium events.

确实如此。这让我想到复杂性与混沌之间的良好关联，虽然它们是两个不同概念，对吧？但彼此相关。混沌理论部分观点认为微小偏差会导致截然不同的未来，这似乎与事物无法稳定下来的特性相关，但我还无法完全理清思路。

Very, very much. And it does make me think that this is a good connection between complexity and chaos, which are two different things. Right? But they're related to each other. Part of the spiel and chaos theory is that small deviations get you very different futures, and that does seem to be related to this inability for things to settle down, but I can't quite put it together in my head.

其实我想说KS理论包含两点：一是对初始条件的敏感依赖性，就是你刚说的——状态空间中原本无法区分的邻近点，最终可能分道扬镳。另一部分是内生运动。

Well, let me actually I would say KS has two things. One is sensitive dependence on initial conditions, which is what you just said. Things that are, you know, maybe so close together in the state space that you can't distinguish them, and therefore could correspond to the same measurement, could eventually become very far apart. So that's the sensitive dependence part. But the other part is endogenous motion.

混沌本质上可以通过两种方式在动力系统中产生内生运动（这是我转行经济学前的研究领域）。其一是振荡，虽然振荡本身不必是混沌的，但有定理表明：当存在两个以上振荡频率时，几乎必然产生混沌。这意味着如果你观察的内生运动不是周期性或准周期性的（两个周期内），那必定是混沌现象——这个观点尚未被经济学界充分吸收。嗯哼。

Chaos is really you can get endogenous motion two ways in dynamical systems, which is what I did before I started doing economics. And one is through oscillations, which don't need to be chaotic, but there's even a theorem that says once the oscillation once you have more than two frequencies oscillating, you almost always get chaos. And so you so that says that if you're looking at an endogenous motion and it's not periodic or quasi periodic with two periods, then it's gotta be chaos. And and that has not been absorbed by the economics community. Uh-huh.

而且我认为这至关重要，因为本质上，如果你想产生不规则的内生经济周期，它们必须是混沌的——别无他法。

And and and I think it's essential because, basically, if you wanna have irregular business cycles that are endogenous, they must be chaotic. There's no other way to do it.

好吧，我想这正是我努力想更深入理解的。我的部分思维想说：让我们退一步，你刚说的关于两个频率或周期的问题，在双摆系统里就完全说得通。

Well, I guess, yeah, that's what I'm I'm try I'm striving to understand this a little bit better. I mean, part of me wants to say okay. Let's back up. The thing you just said about two frequencies or two periods, that makes perfect sense to me. If we have a double pendulum.

对吧？如果一个摆锤悬挂在另一个摆锤上，那就是混沌系统的典型范例——难以预测。但如果存在些许摩擦力，双摆最终会回归平衡。这让我联想到：如果经济系统不受外部冲击，或许也会因存在某种'摩擦力'而逐渐稳定。

Right? If we have one pendulum hanging off another one, that is a paradigmatic example of a chaotic system. Hard to predict. But if I have a little bit of friction, the double pendulum will eventually settle down into an equilibrium. And part of me wants to think the that the economy, if it didn't have any outside shocks, would have a little bit of friction and settle down.

但你告诉我的却不同，我正试图理解这是怎么回事。

But you're telling me different, and I'm trying to understand how that goes.

是的。想象一下，假如我们不知怎的没有遭受冲击。我们生活在一个平稳的世界里，不会遇到任何意外。

Yeah. Well, you know, imagine imagine we didn't have shocks somehow. We just live in a smooth world where we're not getting hit by surprises.

嗯。

Yeah.

首先，这并不意味着我们不会增长。对吧？那是另一种内生现象。

Now, first of all, that doesn't mean we're not gonna grow. Right? That's an endogenous another endogenous phenomenon.

当然。

Sure.

另外值得注意的是，经济中充满了反馈循环。这是凯恩斯的卓越洞见之一——生产需要需求支撑。如果需求下降，比如因为人们失业了，那么我们自然就会减少生产，导致更多人失业，进而需求进一步减少。经济中存在这类反馈循环。我们会采取措施试图控制这些循环，这也是另一个精辟见解。

And and and it's also worth noting that the economy is full of feedbacks. That's one of Kane's brilliant insights, is that if, it requires demand to produce stuff, And and if we have a situation where demand falls, for example, because people become unemployed, then we automatically are gonna produce less, which means more people are gonna become unemployed, which means we're gonna have less demand. So there are feedback loops like that in the economy. And and we do things to try and control those feedback loops. Another, you know, brilliant insight.

我们设立中央银行的原因，就是为了通过调控利率、制定规则（根据需要调整规则）来保持经济平稳运行。但如果我们做得不够完美，就可能出现波动。这又回到了我之前关于醉汉在蜿蜒道路上开车的比喻。或者，在现场讲座中，我喜欢用平衡杆来演示这个概念。

The reason we have central banks is to do things like control interest rates and set the rules up, you know, adjust rules as we need to keep the economy on a steady path. But if we do that imperfectly, then we might expect oscillations. It's back to my point about the drunk driver on the windy road. Yeah. Or, you know, in a in a live lectures, I like to illustrate pole balancing.

如果你拿一根杆子，它得比三英尺长些。你用手握住它，试着保持杆子垂直，尽可能让它直立。虽然你大致能保持垂直，但它会围绕垂直位置摆动。现在把杆子想象成经济，我的手则是中央银行或所有不断做出经济决策的规划者，试图预测经济走势。明白吗？

If you take a pole, it's gotta be about longer than three feet. You put it in your hand, and you try and preserve the pole vertical, maintain the pole as vertical as possible. Well, you can keep it more or less vertical, but it oscillates around vertical. Now think of the pole as the economy, and think of my hand as the central bank or all the planners that are making economic decisions all the time trying to anticipate what the economy's gonna do. You know?

我决定创业、决定借钱或放贷。这些行为都像我的手在试图操控那根杆子。让杆子完全垂直意味着让经济保持在稳定轨道上，所以一旦偏离垂直，我就移动手来纠正。

I decide to start a business. I decide to borrow money, lend money. All those things are like my hand trying to steer that pole. Now and having a pole be perfectly vertical is keeping the economy on a nice steady path, so it it deviates from vertical. I move my hand to correct.

我往往会过度或不足地纠正。杆子会向中心回摆，但我总是错过最佳时机，直到杆子自身来回摆动。为了说明观点差异，假设杆子代表经济，在标准模型中故事是这样的：我是个完美的杆子平衡者。所以有人推了一下杆子。

I tend to over or under correct. The pole then comes back toward the center, but I miss I miss it as I come back until the pole endogenously wobbles back and forth. Now to illustrate the difference in viewpoint, if if, again, the poll is the economy, in a standard model, the story goes like this. I am a perfect poll balancer. So somebody knocks the poll.

对吧？杆子被推了。然后我精确计算如何移动手，让杆子平稳地回到顶端，在此过程中最大化经济消费。就在我快到达顶端时，又有人推了杆子。这两个故事其实都有合理成分。

Alright? So the pole gets knocked. I then calculate exactly how to move my hand to bring the pole smoothly to rest back at the top, maximizing, you know, consumption in the economy as I do it. And before I get to the top, somebody knocks the pole again. Now there can be elements of both stories.

如果我站在暴风雨中的帆船上，就会不断受到冲击。这种情况确实存在。但关键在于，即使站在平地上，在没有微风、没有外界影响的情况下，杆子也会摆动。它不需要外界影响。

If I'm standing on a sailboat in a storm, I'm getting knocked all the time. That happens. But the point is even if I'm standing on level ground, the pole is oscillating, and there's no breezes. There's no no outside influences. It doesn't take outside influences.

这就是主流理论缺失的那部分叙事，而主流经济学试图用我前面说的方式来解释一切。

So that's the side of the story that's missing, whereas mainstream tries to explain everything with in the way I said.

当然，那根杆子本身并不处于均衡状态。它处于不稳定均衡点对吧？所以你的意思是，如果古典经济学发现的均衡实际上是隐秘不稳定的，微小扰动就会导致失衡，这会改变我对许多事情的看法。

So it of course, that poll by itself is not in equilibrium. It's it's at an unstable equilibrium. Right? So is that I mean, that would be that would change my mind about a lot of things if if you're arguing that the equilibria discovered by classical economics are secretly unstable and small perturbations would tend to grow.

嗯，经济学家们都知道这一点。

Well, the the economists know that.

好的。

Okay.

它们实际上是内在不稳定的。但你必须假设存在一个理性的决策者，这个理性决策者能够将其稳定在平衡点上。

They are secretly unstable. And they but you have to assume, like with a rational decision maker, the rational decision maker can keep it pinned at its stable point.

明白了，好的。

Got it. Okay.

站在他们的立场来看，他们会说，也许将其视为处于均衡状态是个足够好的近似。确实如此。在某些情况下，只要说杆子是垂直的就足够了，即使它在10度角内摆动，10度的偏差也不算大。但另一方面，我认为经济虽然总体向上发展，但商业周期就是围绕这个趋势的波动，就像杆子偏离平衡状态一样。

And in their favor, you know, taking their side of viewing things, they would say, well, maybe it's a good enough approximation to say it's in equilibrium. It's true. For some purposes, just saying the pole's vertical is good enough, you know, if it's oscillating through a 10 degree angle, well, 10 degrees, you know, it's not a big deviation. But on the other hand, I would argue that, yeah, the economy tends to go up, but business cycles are the swirls around it, and that's the pole deviating from equilibrium.

是的。不，这非常有帮助。所以基本上，不同于我原先想象的简单画面——存在一个均衡点，通过耗散最终稳定下来——实际上存在的是不稳定的均衡，而我们周围的事物在不断变化。就像你说的，经济在增长，我们不断发现新事物、新资源或新技术等等。

Yeah. No. That's extraordinarily helpful. So, basically, rather than the naive picture I had in mind where there is an equilibrium and there's dissipation and you settle into it, there's an unstable equilibrium and things around us are gradually changing. Like you said, the economy is growing, we discover new things, new resources or technologies or whatever.

因此，在不稳定均衡状态下自然会有持续的小幅波动，还存在非线性反馈，这些波动会倾向于扩大除非我们加以纠正，而纠正过程本质上就是一个动态的来回调整过程。

So of course, there's constant jiggles at the unstable equilibrium, and there's nonlinear feedback, and they will want to grow unless we correct, and that correction is going to be kind of a back and forth process that is intrinsically dynamic.

对，完全正确。

Yeah. Exactly right.

好的，明白了。我现在理解得更透彻了。那我们来探讨下应该采取什么行动，而不是像那些迂腐平庸的经济学家那样。我是说，有限理性理论在这里是如何体现的？

Okay. Good. I understand a lot better now. So let's try to figure out what we should be doing instead of those benighted, ordinary, economists. I mean, how does the statement of bounded rationality play into this?

显然，这个理论表明完全理性的假设过于理想化，但你要如何在改进的思维方式中实践这一点呢？

I mean, clearly, it's a statement that the assumption of perfect rationality is too strong, but then how can you implement that in your better way of thinking?

嗯，回到我之前提到的基于主体的模型。这类模型其实就是计算机模拟，其中包含需要做决策的主体。难点在于你要弄清楚这些主体如何做决策——在每种情境下他们会做出什么选择？以我做的疫情模型为例...

Well, going back to, you know, what I described before is called an agent based model. We have it's a agent based models are just simulations on computers with that involve agents who make decisions. And the challenging part is you have to figure out how those agents are making decisions. What decisions are they making in each situation? Now now, first of all, take the example of the COVID model I made.

那个模型实际上并没有需要做决策的主体。

That model actually has no agents making decisions.

好的。

Okay.

因为我们只需要明白：当行业缺乏需求、劳动力或必要生产资料时就无法生产。所谓的生产函数——即制造产品的配方——承担了所有工作，而模拟动态则追踪所有要素的变化。当然很多时候确实需要决策主体。顺便说，那个模型很有效，它非常简单且没有涉及价格因素。

Because all we have to understand is the industry can't produce if it doesn't have demand, labor, or the inputs it needs. So the production function, as it's called, the recipe for making stuff, does all the work, and the dynamics of the simulation, which tracks what everything's doing. Now that's you often do need agents making decisions. That that model, by the way, it worked. It was very simple and it didn't have prices.

但这意味着我们无法解决通胀等问题。现在，我们猜对了。我们说，我们认为这没关系，因为在大约第一年内，我们认为不会出现通胀。我们实际上明确表示，在那之后，一切都不确定，我们对此感到担忧。所以现在你想引入通胀，你必须让代理做出决策，因为他们会前瞻性地思考，哦，我们会有通胀吗？

But that meant we couldn't address things like inflation. Now, we correctly guessed. We said, we think that's okay because for the first year or so, we don't think there's gonna be inflation. We actually explicitly said after that, all bets are off and we're worried about it. So now you wanna bring inflation, and you gotta have agents making decisions because they're looking ahead going, oh, are we gonna have inflation?

发生了什么？利率在做什么？他们必须考虑所有这些事情。但正如我所说，在基于代理的模型中，我们是从另一端来处理的。我们通常从简单的开始。

What's what's happening? What are interest rates doing? They have to be thinking about all that stuff. Now so but as I said, in an in an agent based model, we kind of come at it from the other end. We usually start simple.

比如，我有一些模型，代理实际上只是通过抛硬币来做决策。

Like, I have models where the agents actually just flip coins to make decisions.

好的。

Okay.

所以确实存在零智能代理。这些模型可能非常有用。它们已经做出了有用的预测。例如，如果你想预测金融市场中的买卖价差，即买入价和卖出价之间的差异，它如何取决于订单流入市场的方式？你可以用零智能模型来计算。

So there are literally zero intelligence agents. And those models can be quite useful. They they have made useful predictions. For example, if you wanna predict how the bid ask spread in a financial market, that is the difference between the buying and selling price, how does that depend on the way orders are flowing into the market? You can calculate it with a zero intelligence model.

你知道，订单流入可能就像随机流入的沙粒。但你很快意识到，哦，不。我们需要改进它。所以你现在需要做一些更现实的东西，比如价值投资者购买被低估的资产。

The the the, you know Wow. Orders flowing in could be sand grains coming in at random. But you quickly realize, oh, no. We need to refine it. So you now need to make something a little more realistic, like value investors buy undervalued assets.

所以有几个不同的基于代理的模型，规则是，这个人是价值投资者或投资者。他们做什么？他们有某种评估资产的方法。当估值低于价格时，他们买入并持有。当估值高于价格时，他们卖出。

So several different agent based models where the rule is, this guy's the value investor or investors. And what do they do? They have some way of valuing the asset. When the valuation's under the price, they buy it and hold it. When it's over, they sell it.

这类简单的规则。我还有其他模型，比如回到经济周期和平衡问题上，我们假设有一群非常、非常愚钝的智能体。他们只会观察邻居的行为，然后选择在特定时期内消费最多的邻居——也就是目光最短浅的那个——并效仿其储蓄率。因此他们各自有不同的储蓄率，都在采用不同的储蓄策略。

And so rules simple rules like that. I have other models where, you know, back to business cycles and pull balancing, we assume a network of agents who are really, really dumb. All they do is look at their neighbors to see what their neighbors are doing, and the neighbor that's consuming the most in that period, so myopically consuming the most, they go, I'll adopt that savings rate. So they all have different savings rates. They're all adopting different savings rates.

顺便回到经济不稳定性的话题——如果储蓄过多，经济就无法运转。必须找到恰当的平衡点，因为要卖出商品，就需要有消费者购买，而这些消费者又需要通过工作获得收入来消费。这个循环不断持续，平衡点就在某个中间位置。神奇的是，只要这些智能体不频繁调整策略，经济就会因储蓄率的动态变化而自发产生波动。

And by the way, back to the instability, you know, in in the economy, you have to If you save too much, the economy doesn't work. You've got to strike the right balance because, again, to sell stuff, you've got to have consumers to buy the stuff, and those consumers then have jobs doing something that allows them to sell stuff. So you're going around that loop all the time. The balance is somewhere in the middle. So amazingly, as long as these agents don't update their strategies too often, the economy actually spontaneously starts oscillating because agents are changing their savings rates dynamically.

但更让我惊讶的是，尽管这些智能体非常愚钝，他们却能非常接近最优储蓄率。完全是选择机制在起作用。看到了吗？他们都在追逐那个消费最多的智能体。

But also, almost to me, almost more amazingly, they get pretty darn close to the optimal savings rate even though they're absolute dummies. And selection's doing all the work. You see? Because you're Yeah. Toward the agent that consumes the most, they're chasing that agent.

那个被追逐的智能体可能正在疯狂消费并即将破产，所以这个机制并不完美。但他们会接近最优策略的百分之几范围内。这虽然有点跑题，但可以说明我们如何让智能体自主决策。关键在于，这种方法不仅在许多场景中更贴近现实，还能更自然地捕捉经济动态——因为你能看到经济不完美导致的偏差，同时模型仍具有可操作性，能模拟数百万智能体而非局限于少数几个。

Now that agent might be on a spending spree and about to go bust, but so it's not perfect, but, you know, they get within, well, a few percent of the optimal strategy. So, anyway, a bit of a tangent, but just to illustrate the kinds of things we do to have the agents make their decisions. And then the key point is that not only do we think that can be more realistic in many settings, it can capture dynamics more naturally because you can see the deviations from the imperfections of the economy, but, it's tractable. So we can run simulations with millions of agents instead of being stuck with just a few.

对。再为从未接触过这类模型的听众说明一下：传统经济模型会有供需、通胀率等宏观变量，而你们的计算机模拟中是用个体变量来代表数百万不同智能体的状态和期望？

Right. And so just to clarify again for the audience members who've never made a model of anything of the of this sort, the difference would be in a in a standard economic model, you would have things like supply and demand and, you know, inflation rate or whatever. And here, you have individual variables in your computer simulation representing the states and aspirations of a million different agents?

没错。但要强调，我们模型里同样存在供需关系和通货膨胀。

That's right. But let me emphasize, we also have supply and demand and inflation.

好的，明白。

Okay. Sure.

因为这些中介促成了供需关系的形成。对吧？如果我们观察到的波动中存在这种情况，那可能是供大于求或求大于供的波动。在某些市场中，长期来看，说供需平衡是个不错的近似。而在像房地产市场这样的市场中，供需可能会严重失衡。

Because those agents make supply and demand happen. Right? And and if that's in the oscillations we're seeing, they may be oscillations where there's more supply than demand or more demand than supply. And in some markets, you know, some markets saying supply equals demand is not a bad approximation over sufficiently long time scale. In other markets like housing markets, supply and demand can be wildly out of balance.

这要追溯到房地产市场价格的形成方式。你知道，如果你买了房子，你会怎么做？你会找类似房源，去找房产中介，他们会提供一些可比房屋。你只需稍微调高或调低价格，然后尝试以那个价格出售你的房子。

And it goes back to the way prices get formed in housing markets. You know, if you bought a house, what do you do? You go find a comparable you go to a real estate agent who says, here are some comparable houses. You know, you would just tweak it up or down a little bit. You try and sell your house at that price.

如果卖不出去，你就降价。还是卖不出去，过一两个月再降价。最后你可能会说，我不想卖房了。

It doesn't sell. You mark it down. Doesn't sell again. After a month or two, you mark it down again. Maybe at the end you go, I don't wanna sell my house.

价格太低时，你会撤下房源。因此在房地产市场中，可以看到供需失衡程度超出一个数量级。就像经历房地产危机时，我们从需求严重过剩的市场转变为供应严重过剩的市场，而价格对此反应非常迟缓，主流模型无法捕捉这种现象，因为你无法简单地用方程来描述它。

This is too cheap, you pull it off the market. So there in housing markets, can see supply and demand imbalances that are more than an order of magnitude. And you can see, like you go through the housing crisis, we flipped from a market where there was a huge excess of demand to a market where there was a huge excess of supply, and prices are responding very sluggishly to that, those much more and you can't capture that in a mainstream model because you can't write it down in an equation simply.

嗯，在主流模型中，如果我没理解错的话，它们不包含个体。个体的角色只是被吸收到供需的集体概念中。

Well, in the mainstream models, I guess, they have no individuals in them, if I understand correctly. Like, role of the individual is just to be absorbed into the collective notion of supply and demand.

是的。让我说得更准确些。

Yeah. So let me be let's be a little more precise there.

好的。

Okay.

在宏观层面，这大致没错，不过最近宏观经济学的一个热点是采用异质主体模型，比如用一维高斯分布来表示主体，其中某些主体的收入能力高于其他主体。

In macro, that's more or less true, though these days, a hot topic in macro is having heterogeneous agent models where you have things like a Gaussian distribution of agents, just one dimensional Gaussian, with, some agents, say, having more earning power than others.

好的。

Okay.

对吧？所以他们在模型中引入这个。但在我们的模型中，我们怎么做呢？我们创建了一个包含百万主体的合成人口，力求匹配所有人口统计特征——收入能力、教育程度、年龄，甚至种族和性别。

Right? Whereas okay. So they're putting that in. But in our models, what do we do? We create a synthetic population with a million agents where we try and match all the demographics, earning power, education, age, even race and gender.

这样我们就能匹配人口的所有特征。我们甚至可以分地区进行，所以可能存在差异。由于我们能模拟数百万主体，这使得模型能更丰富、更精确。

And so we match all those characteristics of the population. We can even do it regionally, so it may vary. So we have the power because we can have millions of agents to really make all this much more much richer and much more accurate.

根据你书中的观点，这种方法的一个优势是模型中的主体能以特定方式实现专业化。你用了个比喻，说经济有点像新陈代谢，是文明的新陈代谢。

And one of the advantages of this approach, as I understand it from your book, is that the agents in your model can take on specializations in a certain way. I mean, you use the analogy that the economy is kind of like a metabolism, the metabolism of civilization.

对，这算是把几个隐喻很好地融合在一起。那么经济为我们做了什么呢？

Yeah. Yeah. So so mixing together a couple of metaphors there, but in a good way. Yeah. What does the economy do for us?

首先，写书时我意识到这让我更懂得欣赏经济的作用，因为我真正思考过它为我们做了什么。我认为它就像文明的消化系统，是新陈代谢。就像我们的新陈代谢——我们摄取外界食物，然后转化这些食物。

So first of all, it's you know, when I wrote my book, I realized it made me appreciate the economy more because I really tried to reflect on what is this thing doing for us. And I would argue it's like our it's the digestive system of civilization, the metabolism. It takes in, just as our metabolism, what does it do? We take in We take in food, right, from the outside world. We reform that food.

我们将其分解重组，创造出新事物。经济的作用是什么？它吸收自然资源，结合劳动力，将其转化为我们或其他产业消费的商品与服务。这就是底层引擎——经济运转如同每日摄入均衡营养，让你获得行动与完成任务所需的能量。

We break it into pieces, and we make it into something else. What does the economy do? It takes in natural resources and then combines it with labor, and we reform it into goods and services that we consume or that other industries consume. So it's the engine at the bottom. It's, you know, it's like having the economy work is like having a square meal every day and having that energy allow you to walk around and do the task you need to do.

这是我们一切活动的基础。生态学类比源于这样一个事实：生态学本质上是关于专业分工的理论。草是什么？草是将阳光、土壤和水转化为草本的生物；斑马是将草转化为斑马的生物。

Very fundamental to everything we do. And now the ecology analogy comes from the fact that ecology is really a theory about specialists. I mean, what is grass? Grass is an organism that takes sun and earth and water and makes grass with it. A zebra is an organism that turns grass into zebras.

狮子是将斑马转化为狮子的生物。它们都是高度特化的。没错。但它们的相互作用可以相当深远——若消灭狮子，草场就会遭殃。

A lion's an organism that turns zebras into lions. They're all specialized. Yeah. But the interactions can be rather long range. Get rid of the lions, grass gets hammered.

因此尽管狮子除了偶尔在草丛打滚外并不直接与草互动，但由于狮子控制斑马数量，斑马控制草场生长，整个系统紧密相连。正如亚当·斯密曾指出过的，我们的模型在有限理性框架下明确展现了这种关联——我们之所以专业化，正是因为人类理性有限，只能精于特定领域，无法胜任所有工作。

So even though the lion might not interact with the grass except to roll around in it occasionally, because the lions are controlling the zebras and the zebras are controlling the grass, it's all connected. So economists sort of I mean, Adam Smith already said something like that, but but our models very explicitly do that in a bounded rational setting because why are we specializing? We're specializing because we're only boundedly rational. We get good at doing very specific things. We can't do everything.

我们将这个前提植入模型，通过加入更多制度结构等要素，得以更丰富地思考生态层面的问题。回到新陈代谢部分——这与人体代谢不同，因为这种代谢由它供养的生态体系维持。代谢滋养着我们所有人，而我们通过各自的专业角色实现这一点。就像消化系统中的细胞，它们因参与消化而获得养分，但这些细胞可以高度特化，执行截然不同的功能。

And so we put that in from the start, and and our models, because we can put in more more institutional structure and so on, allow us to think about the ecological part of the story with more richness. Now back to the metabolism part of the story. The metabolism, it's a bit different from your body in that metabolism is maintained by an ecology that the metabolism is feeding. Metabolism feeds all of us, but we're making that happen by each of our specialized role. We're like the little cells inside your digestive system that are themselves being fed by the fact that we're digesting food, but those cells can be very specialized, and doing very different things.

你们甚至准确预测到：专业化程度更高的行业将通过效率提升实现降价。

And you even have some prediction that turned out correctly about how different industries that were able to specialize more would find efficiencies and lower prices.

是的。我们受到生态学启发——在圣塔菲研究所的妙处就是能涉猎广泛。我们意识到经济中正在发生同样的基础过程，因为这里充满专业分工者。生态学的核心概念之一是营养级，这尤其具有启发性。

Yeah. So we we we were inspired by ecology. One of the nice things about hanging out at SFI is get to know a little about a lot of things. And and we said, the same basic thing is going on in the economy because we have all these specialists. And in particular, in ecology, one of the central ideas is that of a trophic level.

以草为例，草的营养级为一级，斑马为二级，狮子为三级。计算营养级的方法是：生物体的营养级等于1加上其食物平均营养级。对吧？所以你可以把这个原理写下来，推导出经济学的关键方程。在这种情况下，工业的营养级等于1加上其投入品的营养级，而劳动力被我们视为基础。

With the grass, grass has a trophic level of one, zebras have a trophic level of two, Lions have a trophic level of three. And the way you compute trophic levels is you say an organism is equal to one plus the average trophic level of the things it eats. Right? So so and you can actually just write that statement down and derive a key equation about the economy. Because in this case, what happens is industries trophic level in industry is one plus the trophic levels of its inputs, and labor, we put as the foundation.

那是零级营养级。因此纯靠劳动力的工业营养级为一级。实际上这与经济学中的'产出乘数'概念高度相关。这个指标虽然用途不同，但定义方式相似。

That's trophic level zero. So an industry that purely has labor has trophic level one. It turns out this is very closely related to a concept in economics called output multiplier. Okay. Which is used in a different way, but defined in a similar way.

那么这如何关联呢？由此得出的预测是：一个产业的供应链越深，其进步速度就越快。你可能会觉得这听起来有点奇怪。

Now okay. So so this relates how does this relate? What the prediction then is that the deeper your supply chain is as an industry, the faster you'll improve. Now you go, oh, wait a minute. That sounds weird.

首先，供应链深度就是这里的营养级。为什么？假设你使用的劳动力投入很少，所有投入品都已具有高营养级，那么你的营养级会比投入品更高。你需要不断回溯直到劳动力环节。实际上，营养级衡量的是一个产业支付的美元需要多长时间才能流入劳动者口袋，或者说沿着产业链回溯到所有参与生产的劳动力。

Well, first of all, the depth of the supply chain is the trophic level here. Why? Because if let's suppose you don't take in very many labor inputs, and all your inputs are things that already have high trophic levels, then you're gonna have an even higher trophic level than your inputs. And you keep going back down until you get back to labor. So in fact, the trophic level is the average time it takes a dollar that an industry pays to its labor to get into somebody's pocket or get in in other words, for to get all the way back to all the labor that went into making the thing as you go down the chain.

这样你就能计算各产业的营养级。就像生物学中生物通常不只吃一种食物，它们的营养级...

So this allows you to compute these trophic levels for industries, which typically, as in biology, you know, organisms eat more than one thing. Their trophic levels

对。

Yeah.

并非简单整数而是更复杂的。同理，这里可以计算产业的营养级。现在我们假设所有产业的创新速率大致相同——这是个大胆假设，但很适合作为研究起点。

Aren't just integers. They're more complicated. Similarly, here you can compute trophic levels for industries. Now, suppose we assume that every industry is innovating at about the same rate. Bold assumption, but good place to get started.

我们可以允许差异存在，但首先假设所有行业的创新速度相同。那么，如果你的供应链足够长，沿途会有许多行业在创新。因此你会体验到供应链上游所有创新带来的成果。比如，如果苹果使用的钛金属降价，我的笔记本电脑就会更便宜；如果芯片降价，同理类推。

We can let it be different, but let's start it by assuming they all innovate at the same rate. Well, then if you have a deep supply chain, there are many industries that innovate on the way up to your industry. So you experience the product of all those innovations coming up the supply chain. So, you know, if my laptop if if the titanium that Apple is using gets cheaper, that helps make my laptop cheaper. If the chips get cheaper, etcetera.

除了笔记本设计师自身的改进外，你还继承了所有这些上游改进。因此我们计算出：具有深层营养级的产品改进速度会更快——意味着它们会变得更好、更便宜或两者兼而有之。我们分析数据时发现，由于营养级随时间变化缓慢，基本可以假设其恒定不变，仅凭这点就能提前十四年预测哪些产品会降价。这个预测相当准确，而且随着时间跨度延长，预测效果会更好。

You're inheriting all those improvements in addition to the improvements the laptop designers themselves make. And so then we just computed that this means things with deep trophic levels should their products should improve faster, meaning they should get better or cheaper or some combination of the two. And sure enough, we looked at the data, and and we took advantage of the fact that trophic levels change slowly through time. So you can, roughly speaking, assume they stay constant, and you can predict fourteen years ahead which products are gonna be cheaper or not just based on that assumption alone. The prediction's quite good and amazingly gets better as you go forward further forward into the future.

作为一个经常做预测的人，我必须说这种预测方式相当不寻常。

As as someone who does a lot of predicting, that's pretty unusual prediction.

生物学家思考进化时，长期困惑于生物复杂度随时间增长的现象。为什么生物似乎会变得越来越复杂？一个可能的答案是：它们找到了新的效率途径。我想你们研究的经济版本也是类似逻辑。

Well, biologists who think about evolution have long wondered about the development of complexity over the course of biological time. Why is it that organisms seem to become more complex? And one answer, possible answer is they find new efficiencies. And I guess that's kind of you're doing the economic version of that.

有趣的是，在生物学中竞争是进化论的核心，但进化生物学家认为竞争会导致物种分化——这是达尔文的关键见解。而经济学主流如米尔顿·弗里德曼则认为竞争会快速达到均衡静止状态。两者都使用'竞争'概念，却得出完全相反的结论。

Yeah. Now, interestingly, in biology, you know, competition is a key part of evolutionary theory, but evolutionary biologists view competition as what leads us to speciation. That's one of Darwin's key insight. Whereas in economics, the mainstream people like Milton Friedman say, no, competition means you quickly come to equilibrium, the state of rest. So they arrive at a completely different conclusion, both using competition.

部分原因在于经济学家以理性为出发点，假设人类都很聪明能想通一切，从而快速达到均衡。而生物学中，随机变异会在竞争中被放大，最终导致物种分化。我认为经济领域同样如此——在某些简单情况下我们可能表现理性，但那些能想明白的情况恰恰因为事情足够简单。

Now it's in part because economists start with rationality. They assume we're all really smart, so we figure everything out, and that gets us to this equilibrium quickly. Whereas in biology, you know, there is random variation that's being amplified as a result of the competition and actually causes species to diverge. I would argue that's also happening in the economy because, well, in some cases, we may behave rationally. I'd say the cases where we behave rationally are the ones where things are really simple so we can figure them out.

但大多数时候情况非常复杂，理性假设就不太适用。我们往往会反应过度、反应不足，最终走向差异化发展。

But most of the time, it's pretty complicated, so that's not such a good approximation. And we, you know, overshoot, undershoot, and differentiate.

是的。创新在这里扮演什么角色？创新是内生的还是外生的？

Yeah. What is the role of innovation here? Does innovation count as an endogenous happening or an exogenous one?

我认为从根本上说是内生的，这在经济学中建模是个挑战。在生物学里，你可以直接说创新就是随机性，比如基因随机突变。或者更复杂些，要考虑重组。但这些都是可以用算法明确描述的过程，因为所有生物都遵循统一的遗传密码。

Well, I think it's fundamentally endogenous, and it's a challenge to model in economics. In biology, you can just say, Well, innovation is randomness. You know, you randomly change a gene. Or, okay, you have to deal with recombination, which is more complicated. But it's just a process you can specify in an algorithm because we have this universal biological code that everything follows.

而我们没有这么清晰的规律。在经济领域，创新取决于人类理性。不过有时假设它是随机发生的也不失为好的起点。有个叫赖特定律或干中学的理论（有多个名称），能有效预测技术进步。讽刺的是，这个理论最初其实是由约翰·穆思提出的。

Whereas we don't have anything that clean. In the economy, innovation depends on human reasoning. But, you know, sometimes just assuming it happens randomly is not a bad start. There's something called Wright's Law or learning by doing, comes under several different names, that's a very useful way to predict technological improvement. The theory for that actually, ironically, was originally started by John Muth.

约翰·穆思就是1960年提出理性预期理论的那个人。

John Muth is a guy who actually invented rational expectations in 1960.

好的。

Okay.

但他其实是个左右开弓的学者。他说假设发明者只是随机投掷飞镖，但他们足够聪明能识别出更好的投掷。由此他推导出了赖特定律，虽然指数只能是1——这是他唯一能推导出的指数。后来有篇模拟论文进一步发展了这个观点。

But he also he was a he could work with both hands. He said, Let's assume that inventors just throw darts at a dartboard at random, and let's assume they're just smart enough to see when they've made a better throw. And so he then showed that, you got Wright's Law, although only with an exponent of one, right? That's the only exponent he could draw. Fast forward, there was another simulation paper.

我们又写了篇论文，通过观察技术组件间的关联性来完善该理论。比如改造化油器系统可能需要同步调整点火系统。我们借鉴了汽车等复杂产品设计师使用的设计结构矩阵来分析交互关系，用物理学方法推导出赖特定律指数，证明系统越复杂、模块化程度越低，指数就越小，进步速度越慢。这个结论已得到验证。

And then we wrote a paper again, we enhanced that idea by looking at, you know, the fact that technologies are connected within a device. You know, if you model if you change the carburetation system, you may need to change the ignition system. So you look at what's called the design structure matrix that automobile designers and other designers of complex things use to understand interactions. So we enhanced the theory to deal with that, derived a bunch of stuff physics style, and we were able to show that derive the Wright's law exponent and show that the more complicated and less modular the system is, the lower that exponent is and the slower it improves. And that's been tested.

这似乎大体上是正确的。这又是一个例子，说明向飞镖盘投掷飞镖实际上足以让你达到目标，正如理性预期理论的创始人约翰·穆思讽刺性地意识到的那样。但不知何故，经济学被锁定在一个框架中，你无法再像穆思那样做了。假设人们只是抛硬币决定，这简直太糟糕了。不行。

It seems to be more or less true. It's another example of throwing darts at a dartboard actually is good enough to get you there, as was ironically realized by John Huth, the founder of rational expectations. But somehow economics got locked into a framework where you couldn't do what Muth did anymore. That's just bad cricket to, assume that people just flip coins. Not okay.

我认为很多时候，如果你回顾某个领域的经典论文，它们比那些流传到后世的、高对比度的版本要深思熟虑和细致得多。

I think it's often the case that if you go back to some of the classic papers in a field, they were much more thoughtful and nuanced than the sort of high contrast version that survives into subsequent generations.

是的。我认为物理学和其他许多学科也是如此。绝对如此。因为那些人必须绞尽脑汁

Yes. I think the same is true in physics and Absolutely. Many other disciplines. Because those guys had to wrestle

是啊。

Yeah.

首先想出这些概念。所以他们比后代更清楚自己所站立的 slippery 地面。

With coming up with the concepts at the first in the first place. So they understood the slippery ground they were standing on better than subsequent generations.

没错。你多次提到的一个观点是，我们现在可以使用巨型计算机。对吧？我们可以进行基于代理的建模，可以设置一百万个不同的代理。

Exactly. So one thing you've mentioned a few times is this idea that we now have access to giant computers. Right? We can do agent based modeling. We can have a million different agents.

即使实际社会要建模的对象有几亿人，一百万的样本量也相当不错了，对吧？但你暗示说，这样得到的结果是我无法通过分析推导出来的，不做模型就无法理解。这让我有点难过。我是个喜欢用纸笔的人。

And even if the actual society wanna model as a few 100,000,000, surely, a million is a pretty good sampling. Right? But you sort of hinted at the idea that therefore you get results that I couldn't derive analytically, that I couldn't figure out without doing the model. That would make me sad. I'm a pencil and paper kind of person.

我们有多确定自己只是暂时未能推导出这些结果？

How well do we know that we just haven't yet been able to derive some of these results?

我认为，实际上，模拟将帮助我们推导出更好的理论。不过，这些推导出的理论与我给你的标准模板不同。当然，有时如果系统达到均衡，理论可能成立，对吧？但我们复杂性经济学家使用的理论往往更类似于统计力学或进化生物学模型，这些模型可能包含也可能不包含均衡状态。

I think I think, actually, the simulations will help us derive better theories. The theories that get derived, though, are different than that standard template I gave you. Sure. Although sometimes it could be that, if it goes to equilibrium, could work, right? But the theories that we, complexity economists, use are often more like statistical mechanics or evolutionary biology models, which may or may not have equilibrium in them.

因此这是一个灵活得多的理论框架。但我想用流体流动做个类比——你知道纳维-斯托克斯方程可以从牛顿定律推导出来。

And so it's a much more flexible theoretical framework. But I'd like to draw an analogy to fluid flow. You know, the Navier Stokes equations, you can drive them from Newton's laws.

是的。

Yep.

你可以写下这些方程，它们看起来相当简洁，就一行公式。那些倒三角符号可能会让微积分初学者困惑，但一旦理解了数学含义，书写起来很简单。但要解这些方程，通常是无解的。

But and you can write them down. They, you know, take one line that looks pretty simple. A few little upside down triangles that, you know, confuse the novelist of calculus. But once you understand what the math means, simple to write down. Solving them, they're not solvable in general.

确实。我们现在知道原因了——因为它们具有混沌解。当存在混沌解时，通常没有捷径可走，只能一步步进行数值计算。在这方面它们具有本质的复杂性。

Sure. We now know why. It's because they have chaotic solutions. When you have chaotic solutions, there are typically no shortcuts to just grinding things out numerically one step at a time. They're intrinsically complex in that regard.

现在回到理论。流体力学的一个重大变革是我们现在有了数值流体计算技术。我们可以利用计算机能力相当准确地模拟流体行为。但这同时也极大推动了理论发展，因为现在你无需搭建风洞或申请NSF大额资助就能验证理论。因此模拟工作者和方程研究者之间形成了丰富的互动。

But now back to theory. Well, one of the big changes in fluid dynamics is we now have numerical fluid computation. We can make use of computer power to simulate what fluids do pretty accurately. But that's also been a big driver of theory because now you can test your theory without having to set up a wind tunnel and, you know, get a big grant from the NSF. And so there's a rich interaction between the simulators and the equation guys.

因此我认为，模拟能力最终会让我们获得更深层次的理论理解。顺便说一句，当我们在基于主体的模型中看到某个现象时，首先要做的就是简化它。我们会说：'让我们抓住引发这种现象的核心因素'。于是我们开始剔除无关要素，或使用非常简单的组件替代版本。如果现象仍然存在，我们就知道那不是原因。

So I actually think being able to simulate is ultimately gonna give us a deeper theoretical understanding. And by the way, let me say, when we see a phenomenon in an agent based model, the first thing we do is try and strip it down. We go, Let's get at the pulse of what's causing this. So we start throwing stuff away or using really simple dummy versions for component. If it keeps on doing it, we go, okay, that's not the cause.

所以我们尝试通过生物学家所称的'敲除实验'来找出因果关系。此外，我们经常通过'加法实验'来重现现象——即从简单模型开始，逐步添加特征并观察变化。这样你可以从两个方向来锁定因果关系。一旦完成这些，理论学家就能介入，尝试建立精简的数学模型，在某些情况下解释现象成因。

So we try and figure out the causality by doing what biologists would call knockout experiments. Also, we often get the phenomenon by doing addition experiments, meaning we start simple, we add a feature, we look at what happens, we add another feature, we look at what happens. So you can go from either direction to try and pin down the causality. And then once you do that, the theoretician can step in and try and make a stripped down mathematical model and in some cases explain what's happening.

当你说'我们'时，我猜是指复杂经济学派？我想尽量客观地问：这种理论在整个经济学界处于什么位置？我知道自己接触的经济学家样本有偏差——因为我常驻圣塔菲研究所。但你在牛津这样的顶尖学府，那里可不是什么偏远小地方。学界是否尊重这种新的经济学研究方法？

So when you say we, as in the complexity economists, I presume, I mean, trying to be as fair as possible, how does that fit into the larger economics profession? I know that I'm completely biased in the economists I talk to because I hang out at SFI, but, you know, at the major departments you're you're at Oxford. It's not exactly a small back of the, backwoods place. Are people respecting this new approach to economics?

大体上说没有。少数杰出人士是认可的——比如我的书获得了拉里·萨默斯的推荐。

By and large, no. A few exceptional individuals do. My book has an endorsement by Larry Summers

这就对了。

There you go.

这真的让我很意外。因为我把书稿初稿寄给他时写道：'拉里，我在书中多次提到你。你可以直接搜索你的名字，看看我的表述是否妥当。我希望对所有人都保持公正。'

Who really surprised me because I sent the book to the early manuscript to him saying, Larry, I use your name several times in the book. Just search for your name. Look and see if what I said is okay. And let me know. I want to be nice to everybody.

令我震惊的是，他回复说：'我读了你的书，非常认同你的观点。'虽然他指出了我的一些错误并做了修正，但总体表示赞同。这简直让我受宠若惊。

So to my astonishment, he said it back saying, I read your book and I really agree. I think you have a really good point. He made some errors. He corrected a bunch of my errors, but he said, I overall agree. So, wow, I was blown away.

我的老朋友约翰·根纳科普洛斯，他其实是拉里·萨默斯在哈佛读研时的室友。我们从八十年代末就开始争论这些问题。是的，他很欣赏这一点。我和他合著过论文。还有我在MIT的同事安德鲁·洛，像这样的人不多。但总的来说，我们的工作被主流忽视了。

My old friend John Gennakopoulos, who was actually Larry Summers' roommate when they were graduate students at Harvard, He's also we've been arguing about this stuff since the late eighties. So, yeah, he appreciates it. I've coauthored papers with him. My colleague Andrew Lowe at MIT, there's a few people like that. But by and large, what we're doing is ignored by the mainstream.

我们无法在他们的期刊上发表。他们直接说：'你们提出的理论不符合我们的标准'。就像研究圈量子引力的人想在弦论期刊上发表论文一样。是的，了解这个争议的人应该能明白。

We can't publish in their journals. Just say, You're not making the kind of theory we consider acceptable. So, you know, it's like a loop quantum gravity person trying to publish in a string theory journal. Yep. So for those of you who happen to know that controversy.

确实，我们在主流学界没有太多影响力。不过情况正在改变，我看到了一些裂缝。央行行长们开始关注我们，我们改进后的住房模型已被欧洲大约六到八家央行采用。

So, yeah, we're there's not a lot of traction with the mainstream. Now, things are changing. I've seen some cracks opening up. We're getting interest from central bankers. We now have a variation on our housing model is used by, I would say, about six or eight central banks in Europe.

现在加拿大央行正在使用一个基于主体的宏观模型，这个模型由意大利央行开发。我们在这个领域开始获得关注。我还创办了一家名为'宏观世界'的公司，致力于扩大解决方案规模并简化流程。如果有记者问我：'乌克兰战争会怎么发展？'

There's now an agent based macro model being used by the Bank of Canada, being developed at the Bank of Italy. So we're starting to get traction in that domain. And I've started a company called Macrocosm that is dedicated to scaling up the solutions and reducing the practice. So, you know, if a journalist calls me and said, What's going on? You know, What's going to happen with the war in Ukraine?

我只需要看模型运行结果，因为它时刻都在吸收最新数据。我们开始看到实际应用了。我的变革理论是：当这些实际应用获得足够关注，就像经济学家说的'要用模型打败模型'。

I just look at what the model's doing because it's ingesting the inputs all the time and staying up to date. And so we were starting to see practical applications. And my theory of change is that once those practical applications get enough traction and once you know, as economists will say, it takes a model to beat a model.

嗯。

Yep.

当我们的模型在严格的实证层面开始超越他们的模型时，他们就不得不重视起来了。

Once our models start beating their models in hard empirical terms, they'll have to start paying some attention.

确实。你必须真正拿出成果、取得一些成功，人们才会听你的，而不仅仅是因为你觉得这很酷。这在任何学术领域都是如此，对吧？

Yeah. You have to actually have a result, have some success, then people will listen to you, not just because you think it's cool. That's that's true in any academic field. Right?

对对。嗯...有时候让我稍微纠正一下。如果你觉得某件事很酷，并且它属于主流范式范围内...

Yeah. Yeah. Well Well, sometimes let me correct that a little bit. If you think it's cool and you're within the main paradigm

哦，是的。

Oh, yeah.

你可能会因为研究内容很酷而发表论文。但如果你偏离了主流范式，无论多酷，除非有实证结果，否则他们不会关注你。

You can get a paper published because it's cool. But if you're outside of the main paradigm, no matter how cool it is, they're not gonna pay attention to you till you have empirical results.

说到这些结果，我的意思是，说'2008年危机我本可以预测'是一回事。这对我来说有道理。但对于下一次危机，我们能量化到什么程度？你刚才说的让我有点担心，似乎危机几乎不可避免。

Well, speaking of those results, I mean, it's one thing to say, oh, yes. The two thousand and eight crash, I could have predicted that. You know, it makes sense to me. How quantitative can we be about the next crash? I'm a little bit worried from what you said that it's almost inevitable that there will be one.

嗯，我认为危机确实不可避免。只要看看经济史就知道，它们定期发生。1913年美联储成立前，美国平均每七年就会发生某种形式的金融危机。成立后频率降低了，但爆发的危机都成了重磅事件。

Well, I mean, I think it is inevitable there will be one. You you just look through the history of economies. They happen regularly. Before we instituted the Federal Reserve in 1913, The US on average had a financial crisis of some form about every seven years. And now, since we did the Federal Reserve, they're less frequent, but the ones we've had have been doozies.

没错。我们经历过大萧条和次贷危机，都是些大家伙。这说明我们仍不知道如何正确调控经济。在掌握方法前，应该预期还会发生危机。

Right. You know, we have the Great Depression and the Great Financial Crisis. We've had some whoppers. So we still don't know how to control the economy properly. And until we do, we should expect we're gonna have crashes.

甚至可能我们目前的控制方式在大多数时候运作良好，但当它失效时，造成的崩溃会比过去更严重——在那个我们几乎不控制任何事物的年代，除了用我们基于金属的货币对基础金属做些奇怪操作，那本身就相当随意。是的。所以我认为我们将面临更多崩溃。不过我相信基于代理的模型能更好地引导我们。或许等我们建立优质模型后，就能缓解这些崩溃的强度，甚至学会如何预防它们发生。

And it could even be that the way we're controlling it works well most of the time, but when it fails, it actually makes a bigger crash than would have happened in the old days, where we weren't really controlling much of anything, other than doing weird stuff with the base metal we used with a metal we used based currency on, which was pretty arbitrary. Yeah. So, yeah, I think we are gonna have more crashes. Now, I do think that agent based models could help guide us better. And maybe once we get good models, we can soften the intensity of those crashes, or maybe even just understand how to keep them from occurring.

我不知道。尚无定论，但我持乐观态度。

I don't know. The jury's out, but I'm I'm optimistic.

说到2008年危机，你和其他人都提到过新型金融工具的关键作用。我不确定复杂性经济学是否能帮上忙，但我们是否能更早意识到这类风险？

Well, I guess for the 2008 crash, you mentioned, everyone has mentioned the crucial role played by novel financial instruments. Is that something I don't know if complexity economics helps us here, but is that something that we can sort of be more cognizant of the dangers of ahead of time?

没错。我在书中讨论过所谓的市场生态学。传统市场理论——主导大量讨论的有效市场理论认为你无法战胜市场，这既是信息效率，也是配置效率。

Yeah. Definitely. One of the things I talk about in my book is what I call market ecology. The classic theory of markets, you know, that's dominated a lot of the discourse is efficient market theory, and the idea is that you can't beat the market, but also so that's informational efficiency. But also it's allocational efficiency.

我们对不同活动的资源配置是正确的。市场信息效率很高，但我在预测公司却能持续战胜它。明白吗？

The allocations of effort we're making to different activities are correct. And the market's pretty informational efficient. I managed to beat it. We managed to beat it at a prediction company by a pretty steady rate. You know?

我们只是幸运猴子的概率几乎为零。但资源配置可能大错特错，危机就是这样发生的。市场生态学理论就像我之前说的生产系统与实体经济理论，但理解角度是那些专业化的投资者物种——巴菲特有他的方式，

Odds that we were just lucky monkeys are so close to zero as to be negligible. But the allocations can be quite wrong. And that's what happens in crises. So And under the theory of market ecology, it's like the theory I said before about the production system and the real economy, but the way to think about it is those different species of investors, and they're all specialized. Warren Buffett does his thing.

还有趋势跟踪者约翰·亨利，他认为上涨趋势会延续，通过价格微小模式决定买卖。做市商也是，我能轻松列举金融市场15到20种不同策略类型。

There's another guy, John Henry. He's a trend follower. He says if the market's been going up, it'll keep going up and looks at little patterns and prices to decide when to buy and sell. You know, they're market makers. You can make a list of, I can easily write down 15 or 20 different types of strategies in financial markets.

尽管人们执行这些策略的方式各有不同，但大体上就像物种与生物学的关系。根据市场生态学理论，我们需要将市场视为一个由专业参与者组成的生态系统。它们以市场低效为食，正是这些行为推动着市场效率，但永远无法达到完全高效。我们能看到围绕完全效率的波动，尤其当新情况出现时。

While there's variation in how people execute those strategies, they're broadly, it's like species and biology. And so under the theory of market ecology, we need to think of the market as an ecosystem with specialized actors. They feed off of the inefficiencies in the market. There's what they are what's making the market efficient, but they never achieve perfect efficiency. We see swings around perfect efficiency, particularly when new stuff happens.

要知道，抵押贷款支持证券只是导致市场出问题的几种新型金融工具之一。根据这个理论，你可以模拟市场动态并理解市场失灵的原因。有效市场理论假设市场完美运行，所以无法解释失灵原因。这就像支撑杆平衡的低效市场理论。

You know, Mortgage backed securities are only one of several examples of new financial instruments that caused bad stuff to happen in markets. Yeah. And so under that theory, you can then simulate what's going on in markets and understand why markets malfunction. The efficient market theory assumes they work perfectly, so it doesn't give you any insight into why they malfunction. It's like the pole balancing inefficient market theory.

它总是笔直向上的。如果想理解为何会偏离垂直状态，就需要其他方法，我认为市场生态学理论是关键。这能让我们——回到你的问题——我认为监管机构应该模拟市场。他们掌握所有数据来理解这些'物种'及其互动方式，因为他们能观察到每个人的行为。

It's always straight up. Right. If you wanna understand why it deviates from straight up, you have to do something else, and I maintain theory of market ecology is the key. And that then will allow us one of the things, back to your question, regulators, I argue, should be simulating the market. They have all the data to understand the species and who they are and how they interact, because they can see what everybody does.

我们可以建立标准市场模拟，每当新型金融工具出现就将其纳入。就像生态学中的入侵物种，我们通过测试来观察其副作用。如果在金融危机前就这么做，我们本可以预见到

We could have a standard simulation of markets, and whenever a new financial instrument comes in, we put it in there. It's like an invasive species in ecology, and we test it out to see what its side effects are. And if we were doing that leading up to the great financial crisis, we would have seen

我明白了。

I see.

是的，高杠杆使用抵押贷款支持证券的副作用。

Yeah. The side effects of mortgage backed securities used with high leverage.

所以你是想说服央行、规划者和预测者们主动采取这种方案？

So you're trying to convince, central banks and and planners and and prognosticators to take this approach proactively?

实际上我正在尝试说服SEC（美国证券交易委员会），因为你看，中央银行作为央行，只能看到自己那部分情况。你清楚自己的立场，但不知道其他人在做什么。而SEC可以查看任何它想查看的内容，并在出现问题时介入。

Well, I'm actually trying to convince the SEC because, see, central banks, as a central bank, you only see your part of the story. You know your positions. You don't know what everybody else is doing. But the SEC can look at anything it wants to and does whenever something goes haywire.

好的，这说得通

Okay, that makes

数据可能源源不断地流入，他们可以模拟正在发生的情况，并进行反事实实验。那么我们需要担心这里的杠杆率过高吗？调高杠杆。哦等等，我们得让人们降低杠杆。

And so the data could just be flowing in, they could be simulating what's happening, and they could be doing counterfactual experiments. So do we need to be worried about leverage getting too high here? Crank it up. Oh, wait a minute. We gotta get people to lower their leverage.

所以你是在计算机里研究各种可能性世界。

So you're investigating possible worlds in your computer.

没错，正是如此。

Yeah. Exactly.

我要说明一下，SEC是指美国证券交易委员会，这是为那些非美国听众解释的。好的。我想关于复杂性的最后一个宏观问题是——像我们这样热衷于复杂系统的人常有的一个担忧是：这些系统缺乏实质关联。经济、生物、互联网，这些都是截然不同的领域。

And I should say SEC is Security and Exchange Commission for those non Americans listening to us. Okay. I mean, I guess maybe the last thing to ask about one more big picture question about complexity. One of the worries about people who are enthusiastic about complex systems such as ourselves is that there's no there there. There's the economy, and there's biology, and there's the Internet, and these are very different things.

确实。你对经济的研究在多大程度上受益于纯粹从复杂系统角度思考，或是从非经济学的类比系统（比如你已提到的生物学案例）中获得的启发？

Yeah. To what extent have your investigations into the economy actually been helped by thinking about complex systems for their own sake or or analogous systems that are not economics? You've already given us some examples with biology.

是的，他们确实因此受益。你知道，在我的职业生涯中，我一直面临无法融入任何单一学科的问题。所以我成为了最跨学科的人之一，因为我的整个职业生涯都在跨越不同学科领域。顺便说一句，你刚才说我在牛津大学，但实际上我隶属于地理与环境学系。

Yeah. Well, they've certainly been helped by that. You know, in my career, I've always had the problem that I never fit into any discipline. So I'm one of the most interdisciplinary people around because I've straddled disciplines without being in one through my whole career. And by the way, you said, Oh, you're at Oxford, but actually I'm in the Department of Geography and the Environment.

我很抱歉。

I'm sorry.

我并不是经济系的。这没什么错。

I'm not in the Economics Department. That's not wrong.

好的，非常好。

Okay. Very good.

这是典型现象。美国没有哪个经济系在研究复杂性经济学。我是说，好吧，布莱克·利伯林确实在布兰迪斯大学的经济系。

And that's characteristic. There are no economics departments in The U. S. That do complexity economics. I mean, okay, Blake Liberin sits in an economics department at Brandeis.

他算是个例。还有少数几个人，但一只手就能数得过来。乔治梅森大学有个由罗布·阿克塞尔主持的计算社会科学项目，但那不属于经济学。在欧洲确实有些经济系在做这个方向，但他们在该领域地位不高。

He's one guy. There's a few people, but I can name them on the fingers of one hand. Yeah. You know, there's a computational social science program at George Mason run by Rob Axtel, but it's not economics. Now in Europe, there are some economics departments that do this, but they're not the ones with a lot of status in the field.

他们被迫在那些被视为低级的期刊上发表文章。所以这是个边缘群体。想从事这方面研究的学生仍然很难找到工作。经常有敏锐的年轻学生来找我，说'我对现在或将接受的经济学教育不满意，能跟您学习吗？'

They're forced to publish in what are viewed as inferior journals. And so it's an out group. So it's still a struggle to get jobs for students that wanna do this kind of thing. I'm approached by, you know, perceptive young students who say, I'm not happy with what I'm getting taught or would be taught in economics. Can I work with you?

首先我要说的是，没错，但你毕业后不会在哈佛经济系找到工作。是的。你知道，我现在有些学生在世界银行和国际货币基金组织工作。他们正在西班牙银行崭露头角。我有学生现在从事那方面的工作，但那边思想更开放些。

And the first thing I have to say is, yes, but you're not gonna get a job in the Harvard economics department when you're done. Yeah. You know, I do have students now who are at the World Bank, at the International Monetary Fund. They're getting out there at the Bank of Spain. I have students who are now in that side of things, but there's more open mindedness there.

你知道，这是市场低效的表现。一个院系可以通过更认真地对待这些事情实现跃升。是啊。是啊。让我们希望他们能做到吧。

You know, it's a market inefficiency. A department can, leap upward by taking this stuff more seriously. Yeah. Yeah. Let's let's hope that they do.

多因·法默，非常感谢你参加《心智景观》播客。这真是太棒了。

Dohin Farmer, thanks so much for being on the Mindscape Podcast. This is very great.

这是我的荣幸。

My pleasure.

嗨。

Hi.