The video explores the perceived inadequacies of university-level economics education, advocating learning system dynamics as a more practical alternative. The narrator criticizes modern economic curricula as outdated and disconnected from real-world economic behavior. According to the speaker, institutions persist in teaching theories proven empirically false, such as the concept of rising marginal costs, which starkly contrasts with what businesses report about constant or declining costs.

The discussion moves on to Steve Keene's stance, which suggests abandoning traditional economics degrees in favor of system dynamics. By providing insights into the studies and research supporting his claims, the economist underscores the necessity for a dynamic understanding of economic systems using approaches developed by pioneers like Jay Forrester, emphasizing that system dynamics align better with real-world economies characterized by non-equilibrium.

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Key Vocabularies and Common Phrases:

1. empirically [ɛmˈpɪrɪkli] - (adverb) - Based on observation or experience rather than theory or pure logic. - Synonyms: (pragmatically, experimentally, observationally)

He's still teaching this stuff, even though he found out 15 years ago it's empirically false.

2. prototype [ˈproʊtəˌtaɪp] - (noun) - An early sample, model, or release of a product built to test a concept or process. - Synonyms: (model, sample, example)

So when you put it together and say, what does that mean in terms of cost of production? What do firms actually do? You find that they, first of all, when they build a prototype, they work out the cost of production, the variable cost of each unit

3. epicycles [ˈɛpɪˌsaɪkəlz] - (noun) - A geometric model used to explain the variations in speed and direction of the apparent motion of the Moon, Sun, and planets. - Synonyms: (loop, orbit, cycle)

Learning economics at a university is like learning to make astronomy, earth-centric equilibrium epicycles being added to make your models fit the data.

4. diminishing [dɪˈmɪnɪʃɪŋ] - (verb) - Making or becoming less. - Synonyms: (reducing, decreasing, diminishing)

They talk about what's called diminishing marginal productivity, which lies behind the idea of rising marginal cost.

5. sustainability [səˌsteɪnəˈbɪlɪti] - (noun) - The ability to be maintained at a certain rate or level. - Synonyms: (durability, endurance, maintainability)

It's naive. Best way to describe it.

6. dynamics [daɪˈnæmɪks] - (noun) - The forces or properties that stimulate growth, development, or change within a system or process. - Synonyms: (forces, changes, activity)

We should have to treat it as a problem of motion, of problem dynamics.

7. equilibrium [ˌiːkwəˈlɪbriəm] - (noun) - A state in which opposing forces or influences are balanced. - Synonyms: (balance, stability, steadiness)

It is as absurd to assume that the variables in the economic organization will stay put in perfect equilibrium as to assume that the Atlantic Ocean can never be without a wave.

8. ravel [ˈrævəl] - (verb) - To untangle or unravel something. - Synonyms: (untangle, unravel, unfold)

I designed a program called Minsky, which is now called ravel.

9. algebraic [ˌælʤəˈbreɪɪk] - (adjective) - Relating to or involving algebra, a field of mathematics. - Synonyms: (mathematical, numerical, arithmetic)

Instead, they use algebraic equations.

10. desiderata [dɪˌzɪdəˈrɑːtə] - (noun) - Things that are needed or wanted. - Synonyms: (needs, requirements, essentials)

Virtually all economic theories have a primary desiderata that the behavior described must be consistent with some sort, some notion of equilibrium.

Unveiling the Obsolete Economic Theories Refreshing the Approach

So it's not that economics is not a discipline worth deeply studying, it's that the university education around economics is so bad, is bad. All of them teach this guff. He's still teaching this stuff, even though he found out 15 years ago it's empirically false. And you'd think he'd change what he teaches in his textbook. 70 surveys that have found this and economists have ignored it.

So I'd say learn system dynamics, learn that technology, learn how to apply that. You can apply it in any field whatsoever. It handles what the real world actually is, which is a dynamic non-equilibrium system.

The influential contrarian economist Steve Keene, brilliant economist that criticizes much of modern economics. The research fellow at the Institute for Strategy, Resilience and Security at University College in London. He is someone that each and every one of us has to listen to, whether we agree or disagree.

Here's Steve Keene. Why I reckon you should not study economics at the university. It sounds ridiculous. Advice. Don't study economics if you're interested in economics, but it's genuinely realistic. Let's see why.

Can you give advice to young people in high school and college? Maybe they're interested in economics. What advice would you give them about a career they can have that they can be proud of, or a life they can be proud of, mainly in a career? I say don't do an economics degree and say screw it to an economics degree. Yeah. Because what you learn is an obsolete technology.

Learning economics at a university is like learning to make astronomy, earth-centric equilibrium epicycles being added to make your models fit the data. So it's not that economics is not a discipline worth deeply studying, it's that the university education around economics is so bad. Is bad, yeah.

So I'd say learn system dynamics. I'll get system dynamics in the next part of the reaction here, but I want to show you why I make that claim about just not being worth your while to learn economics at universities. This is one of the slides in one of the set of lectures I give. This is an extract from Mankiew's textbook showing the cost of production of producing lemonade. This is from Alan Blinder talking about. I think he's making the cost of making garage.

This is from Nordhaus's textbook. All the same basic sort of idea. Each actual unit you produce costs more to produce produced because of what they call diminishing marginal productivity. So they all got rising per unit cost. As you increase the level of output, profit maximizing, that's enough just to see what they'll teach, all of them teach this guff.

And why is it guff? Because if you actually go and take a look at what firms tell you, they tell you this is their cost structure. They say they have constant per unit costs. In fact, often falling per unit cost, average fixed cost, obviously must come down like a rectangular hyperbola, because you've got a fixed cost dividing by larger and larger number of units. And they all set a list price at which they sell.

And there's therefore a gap between the variable cost and the money they get for selling each unit right out from the start of production out to the capacity of the plant. Now, about 70 surveys that have found this, and economists have ignored it.

And the intriguing thing is that in the late 1990s, Alan Blinder, who's very prominent neoclassical economist, did a survey of firms and found the result. I'm showing down in yellow here, the overwhelmingly bad news for theory here is that only 11% of GDP is produced under conditions of rising marginal cost, almost half as produced under constant marginal cost and a 40% declining marginal cost. This is the drawing he did, rather than showing the rising marginal cost that was on.

That's what they all draw, marginal cost rising. And he went out and found, well, actually, Marshall cost is falling, well, constant. Now, he writes a textbook, and you'd think that after doing his research, he'd change what he teaches in his textbook. This is what he teaches in his textbook that was published 1215 years after he did that research. He talks about what's called diminishing marginal productivity, which lies behind the idea of rising marginal cost.

He's still teaching this stuff, even though he found out 15 years ago it's empirically false. And what I do in the courses that I teach is I go and show what the actual cost situation is for firms, how they actually do maximize profits. So I start from this idea, which, as I said, it's been found in about 70 surveys. And economists continue ignoring this work.

So when you put it together and say, what does that mean in terms of cost of production? What do firms actually do?

You find that they, first of all, when they build a prototype, they work out the cost of production, the variable cost of each unit. So when Tesla first designed the model three, they worked out what the cost was for the variable inputs, the labor, the sheet metal, the magnets, et cetera, et cetera. And they just put that's what they expect to pay that cost or lower as they increase the volume. Then they work out the markup. They can get away with how much price.

How much profit margin can they make? That depends upon the competition from the car manufacturers. And of course, they could put a large markup when they first started, because they were the first electric car. Volume sales, that's the price they charge at. And then they work out what target sales they'd like to achieve. They work out whether they can make a factory, that producers can produce that and still make a profit at that level of output.

And that's what they do. They work at breakeven point, and if they think they can sell more than the breakeven, you go ahead and build the factory. And then as you increase production, the more you increase production, the more you make a profit. So if you reach your target, you make a target level of profit. But if you manage to sell more than your target, you make a bigger profit with actually a bigger margin between your total costs and your price, and a larger quantity as well.

That's why the basic rule of any large company is sell as many units as you can not equate marginal cost and marginal revenue, which is what textbooks will teach you, which is the myth, because the conditions that make that a way of maximizing profits simply don't apply in the real world.

So don't study an economics degree, they'll fool you ahead with nonsense. And that's why I say forget about it. If you want to learn how to think about the economy, I'd say learn system dynamics. Do a course in system dynamics which you can apply in any field, and then apply what you learn out of system dynamics to the issues of economics, if that's what interests you.

So get a sort of base engineering education. A base engineering education that is far better than doing an economics degree. Let's see what I'm talking about with system dynamics. That began with the work of a guy, engineer called J. Forrester. Back in the 1950s.

Forrester had been responsible for designing the survey mechanisms on the gun turrets of american warships during World War two, which enabled them to shoot accurately even though they're going up and down in shopee sees, then became an expert in management, as you can see here, the professor of management at the MIT Sloan school.

And in the late 1950s, he decided to take a look at economic theory. And this is the report that he gave to his faculty seminar. This is reproduced as the most important initial paper in system dynamics in 2003. And what he did was he read a small part of literature on the economics of the industrial field, and he was frankly horrified.

One of the striking shortcomings is their failure to reflect adequate structural form of regenerative loops, feedbacks that occur, the flow of models, money, materials, etcetera. This has to be shown as a set of cycles. And he says they don't do it. Instead, they use algebraic equations. So the recent models neglect the flows of money, goods, information and labor.

He carries on a bit further here and says they use linear equations. When the essential features of the model, that the system that are being modeled are nonlinear, they might be suitable for long range prediction, but they use them for short run. And this is the crucial point here.

The models are formulated in terms of systems of simultaneous algebraic equations. These impress me as being particularly unsuited to the kind of behavior being studied now. These are models in the fifties. What he said back then is even more true of what models are done today by neoclassical economists.

Again, this is very often the model and its results are judged by the logic with which it's derived over its founding assumptions, whereas the failures probably lie on those assumptions, so on and so forth. Now he's castigating neoclassical economics.

But if you go back in time and take a look at the founders of neoclassical economic, you'll find that they were actually aware of the sorts of issues that Forrester raised almost a century later. So have a look at this.

We must carefully distinguish between the statics and dynamics of the subject. The real condition of industry is one of perpetual motion and change. If we wish to have a complete solution of the problem in all its natural complexity, we should have to treat it as a problem of motion, of problem dynamics.

But it would surely be absurd, and this is unfortunately where it all went wrong. It would surely be absurd to attempt the more difficult question when the more easy one is yet so imperfectly within our grasp. In other words, Jevon, and this is quoting from Jevons theory of political economy, saw working with algebraic equations and equilibrium and so on as a stopgap until the methods to do dynamic analysis were developed, which of course happened about a century later.

And Forrester played a major role in doing that. Now, all sorts of reasons which, if you join me in my courses as to why this happened. But over time, the vision of the economy as a short gap of a clud necessary to just make analysis possible before we had the tools to handle dynamics.

Instead, it became seen as a fundamental defining characteristic of capitalism. Economic models here strictly the importance of equilibrium as the part of any theory. And this is seen as being scientific behavior.

As in the physical sciences, equilibrium is a central concept in economics. Virtually all economic theories have a primary desiderata that the behavior described must be consistent with some sort, some notion of equilibrium. It's naive. Best way to describe it.

This is by Ed Lazare, who actually quite a nice guy. I met him about 15 years ago in Australia after the financial crisis, during which he'd been Bush's chief economic advisor, telling him everything was going to be hunky dory. And then the whole world fell apart around him.

So he realised there was something wrong with his thinking, but it was too late. That's what he was locked into believing. Notice the title of the paper, economic imperialism. This is a paper commissioned by the quarterly journal of economics, which is the dominant journal in the discipline and completely dominated by neoclassical thought. And it was his responsibility to write a survey of where economics was in 2000.

Looking forward, and aren't we a great science, etcetera. Now, this fantasy that equilibrium is a way to describe capitalism is something which has dominated neoclassical economics ever since. Jevons, comets, unfortunately.

And occasionally economists get a wake up call when they take their own ideas too seriously. And this is Irving Fisher, who was the leading neoclassical mathematical economist before the Great Depression. And then in the Great Depression, in the great crash, he was wiped out by the stock market crash. And then he looked back and said, what led me astray?

And this is his fabulous paper called the debt deflation theory of great depressions. And the most important thing, he realized, Washington, you should not rely about the economy as if it's an equilibrium. So this is a set of points you made in that paper.

We may tentatively assume that almost all variables tend in a general way towards a stable equilibrium, so still that idea. But the exact equilibrium thus sought is seldom reached and never long maintained. New disturbances are sure to occur, so that, in actual fact, any variables almost always above or below the ideal equilibrium.

And then he says, it is as absurd to assume that the variables in the economic organization will stay put in perfect equilibrium as to assume that the Atlantic Ocean can never be without a wave. That's what we should be understanding, and that's what Jay Forrester realized.

And then, a few years after he wrote that paper, he started to develop the methods for system dynamics, which involve the capacity to model dynamic systems properly. And he was commissioned to investigate why a particular manufacturing company found it had cycles in its manufacturing system.

And he found it was actually a question of all the time lags, delays and feedbacks involved in going from initially deciding to produce a product through to marketing, through to the sales, and then its effect upon other elements of the system and so on. So Forrester then commissioned the writing of the very first system dynamics program, which is called simple, which stands for simulation of industrial management problems with lots of equations. Very clever acronym.

And so, as we learned from that, ultimately software called Vensim, or stellar, took over the field. And this is what people learn when they learn system dynamics today. Now, I, having spent 20 years of my life working in the computer industry, I regarded this as a very old fashioned and cludgy user interface.

And if you just take a look at the arrows going everywhere on this system, capital, et cetera, et cetera, there are no equations visible there. And if you want to see the equations, you have to click on this tool and then point at what you're looking at, and you get a box that comes up, and there's your equation.

So the rate of change, or capital, is the integral of capital investment minus capital depreciation. And all the equations are like that. So, if I take a look at the one for a fraction of agriculture, they're all text written equations, very much like what you put in a standard computer modeling program, putting it in python, all that sort of thing.

So I never particularly even. I like the concept. I never liked the technology that was used to do system dynamics. So I designed a program called Minsky, which is now called ravel.

Their major idea was to make the equations visible on the canvas itself. You don't have to click inside a box. You see them straight away. So, this very simple model here has output divided by an output.

Labor ratio is labor divided by population is the employment rate, wage, the rate of change of wages is here, etc, etcetera. So the equations are all visible. And if I simulate this model, which I'll just quickly start doing now, you see the simulation running live. You don't see a static result of the model later. And that is what I teach in my courses.

And the thing which is unique about Minsky, the reason ravel, now we call it that I designed it in the first place, is because it was incredibly hard to use this flow sharp paradigm to mold the monetary system. Now, what do accountants use? They use double entry bookkeeping.

So what this part of ravel does is lets you build a model using double entry bookkeeping. And you simply say, well, credit increases loans and increases deposit. Interest. Payments go from deposits to the bank. The banks spend back onto the customers. There's not a single differential equation in sight there, but they're all being generated by the program in the background.

And then you can run a model like this, and you can see what happens if you have a boost to credit or a fall in credit and so on. You can use it as a control panel. And it's not just usable for economics.

I think that's where it's most important. You can simulate any system at all. So this is what's called Lorenz strange detractor. This was the very first chaotic model discovered.

Well, the second model, but the first one simulated, done by meteorologist Edward Lorenz in the mid-1960s. And what he got out of it was he expected to see an unstable system, but the nature of the dynamics was a complete surprise to him.

And this now is the sort of modeling that is at the basis of all the weather forecasting we see these days. There's much more to weather forecasting than just this approach. But the idea that the weather is unstable, that you have feedback effects, et cetera, et cetera, that is commonplace in meteorological modelling. Economists haven't got the message yet. They still think they can do everything with equilibrium.

So the advice is learn that technology, learn how to apply that. You can apply it in any field whatsoever. It handles what the real world actually is, which is a dynamic non equilibrium system.

Go to university, they'll teach you about equilibrium. You might as well learn epicycles and equants. You might as well believe the earth is the center of the universe for how relevant that is to a real economy.

Economics, Education, Technology, Steve Keene, System Dynamics, Innovation