A New Model of Economy

Economic models

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Economic Models: Simulations of Reality

Inequality seems to have become the topic of our times, even though barely a decade ago it was politely kept off the agenda. So what kind of economic mindset can help bring it about? Certainly not 20th-century thinking on inequality, which was ruled by a spurious economic law of motion. In , Kuznets gathered together patchy historical data on income distribution in the US, UK and Germany, and he thought he saw a pattern: Plotted on the page, it looked like an upside-down U.

Kuznets was the first to acknowledge that this finding went against his intuition: The underlying message — that rising inequality is an inevitable stage on the journey towards economic success for all — was too good a story to doubt and the Kuznets Curve was taught to every student for at least the next 50 years. That matters because it wordlessly whispers a powerful message: So what new paradigm can replace this outdated myth and its accompanying intellectual graffiti?

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And thanks to the emergence of network technologies — particularly in digital communications and renewable energy generation — we have a far greater chance of making this happen than any generation before us. As we do so, we should also deepen the ambition of the redistribution agenda. In the 20th century, policies promoting redistribution were largely focused on redistributing income — by raising taxes, increasing transfers, and implementing minimum wages — along with investing in key public services such as health and education.

Instead of focusing foremost on income, 21st-century economists will seek to redistribute the sources of wealth too — especially the wealth that lies in controlling land and resources, in controlling money creation, and in owning enterprise, technology and knowledge. And instead of turning solely to the market and state for solutions, they will harness the power of the commons to make it happen.

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Most economic models rest on a number of assumptions that are not entirely realistic. For example, agents are often assumed to have perfect information, and markets are often assumed to clear without friction. Or, the model may omit issues that are important to the question being considered, such as externalities. Any analysis of the results of an economic model must therefore consider the extent to which these results may be compromised by inaccuracies in these assumptions, and a large literature has grown up discussing problems with economic models , or at least asserting that their results are unreliable.

One of the major problems addressed by economic models has been understanding economic growth.

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An early attempt to provide a technique to approach this came from the French physiocratic school in the Eighteenth century. These tables have in fact been interpreted in more modern terminology as a Leontiev model, see the Phillips reference below. All through the 18th century that is, well before the founding of modern political economy, conventionally marked by Adam Smith's Wealth of Nations simple probabilistic models were used to understand the economics of insurance.

This was a natural extrapolation of the theory of gambling , and played an important role both in the development of probability theory itself and in the development of actuarial science. Many of the giants of 18th century mathematics contributed to this field. Around , De Moivre addressed some of these problems in the 3rd edition of The Doctrine of Chances.

Even earlier , Nicolas Bernoulli studies problems related to savings and interest in the Ars Conjectandi. In , Daniel Bernoulli studied "moral probability" in his book Mensura Sortis , where he introduced what would today be called "logarithmic utility of money" and applied it to gambling and insurance problems, including a solution of the paradoxical Saint Petersburg problem. All of these developments were summarized by Laplace in his Analytical Theory of Probabilities Clearly, by the time David Ricardo came along he had a lot of well-established math to draw from.

In the late s the Brookings Institution compared 12 leading macroeconomic models available at the time. They compared the models' predictions for how the economy would respond to specific economic shocks allowing the models to control for all the variability in the real world; this was a test of model vs. Although the models simplified the world and started from a stable, known common parameters the various models gave significantly different answers.

Partly as a result of such experiments, modern central bankers no longer have as much confidence that it is possible to 'fine-tune' the economy as they had in the s and early s. Modern policy makers tend to use a less activist approach, explicitly because they lack confidence that their models will actually predict where the economy is going, or the effect of any shock upon it.

The new, more humble, approach sees danger in dramatic policy changes based on model predictions, because of several practical and theoretical limitations in current macroeconomic models; in addition to the theoretical pitfalls, listed above some problems specific to aggregate modelling are:. Complex systems specialist and mathematician David Orrell wrote on this issue in his book Apollo's Arrow and explained that the weather, human health and economics use similar methods of prediction mathematical models. Their systems—the atmosphere, the human body and the economy—also have similar levels of complexity.

Economic model

He found that forecasts fail because the models suffer from two problems: This is because complex systems like the economy or the climate consist of a delicate balance of opposing forces, so a slight imbalance in their representation has big effects. So for example, in chemistry, chemists have tried to understand at a high level, well, how do molecules in a container behave?

Let's say molecules of gas. Well, you could imagine if you have a container here with trillions upon trillions of molecules, this is incredibly complex but by making some simplifying assumptions about the type of interactions these particles will have or don't have, they can come up with models like the ideal gas law which you might be familiar with or not from your chemistry class that relates the pressure to the volume to the number of particles you have to the actual temperature and so this right over here where you're taking something that's hairy and complex and making simplifying assumptions to help you understand it, this thing right over here is a model and this is in other fields as well.

Sometimes, it's not an equation. Sometimes, it might be a simpler organism. For example, in biology, human beings are incredibly complex organisms and not only are they incredibly complex but certain forms of experimentation would also feel fairly unethical to our modern moral ethos and so what do biologists do?

Well, they make simplifying assumptions or they pare down, they say, okay, we can't do that study on human beings but maybe we can simplify the problem by looking at simpler organisms. Maybe you can look at an individual cell right over here. Maybe you can look at things like fruit flies which are famous in the study of genetics.

Maybe you can even look at fairly complex organisms. Even a mouse is a very complex thing but it's still simpler than a human being and at least to our modern ethics, we're willing to do certain things to mice that we aren't willing to do to human beings and so that's why in a biological context, you will hear people talk about things like a mouse model where they will test a drug on a mouse or try to understand how something happens in a mouse and then say, well, that's a pretty good indication that might be happening to human beings.

In fact, when they do drug trials in medicine, they often will do it on mice first and when they have good confidence that it works there and that it's fairly safe, only then will they start to do the experiments on human beings. Well, economists are doing the same thing.

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