As AI technology continues to progress, its effects may forever change the world of economics and economic policymaking. I recently had the pleasure of joining David Beckworth on the Macro Musings podcast to discuss these transformative changes. Here’s a summary of our conversation and my thoughts on the matter. Listen to the podcast here.

My Move from Macro to AI

I’ve been an economist for nearly 30 years, with a background in government, the banking sector, and running my own consultancy. Data has always played a significant role in my work. However, with the advent of the internet, increased computing power, and advancements in machine learning, we’re seeing a shift where all markets are beginning to behave like financial markets, with prices becoming less sticky.

In 2015, I resigned from my banking job to start Markets & Money Advisory, with the goal of doing more academic work and exploring sports economics and analytics. The release of ChatGPT sparked my interest in AI.

Initially, I used it for Python coding and econometric analysis, leading to significant productivity gains. This led me to establish an AI advisory company called PAICE with my partner Christian Heiner Schmidt, where we advise financial and retail companies on using AI and data to make decisions.

The Basics and Implications of Dynamic Pricing

Dynamic pricing, or flexible pricing, reacts to supply and demand changes. While there are costs associated with changing prices (menu costs), technology is reducing these costs. In Scandinavian supermarkets, for instance, electronic price tags allow for frequent price adjustments from headquarters.

This shift resembles the transition in finance from open outcry trading to electronic trading. As the cost of implementing dynamic pricing decreases, we’re moving towards a world where retail prices could change as frequently as gas prices do today. Imagine a future where Jerome Powell’s statements influence the price of milk in Copenhagen instantaneously. This would result in a world where prices are much more flexible, significantly impacting monetary policy.

Using AI for Econometric Analysis

I’ve been exploring AI’s potential in econometric analysis. For instance, I used ChatGPT to model Gary Becker’s Rational Addiction Model for calories and anti-obesity medicine. More recently, I asked ChatGPT to create a New Keynesian DSGE model (see my post on this here). The results were impressive, as it provided Python code and helped troubleshoot errors, demonstrating AI’s potential as a powerful tool for economic analysis.

This technology allows us to perform complex econometric analysis much faster, improving productivity and enabling more sophisticated research. However, the quality of analysis still depends on the economist’s expertise and understanding of economic principles.

The Implications of AI for the Economics Field

AI is essentially advanced statistics, evolving from traditional econometrics. While it enhances our productivity, it doesn’t replace the need for critical thinking and economic insight. Economists must still understand the underlying principles and implications of their analysis.

In the future, the role of economists may shift from government and finance to the broader economy. Just as the quants moved into finance in the ’90s, we might see economists applying their skills in diverse industries like retail, where dynamic pricing and data-driven decision-making become more prevalent.

How Will AI Impact the Federal Reserve and Its Policymaking?

AI’s ability to process and analyze vast amounts of data could transform the Federal Reserve’s operations. As markets and prices become more flexible, the Fed’s traditional tools and models may become less relevant. AI could enable more accurate macroeconomic indicators and better policy decisions.

However, the need for a large staff of economists at the Fed might decrease. As technology reduces the costs of monetary policy mistakes, the Fed’s role could shift towards monitoring and adjusting to real-time data rather than extensive analysis and forecasting.

Deflation as a Response to an AI-Driven Productivity Shock

George Selgin’s idea of allowing mild, gentle deflation in response to rapid productivity gains is intriguing. In a world where AI drives significant productivity improvements, output prices could fall, benefiting consumers and sharing real gains widely. Keeping demand stable while allowing prices to decrease gradually could ensure that everyone benefits from AI’s advancements.

In conclusion, AI has the potential to revolutionize economics and monetary policy, making processes more efficient and data-driven. While there are challenges and adjustments ahead, embracing this technology will enable us to navigate the future with greater insight and agility.

Contact:

Lars Christensen

LC@paice.io

+45 52 50 25 06

In my AI/ChatGPT workshops, I often ask ChatGPT to create an image of “an economist and a Muslim woman” to illustrate how AI “thinks.” Artificial intelligence, in its broadest sense, is fundamentally about pattern recognition or prediction.

In economic theory, we talk about “statistical discrimination,” which occurs when a person is discriminated against based on the “median” or “average” characteristic of a group. A classic example is the police tending to stop more ethnic minorities in various checks because this group is overrepresented in crime statistics as a whole.

Similarly, it is statistical discrimination when people say that women earn less than men. It is true on average, but that doesn’t mean ALL women earn less than men. Often, it can be quite reasonable to use statistical discrimination. If the police are looking for a criminal, they don’t search in school classes or nursing homes. At least, they don’t start there if they want to be successful in solving the crime.

Generally, our brains use “grouping” to solve problems, and this is often a sensible strategy, at least initially. Large Language Models (LLMs) like ChatGPT are no different. Thus, when LLMs try to provide an answer, it is based on pattern recognition from the data the model has been fed. And if you ask ChatGPT to create an image of an economist and a Muslim woman, you get an “average” response.

Below is an example. I asked ChatGPT for “a funny drawing of an economist, a basketball player, and a Muslim woman.” What do we see?

• The economist is a man. He is wearing a suit and tie (and has a huge calculator).
• The basketball player is a man – he is black (and tall).
• The Muslim woman is wearing a headscarf.

We get precisely what we expected, but some might argue that this is a bias. However, I would say it is more an expression of “averageness.” We get the most likely answer.

The way ChatGPT presents these three characters is probably the way most of us would have drawn them if we could draw that well. This doesn’t mean there aren’t female economists, short white basketball players, or Muslim women without headscarves. These are, however, all minorities.

We must remember that ChatGPT and other language models are trained on the data that exists in the real world. If you do a Google search for “Muslim woman,” you will find images of women, almost all of whom are wearing headscarves. So the “reality” that ChatGPT sees is precisely the same “reality” that Google sees/creates. This does not mean it is the only reality, but it is the average reality.

The Average Reality is Boring

Language models generate text and images (and sound and film, for that matter) in the same average way. So when, for example, you ask ChatGPT to write a text on a subject, it often feels “bland” – boring or without edge. A text about economics often feels like it was written by a dull bank economist. Correct – but not very exciting to read and certainly not thought-provoking.

This is because when ChatGPT and other LLMs write a text, it is essentially an expression of the average text the model has been trained on. Therefore, the result is average. And it is often good enough. Often, we need text that is neutral and completely without color and edge.

That said, you can create edge by “prompting” the language model correctly. For example, you can ask ChatGPT to write a text about inflation as if it were written by a pirate or as a script for a Pink Panther movie. But LLMs can be trained on a different reality.

Alternatively, and more effectively, you can train a language model on your data so that the output reflects the data you have input into the model. You could, for example, imagine a Lars Christensen chatbot, where we input all the texts I have written about economics on LinkedIn, Facebook, Twitter, form this blog (The Market Monetarist), and in the media over the years. Then we could train it to write in my style (whatever that is) and with the typical viewpoints and angles I have. It would still not be me, but it would be more like me than what ChatGPT produces when I ask it to write about economics, as ChatGPT is average.

My point with all this is that the problem, if there is one, is not the technology behind ChatGPT and other LLMs but what we train the models on. And it is a balance. We want an average response. If we ask for specific facts: Is it legal to cross on red? What is 2+2? Will I die if I jump out the window from the 12th floor?

In all these examples, we want a concrete answer, and since the average answer is often the correct answer (but not always), it is what we want. In other words, I don’t see a problem when I ask for a picture of an economist, a basketball player, and a Muslim woman, and I get what I get. But it is important to know WHY it is the answer that comes out.

I hope this article has contributed to your understanding of language models. If your company or organization wants to hold a workshop on AI and ChatGPT, you can find more information here.