Markets and Machines

Viewpoint showing why even artificial intelligence cannot eliminate the information gaps that limit planned economic models in order to replace the dynamic market discovery process

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This Viewpoint was prepared by Peter J. Boettke, Professor of Economics and Philosophy at George Mason University, in collaboration with Gabriel Giguère, Senior Policy Analyst at the MEI. The MEI’s Regulation Series aims to examine the often unintended consequences for individuals and businesses of various laws and rules, in contrast with their stated goals.

Every few years, someone announces that we are finally on the verge of solving the problem of the untamed economy with modern tools of management and techniques of calibration.(1) In the early 20th century, it was the experience of wartime planning that first caught the imagination, then Taylorism and scientific management, then operations research and linear planning, and eventually cybernetics and advanced computing power.

Today, the promise comes wrapped in artificial intelligence. With enough data, enough sensors, and enough computing power, we are told, we could simulate the entire economic system, forecast crises before they happen, and steer production and consumption toward social goals. No more waste. No more chaos.

Alibaba’s Jack Ma has suggested that AI and big data could eliminate the information gaps that have historically limited planned economic models, implying that computational systems might finally overcome the hurdles that doomed 20th-century socialism.(2) Complexity scientist J. Doyne Farmer has argued that with our modern computing capacity we can assemble data from every business on Earth and use it to make realistic decisions that change as the economy changes: “From this astonishing complexity would emerge forecasts of unprecedented clarity—no more flying blind into global financial crashes, no more climate policies that fail to shift the dial.”(3)

An Old Dream in a New Package

In 1776, Adam Smith offered a humbler but more profound insight. Even the production of something as ordinary as a woolen coat, he observed, required the cooperation of so many hands—farmers, spinners, dyers, merchants, shipbuilders—that the full network of exchanges would “exceed all computation.” The point was not that the coat could not be made—clearly it was. The point was that no single mind directed the process. It takes many hands (and minds) to make up the “invisible hand” of the market.(4)

In the late 19th century, the economist Vilfredo Pareto made the same observation in more technical language. If one tried to coordinate even a modest economy by solving a system of equations—matching resources to uses, goods to preferences—the number of equations would explode. For a modern economy, the task would be staggering.(5)

So perhaps, one might think, we simply needed better mathematics and faster machines. That is what today’s AI enthusiasts believe. But here is where Friedrich Hayek, the recipient of the Nobel Prize in Economics in 1974, changes the argument. Hayek agreed that the economy is too complex to be centrally computed. Yet he insisted that this is not the deepest problem. The real issue is not that the system of equations is too large; it is that the relevant “data” do not even exist in the form the planner imagines. They are of a form, Hayek tells us, that cannot be treated as statistics to be fed into a machine.(6)

Prices Emerge

Economic coordination does not begin with a giant spreadsheet of given facts. The knowledge that matters is dispersed across millions of individuals. It is local, contextual, and often tacit. A shop owner knows her neighbourhood customers. A machinist senses subtle changes in production. An entrepreneur imagines a product that has never existed before. Much of this knowledge cannot be fully articulated, let alone uploaded into a database.

Most importantly, prices—the signals that guide decisions—are not raw facts about the world waiting to be harvested by an algorithm. Prices emerge from real exchanges based on private property and freedom of contract. When the price of lithium rises, it is because buyers and sellers are competing over scarce resources. The price increase communicates something about relative scarcity, but it also gives people an incentive to adjust in order to conserve, to innovate, to search for substitutes.

Prices are not inputs to the system, but outputs of a dynamic discovery process (see Figure 1). Without the process of exchange and production, without the haggling and bargaining in the market, the knowledge embedded in a price simply doesn’t come into existence. This generative nature of the knowledge of the market is what Hayek was trying to get his peers to see, and why he even resorted to using the word “marvel” in his description of the price system.

This is also why Hayek described competition as a “discovery procedure.”(7) Markets do not merely allocate known resources among known ends. They generate new knowledge about what is possible, what is valued, and what works. Entrepreneurs test ideas. They bear losses when they are wrong and earn profits when they are right. Through this process, the economy learns.

Artificial intelligence can process vast quantities of historical data. It can detect patterns, forecast trends, and optimize within a given framework. But economic life is forward-looking and creative. The future is not simply an extension of the past. New tastes emerge. New technologies appear. New scarcities arise. Much of what matters tomorrow has not yet been imagined today.

The lesson for the age of AI is not hostility to technology. AI is a powerful tool, and firms will use it to improve logistics, manage inventories, and analyze markets. But tools operate within institutional frameworks. They do not replace them.

The dream of a fully computable society is alluring. Yet, the more we understand how knowledge actually works, the more we see why freedom, not centralized intelligence manipulated by the government, remains the foundation of prosperity.