Not Even Artificial Intelligence Can Make Central Planning Work - Part 2
May 4, 2024 20:13:36 #
Link to Part 1
https://www.onepoliticalplaza.com/t-315782-1.html
https://reason.com/2024/05/04/wicked-problems-remain/
Not Even Artificial Intelligence Can Make Central Planning Work
Revolutionary AI technologies can't solve the 'wicked problems' facing policy makers. PART 2
Arnold KlingFrom the June 2024 issue
Central Planning Still Won't Work
Economic organization is a wicked problem. Your intuition might be that the best approach would be for a department of experts to determine what goods and services get produced and how they are distributed. This is known as central planning, and it has not worked well in reality. The Soviet Union fell in part because its centrally planned economy could not keep up with the West.
Some advocates of central planning have claimed that computers could provide the solution. In a 2017 Financial Times article headlined "The Big Data revolution can revive the planned economy," columnist John Thornhill cited entrepreneur Jack Ma, among others, claiming that eventually a planned economy will be possible. Those with this viewpoint see central planning as an information-processing problem, and computers are now capable of handling much more information than are individual human beings. Might they have a point?
F.A. Hayek made a compelling counterargument. In a famous paper called "The Use of Knowledge in Society," first published in 1945, Hayek argued that some information is tacit, meaning that it will never be articulated in a form that can be input to a computer. He also argued that some information is dispersed, meaning that it is known only in small part to any one person. Given the decentralized character of information, a market system generates prices, which in turn generate the knowledge necessary to efficiently organize an economy.
A central computer is not going to know how you as an individual would trade off between two goods. You may not be able to articulate your preferences yourself, until you are confronted with a choice at market prices. The computer is not going to know how consumers will respond to a new product or service, and it is not going to know how a new invention might change production patterns. The trial-and-error process of markets, using prices, profits, and losses, addresses these challenges.
Economists have a saying that "all costs are opportunity costs." That is, the cost of any good is the cost of what you have to forgo in order to obtain it. In other words, cost is not inherent in the nature of the good itself or how it is produced. It is impossible to know the cost of a good until it is traded in the market. If central planners do away with the market, then they will not have the information needed to calculate costs and make good decisions. Forced to use guesswork, planners will inevitably misallocate resources.
In a market system, bad decisions result in losses for firms, forcing them to adapt. Without the signals provided by prices, profits, and losses, a central planner's computer will not even be aware of the mistakes that it makes.
Learning From Simulations
The problem of organizing an economy is too wicked to be solved by computers, whether they use pattern matching or other methods. But that does not mean that advances in computer science will be of no help in improving economic policy.
New software tools can be used to create complex simulations. The tools that gave us chatbots could be used to create thousands of synthetic economic "characters." We could have them interact according to rules and heuristics designed to mimic various economic policies and institutions, and we could compare how different economic policies affect the outcomes of these simulations.
Among economists, this technique is known as "agent-based modeling." So far, it has been of only limited value, because it is difficult to create agents that vary along multiple dimensions. But it may be improved if we can use the latest tools to create a richer set of economic characters than what modelers have used in the past. Still, this improvement would be incremental, not revolutionary. They will not permit us to hand off the resource allocation problem to a central computer.
The latest techniques for using large datasets and pattern matching offer new and exciting capabilities. But these techniques alone will not enable us to solve society's wicked problems.
https://www.onepoliticalplaza.com/t-315782-1.html
https://reason.com/2024/05/04/wicked-problems-remain/
Not Even Artificial Intelligence Can Make Central Planning Work
Revolutionary AI technologies can't solve the 'wicked problems' facing policy makers. PART 2
Arnold KlingFrom the June 2024 issue
Central Planning Still Won't Work
Economic organization is a wicked problem. Your intuition might be that the best approach would be for a department of experts to determine what goods and services get produced and how they are distributed. This is known as central planning, and it has not worked well in reality. The Soviet Union fell in part because its centrally planned economy could not keep up with the West.
Some advocates of central planning have claimed that computers could provide the solution. In a 2017 Financial Times article headlined "The Big Data revolution can revive the planned economy," columnist John Thornhill cited entrepreneur Jack Ma, among others, claiming that eventually a planned economy will be possible. Those with this viewpoint see central planning as an information-processing problem, and computers are now capable of handling much more information than are individual human beings. Might they have a point?
F.A. Hayek made a compelling counterargument. In a famous paper called "The Use of Knowledge in Society," first published in 1945, Hayek argued that some information is tacit, meaning that it will never be articulated in a form that can be input to a computer. He also argued that some information is dispersed, meaning that it is known only in small part to any one person. Given the decentralized character of information, a market system generates prices, which in turn generate the knowledge necessary to efficiently organize an economy.
A central computer is not going to know how you as an individual would trade off between two goods. You may not be able to articulate your preferences yourself, until you are confronted with a choice at market prices. The computer is not going to know how consumers will respond to a new product or service, and it is not going to know how a new invention might change production patterns. The trial-and-error process of markets, using prices, profits, and losses, addresses these challenges.
Economists have a saying that "all costs are opportunity costs." That is, the cost of any good is the cost of what you have to forgo in order to obtain it. In other words, cost is not inherent in the nature of the good itself or how it is produced. It is impossible to know the cost of a good until it is traded in the market. If central planners do away with the market, then they will not have the information needed to calculate costs and make good decisions. Forced to use guesswork, planners will inevitably misallocate resources.
In a market system, bad decisions result in losses for firms, forcing them to adapt. Without the signals provided by prices, profits, and losses, a central planner's computer will not even be aware of the mistakes that it makes.
Learning From Simulations
The problem of organizing an economy is too wicked to be solved by computers, whether they use pattern matching or other methods. But that does not mean that advances in computer science will be of no help in improving economic policy.
New software tools can be used to create complex simulations. The tools that gave us chatbots could be used to create thousands of synthetic economic "characters." We could have them interact according to rules and heuristics designed to mimic various economic policies and institutions, and we could compare how different economic policies affect the outcomes of these simulations.
Among economists, this technique is known as "agent-based modeling." So far, it has been of only limited value, because it is difficult to create agents that vary along multiple dimensions. But it may be improved if we can use the latest tools to create a richer set of economic characters than what modelers have used in the past. Still, this improvement would be incremental, not revolutionary. They will not permit us to hand off the resource allocation problem to a central computer.
The latest techniques for using large datasets and pattern matching offer new and exciting capabilities. But these techniques alone will not enable us to solve society's wicked problems.
May 4, 2024 20:20:03 #
AI is for salesman.
They never solve, just sell.
They never solve, just sell.
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