Not Even Artificial Intelligence Can Make Central Planning Work
The term wicked problem has become a standard way for policy analysts to describe a social issue whose solution is inherently elusive. Wicked problems have many causal factors, complex interdependencies, and no ability to test all of the possible combinations of plausible interventions. Often, the problem itself cannot be articulated in a straightforward, agreed-upon way. Classic examples of wicked problems include climate change, substance abuse, international relations, health care systems, education systems, and economic performance. No matter how far computer science advances, some social problems will remain wicked.
The latest developments in artificial intelligence represent an enormous advance in computer science. Could that technological advance give bureaucrats the tool they have been missing to allow them to plan a more efficient economy? Many advocates of central planning seem to think so. Their line of thinking appears to be:
- Chatbots have absorbed an enormous amount of data.
- Large amounts of data produce knowledge.
- Knowledge will enable computers to plan the economy.
These assumptions are wrong. Chatbots have been trained to speak using large volumes of text, but they have not absorbed the knowledge contained in the text. Even if they had, there is knowledge that is critical for economic operations that is not available to a central planner or a computer.
The Promise of Pattern Matching
The new chatbots are trained on an enormous amount of text. But they have not absorbed this data in the sense of understanding the meaning of the text. Instead, they have found patterns in the data that enable them to write coherent paragraphs in response to queries.
Loosely speaking, there are two approaches to embedding skills and knowledge into computer software. One approach is to hard-code the sort of heuristics that a human being is able to articulate. In chess, this would mean explicitly coding formulas that reflect how people would weigh various factors in order to choose a move. In loan underwriting, it would mean spelling out how an experienced loan officer would regard a borrower’s history of late credit-card payments in order to decide whether to make a new loan.
The other approach is pattern matching. In chess, that would mean giving the computer a large database of games that have been played, so that it can identify and distinguish positions that tend to result in wins. When the computer then plays the game, it would select moves that create positions that fit a winning pattern. In loan underwriting, pattern matching would mean looking at a large historic sample of approved loans to find characteristics that distinguish the borrowers who subsequently repaid the money from those who subsequently defaulted. It would then recommend approving loans where the credit report resembles the pattern of a borrower who is likely to repay.
Human beings use both pattern matching and explicit heuristics. An experienced chess player will not try to calculate the advantages and disadvantages of every single possible move in a position. Instead, the player will immediately recognize a pattern in the position, and this will intuitively suggest a few possible moves. The player will then make a more careful analysis to choose from among those moves. In speed chess, a player relies more on pattern recognition and less on heuristics and careful thought.
If you are on a hike, you may instinctively flinch when you see something that resembles the pattern of a snake. But then you will stop and reason about what you see. If it is not moving, you may conclude that it is merely a stick.
In American football, the quarterback may call a play based on careful reasoning about what the defense is likely to do in a situation. But once the play starts, the quarterback has to make instantaneous decisions based on what his instinct tells him about what the defense is doing. For these decisions, the quarterback is pattern matching.
We tend to pride ourselves on our ability to use heuristics and careful reasoning. When we examine our own thought processes, we do not think of ourselves as mere pattern matchers. But the latest advances in computer science rely heavily on pattern matching. ChatGPT has studied an enormous corpus of text in order to find patterns in how words are used in relation to one another, without having been given any instruction about what the words mean. Many experts, who assumed computers would have to be programmed to know the meaning of words, are surprised that this pattern matching works as well as it does. When you type a comment or a question into
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