Strong intelligence
The economy is an intelligence capable of learning and solving novel problems. But it doesn’t work the way we might ordinarily envision intelligence: as something involving representation, prediction, generalization, etc. The economy is an example of a strong intelligence: an intelligence that does strong anticipation via strong internal models.
Strong intelligence has a few unusual properties as compared to the traditional picture of intelligence, which is weak intelligence, or intelligence that does weak anticipation via weak internal models.
First, strong intelligence is a whole-body phenomenon. There is nothing in the economy analogous to what the brain is typically thought of as being: the place where the intelligence happens. Instead, the economy assembles intelligence across its entire being. For example, when a supply shock occurs, the economy as a whole rapidly adapts, even though no individual or no part of the system represents the problem as a whole or forecasts the full solution.
Second, strong intelligence is the ability of the system to coordinate itself to solve problems. Intelligence isn’t something the economy has; it’s something the economy does.
Third, strong intelligence doesn’t operate, at a system level, on forecasting, thinking, representing, generalizing, or any of the other things that are the hallmarks of weak intelligence. Strong intelligence occurs as the absorption of novelty into an existing infrastructure for translating surprise into a shared system of constraints that maintain coherence across the collective intelligence, environment, task, and structured space patterns.
Fourth, strong intelligence solves problems via reorganizing itself to maintain task-relevant coordination. Whereas weak intelligence constructs and manipulates representations, strong intelligence just adapts its own body to the task. A classic example is writing a signature with a pen: each pen and each surface are different, there’s all kinds of minor perturbations, but the body’s dynamics naturally produce a consistent signature without some weak internal model first needing to take in all of the information about the environment and plan an optimal motor behavior.
Humans are also examples of strong intelligence, though we can do weak intelligence as well. The A-not-B error is an excellent example of humans being strong intelligences. In this error, an infant repeatedly reaches to a location that was successful in the past but no longer is despite the fact that the infant can now see where the correct location is to reach. Weak-intelligence explanations, like the idea that infants lack object permanence or have limited short-term memory, were unsuccessful at explaining this.
Instead, the error emerges for reasons of strong anticipation. The infant perseverates in reaching to A instead of B not because it’s misrepresenting the world but because it’s stuck in a previously successful way of being in the world. The infant has the right believes in a weak anticipation sense, but it’s bad at reconfiguring its body in the circumstances in which perseveration occurs. Ironically, the infant errs because it’s too good at stabilizing successful patterns.
Normally, we think of learning as consisting of adding content—getting more information packed into the intelligence part of the system. Strong intelligences “learn” by improving their ability to reconfigure themselves over time. In the economy, this process is called economic growth, which is primarily a process of improving the system’s ability to flexibly reconfigure on the face of novelty, not accumulation of resources.
Ultimately, weak intelligence changes what the system knows; strong intelligence changes what the system is able to do as a coordinated whole. Weak intelligence cares about information processing; strong intelligence cares about constraint reorganization (e.g., relative price changes).