Translating the economic code

I recently had the pleasure of talking to Patrick McMillen about his new preprint on translating the bioelectric code. We didn’t record it, but he gave a really cool presentation. Now I wanted to share some thoughts about translating the economic code.

The economic analogy to the bioelectric code is of course the price code. There are a few things we know about translating the price code.

—Individual prices are meaningless. Knowing that an apple costs $1 tells you nothing. It’s only when you compare the price of a $1 apple to a $2 orange that prices become meaningful. Prices are relationally real.

—There is a sense in which we know exactly what (relative) prices tell us: Relative prices denote relative scarcities. If an apple is half the price of an orange ($1 vs $2), then apples are half as scarce as oranges. In this sense, translating the economic code is already accomplished: we know what prices are saying.

—There is no way to translate prices so that they tell us why one good is half as scarce as another good. Maybe apples are easy to grow, or oranges are very popular, or any combination of any number of other factors. There is no way to decode prices to get this information back out of them, even though this information played an important role in forming those prices. This makes for an efficient price system, but also places a hard limit on one concept of translating prices.

—The most useful view of translation is that prices mean what the people interacting with the prices use them to accomplish. In general, prices are used to form feasible budgets, or sets of things you can buy. Then you try to buy the feasible bundle that you most prefer. So the same set of prices can produce very different outcomes depending on the people in the economy. Therefore, there is a many-to-many relationship between prices and economic outcomes, specifically outcomes that are allocations of scarce resources. The task of using the price system to achieve social goals is a challenge of translating price system inputs into economic outcomes, a challenge made difficult by the many-to-many mapping.

—Economics doesn’t have a Rosetta Stone. However, financial markets can be used to figure out which prices will achieve which outcomes by drawing on information from the people who will be using the prices to achieve the outcomes. The brain may play an analogous role in the human organism.

This last point holds the most potential for future research. Solving these many-to-many mappings by amassing lots of data is very hard. Solving them by getting the people whose behavior constitutes the solution to tell you what they’re going to do is relatively easy. Figuring out if and how such a process happens in biological systems may be key for advancing our ability to understand and use the bioelectric code.