Microeconomic theory gets little attention. The public usually only hears about macro, tax or labor economics — the things that affect day-to-day life. But deep within the stygian recesses of academia, bright mathematical minds are working on the economics of the next century.
One of these is Yuliy Sannikov, a professor at Princeton. Known throughout his life as a mathematical genius, Sannikov recently won the John Bates Clark Medal, a notable award given each year to a prominent economist under the age of 40. In recent years, that award has been given mostly to empirical researchers, reflecting econ’s turn toward data-driven work. Sannikov is among the few who work with pure math and abstract concepts.
Since 2008, a lot of people have looked very unfavorably on purely mathematical economic theory. But in microeconomics, this kind of theorizing has been quite successful: It has enabled advances in online auctions, organ transplants and a number of other areas. This work is not as glamorous as the research done by people who claim to be able to explain recessions and unemployment, but by keeping a low profile, it is able to stay a lot more grounded in reality.
Sannikov’s work doesn’t have a lot of application to the world as it exists today. It has to do with the way parties would design and update contracts at incredibly high frequencies. For a more complete description of this research, check out the summary by the excellent econ blogger Kevin Bryan. In a nutshell, Sannikov’s theories describe how an employer would adjust a contractor’s compensation in real time, in response to changing performance and external conditions. Where most previous researchers only allowed contracts to be updated at regular intervals, Sannikov described what happens when they can be changed infinitely fast.
In reality, of course, Sannikov’s work is just pure math conducted in an econ department. No one adjusts contracts by the microsecond. Yet, that is. In the future, we will almost certainly design computers to manage our economic interactions for us. Instead of teams of human lawyers drawing up fixed rules on sheets of paper, machines will talk to one another, exercising broad powers to renegotiate deals on the spot. At that point, Sannikov’s work will go from esoteric math to reality.
Thirty years ago, stocks were traded by humans waving sheets of paper in a crowded room in lower Manhattan. Today, they’re traded thousands of times a second, by computer algorithms relying on data sets too big to be comprehended by the human mind. Imagine that same transformation applied to many areas of business.
Picture supply chains managed by algorithms, with purchasing contracts adjusted at high frequencies in response to constant flows of data about sales, shipping times, manufacturing costs, and so on. Imagine demand shifting back and forth across continents at close to the speed of light, switching small-batch production and shipping orders from one bidder to the next. Instead of high-frequency traders, imagine high-frequency lawyers, adjusting contracts to reward contractors appropriately for tiny improvements in efficiency, using sophisticated statistical analysis to figure out whether performance is being driven by outside conditions or by the contractor’s skill and effort. Of course, in this sci-fi vision, the contractor will also be a robot, using equally sophisticated procedures to optimize its payment relative to its cost.
This may be our future: a whole economy of ultra-fast robots, negotiating, making agreements and adjusting incentives. Sometimes, just like their financial counterparts, the high-frequency lawyers will try to fake each other out. They will try to shirk, to disguise their lack of effort and corner-cutting, to save money at the purchasers’ expense, just like human contractors do now. The government will even have its own high-frequency lawyers, monitoring private-sector robots for regulatory violations. The economy will become a beautiful ballet of data and math, dancing to the beat of equations like Sannikov’s.
And where will we fit into the equation? That, of course, is the big question with all automation. High-frequency lawyers will threaten the jobs of human lawyers, white-collar office workers, and business service providers of all types. With advanced enough machine-learning techniques, they may even come for the jobs of top managers and executives. There will be near-infinite wealth floating around the economy, and our traditional systems for allocating it to human beings will become less and less useful. Business service robots will bring forward the day when we need to think hard about how to spread the massive wealth created by the automation of everything.