For the final part of my Average is over posts I want to share some of Tyler Cowan’s thoughts on the future of Economics.
In chapter 11 of the book he writes about the end of average science. He argues that science is going to become harder to understand for three reasons:
- The problems are ever more complex and simple, intuitive, big breakthroughs are unlikely.
- Individual contributions are becoming more specialised, and
- Soon intelligent machines will become researchers in their own right.
He goes on to explain how machine intelligence and specialisation are reshaping theoretical mathematics and physics, and how the average age of Nobel Prize winners has increased over time. Once machine science goes beyond human understanding – think of proofs in the multi-dimensions of string theory – science will also become harder to regulate. But what does all that mean for Economics?
Cowan argues that in recent times data gathering and crunching has been pushing out theoretical intuition. (if this sound interesting, also read Noah Smith’s post on the death of theory) Economists still like their models but, “the real action and value-add comes from the data and its handling, including data from field experiments, laboratory experiments and from randomised control trials”. Sooner rather than later, big data will reinforce the use of machine intelligence in Economics. Data from, for example, your social media profiles, online shopping and loyalty cards will be looked at by a computer programme and it will search for patterns in more complex ways than researchers can do. Cowan hopes that this will reinforce our understanding of some basic regularities behind economic phenomena. He goes on to write:
We are not far a way from having a single de facto, more or less unified, empirical social science. In that social science, researchers invest a lot in learning empirical techniques and then invest some marginal energies in the simpler theories that surround their chosen field of study. Finally, they spend their research time looking for new data sets, or looking to create that data, whether by detective work or by lab and field experiments.
I’m sure this sounds familiar.
He goes on to write that economists who favour more intuitive approaches will have to take a different approach to survive. They will focus less on producing their own original research and will become clearinghouses for and evaluators of the work of others. That means translating the work of others for fellow economists and for the public. It is about seeking out, absorbing and evaluating information.
If you are blogging already, this will sound familiar too.