The intersection of behavioral economics and machine learning to understand Big Data
I am often asked which jobs will thrive as we move into the next phase of the robot revolution. My answer is that people will need to be multi-skilled. They will need critical thinking and design skills, they will need to be able to think statistically, and they will need a deep knowledge of human behavior.
One area that I see growing in demand is those with machine learning and data science backgrounds, however increasingly, computer programmers and data scientists require dual expertise in both social science and computer science, adding competence in economics, sociology, and psychology – collectively known as Behavioral Economics — to more traditionally recognized requirements like algorithms, interfaces, systems, machine learning, and optimization.
This combined expertise in computer science and behavioral economics helps to bridge the gap between modeling human behavior, data mining and engineering web-scale systems. At Harvard School of Engineering and Applied Sciences they say that: “an emerging area in both artificial intelligence and theoretical computer science, computational mechanism design lies at the interface of computer science, game theory, and economics.” Similarly at Yale School of Management we now find professors working on the intersection of behavioral economics and machine learning.
Many of the major tech companies are recognizing the benefits of combining these skill sets. Microsoft Research call their internal machine learning and behavioral economics department Algorithmic Economics.
A recent paper by Hal Varian, Chief Economist at Google, titled, “Big Data: New Tricks for Econometrics” (Incidentally Hal is author of one of my favorite books: Information Rules), … provides an extremely readable introduction to the collaboration of machine learning, big data and behavioral economics.
Hal also offers a valuable piece of advice:
“I believe that these methods have a lot to offer and should be more widely known and used by economists. In fact, my standard advice to graduate students these days is ‘go to the computer science department and take a class in machine learning’.”
Michael Bailey and Economist at Facebook writes on Quora:
I currently (Feb 2014) manage the economics research group on the Core Data Science team. We are a small group of engineer researchers (all PhDs) who study economics, business, and operations problems. As Eric Mayefsky mentioned, there are various folks with formal economics training spread across the company, usually in quantitative or product management roles. The economics research group focuses on four research areas:
Core Economics – modeling supply and demand, operations research, pricing, forecasting, macroeconomics, econometrics, structural modeling.
Market Design – ad auctions, algorithmic game theory, mechanism design, simulation modeling, crowdsourcing.
Ads and Monetization – ads product and frontend research, advertiser experimentation, social advertising, new products and data, advertising effectiveness, marketing.
Behavioral Economics – user and advertiser behavior, economic networks, incentives, externalities, and decision making under risk and uncertainty.
I think a more interesting question is “what *could* an economist at Facebook do?” because there is a LOT of opportunity. There are incredibly important problems that only people who think carefully about causal analysis and model selection could tackle. Facebook’s engineer to economist ratio is enormous. Software engineers are great at typical machine learning problems (given a set of parameters and data, make a prediction), but notoriously bad at answering questions out of sample or for which there’s no data. Economists spend a lot of time with observational data since we often don’t have the luxury of running experiments and we’ve honed our tools and techniques for that environment (instrumental variables for example). The most important strategic and business questions often rely on counterfactuals which require some sort of model (structural or otherwise) and that is where the economists step in.
In the following video (Machine Learning Meets Economics: Using Theory, Data, and Experiments to Design Markets) Stanford University’s Susan Athey discusses suggestions about research directions at the intersection of economics and machine learning.
I previously wrote on more of the crossover between Behavioral Economics, Machine Learning and Big Data and will continue to evolve this series of articles in the coming weeks.