Machine Learning (ML) is the study of leveraging data and computational resources to obtain prediction and decision-making algorithms that function well in the presence of uncertainty. The techniques employed to design and study ML algorithms typically involve concepts and tools from probability, statistics, and optimization; the language of economics, on the other hand, is conspicuously absent. It is rare to encounter terms such as marginal price, utility, equilibrium, risk aversion, and such, in the ML research literature. This gap is significant and belies the reality that the broad interest in Machine Learning, and its sudden growth spurt as a research field, can be ascribed to its potential for generating economic value across many segments of society. This NSF CAREER projectadvances an already-emerging relationship between Machine Learning and the fields of microeconomic theory and finance. This will begin with the development of mathematical tools that enable a semantic correspondence between learning-theoretic objects and economic abstractions. For example, the project shows that many algorithms can be viewed as implementing a market economy, where learning parameters are associated with prices, parameter updates are viewed as transactions, and under certain conditions learned hypotheses can be extracted as market-clearing price equilibria. In addition to developing this link, the project research raises a number of intriguing questions and explores several surprising and novel applications with benefits to computer science more broadly.
Among several such applications stemming from the new theoretical connections are: 1. Developing new models for distributed computing for learning and estimation tasks: The economic lens gives new insights into a robust and effective model for decentralization of data-focused tasks. 2. Designing new techniques for crowdsourcing and labor decentralization via collaborative mechanisms involving financial payment schemes: This builds off of the success of platforms like Amazon's Mechanical Turk as well as the Netflix Prize and the prediction challenge company Kaggle. 3. Developing a market-oriented model for data brokerage and financially-efficient learning: As information is increasingly traded in market environments, we aim to answer questions such as "what is the marginal value of a unit of data?"
The project will also develop the Michigan Prediction Team, a data-science focused program for formulating and solving prediction and learning challenges that develop from across the University of Michigan as well as externally. The group primarily targets undergraduates with graduate student mentors, and Team has a strong interdisciplinary focus.
