Agency Theory: The existence of massive data sets in many arenas is creating new challenges. Most people know about the issue of spurious correlations that do not represent true cause-and-effect. However a second challenge is more insidious and costly: this is the expense – which can run into the billions of dollars – of managing data that does not lead to actionable and valuable outcomes for an organization. For this reason, organizations that can identify the 20% of data that represents 80% of value realize a substantial advantage.
In this talk, I introduce Agency Theory, which is a mathematical framework for analyzing decision models to solve this problem. Agency theory borrows key ideas from machine learning, to solve a different purpose: rather than finding a set of parameters that best fits a data set, the objective is to find a set of decisions that leads to the most favorable set of outcomes, along with the data that is most valuable in supporting those decisions. Just as many foundational aspects of machine learning can be understood using information theory, I’ll describe how entropy and related concepts underlie Agency, and how to use this approach to prioritize data management and improve decision making.