A spatially explicit agent-based vehicle consumer choice model is developed to explore sensitivities and nonlinear interactions between various potential influences on plug-in hybrid vehicle (PHEV) market penetration. The model accounts for spatial and social effects (including threshold...

The Office of Financial Research (OFR) has a mandate to measure and monitor risks to U.S. financial stability. To help fulfill that mandate, the OFR launched the Financial System Vulnerabilities Monitor (FSVM) in 2017. The monitor is a starting point for assessing vulnerabilities in the U.S....

Machine learning and artificial intelligence methods are often referred to as \black boxes" when compared to traditional regression-based approaches. However both traditional and machine learning methods are concerned with modeling the joint distribution between endogenous (target) and exogenous...

Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn relationships that yield de facto discrimination. How can we understand the behavior and potential...

We present a variation on literate programming (see Knuth: 1984, 1992) targeting multiple simultaneous readerships, both human (e.g., coders, testers, analysts, etc.) and compilers/interpreters (e.g., C , Python, Fortran, etc.). The technique exploits existing commenting syntax available in all...

Systemic risk must include the housing market, though economists have not generally focused on it. We begin construction of an agent-based model of the housing market with individual data from Washington, DC. Twenty years of success with agent-based models of mortgage prepayments give us hope...