Application of artificial intelligence (AI) machine learning techniques in industry and government for data-driven predictive analytics has been based primarily on statistical econometric models. However, one of the limitations of this approach is the “black-box” nature of the resulting models, which frequently result in a lack of interpretability.
New research from Maryland Smith’s Michael C. Fu offers a rigorous way to analyze statistics generated from simulation models.
The new result fills a gap in probabilistic simulation modeling and analysis. Fu, the Smith Chair of Management Science in the Decision, Operations and Information Technologies department at the University of Maryland’s Robert H. Smith School of Business, worked with four co-authors, two at Stanford University and two in China at Fudan University and Peking University.
Managers who rely on computer models to help with decision-making bump into a dilemma when it comes to allocation of scarce resources in complex environments with many moving parts.
Organizations want immediate economic benefits, which means sticking with the surest path to profit. But they also want to make better decisions in the future, which means experimenting with risky or unproven ideas. The trick is finding the right balance between two opposing strategies: exploitation or exploration.
Political candidates, manufacturers and even online game designers can hit their performance targets with increased regularity using a new algorithm developed by professor Ilya O. Ryzhov at the University of Maryland’s Robert H. Smith School of Business.
The prediction model works best in situations where decision makers have a complex set of variables to consider and a predetermined target — rather than a general desire to maximize results as much as possible.
The Department of Decision, Operations & Information Technologies at the University of Maryland’s Robert H. Smith School of Business has been selected as a finalist for the 2019 UPS George D. Smith Prize.
Estimating Sensitivities in the Simulation of Complex Systems
Numbers can be a tricky business.
Michael C. Fu, the Smith Chair of Management Science and chair of the Decision, Operations and Information Technologies Department at the University of Maryland’s Robert H. Smith School of Business, understands just how tricky they can be.