Operations Research

A Mathematically Rigorous Way To Analyze Statistics from Simulations

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.

Finding Your Way in a World of Tradeoffs

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. 

Finding the Best Path to Your Target

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.

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