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The Multiscale Decision Making (MSDM) Laboratory at Virginia Tech conducts fundamental and applied research on complex systems that exhibit multi-level, multi-period, and stochastic interactions among and between decision-makers and their environment. MSDT is a modeling framework that incorporates game theory, mechanism design theory, Markov decision processes, information science, and multi-agent systems. MSDT has been applied to manufacturing and service operations, management systems, supply chain management, and healthcare. Emerging application areas are unmanned vehicles & autonomy, and energy & climate change.


The MSDM lab currently focuses on healthcare system modeling, analysis, and improvements using MSDT, but also other operations research and management science methodologies. Healthcare is a complex, socio-economic system with multiple stakeholders across different system levels that make decisions at different frequencies and with different information.


MSDT provides a graphical modeling tool to capture key stakeholders, their decisions and interdependencies. The dependency graph is a communications and modeling aid to derive a validated, mathematical multiscale formulation of the system.


Using MSDT, we have modeled and analyzed the effects of incentives in healthcare. We found that while incentives from payers, such as Medicare, can reduce healthcare costs and improve quality, the effectiveness depends on a careful design of the incentive program. MSDT supports the evaluation and calibration of existing and newly proposed incentive programs. Moreover, it supports stakeholders at all levels, including patients, physicians, and hospitals, to make better decisions in response to these incentives.


In another healthcare project, the MSDM lab has applied decision analysis and system dynamics to help hospitals make objective, equitable and evidence-based capital investment decisions. The physician and patient demand for beneficial but also expensive medical technologies, such as CT, MRI, PET scanners or surgical robots, typically exceeds the available financial resources of the hospital. Deciding which portfolio of equipment to acquire is complex due to multiple organizational objectives, conflicting stakeholder interests, uncertainties regarding technology and economics, and organization-wide effects and independencies of and among investments. We have developed a data-to-decision tool that addresses these challenges and supports hospital executives in their decision process. We have piloted the implementation of this tool with one of our hospital partners.

These were two examples from a number of projects the MSDM Lab has conducted in recent years. Below is a list of selected recently funded research projects:

  • Designing Multi-Level Incentives for Health Care Systems, National Science Foundation (NSF).
  • Analyzing and Improving Technology Investment Decisions at Hospitals, Agency for Healthcare Research Quality (AHRQ).
  • Modeling the Effect of New Payment Structures on Radiology and Accountable Care Organizations, Harvey L. Neiman Health Policy Institute of the American College of Radiology.
  • Assessing Regional Differences in Healthcare Technology Use and Quality, Carilion Clinic Research Acceleration Program (RAP).
  • Next Generation Data-to-Decision Methods for Disaster Response, Institute of Critical Technology and Applied Science (ICTAS), Virginia Tech..
  • Contract Analysis and Design to Leverage Probabilistics Methods, Rolls Royce.
  • Usage Based and Probabilistic Lifing Scenarios, Rolls Royce.
  • Fulfillment Process Improvement, James Hardie Corp.