Multiscale decision-making, also referred to as multiscale decision theory (MSDT), is an approach in operations research that combines game theory, multi-agent influence diagrams, in particular dependency graphs, and Markov decision processes to solve multiscale challenges across organizational hierarchies, time, space (e.g., topology and geography), and size (e.g., number of nodes, users).
Multiscale decision theory is a fusion between decision theory and multiscale mathematics. Multiscale decision theory can model and analyze hierarchical decision-making networks which exhibit multiscale phenomena. The theory's results can be used by mechanism designers and decision-makers in organizations and complex systems to improve system performance and decision quality.
Multiscale decision theory has been successfully applied to manufacturing enterprises, service systems and supply chain management, among others. Current research focuses on identifying multi-level incentives to improve health care value (quality of outcomes per dollar spent); see also pay for performance (healthcare). Furthermore, researchers are applying multiscale decision theory to improve performance and reliability of power networks (electricity distribution), the Internet, homeland security and defense (military) systems.
Multiscale decision theory is related to:
- Multiscale modeling
- Decision analysis
- Cooperative distributed problem solving
- Decentralized decision making
- Multiscale Mathematics Initiative: A Roadmap
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