Optimization of U.S. Government Research and Development Framework with Emphasis on Discovery Primacy and Resource Efficiency
Open AccessThe U.S. advocates the use of quantitative-based methods in policy planning of R&D; which may be done through least-regret decision making, investment balancing, and comparative methods (Fang, 2021 and Santiago, 2020). Current methods rely on road mapping, communities of interest, and peer reviews to develop program priorities and objectives (Clardy et al., 2017). While valuable, these methods lack the fidelity needed in today’s planning environment (GAO, 2021). This research aims to increase the probability of discovery primacy and efficient use of limited resources using bibliometric analysis, and application of economic methods, such as Modern Portfolio Theory. Quantum Information Science is used as a test case and can be affected through various governing plans. Results from cluster analysis and Generalized Methods of Moments give predictors for use in the Herfindahl-Hirschman Index, Data Envelopment Analysis, Box Score Method, and system dynamics. This allows for adjustment of leading nation-state frameworks while providing an overlay of external factors, such as adversarial effects and research intensity by topic. Adjustments are validated via Two-Sample T-Tests and agreement with literature. This research bridges analysis and application in policy planning and provides recommended improvements to the U.S. quantum framework. Keywords: cluster analysis, predictors, framework, generalized methods of moments, key performance indicators, modern portfolio theory, policy, program, quantum research
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