Dynamic Centralized Public R&D Project Portfolio Selection

Abstract

Many public funding agencies adopt the one-stage call-based mode to select R&D projects. The funding decision-making in this mode is characterized by two primary features: 1) it is static, meaning that decision-makers solely concentrate on projects submitted within the current call, and 2) it is decentralized, indicating that funding decisions are made independently across diverse sectors/disciplines. As a result, the administrative burden on funding agencies is reduced. However, due to the budget constraint, the funding agencies may fail to fund better projects that emerge in future calls or appear in other sectors/disciplines. Therefore, we investigate whether a better project portfolio can be achieved by selecting public R&D projects in a dynamic and centralized manner. We formulate a Markov decision process (MDP) model for the dynamic centralized public R&D project portfolio selection problem. We develop an approximate dynamic programming (ADP) approach that combines learning-based Monte Carlo simulation and a two-stage rollout algorithm to efficiently solve the MDP model. Based on extensive computational experiments, we compare our ADP approach with a threshold heuristic that is representative of the static and decentralized funding mode, as well as two baseline algorithms. The results indicate that our ADP approach is effective, and it is beneficial to adopt the dynamic centralized decision-making mode in public R&D project portfolio selection. We also investigate our ADP approach utilizing a case study based on real-world data from the National Natural Science Foundation of China. Our ADP approach can be integrated into the decision support systems of funding agencies, enabling automated, dynamic, and centralized selection of public R&D projects.