Effective R&D Decision Making in Competitive Environments: A Quantitative Framework

R&D decision making is a critical element of engineering management. As decision makers consider R&D opportunities in practice, competition is often a driving factor in determining opportunity value. This dynamic is reflected in the classic case of multiple organizations researching a new technology in which one is first to patent and receives the majority of the rewards. Despite its importance, competition is challenging to analyze and account for in decision making. In this article, we establish a quantitative framework to determine effective decision-making behaviors for competitive environments. The approach is built on the concept that R&D decision making can be mathematically optimized for the resulting postcompetition value (PCV) of opportunities, rather than for their initial precompetition value. Our framework utilizes an agent-based model to simulate the forms of interactive competition and determine PCV Within the framework, we consider candidate decision-making behaviors and characterize their performance in different competitive environments. This defines the optimal strategy region for decision making and, correspondingly, the relative gains in PCV that can be attained in each competitive environment through improved decision making. We synthesize the results into practical heuristics, which can be implemented by engineering managers to effectively account for competition in their R&D decision process and gain a competitive advantage.