Engineering impactful science in a research enterprise by dynamic modeling of innovation life cycle and evolution

Abstract

Despite record-high global R&D investments, we are witnessing an "Innovation Paradox" where research outputs are yielding diminishing returns. This national innovation slowdown is evidenced by the United States falling to No. 6 in the 2023 Global Innovation Index. This crisis is particularly acute in the Department of War's S&T enterprise, where the Secretary of War has explicitly identified that the traditional "linear model" of R&D management is "dangerous to mission accomplishment." Recently, at a Reagan Institute event on the National Security Innovation Base, the Fourth Annual National Security Innovation Base Report Card was published. It stated that, "America has the resources it needs to achieve technological superiority over global adversaries, and Washington has signaled its intent to transform and modernize the National Security Innovation Base, but amid encouraging progress, roadblocks remain," Traditional frameworks for managing and measuring scientific progress are failing, treating innovation as a static, linear process. This linear assembly line perspective is in direct conflict with the reality of complex research ecosystems, such as that of the Department of War (DOW). Innovation systems are governed by dynamic feedback loops, emergent properties, and evolutionary pressures that linear models cannot capture. Not treating innovation as evolutionary leads to flawed forecasting and perpetuates systemic organizational failures, such as policies that favor low-risk exploitation over the exploration required for true breakthroughs. This is a classic representation of the "Innovator's Dilemma." The main question that persistently exists for governments, industries, and academia is, "how do we balance risk with the probability of innovation". This dissertation develops and validates a new computational systems model that reconceptualizes innovation as a Complex Adaptive System. It posits that breakthrough ideas do not merely appear; they evolve. To model this, the research integrates principles of biological evolution with a multi-model simulation environment. It utilizes a rigorous suite of systems thinking tools, including agent-based modeling (ABM) to simulate the competitive and adaptive interactions between researchers, and genetic algorithms to model the "natural selection" and procreation of ideas. The primary contribution of this research is a dynamic, six-level framework that provides a more accurate method for forecasting the structured temporal progression from incremental to breakthrough innovation. The model demonstrates how small refinements categorized as incremental, cumulatively build toward larger advances that are categorized as modular and architectural. These advances in turn can trigger disruptive and radical paradigm shifts. Ultimately, this dissertation establishes a robust, systems-aware methodology for strategic R&D investment. By providing new quantitative metrics for classifying and measuring innovative potential, such as the proposed Innovation Procreation (IP⁰), this work provides a dynamic framework for investment strategies aimed at fostering the high-impact, transformative research essential for national and economic security. The views expressed in this article are those of the authors and do not reflect the official policy or position of the Office of Naval Research, the U.S. Naval Research Laboratory, the Department of War, or the U.S. Government. All information and sources for this paper were drawn from unclassified materials.