Abstract for: Developing Adaptive Logic Models for Planning and Evaluation of Convergence Research Programs
Convergence research emphasizes the strategic focus on vision-inspired goals that address complex societal problems through scientific and technological innovations. In the United States, agencies such as the National Science Foundation have focused their funding efforts on encouraging and accelerating the deep integration of disciplines toward resolving pressing societal problems through convergence research. With this surge in strategic investment, there is a growing need to refine policy by evaluating convergence research programs. As a visual aid in theorizing a means-ends chain, the traditional logic model is a near-ubiquitous tool in program planning and evaluation. Once constructed, however, it can be onerous to adapt traditional logic models to reflect changing implementation contexts. Furthermore, since multiple feedbacks are difficult to represent without sacrificing clarity, traditional logic models tend to be less conducive to specifying and understanding multi-level interactions at a system level. This work-in-progress paper aims to describe how to develop responsive logic models for planning and evaluating strategic efforts toward advancing convergence research. Our approach focuses on leveraging both agent-based and system dynamics modeling methods to transform traditional logic models into adaptive logic models that represent the unintended consequences and changing contexts associated with convergence research program implementation.