Convergence Research and Collaborative Modeling: New Approaches are Needed to Address Complex Challenges

In the emerging realm of Convergence Research, there is growing recognition that disciplinary models are unable to adequately address complex science and resource issues facing society. Instead, collaborative modeling of coupled human and natural systems, is increasingly used for discovering and understanding system dynamics, investigating societially-relevant and complex problems, and exploring and evaluating solutions. However, collaborative modeling is subject to the same complexity as the systems they are attempting to model. Based on years of experience, we outline the fundamental challenges in, and offer best practices for effective development and application of collaborative models. The best practices that are summarized, with examples, involve: 1) Aligning model and project objectives and expectations; 2) Anticipating and funding the high and persistent costs (time and effort) of collaboration; 3) Planning for and communicating about complex model calibration and validation; 4) Developing tools and expertise for analyzing, communicating and synthesizing massive model output; and 5) Exceptional leadership for managing personalities and budgets. In developing and implementing these practices, the science community (i.e. funding agencies, scientists, and practitioners) will develop the skills and approaches needed to conduct collaborative modeling effectively. Successful convergence research and collaborative modeling requires that we learn from past experience or risk wasting resources, losing opportunities for discovery, understanding systems incorrectly, and losing community trust.