Journal on Policy and Complex Systems Volume 6, Number 1, Spring 2020 | Page 97

Journal on Policy and Complex Systems • Volume 6, Number 1 • Spring 2020 A Predictive, Interactive, and Computational Methodology for Multiple-Criteria Decision-Making, with Application to Stimulating Biomedical Innovation Brian Thompson*, Kevin Gormley, Ken Hoffman, Leslie Platt The MITRE Corporation, McLean, VA Leslie Platt & Associates, Tucson, AZ 85704 * Corresponding author: [email protected] Abstract Federal and state policymakers face a host of challenges, such as balancing budgetary constraints, managing risk and uncertainty, and anticipating the response of other stakeholders and affected populations. In the healthcare domain, stimulating private sector investment in biomedical innovation by providing appropriate market incentives has been recognized as a promising approach to improving health outcomes, if the above challenges can be adequately addressed. In this paper, we present the PIC (Prediction, Interaction, Computation) framework and an accompanying methodology for multiple-criteria decision-making. We then demonstrate an application to the biomedical innovation domain by prototyping a system that we call DATSBI (Decision Analysis Tool for Stimulating Biomedical Innovation). By combining automation with feedback solicited from DATSBI users, the DATSBI system is designed to help decision-makers choose a combination of policies and budget allocations that best achieves a desired set of outcomes, including reduced disease burden, reduced healthcare costs, and increased industry net present value. Although the DATSBI system is tailored to the context of biomedical innovation, our framework and methodology are more general and could be applied to multiple-criteria decision-making in a wide variety of domains. Keywords: multiple criteria; multi-objective, optimization; modeling and simulation; complex systems; medical attention; medical attention; politics; biomedical; innovation 93 doi: 10.18278/jpcs.6.1.6