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
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doi: 10.18278/jpcs.6.1.6