Decision Making in a Convergent Society

Abstract

Decision analysis can help organizations which fund research (e.g., government agencies, technology incubators) to develop guidelines for promoting breakthrough interdisciplinary science in a transparent and replicable manner. An evaluation of the methods that encourage convergence requires data and preferences from varying temporal, spatial, and organizational scales and domains. It is necessary to identify and incorporate objectives of social, economic, and technical importance in the decision-making process. This necessary and holistic evaluation is of such complexity that individual decision-makers cannot effectively consider all these factors and their interactions at the same time (Linkov Cormier et al. Risk Anal 32(3):374–380, 2012; Roco et al. Convergence of knowledge, technology, and society: beyond convergence of nano-bio-info-cognitive technologies. Springer, New York, 2013). With regard to convergence, current guidelines are qualitative in the form of vision statements and road maps. Decision analysis can facilitate convergence by enhancing decision-making with quantitative, holistic, and structured tools that prioritize objectives in a transparent and replicable way and helps decision-makers to cope with overwhelming complexity. Multi-criteria decision analysis (MCDA) is of particular interest in enhancing this decision process. Once an objective is identified, then decision criteria, preferences for criteria, and alternatives are defined. Based on available scientific information, MCDA identifies feasible alternatives (e.g., training, changes to organizational structure, funding) and decision criteria (e.g., cost, importance, network diversification), assess the performance of each alternative relative to those criteria, and elicits or explores relative priorities among the continuum of incommensurable criteria (Linkov and Moberg Multi-criteria decision analysis: environmental applications and case studies. CRC Press, Boca Raton, 2012). Further analysis like value of information (VoI) boosts MCDA by identifying which uncertainties to reduce, e.g., by increasingthe accuracy of the information on which the decision is based, that results in a change in preference for alternatives or an increase in value for an alternative that is already preferred. When promoting convergence within the scientific community, MCDA provides research funding organizations with the ability to quantify the values and the trade-offs between criteria to provide practical and decision-relevant guidelines for individual scientific organizations to follow (Roco et al. Convergence of knowledge, technology, and society: beyond convergence of nano-bio-info-cognitive technologies. Springer, New York, 2013).