Science Policy Research Report: The Use of Research Portfolios in Science Policy

供资组织在同行审查的指导下非常成功地选择了个别项目。然而，随着研发活动在新的专题、部门和地理区域的规模和范围的扩大，越来越难以对研发资金的方向和实际优先事项进行战略思考。研究组合方法采用项目管理的研发景观视角，以促进战略思维。从所有获得资助的项目来看，人们可能会想，是否某些类型的相关研究资金不足，或者考虑到研究的高度不确定性，是否有提高投资效率的空间。该项目补充了当前管理层对研发项目质量的关注，并系统地考虑了项目风险、协同效应和对社会需求的响应。它为研发项目经理/决策者提供了考虑总体研发组合的工具。该政策报告将解释分析方法，以更好地处理项目风险和协同作用在计划层面。它还将解释方法来分析R D计划任务和计划组合的预期社会成果之间的一致性。这份科学政策报告是基于这样一个概念，即R D组合不仅仅是组合中包含的项目的总和。原因是，失败风险和项目学习不是相加的，而是以复杂的方式相乘的：通过考虑项目之间的相互作用以及规模经济和范围经济，可以减少风险，加强总体学习。 为项目组合管理显示了三个特定的近似值。首先，单目标投资组合的方法开发的情况下，如能源消耗在一个行业或公共R D的逻辑。这些方法可以更好地处理技术和市场的不确定性，以及在研究项目的生命周期中考虑所有的停-走决策。他们是基于文献使用决策树和真实的选项的方法。其次，该项目为具有多个目标的研发项目开发方法，如公民福祉，环境和安全。在这种情况下，项目分析多标准方法或数据包络分析。第三，该项目引入了过去几年开发的数据和可视化技术，这些技术有助于确定知识差距，并有助于使目前的研发投资重点更加透明。

Funding organizations have been very successful at selecting individual projects under the guidance of peer review. However, the growth in size and scope of R&D across new thematic, sectoral and geographic areas has made it increasingly difficult to develop strategic thinking about the directions and de facto priorities of R&D funding. Research portfolio approaches take an R&D landscape perspective of project management to facilitate strategic thinking. Looking at the ensemble of funded projects, one may wonder if some type of relevant research is underfunded, or whether there is room for improving efficiency of the investments taking into consideration the high uncertainties of research. This project complements current management focus on R&D program quality with systemic considerations on project risk, synergies and responsiveness to societal needs. It provides tools to R&D program managers/decision makers to consider aggregate R&D portfolios. The policy report will interpret analytical methods for better handling project risk and synergies at the program level. It will also explain methods to analyze the alignment between R&D program missions and the expected societal outcomes of program portfolio.This science policy report is based on the notion that an R&D portfolio is more than the sum of the projects contained in the portfolio. The reason is that the risks of failure and project learning are not additive but multiplicative in complex ways: by considering the interactions among projects and economies of scale and scope, risks can be reduced and overall learning enhanced. Three specific approximations are shown for portfolio management. First, methods for single-objective portfolios are developed for cases such as energy consumption in an industry or within the logic of public R&D. These methods better handle technical and market uncertainty, as well as account for all stop-go decisions in the lifetime of a research project. They are based on the literature using decision trees and real options methods. Second, the project develops methods for R&D programs that have multiple goals such as citizen wellbeing, the environment, and security. In such cases, the project analyses multicriteria methods or Data Envelopment Analysis. Third, the project introduces data and visualization techniques developed in the past few years that facilitate the identification of knowledge gaps and help in making more transparent the current focus of R&D investment.