Research Design and Research Systems: An Application of Agent-Based Modelling to Research Funding
Hassanpour, Ebrahim 1973-
Governments nurture their multi-disciplinary innovation systems by funding several public organizations to help universities and research institutes support research projects and associated infrastructure. To study the impact of research funding, a generic stylized model is developed using Agent-Based Modelling (ABM) to simulate the outcomes. To provide context, the analysis anchors the problem in the context of Genome Canada’s research funding efforts. The process of academic research and the impact of grants on its speed and output (papers published) is simulated. To compare the outcomes for policy choices, two measures or indices are developed for the outcomes: efficiency is measured by number of papers per granted money and equity is measured by a Gini coefficient (for papers and money granted); the Matthew effect is also tested to check for effects on equity. Defining academic investigators as the main agent and having investigations and grants as subagents, along with assumptions for the procedures and parameters, an ABM is designed in which investigators conduct individual research using grant and non-grant funds. The simulation model is then tested and verified to be used for evaluation and comparison of policy scenarios. The results revealed that the instruments of allocated budget per competition, the gap between competitions, the sum granted for any proposal, and the size of the target group may be utilized to improve the efficiency and equity of the system. However, there is usually a trade-off between these two objectives and a loss in one of them is necessary to achieve a gain in the other. The tools can be combined in order to secure better results, but there are other factors that should be taken into account in making decisions. Although some lessons can be learned from such a simple model, making it applicable to policy making and to real-world issues, other factors such as investigator heterogeneity, collaborations, and grant administration complexities should be taken into account.
DegreeMaster of Public Policy (M.P.P.)
DepartmentJohnson-Shoyama Graduate School of Public Policy
SupervisorPhillips, Peter W.B.
CommitteeLongo, Justin; McNutt, Kathleen; Evans, Samantha; Mou, Haizhen
Copyright DateAugust 2017
research funding, agent-based modelling, Genome Canada