Fostering breakthrough technologies — How do optimal funding decisions depend on evaluation accuracy?

Abstract

There is a growing interest in fostering breakthrough technologies that offer exceptionally high value to society. However, when starting technology projects, it is impossible to know which of them have the potential to lead to breakthroughs. Therefore, organizations have adopted funding policies in which on-going projects are subjected to interim evaluations based on which some projects may be abandoned to release resources for seizing new opportunities. In this paper, we study which funding policies are optimal when the objective is either (i) to maximize the expected value of the project portfolio, or (ii) to maximize the expected number of exceptionally excellent projects that may lead to breakthrough technologies. We show that the optimal policy for funding exceptionally excellent projects is to start a large number of projects and abandon a high proportion of them later, whereas the optimal policy for maximizing the expected value of the project portfolio is to grant long-term funding to a smaller set of projects based on initial evaluation. Furthermore, we show how the trade-off between these two objectives depends on the initial project evaluation accuracy and the rate at which this accuracy improves. Our results suggest that this trade-off is particularly significant when the initial project evaluations are very uncertain but become more accurate soon after the projects have been launched. In such a setting, policies that seek to maximize the expected portfolio value may fail to promote breakthrough technologies.

Assessing the Performance of Japanese Major Universities through the Research Funding System

“…Muscio, et al (2013) showed that the government funding to universities complements funding from research contracts and consulting, contributing to increasing universities' collaboration with industry and activating knowledge transfer processes. Vilkkumaa et al (2015) investigated optimal funding decisions depending on evaluation accuracy. Focusing upon the policies maximizing the expected value of the project portfolio, they showed that the optimal policy for funding exceptionally excellent projects was to start a large number of projects and abandon a high proportion of them later.…”

“…Focusing upon the policies maximizing the expected value of the project portfolio, they showed that the optimal policy for funding exceptionally excellent projects was to start a large number of projects and abandon a high proportion of them later. In general, Vilkkumaa et al (2015) emphasize that the optimal policy for maximizing the expected value of the project portfolio is to grant long-term funding to a smaller set of projects based on initial evaluation. McKinney and Hagedorn (2017) proposed a performance-based funding model for community colleges in Texas, USA.…”

Funding Risky Research

“…Can such a staging approach also be used by science funding agencies, allowing them to take more risk? Interim evaluation is especially useful when initial estimates are unreliable, but can be quickly updated and when investments can start at small scale and can be later scaled up (Vilkkumaa et al 2015). It is thus especially suitable for research that can make substantial steps forwards in a relatively short period of time and does not require large fixed-costs to be started (Ewens, Nanda, and Rhodes-Kropf 2018).…”

Sowing the seeds of the future: Policies for financing tomorrow's innovations

“…While their importance has been highlighted in the literature, and is likely to only increase (Colombo et al, 2011), there are still important issues which remain unresolved. Firstly, the qualitative and analytical models using market failure theory to justify STI policy are potentially limited when the innovation in question are related to transformational shifts and grand challenges (Nelson, 1994;Mazzucato, 2013a), and secondly, solid quantitative and empirical models to support the design of policies promoting technological breakthrough innovation are still scarce (Vilkkumaa et al, 2015).…”