Government’s new metascience unit an opportunity to transform innovation funding

An eye-catching aspect of the UK Government’s response to the Independent Review of the UK’s approach to research, development and innovation, was a proposal to create a new ‘metascience unit’ to evaluate different funding models in research and innovation. The Government plans to support a dynamic and diverse innovation system in relation to this.

We believe initiatives of this type are vital for building a better evidence base for innovation policy.

Innovation is the main driver of a country’s long-term economic growth, making it critical to national and global interests. Given the centrality of innovation to human progress and rising prosperity, the effectiveness of innovation policy really matters. It is key to tackling emerging threats, as illustrated in the critical role new vaccines played in the Covid-19 pandemic.

Initiatives such as the new metascience unit, therefore, can help address some clear evidence-needs. The deficiencies of evaluation in science and innovation policy are relatively well-documented, including acknowledgement of the wider systemic gaps. The former chair of UKRI, Sir John Kingman, openly remarked on how innovation and R&D policy “tends to turn on gut feel of the individuals involved, than on hard evidence and analysis”.

This ‘vibes-based’ approach to innovation policy risks neglecting the real, measurable impact of innovations, overweighting the importance of stories and messaging. More critically, it fails to give due attention to the role that empirical evidence should play in shaping effective and impactful innovation policies.

Steering the science of science (and innovation) 

The practice of ‘metascience’, i.e. the ‘science of science’, uses rigorous experimentation and data collection to enable better evaluation of science and innovation funding. In the UK, ARIA is an immediate example of an innovative approach to funding RD&I. In the United States, R&D priorities from the Office of Management and Budget (OMB) and the Office of Science and Technology Policy (OSTP) emphasise how agencies should be experimenting with their funding processes, as is being put in motion by the U.S. National Science Foundation (NSF) and Institute for Progress (IFP). The CHIPS and Science Act of 2022 has given the NSF a mandate to explore alternatives to its traditional processes, opening the door for IFP and the Metascience Working Group to conduct economic and social experiments and statistical tests to improve NSF’s funding mechanisms.

Strategies and analysis cases are also being put forward in favour of more ‘pull funding’ in innovation. In the funding of innovation, push funding involves paying for inputs or reimbursing costs (e.g. research grants), whilst pull mechanisms pay for outputs and outcomes (e.g. challenge prizes). Earlier this year, a delinkage study was conducted as part of a bill to reauthorise the Pandemic and All-Hazards Preparedness and Response Act (PAHPA), to investigate serious alternatives to time-limited patent or regulatory monopolies to stimulate investments in R&D. The study suggests new models to stimulate funding for biomedical research and developments that delink R&D costs from the prices of drugs.

Pull-funding tactics are also evidenced in the Market Shaping Accelerator (MSA), a novel programme from the University of Chicago. It is spurring efforts to tackle major global challenges such as climate change and pandemics by harnessing the power of incentives and competition. The MSA intends to utilise market-shaping mechanisms to address market failures that occur when commercial incentives for innovation do not align with societal needs, including advanced market commitments (AMCs), prizes and milestone contracts. These examples of pull mechanisms, which align funding with the successful delivery of innovative solutions, evidence a shift away from traditional push-funding models and align with a case for more pull-financing.

New models such as research impact bonds are also emerging, which aim to redistribute accountability and risk in science and innovation funding. These models sit alongside more established ideas (that have perhaps yet to be fully realised) on more equitable approaches to science and innovation – i.e. randomly-allocated research funding and lotteries. The Innovation Growth Lab, in partnership with the Research on Research Institute, has also examined research and innovation funding processes and the application of random selection and experimental methods.

Where next?

The creation of this metascience unit is arguably not just a response to evidence gaps in science and innovation policy. It points to a need to constantly evolve and adapt our strategies to adapt to the rapidly changing challenges our climate and society are facing.

Though clearly interrelated, science and innovation policy each have different focuses and characteristics. Science policy, defined broadly here as a focus on the production of scientific knowledge and the allocation of resources to different scientific activities, might be considered as having been broadly effective, given the UK’s reputation in basic science. In innovation policy, however, and the measures, programmes and incentives put in place to support the development and diffusion of innovations, there is much discussion on how good the UK is at innovation. Hence, a better understanding of what works in innovation policy takes on even greater importance.

Incubating ideas

In addressing the lack of evidence on what works, Nesta has previously taken a lead in commissioning a comprehensive database of empirical evidence on the effectiveness of innovation policies – including a report on the impact of innovation inducement prizes – or challenge prizes.

As a Nesta enterprise, we at Challenge Works are eager to build on this existing evidence – to improve understanding of the effectiveness of the challenge prize method. We want to inform decision-making for policy makers’ about when prizes may be the most appropriate tool to spur innovation.

Here are just some of the specific ideas we have for evidence-building on challenge prizes that we would be keen to explore and implement:

• What is the optimal time frame for a challenge prize in a specific problem space and what is the impact of varying time frames on the speed and efficiency of innovation efforts. Does a shorter time frame for a challenge prize accelerate the pace of innovation and how does the duration of a challenge prize affect the risk-taking and creativity of participants?
• What is the value of non-financial support and incentives in the structure and implementation of challenge prizes – how does the non-financial support provided to participants (mentoring, networking, technical and commercial capacity building, access to data etc.) contribute to the quality and success of the innovation?
• How can we best expand the pool of innovators through the design and structure of challenge prizes – to be tested by randomly allocating applicants to different funding structures and analysing when, and for whom, does up-front grant+prize funding generate better results than prize funding alone?
• How do the effectiveness of collaborative and competitive structures in challenge prizes compare – do collaborative prize structures, where participants are encouraged to partner and work together, yield better and more comprehensive solutions? Conversely, how do competitive prizes, where participants work independently, affect the novelty and diversity of solutions?
• What are the characteristics of challenge prize RD&I activities and how do they differ from grant-funded RD&I activities – does the time-bound nature of a challenge prize and the different funding requirements it presents affect the R&D efforts carried out?
• When and where does innovation activity supported through challenge prizes lead to preferable solutions than those that would have been funded through grant programmes?
• How would the outputs compare from a grant structured programme versus a prize structured programme, where the programme and focus is otherwise the same? How would the characteristics of prize solution / technology outputs compare to outputs from a grant programme focused on the same problem, in terms of quality, novelty, and conventionality, and how could this help us better distinguish which innovation problems are better suited to prizes, grants or other innovation mechanisms?

We see the establishment of the new metascience unit as a call to action for all stakeholders in the innovation ecosystem. It’s a chance to test and rethink our approaches, challenge our assumptions, and collaborate in new ways. We look forward to seeing how work unfolds in this new unit and stand ready as an active collaborator that is committed to improving the future of funding and catalysing meaningful innovation, for social good.

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