Primer

Funding Mechanisms: How the Design of Research Money Shapes What Science Happens

David Hilmer Rex · June 2026

Summary

Most debates about science funding are about how much. The more consequential question is how - because the mechanism that distributes research money quietly determines what questions get asked, who gets to ask them, and how much risk the system can bear. This primer surveys the dominant model (project grants allocated by peer review) and its documented pathologies, then the main alternatives - lotteries, prizes and advance market commitments, funding people rather than projects, the ARPA model, and fast low-overhead grants and regranting - before setting out the design dimensions that distinguish them, why funding mechanisms are so hard to evaluate, and the funding landscape specific to Europe and the Nordics.

Why the mechanism is the message ↑

A funding mechanism is a theory, made operational, of how good science gets selected: who decides, on what evidence, how quickly, and what they are rewarded for. Change the mechanism and you change the portfolio of work that gets done - not at the margin but structurally. This is the funding-side counterpart to the institution–problem fit argument in the diversity primer: just as no single institutional form suits every problem, no single allocation rule suits every kind of research. The danger is monoculture - routing nearly all money through one mechanism and inheriting all of its biases.

The default and its pathologies ↑

Project grants awarded by ex-ante peer review are the global default, and their strengths are real: expert judgement, legitimacy, and accountability for public money. But three decades of metascience on the mechanism itself have documented serious weaknesses. When researchers re-ran the NIH process - 43 reviewers scoring 25 proposals that had already been funded - they found essentially no agreement between reviewers, and no reliable ability to distinguish the merely good from the excellent.¹

The mechanism is also expensive in the scarcest resource it has: researcher time. An Australian study found that preparing a single grant proposal took about 34 working days on average, and that one funding round consumed an estimated 550 working years of researchers’ time - with roughly one proposal in five funded, and more preparation buying no greater chance of success.²

It also has a measurable bias against the unconventional. Taking first-time combinations of cited journals as a marker of novelty, Wang, Veugelers and Stephan found that highly novel papers are more likely to become top-cited in the long run but less likely in the short windows that funders and committees actually use; they appear in lower-impact-factor journals and are more often cited outside their own field.³ The bibliometrics that evaluation leans on are themselves biased against novelty, and conservative review of this kind favours safe, incremental proposals over risky ones - optimising the default for defensible consensus rather than for boldness.

And concentrating money in the best-funded labs yields diminishing returns. Analysing NIH grants, Lauer and colleagues found that output per dollar peaks near the median grant size - roughly US$400,000 in total costs per investigator - and falls away on either side, with each marginal dollar at twice that level returning only a quarter to two-fifths as much.⁴ Spreading the same budget across more labs would, on this evidence, buy more science than concentrating it - which makes the default look less like a law of nature than like one mechanism among several, with biases of its own.

The most active current reform within the peer-review paradigm rather than outside it is distributed peer review (DPR), in which applications are assessed by a larger pool of reviewers each carrying a lighter load, spreading the bottleneck across a broader crowd. The UK Metascience Unit at DSIT has run formal DPR pilots within UKRI calls; early results show a 65% reduction in per-application assessment time, and the approach is now rolling out across major UKRI funding lines. The official caution is worth registering: DPR is “not the holy grail” — it cuts overhead without resolving the prior question of whether distributed assessment actually improves which proposals get funded. The unit has announced a follow-on survey specifically on AI-assisted assessment, probing whether algorithmic support can close that remaining gap.¹⁴

Funding by lottery ↑

If peer review cannot reliably rank the proposals above the funding line, one honest response is to stop pretending it can. Funding lotteries screen proposals for eligibility and quality and then randomise among those that qualify. The aim is not to abolish judgement but to bound it: cut the reviewing burden, remove the false precision of fine-grained ranking, and give unusual or risky ideas a fairer shot. The standard design is a modified or partial lottery - peer review filters out the clearly weak, and sometimes fast-tracks the clearly outstanding, while chance decides the contested middle.⁵

This is no longer hypothetical. The Health Research Council of New Zealand allocates its Explorer Grants by lottery after a quality screen; the Volkswagen Foundation, the Swiss National Science Foundation, the Austrian Science Fund and the British Academy have all added randomisation to at least one funding line. The early evidence is encouraging: when the Volkswagen Foundation ran a lottery before peer review in one programme, funded projects from female applicants rose by 23% and the estimated cost of the whole allocation process fell by 68% against conventional single-stage review.⁶ The evidence base is still young, and lotteries fit some funding lines (early-stage, exploratory) far better than others - but they are the clearest example of metascience changing how real money moves.

Paying for outcomes: prizes and advance market commitments ↑

Grants pay for effort - you are funded to attempt the work. Prizes and advance market commitments (AMCs) invert this and pay for outcomes: the reward is contingent on hitting a defined target, and the funder need not pick the winner in advance. This works when the destination is definable but the path is not, and it can attract participants - firms, startups, non-traditional teams - who would never respond to a research grant.

The landmark case is the pneumococcal AMC, launched in 2009 with US$1.5 billion from five governments and the Gates Foundation through Gavi, which guaranteed a market and is credited with accelerating vaccine supply for lower-income countries; Operation Warp Speed used related pull mechanisms at speed.⁷ The limits are equally clear: outcome-based funding works poorly for basic research where the destination is unknown, and rewards can be gamed when milestones are loosely specified.

Funding people, not projects ↑

A different move is to fund the person or organisation rather than the project - long-horizon, failure-tolerant support that removes the incentive to diversify across safe bets. The Howard Hughes Medical Institute funds on exactly this principle, summarised in its slogan “people, not projects.” When economists compared HHMI investigators with comparable NIH-funded scientists, the HHMI model - longer terms, genuine tolerance of early failure - produced more high-impact, original work.⁸

The same logic underlies the full-time employment model of Focused Research Organisations (see the diversity primer) and the block funding of contract-research institutes. The trade-off is the one that recurs across this resource: autonomy and long horizons buy ambition, at the cost of the legibility and accountability that project grants provide.

Mission-driven funding: the ARPA model ↑

Where lotteries and people-not-projects loosen central control, the ARPA model concentrates it - in a person. Modelled on DARPA, ARPA-style agencies hand an empowered, term-limited programme manager a budget and wide latitude to design a programme around a goal, solicit and hand-pick projects rather than wait for proposals, and then actively manage the portfolio: extending and expanding what works, cutting what does not. The fixed term forces an exit strategy and injects urgency. The early evidence is promising - cleantech startups funded by ARPA-E filed patents at roughly twice the rate of comparable firms - though the model’s success appears to depend on scarce managerial talent and genuine institutional autonomy, neither of which is easy to reproduce on demand.⁹

The Franco-German Task Force on Breakthrough Innovation (June 2026) offers the most systematic recent account of what those conditions actually are.¹⁰ Drawing on DARPA and Germany’s SPRIND as its primary cases, it distils five invariants: empowered programme managers (not committee-filtered decisions), mission-oriented and time-bound programmes, a genuine high-risk posture, connected science (tight links between the programme and the broader research community), and institutional independence from standard procurement and ministerial oversight. The task force also argues that France’s gap is institutional, not financial - France 2030 has committed €54 billion, so the binding constraint is not money but the institutional form and the mechanism that runs it. It recommends France establish a dedicated ARPA-type entity interoperable with SPRIND from day one.

Speed and the cost of the cycle ↑

A mechanism’s speed is itself a design choice with consequences. Conventional cycles run six to nine months from application to decision; for time-sensitive or exploratory work that lag is often fatal. Fast grants and regranting compress the cycle to days - a thirty-minute application, a near-immediate decision - by trading exhaustive review for trusted, low-bureaucracy judgement.¹¹ The deeper point, developed in the enabling-infrastructure section of the diversity primer, is that the speed and overhead of the funding decision set the rate at which new ideas can even be tried.

The regrantor model generalises this. Instead of a committee, a funder hands a discrete budget to a trusted, well-networked individual who makes grants on their own judgement - fast, low-overhead, and able to surface opportunities a formal call would miss, including proactively starting projects rather than waiting for applications. Fast Grants, run on this principle, made decisions in around 48 hours and moved more than US$50 million to over 260 COVID-19 research teams in 2020; philanthropic experiments such as Manifund and the FTX Future Fund’s regrantor programme have since extended it. The open weakness is evaluation: regranting spends almost nothing on tracking outcomes, so its track record remains largely anecdotal.¹²

The design dimensions ↑

Most reform is a move along one of a few dials rather than a wholly new invention. Five matter most: who decides (a committee, a programme manager, the crowd, or chance); speed; the unit of funding (project, person, or organisation); risk tolerance; and what behaviour the mechanism rewards. Naming the dials makes otherwise incomparable instruments easy to compare - a lottery and a fast grant both attack reviewing burden but move different dials; an FRO and an HHMI investigorship both shift the unit of funding from project to organisation or person.

Why this is hard to evaluate ↑

A caution runs underneath all of this: funding mechanisms are unusually hard to evaluate, because the value of research is heavy-tailed. A handful of breakthroughs dominate the total return, so what matters is how a mechanism affects the rare right tail - not the average grant, which is what randomised trials and conventional statistics are built to characterise. If outcomes are outlier-dominated enough, sample means and variances are barely meaningful, and two mechanisms can look identical across the bulk of grants while differing entirely in the tail that counts.¹³ This is partly why the field leans on case studies of extreme successes alongside controlled comparison - and why confident claims that one mechanism simply “outperforms” another deserve to be read with care.

The European and Nordic angle ↑

Three features distinguish the European context. The unusual weight of private foundations as research funders - above all the Danish enterprise foundations, whose patient capital is examined in the enterprise-foundations primer. The EU framework programmes (Horizon), whose scale is matched by a heavy administrative load. And the coordination problem flagged by the 2020 EU peer review of Denmark: many capable funders, public and philanthropic, with little strategic alignment among them - a system-level inefficiency distinct from the design of any single mechanism, and one that pooling and coordination, not more money, would address.

The Franco-German Task Force on Breakthrough Innovation (June 2026) makes this last point starkest: it recommends France create a dedicated ARPA-type entity explicitly interoperable with Germany’s SPRIND from launch - a direct proposal to shift from calls-for-proposals as the dominant mode toward a mechanism built around empowered programme managers and mission-oriented time-bound programmes. The report illustrates an observation that applies across European funders: large volumes of public money can coexist with an institutional gap, because the mechanism design and the institution that runs it are independent variables.