A better mythology for science?

Science: A New Golden Age offers a rich vision of innovation, but only if its philosophy is reflected in practice

Introduction:

Most scientists, engineers, and associated policy staff have an internal mythology, a narrative about why they do what they do and how it affects society. A new effort to define the mythology of science was just released on July 21st.¹ Michael Kratsios, President Trump’s Science Advisor and director of the Office of Science and Technology Policy (OSTP), released Science: A New Golden Age (SNGA), a new 100-page report. The report seeks to go beyond an earlier report, Vannevar Bush’s 1945 Science: The Endless Frontier, and Kratsios declares that part of Bush’s core mythology for science, the linear model of innovation “no longer holds” (p. 7). Kratsios/OSTP is clear in their desire for major change, saying “[t]he task, then, falls to our generation to design the institutions, standards, and capabilities that can guide a scientific enterprise faster, and less individually comprehensible than any in history” (p. 72). Their hope seems, in part, to be to set a new mythology to guide future science and engineering.

SNGA has already produced many reactions that touch on its many features. Institute for Progress’s Alec Stapp expressed excitement about proposed metascience units across federal science agencies, portfolio approaches to federal research funding, the creation of new research institutions, experimentation with mechanisms such as fast grants, prizes and advance market commitments, and reductions in the administrative burden on scientists. The Wall Street Journal’s article on the report stated it would redirect funds away from universities and toward individual scientists and the use of artificial intelligence.² Others are more critical, with Ars Technica’s John Timmer labeled it a “a near-random mixture of grievances and real problems,” noting that some issues reflect points “where people in science would likely agree that things are often considerably more complex” but that it lacks a unified focus and ignores harms being done against science today. Strong debates about the politics of science, driven in part by the recent proposal for political appointee review of grant awards and proposed reductions in some areas of research funding, can make it difficult to focus on SNGA as a policy document that is meant to rise above science politics.

As both a former civil servant engineer and a researcher of science policy and its history, my first reaction to SNGA was surprise at its invocation of an unusually broad range of science policy and innovation-related research.³ For myself, with engineering degrees but experience in the philosophy of science, I was surprised by citations to the philosophy and sociology of science, and a focus on how knowledge about new innovations is distributed across people and models.⁴ The breadth of the report makes me very curious to learn about the backgrounds of those who wrote the report - Kratsios himself has a degree in political science and appears to have created an interdisciplinary team in what seems to be a serious effort to engage on what innovation policy should be.

This post is the first of several reflections on SNGA. I admittedly will not fully answer the question in the title here. Instead, I want to explore a series of science policy questions that I think deserve more attention, including the linear model of innovation, tacit knowledge, the high and low churches of science policy, and what it takes for a new mythology to actually shape practice.

I will note here, however, that the report does not entirely escape the mythology it seeks to replace. Its conclusion and especially the budget guidance included in its annex in some ways restores the same science-first hierarchy that its opening chapters reject.

Down with the linear mythology for U.S. science policy!

My second reaction was to the report’s specific attacks on the linear model of science - I felt surprise mixed with appreciation! The persistent negative effects of this model were the focus of my second-ever publication in 2013.⁵ As Vannevar Bush stated in his 1945 report, Science, the Endless Frontier, “Basic research leads to new knowledge. It provides scientific capital. It creates the fund from which the practical applications of knowledge must be drawn” (emphasis added). This framing casts basic research as creating a reservoir of knowledge from which practical applications must be drawn. This makes basic science the source of practical benefits realized through engineering – new advances in engineering seemingly come from earlier advances in basic science. It partially casts advances in engineering as being about the mere application of ideas found elsewhere.

As several historians of innovation have shown, engineers often discover new technologies and approaches without having a solid basis in science, such as how the steam engine developed before the discipline of thermodynamics was firmly established. Indeed, many engineering innovations drive basic insights and interact with ongoing research.

SNGA matches my description and critique, stating:

The Linear Model No Longer Holds: In his 1945 report, Vannevar Bush presented a progression from basic research through applied research to development, later termed the “linear model.” This framework laid out a clear role for each part of the research and development pipeline. Universities would pursue fundamental understanding without the pressure of practical application. Industry would turn discoveries into products. At the time, the separation was natural and productive. (p.7)

...

“We must acknowledge, too, that the linear model of discovery and technical progress no longer holds. The interplay between basic research, regular professional science, and commercialization is far more complex than had been assumed. Meanwhile, the context in which the American innovation enterprise operates has become global, and therefore intensely vulnerable; the technologies we invent rely on production chains that stretch around the world, and they are subject to theft by near-peer competitors (p.12, emphasis added).

I was genuinely surprised to see an OSTP report attack the linear model of innovation so explicitly! I do not recall such discussion in the history of major OSTP reports across the decades. Perhaps this is because many historians and students of science policy have long discredited the linear model - the famous economist Nathan Rosenberg declared in 1991 that “[e]veryone knows the linear model is dead!”⁶ Others debate whether Vannevar Bush used the phrase ‘model’ and exactly what was implied and when.⁷ And for 60 years and more, there has been a proliferation of research-based alternatives to the linear model, though few of those make their way into engineering or policy practice.⁸

Yet, the linear model effectively remains as mythically descriptive of science policy as both Bush and Kratsios take it to be. As I argued in 2013, the linear model remains the implicit epistemology - or theory of knowledge - underlying engineers and scientists who shape innovation and innovation policy. Further, while many engineers have examples in their textbooks of applying laws of physics to conditioned systems, it is often more complicated. Even where scientific laws and models apply, they do not by themselves provide all the knowledge needed to build and operate complex engineering systems. A law-centered, science-first account can mistakenly make engineering development appear too easy, leading some to excessive management approaches to ensure every test meets with success instead of allowing for iterative learning with failures across multiple tests.⁹ And while historians have long discredited the linear model, there are many reasons why the engineers who lead many innovations still believe it. Since the first engineering school in the U.S. at West Point in 1802, engineering education in the United States in some ways reifies the linear model, as students primarily focus on math and science for the first two years of an undergraduate degree before beginning the branch into more applied classes later on.¹⁰

I should note Vannevar Bush’s career as an engineer, as chronicled by his biographer G. Pascal Zachary, was exceptional at how he built mechanical computers and managed complex R&D projects. He was quite good at many aspects of how engineers know the world and I suspect he would surely agree that engineering and applied work can shape scientific inquiry. But Science: The Endless Frontier is written the way it is, and its prose leads readers to take away a too simple narrative of how science works, giving basic science epistemic priority as the reservoir from which practical applications must be drawn

Who is SNGA for? The High and Low Churches of Science Policy

Given how Vannevar Bush’s 1945 report is still heralded as a key part of the mythology for science and engineering policy in the U.S., I think it is a welcome opportunity to examine what myths we hold about science. But it takes some thinking to tease out who exactly the report is intended for, especially if one normally doesn’t spend time thinking about the linear model.

The report’s audience seems to simultaneously be what I’d call both the high church and low church of science policy. ‘High church’ science policy comes from Executive branch presidential executive orders, OSTP reports and budget proposals, as well as legislative branch work, from Congressional action and laws. The much broader ‘Low church’ science policy realm consists of thousands of civil servants at federal agencies and partner organizations who make strategy and implementation decisions about how to do science and engineering. Decisions made by mid-career GS-14 program officers and program managers across the federal government often do much to steer and shape the outcomes of science in a significant way. Many people working in the trenches often just ignore the high church proclamations; work often continues much as it always did, even though the need for thoughtful changes can be paramount.¹¹

SNGA seems to aspire to be a high church policy document that shapes a myriad of actions across the low church of science policy. It seems to want to make the many practitioners in the low church of science policy start to care more about concepts like the linear model of innovation, to reflect more on the nature of knowledge used in engineering new systems. It has many tangible prescriptions for low church policy activities, from creating metascience units, empowering program officers, increasing funding of AI companies, and many others. There are many areas where reform could be beneficial in the low church, and some of the ideas contained in SNGA could be important.

What is the new mythology?

What, exactly, is the new mythology of the SNGA report? There’s not a simple catch phrase for it that matches the brevity of Bush’s 1945 missive. As I noted in my introduction, SNGA seeks to “design the institutions, standards, and capabilities that can guide a scientific enterprise faster” (72). Seemingly the new mythology embodies a broad and collective undertaking in which scientific research, engineering, institutions, acquisitions, manufacturing, tacit knowledge and policy experimentation all interact to produce useful outcomes for society.

To make this new myth worthwhile, there are a lot of engineers and scientists across the federal government and its partners that need time to think through the history of innovation and to research and build upon some of the ideas noted in this report. The new mythology could be important if this administration practices what it preaches and helps low church policy practitioners to shape their work to better outcomes. Science policy practitioners need time to actually reflect, study and then reframe their work based on all of these ideas. To strain the church metaphor, they need time listening at the pulpit, discussing the sermon, and then distilling it into their lives.

Tacit Knowledge: Often Acknowledged, but Sometimes Ignored in Practice.

I must mention another key aspect of SNGA. The report calls for better connecting craft researchers with practicing scientists and engineers. This comes as part of an important discussion of tacit knowledge (p. 47-48 and elsewhere), which is knowledge that technicians, craftspeople and engineers know but cannot explicitly codify or formalize in writing to share with others. Tacit knowledge has long been known to need to be transmitted through interactive approaches, such as apprenticeship and learning by doing. As part of SNGA’s call for a large-scale onshoring of US domestic jobs, SNGA calls for an explicit focus on encouraging and connecting tacit knowledge and partnering those with craft skills with those conducting cutting-edge research.

For students of science policy, invoking the need to carefully consider tacit knowledge in innovation systems is not novel, yet few practicing engineers are such dedicated science policy students, including those in management and policy positions in federal agencies. Many managers underestimate how much tacit and process knowledge is held by government teams, and therefore what is lost when those teams are dispersed. (This critique might augur for the need to carefully preserve the tacit and process knowledge of teams who are currently proposed for budget reductions in the recent President’s Budget Request). A broader and deeper conversation about tacit and process knowledge across government at multiple levels could be both helpful and important. This is one place where SNGA moves beyond a science-first narrative, though it’s one that many historians might readily acknowledge.

Yet, does SNGA actually escape the linear model of innovation?

Getting cultural buy-in to a new mythology for science - especially one that cannot be captured in a single soundbite – is quite hard. And as can happen in ambitious efforts, I do read two conflicting narratives in SNGA that are in tension here. SNGA’s most obvious narrative is that the linear model no longer holds and complex interactions between basic/applied science and engineering must take place. But the report does use language that essentially is identical to Bush’s. From the conclusion:

To sustain this progress, we must invent new ways of doing science. Science is the pool of knowledge that underlies our technological pursuit. The science of the coming decades could produce knowledge that no single person fully grasps, verified by systems that no single person fully audits, yet more reliable than anything we have built before. (p. 71 emphasis added)

The metaphor of a pool of knowledge closely reproduces Bush’s reservoir language. Describing science as the pool of knowledge underlying technological pursuits is essentially the same rhetorical move Bush made. Bush never tried to argue that there could not be strong interactions between basic science, applied science and engineering. SNGA’s emphasis on the complexity of how science and engineering interact is welcome and important. But the above quote shows how easy it is to revert back to language from the linear model. The question is not simply whether SNGA acknowledges interaction and feedback across science and engineering. It is whether science remains the privileged source from which technological knowledge is understood to flow.

And it’s hard to get clear institutional buy-in about a new mythology as well. The report contains an annex that includes a budget strategy document from OSTP director Kratsios and the director of the White House Office of Management and Budget (OMB) Russell Vought. This document repeatedly tells science agencies that they should focus on “foundational research,” and seemingly focus on more laboratory-oriented research vs “later-stage development activities.”¹² Because these are budget instructions rather than high church reflections, they may shape low-church science policy behavior more directly than the report’s repeated rejection of the linear model. The memo’s category of “foundational research” tentatively appears to be broader than Bush’s basic research framing and explicitly includes engineering sciences. But the document nevertheless directs agencies to shift their portfolios toward earlier-stage work and places a special burden of justification on expansions of later-stage development. Its repeated claim that foundational fields “underpin” technologies preserves an upstream-to-downstream hierarchy that sits uneasily with SNGA’s rejection of the linear model.¹³

That tension does not erase the many important and underappreciated points about the nature of innovation and innovation policy contained in SNGA. The linear model is not displaced simply because a high-level report declares that it no longer holds. It is displaced when the categories, budgets, institutions, and practices of the low church of science policy stop acting as though science is the upstream pool from which engineering applications flow. SNGA contains many of the intellectual resources for a richer account of innovation. Its ultimate test is whether the administration allows and encourages those ideas to reshape practice. If this administration helps encourage a reflective culture among the thousands of low-church science policy staff, provides tools to reshape existing science institutions and to design new ones to get better societal outcomes, then perhaps this new mythology for science could be worthwhile. Whether this becomes a better mythology depends on whether the administration practices what it preaches. But that’s a challenging cultural task, especially with so much angst about science politics ongoing.

I actually hope SNGA succeeds in raising the level of reflection about science policy. I think many of the questions it raises deserve sustained discussion. But replacing an old mythology requires more than writing a thoughtful report. It requires changing how thousands of people actually think about and practice science policy. That takes time, which often involves funding, as well as leadership direction and encouragement to enable, likely alongside continued research.

In future posts, I will look more closely at SNGA’s treatment of tacit knowledge, the history and persistence of the linear model, and the harder question of what makes a mythology for science policy successful.

By Zachary Gallagher Pirtle, Ph.D.

Deputy Director, Consortium for Science, Policy and Outcomes at Arizona State University, based at the Barrett and O’Connor Center in Washington, DC. Website.

All opinions and view expressed in this article are my own.

1. ‘Mythology’ is used here in a neutral way to reflect the guiding beliefs and culture of an organization.

2. I am not sure if the report actually calls for reducing university-funded research, though I admit I have not followed all of Kratsios’s recent public comments. The report does say: “Universities remain essential for training scientists and pursuing fundamental questions” (p.5) and of its proposals for non-academic work, it says “This is not a sign that the academy has become less important, but rather that the scientific world has expanded” (p. 6). If Congress keeps appropriating research and development budgets at higher levels than requested in the President’s Budget, I could see university budgets increase alongside new types of R&D funding.

3. While noting cornerstones like Donald Stokes’ Pasteur’s Quadrant, it also cites a broad range of innovation research including work by the economist W. Brian Arthur, management scholar Pierre Azoulay, histories of semiconductor manufacturing, history of technology prizes, and articles from journals such as Management Science, American Economic Review, Nature and Science.

4. It references the “chemist turned philosopher Michael Polanyi” and “sociologist Harry Collins,” both of whom are classic references in science policy but also literature on science, technology, and society (STS). It cites philosopher Matthew B. Crawford’s book on tacit knowledge and an article in the STS journal Science, Technology and Human Values on the subject of technological determinism and how humans sometimes are negligent about actively managing our technological systems.

5. Pirtle, Z., 2013. Engineering innovation: Energy, policy, and the role of engineering. In Philosophy and engineering: Reflections on practice, principles and process (pp. 377-390). Dordrecht: Springer Netherlands.

6. Rosenberg, N., 1991. Critical issues in science policy research. Science and public policy, 18(6), pp.335-346.

7. The renowned historian David Edgerton claimed that the linear model never existed because it was never explicitly articulated as a logical progression or called a “model” in Bush’s report. Yet Edgerton agrees that Bush implicitly evoked a “reservoir model” (see his discussion of Arie Rip) that gave primacy to basic sciences. Edgerton is technically correct but practically irrelevant given the received wisdom in government about the nature of innovation. The late innovation scholar Benoit Godin likewise felt that Edgerton was in agreement with him on his analysis of the linear model despite Edgerton’s focus on the word ‘model’ (P. 79.). Cites: Edgerton, D., 2004. ‘The linear model’ did not exist: Reflections on the history and historiography of science and research in industry in the twentieth century. The science-industry nexus: History, policy, implications, pp.31-57. Godin, B., 2025. Models of innovation:

8. While there has been a century of more advanced science policy research, much of it is a series of specialized digressions, explorations of new models, and an unveiling of complexity. I’ve seen few engineering practitioners engage with this work. There’s no singular new narrative or easy mythology to capture what science and engineering are today. And perhaps that’s appropriate given the complexity of the world, but it sure makes it hard to shape a new generation of innovation policy without a myth. This makes me admire SNGA’s effort to found a new myth and encourage broader reflection.

9. I feel the linear model haunts and shapes engineering practice in a multitude of ways. If major systems engineering challenges are merely a fleshing out of already established knowledge, then some might assume that there shouldn’t be major failures on engineering tests. Yet learning through failure – and encountering the implicit, emergent and unavoidable mishaps that can occur with developing new systems – is core to how engineering should work (see Kossiakoff et al 2011 systems engineering textbook). The linear model still shapes how programs get formulated, what technology readiness level buckets are available for the funding of new technology (with many privileging lower TRL approaches). The linear model can even shape what types of careers talented students seek to pursue, with too few technicians or brilliant minds going into acquisition or other program development roles. Kossiakoff, A., Sweet, W.N., Seymour, S.J. and Biemer, S.M., 2011. Systems engineering principles and practice. John Wiley & Sons.

10. On West Point’s influence on engineering as a discipline, see Will Thomas’s review of Johanesson 2026. Michael Davis’s histories of the engineering profession are also apt here. This sequencing can obscure how much knowledge taught in later engineering courses - and especially on the job in engineering organizations - is not simply an application of the mathematics and scientific laws. This is despite much of the knowledge and insight in engineering practices not being relevant or needed for much of their insight and understanding. Walter Vincenti’s How Engineers Know What They Know gives a strong argument for the kinds of unique knowledge that engineers have to dabble with, which cannot be directly traced to laws or existing bodies of knowledge. Jóhannesson, S.M., 2026. The Scientific-Military State: How Enlightened Engineers Reinvented Early American Government. University of Chicago Press. Vincenti, W.G., 1990. What engineers know and how they know it (Vol. 141). Baltimore: Johns Hopkins University Press. Davis, M., 1996. Defining “engineer:” How to do it and why it matters. Journal of Engineering Education, 85(2), pp.97-101.

11. I can attest to this from having worked in a U.S. federal agency. High level policies do not prescribe the societal outcomes of engineering in total. Federal agency staff make strategic and tactical decisions that strongly shape the ultimate outcomes of science. Of course high church US science proposals and presidential decrees or Congressional laws are important but they sometimes obscure needed changes within agencies.

12. From p. 87-89 of the annex to SNGA, emphasis added: “In their FY 2028 Budget submissions to OMB, agencies should note the R&D character classification of proposed activities as a percentage of their R&D funding portfolio and identify the specific programs through which the agency proposes to shift its portfolio toward earlier-stage work. [ZGP note: this seems to affirm that activities closer to science are more important than engineering activities, per the linear model. ]Where agencies propose to significantly expand later-stage development activities, they should justify why such activities would not occur absent Federal support. Agencies should prioritize funding for:

…
“Agencies should prioritize foundational research in the physical sciences…These fields underpin quantum science, semiconductors, advanced communications networks, future computing technologies, advanced nuclear fission and fusion energy, and space exploration technologies including novel sensing modalities and precision position, navigation, and timing.

…

“Agencies should prioritize foundational research in chemistry and materials science…These fields underpin quantum science and semiconductors and extend across advanced manufacturing, energy production and storage, the nuclear fuel cycle, photonics, and space and hypersonic systems.

…

“Agencies should prioritize foundational research in the mathematical and computational sciences…these fields underpin advanced communications networks and secure information systems, quantum information science and future computing, and the modeling, simulation, and verification on which fusion energy, advanced manufacturing, and space systems depend.

…

“Agencies should prioritize foundational research in engineering sciences…These fields underpin

semiconductors and advanced communications networks, advanced manufacturing, space systems, robotics, and fission and fusion energy”

…

Agencies with general, broad-based life-sciences research missions should prioritize foundational research in the biological sciences….These fields underpin biotechnology and biomanufacturing, neurosciences and brain-machine interfaces, and human health and therapeutics.”

13. This language on prioritizing foundational (seemingly more science-like) research seems in family with past OMB budget memos. One might joke that even if OSTP is feeling idealistic in creating a new mythology for science, getting OMB to approve a change in budgetary practices represents an additional cultural hurdle!