Why I’m Taking Science Policy Insider International

A View from Abroad

Mid-competition week for a panel reviewing proposals on genes and cells: the fifteen-minute clock starts, and the five of us assigned to this proposal dive in. We consider factors such as whether the proposer is early in their career and how the COVID pandemic might have affected their laboratory’s productivity. We carefully assess their plan for mentoring trainees, including their previous track record and plans. The excellence of the proposer is evaluated, not by raw bibliometric measures such as H-index, but by substantive contributions to the field. And we take a very close look at the proposal itself—not only in terms of intellectual merit, but also to make sure that it is distinct from the investigator’s other supported science. Is this an NIH study section? Nope. Is this an NSF panel? Again, no. This is a peer review for another G7 nation, to be unnamed in this post.

What struck me wasn’t that this country did peer review differently than NSF or NIH. What struck me was how similar it was. Same careful attention to mentoring. Same suspicion of bibliometrics. Same concern about overlaps with existing funding. I could have been in any panel room I’d sat in over three decades in Washington. And that’s when it hit me: among the wealthy nations that fund science, we’re all running variations on the same basic system. We argue about details – overhead rates, review criteria, funding durations – but we share fundamental assumptions about how science should work.

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Or so I thought. Until I stepped outside the world of science funding and began looking at how other countries organize technical knowledge. My second book project examines how Boeing, Airbus, and Embraer design commercial aircraft – and that research has revealed something I’d missed in all my years in government and academia.

Civic Epistemologies

The scholar Sheila Jasanoff has a concept called ‘civic epistemologies’ – the idea that different societies have fundamentally different ways of producing and validating knowledge. It’s not about organizational charts or funding mechanisms. It’s deeper than that. It’s about cultural assumptions: What questions are worth asking? What counts as evidence? Who gets to decide? How do we measure success?

When Americans design an airplane, we assume that technical decisions should be made by engineers based on data, with regulators checking compliance after the fact. Europeans embed social and labor concerns directly into the design process – workers’ councils have a say in production methods, and safety regulators are involved earlier. Brazilians organize around different assumptions entirely, shaped by their position as a developing economy entering a market dominated by established players.

Same engineering principles. Same physics. The same goal of building a safe, efficient aircraft. But fundamentally different answers to the question: Who should decide how this gets done?

I saw the same pattern as a working neuroscientist. American neuroscience tends to bet on fundamental discovery—map the circuits, understand the mechanisms, and applications would follow. Recording sea slug neurons during my training embodied this approach: study simpler systems, find conserved principles, apply them to humans. Europeans start closer to the clinic, organizing major research programs around disease categories and patient needs. Japanese neuroscience builds unusually tight links between academic labs and industry—electronics and engineering companies actively embedded in research networks, with clear paths toward commercialization: same neurons, same biology—different assumptions about how knowledge should flow from laboratory to society.

My new book project

So, where is this taking me? The short answer is I’m working on a new book about how American, European, and Brazilian cultures (think Boeing, Airbus, and Embraer) shape commercial aviation technology. Why planes? In my lifetime, I experienced firsthand the jet revolution: I started on the Comet, went on to the Pan Am 707s, and these days still enjoy the grandeur of the big twin aisle giants that connect us across oceans.

In the new book, I’m interested in comparing technical cultures through the lens of those jets (as technical artifacts). But beyond my lifetime fascination with aviation, the same questions apply to science policy itself: why do different countries organize technological knowledge differently? What can we learn from how other G7 nations fund science? And what cultural assumptions shape what gets built (airplanes OR research programs)?

Science Policy Insider Expands Its Scope

This brings me back to Science Policy Insider and where we’re headed. We are broadening our remit. In the future, we’ll expand to include a comparative analysis of research funding systems—both public agencies and private industry—drawing on insights from my aviation research. We’ll examine how different countries handle current challenges: AI governance, climate research, and research security.

On the practical side, we’ll provide insights for American researchers who work internationally or plan to—from navigating different grant systems to understanding why collaborations succeed or fail across cultural boundaries. And above all, we’ll consider what viewing American science policy from the outside reveals about our own system.

We’ll maintain our bi-weekly publishing schedule.

Science Policy Insider started with my promise to explain how American science policy really works from someone who was inside the system. Now we’re also going to explore what it looks like from the outside and what that perspective reveals about our own system.

I continue to invite readers’ questions, now not only about how things work in our own American discovery machine, but also about international science policy.

Why Transformational Science Can’t Get Funded: The Einstein Problem

Proposal declined. Insufficient institutional support. No preliminary data. Applicant lacks relevant expertise—they work in a patent office, not a research laboratory. The proposed research is too speculative and challenges well-established physical laws without adequate justification. The principal investigator is 26 years old and has no prior experience in physics.

This would have been the fate of Albert Einstein in 1905, had the NSF existed as it does today. Even with grant calls requesting ‘transformative ideas,’ an Einstein proposal would have been rejected outright. And yet, that year 1905 has been called Einstein’s miracle year. Yes, he was a patent clerk working in Bern, Switzerland, without a university affiliation. He had neither access to a laboratory nor equipment. He worked in isolation on evenings and weekends and was unknown in the physics community. Yet, despite those disadvantages, he produced four revolutionary papers on the Photoelectric Effect, Brownian motion, Special Relativity, and the famous E=mc²energy-mass equivalence.

Taken as a whole, the work was purely theoretical. There were no preliminary data. The papers challenged fundamental assumptions of the field and, as such, were highly speculative and definitively high-risk. There were no broader impacts because there were no immediate practical applications. And the work was inherently multidisciplinary, bridging mechanics, optics, and thermodynamics. Yet, the work was transformative. By modern grant standards, Einstein’s work failed every criterion.

The Modern Grant Application – A Thought Experiment

Let’s imagine Einstein’s 1905 work packaged as a current NSF proposal. What would it look like, and how would it fare in peer review?

Einstein’s Hypothetical NSF Proposal

Project Title: Reconceptualizing the Fundamental Nature of Space, Time, and the Propagation of Light

Principal Investigator: Albert Einstein, Technical Expert Third Class, Swiss Federal Patent Office

Institution: None (individual applicant)

Requested Duration: 3 years

Budget: $150,000 (minimal – just salary support and travel to one conference)

Project Summary

This proposal challenges the fundamental assumptions underlying Newtonian mechanics and Maxwell’s electromagnetic theory. I propose that space and time are not absolute but relative, dependent on the observer’s state of motion. This requires abandoning the concept of the luminiferous ether and reconceptualizing the relationship between matter and energy. The work will be entirely theoretical, relying on thought experiments and mathematical derivation to establish a new framework for understanding physical reality.

How NSF Review Panels Would Evaluate This

Intellectual Merit: Poor

Criterion: Does the proposed activity advance knowledge and understanding?

Panel Assessment: The proposal makes extraordinary claims without adequate preliminary data. The applicant asserts that Newtonian mechanics—the foundation of physics for over 200 years—requires fundamental revision yet provides no experimental evidence supporting this radical departure.

Specific Concerns:

Lack of Preliminary Results: The proposal contains no preliminary data demonstrating the feasibility of the approach. There are no prior publications by the applicant in peer-reviewed physics journals. The applicant references his own unpublished manuscripts, which cannot be evaluated.

Methodology Insufficient: The proposed “thought experiments” do not constitute rigorous scientific methodology. How will hypotheses be tested? What experimental validation is planned? The proposal describes mathematical derivations but provides no pathway to empirical verification. Without experimental confirmation, these remain untestable speculations.

Contradicts Established Science: The proposal challenges Newton’s laws of motion and the existence of the luminiferous ether—concepts supported by centuries of successful physics. While scientific progress requires questioning assumptions, such fundamental challenges require extraordinary evidence. The applicant provides none.

Lack of Expertise: The PI works at a patent office and has no formal research position. He has no advisor supporting this work, no collaborators at research institutions, and no track record in theoretical physics. His biosketch lists a doctorate from the University of Zurich but no subsequent research appointments or publications in relevant areas.

Representative Reviewer Comments:

Reviewer 1: “While the mathematical treatment shows some sophistication, the fundamental premise—that simultaneity is relative—contradicts basic physical intuition and has no experimental support. The proposal reads more like philosophy than physics.”

Reviewer 2: “The applicant’s treatment of the photoelectric effect proposes that light behaves as discrete particles, directly contradicting Maxwell’s well-established wave theory. This is not innovation; it’s contradiction without justification.”

Reviewer 3: “I appreciate the applicant’s ambition, but this proposal is not ready for funding. I recommend the PI establish himself at a research institution, publish preliminary findings, and gather experimental evidence before requesting support for such speculative work. Perhaps a collaboration with experimentalists at a major university would strengthen future submissions.”

Broader Impacts: Very Poor

Criterion: Does the proposed activity benefit society and achieve specific societal outcomes?

Panel Assessment: The proposal fails to articulate any concrete broader impacts. The work is purely theoretical with no clear pathway to societal benefit.

Specific Concerns:

No Clear Applications: The proposal does not explain how reconceptualizing space and time would benefit society. What problems would this solve? What technologies would it enable? The PI suggests the work is “fundamental” but provides no examples of potential applications.

No Educational Component: There is no plan for training students or postdocs. The PI works alone at a patent office, with no access to students and no institutional infrastructure for education and training.

No Outreach Plan: The proposal includes no activities to communicate findings to the public or policymakers. There is no plan for broader dissemination beyond potential publication in physics journals.

Questionable Impact Timeline: Even if the proposed theories are correct, the proposal provides no timeline for practical applications. How long until these ideas translate into societal benefit? The proposal is silent on this critical question.

Representative Reviewer Comments:

Reviewer 1: “The broader impacts section is essentially non-existent. The PI states that ‘fundamental understanding of nature has intrinsic value,’ but this does not meet NSF’s requirement for concrete societal outcomes.”

Reviewer 2: “I cannot envision how this work, even if successful, would lead to practical applications within a reasonable timeframe. The proposal needs to articulate a clear pathway from theory to impact.”

Reviewer 3: “NSF has limited resources and must prioritize research with demonstrable benefits to society. This proposal does not make that case.”

Panel Summary and Recommendation

Intellectual Merit Rating: Poor
Broader Impacts Rating: Very Poor

Overall Assessment: While the panel appreciates the PI’s creativity and mathematical ability, the proposal is highly speculative, lacks preliminary data, contradicts established physical laws without sufficient justification, and fails to articulate broader impacts. The PI’s lack of institutional affiliation and research track record raises concerns about feasibility.

The panel notes that the PI appears talented and encourages resubmission after:

Establishing an independent position at a research institution
Publishing preliminary findings in peer-reviewed journals
Developing collaborations with experimental physicists
Articulating a clearer pathway to practical applications
Demonstrating broader impacts through education and outreach

Recommendation: Decline

Panel Consensus: Not competitive for funding in the current cycle. The proposal would need substantial revision and preliminary results before it could be considered favorably.

The Summary Statement Einstein Would Receive

Dear Dr. Einstein,

Thank you for your submission to the National Science Foundation. Unfortunately, your proposal, “Reconceptualizing the Fundamental Nature of Space, Time, and the Propagation of Light,” was not recommended for funding.

The panel recognized your ambition and mathematical capabilities but identified several concerns that prevented a favorable recommendation:

– Lack of preliminary data supporting the feasibility of your approach – Insufficient experimental validation of your theoretical claims
– Absence of institutional support and research infrastructure – Inadequate articulation of broader impacts and societal benefits

We encourage you to address these concerns and consider resubmission in a future cycle. You may wish to establish collaborations with experimentalists and develop a clearer pathway from theory to application.

We appreciate your interest in NSF funding and wish you success in your future endeavors.

Sincerely,
NSF Program Officer

And that would be it. Einstein’s miracle year—four papers that transformed physics and laid the groundwork for quantum mechanics, nuclear energy, GPS satellites, and our modern understanding of the cosmos—would have died in peer review, never funded, never attempted.

The system would have protected us from wasting taxpayer dollars on such speculation. It would have worked exactly as designed.

The Preliminary Data Paradox

The contemporary scientific grant review process implicitly expects foundational work in transformative science to present preliminary data, despite knowing that truly groundbreaking ideas often do not originate from such tangible evidence but instead evolves through thought experiments and mathematical derivations, as Einstein did. This unrealistic expectation stifles innovation at its core – the process essentially forces researchers like Einstein to abandon pure theoretical exploration and confine them to a narrow experimental framework, where they cannot freely challenge existing paradigms, even when their work holds no immediate empirical validation yet promises to revolutionize our understanding fundamentally.

The Risk-Aversion Problem

Often, in grant reviews, I see a very junior reviewer criticize work as being too risky—dooming the proposal to failure—while simultaneously sensing their admiration for the promise and transformative nature of the work. The conservative nature and risk-averse mentality of modern grant review panels are deeply rooted in the scientific community’s culture that values incremental advances over speculative leaps – a bias born from career motivations wherein funding decisions can make or break one’s professional trajectory. Reviewers often exhibit reluctance to invest support into proposals like Einstein’s, as they pose potential controversy and may not align with personal research interests due to the associated risks of failure – a reflection of how science has traditionally evolved through evolutionary rather than revolutionary processes within academic institutions.

The Credentials Catch-22

To secure funding in today’s scientific landscape, one often needs institutional affiliation and an impressive publication record that reflects strong research credentials – a catch-22 scenario wherein groundbreaking innovators with no formal backing or prior experience find it challenging to gain the trust of reviewers. This requirement discriminates against fresh perspectives from individuals such as Einstein, who was working outside established institutions and did not have access to mentorship, which is typically deemed necessary for academic recognition – a stark contrast in how transformative outsider thinkers with unconventional backgrounds historically nurtured science.

The Short-Term Timeline Problem

Einstein developed special relativity over years with no milestones, no quarterly reports, no renewals. How would he answer, ‘What will you accomplish in Year 2?” The funding cycle durations set forth by major grant agencies, such as NSF’s typical three to five years for regular grants and the NIH’s maximum of five years, do not accommodate the long periods necessary for fully developing foundational theories that require time-intensive evolution. Such timelines impose an unfair constraint on researchers like Einstein, whose transformative ideas did not evolve within strict milestones but unfolded in an unconstrained fashion – showcasing the incompatibility of this model with truly revolutionary scientific discoveries where a linear progression is unrealistic and even counterproductive.

The Impact Statement Trap

Requirements for demonstrating immediate “broader impacts” or societal benefits pose significant obstacles to transformative research proposals that often envision far-reaching implications beyond their direct applications – an aspect Einstein’s work exemplifies best with its foundational role in advancing physics. The trap lies when reviewers, fearing potential misuse of speculative science or unable to perceive future benefits due to cognitive biases, force research proposals into a mold where immediate practical impact takes precedence over visionary scientific contributions, further marginalizing transformative studies that could potentially unlock new dimensions in various fields.

The Interdisciplinary Gap

The inherent disciplinarity of current grant funding schemes disconnects them from the interdisciplinary essence required for revolutionary research proposals like Einstein’s – a reality where transformative work frequently transcends conventional academic boundaries by merging concepts across multiple fields. This approach often results in an exclusion not only based on institutional affiliation but also because of its challenge to compartmentalized funding models that struggle with the non-linear, cross-disciplinary nature integral to truly transformative science – a significant obstacle for proposals inherently interdisciplinary yet unable to fit neatly within program structures or expertise.

The hypothetical funding scenarios for transformational science, as presented through the lens of Albert Einstein’s groundbreaking work, illustrate the inherent challenges faced by revolutionary ideas. To further highlight this problem, let’s take a look at other seminal discoveries that may have been overlooked or deemed unworthy of support under current grant review criteria:

Copernicus’ Heliocentric Model: In a contemporary setting, Copernicus’ heliocentric model might face skepticism due to its challenge to the widely accepted geocentric view of the universe. Lacking preliminary data and facing resistance from established religious beliefs, his proposal would likely be rejected under modern grant review criteria, despite its ultimate validation through observation and mathematical proof.

Gregor Mendel’s Pea Plant Experiments: The foundation of modern genetics was laid by Mendel’s pea plant experiments, yet his work remained largely unnoticed for decades after its initial publication. A grant reviewer in 1863 would likely have dismissed Mendel’s findings as too speculative and without immediate practical applications, thereby overlooking the fundamental insights he provided about heredity and genetic inheritance.

mRNA Vaccines: Katalin Karikó spent decades struggling to fund mRNA therapeutic research. Too risky. Too speculative. No clear applications. Penn demoted her. NIH rejected her grants. Reviewers wanted proof that mRNA could work as a therapeutic platform, but without funding, she couldn’t generate that proof. Then COVID-19 hit, and mRNA vaccines saved millions of lives. The technology that couldn’t get funded became one of the most important medical breakthroughs of the century.

Why does all of this matter now? First, the evidence is mounting that American science is at an inflection point. The rate of truly disruptive discoveries—those that reshape fields rather than incrementally advance them—has been declining for decades, even as scientific output has grown. Both NSF and NIH leadership recognize this troubling trend.

This innovation crisis manifests in the problems we cannot solve. Cancer and Alzheimer’s have resisted decades of intensive research. AI alignment and safety remain fundamentally unsolved as we deploy increasingly powerful systems. We haven’t returned to the moon in over 50 years. In my own field of neuroscience, incremental progress has failed to produce treatments for the diseases that devastate millions of families.

These failures point to a deeper problem: we’ve optimized our funding system for incremental advances, not transformational breakthroughs. Making matters worse, we’re losing ground internationally. China’s funding models allow longer timelines and embrace higher risk. European ERC grants support more adventurous research. Many of our best researchers now weigh opportunities overseas or in industry, where they can pursue riskier ideas with greater freedom.

What Needs to Change

Fixing this requires fundamental changes at multiple levels—from how we structure programs to how we evaluate proposals to how we support unconventional researchers.

Create separate funding streams for high-risk research. NSF and NIH need more programs that emulate DARPA’s high-risk, high-reward model. These programs should be insulated from traditional grant review: no preliminary data required, longer timelines (10+ years), and peer review conducted by scientists who have themselves taken major risks and succeeded. I propose that 10 percent of each agency’s budget be set aside for “Einstein Grants”—awards that take the view opposite the status quo. Judge proposals on originality and potential impact, not feasibility and preliminary data. Accept that most will fail, but the few that succeed will be transformational.

Protect exploratory research within traditional programs. Even standard grant programs should allow pivots when researchers discover unexpected directions. We should fund people with track records of insight, not just projects with detailed timelines. Judge proposals on the quality of thinking, not the completeness of deliverables.

Reform peer review processes. The current system needs three critical changes. First, separate review tracks for incremental versus transformational proposals—they require fundamentally different evaluation criteria. Second, don’t let a single negative review kill bold ideas; if three reviewers are enthusiastic and one is skeptical, fund it. Third, value originality over feasibility. The most transformational ideas often sound impossible until someone proves otherwise.

Support alternative career paths. We should fund more researchers outside traditional academic institutions and recognize that the best science doesn’t always emerge from R1 universities. Explicitly value interdisciplinary training and create flexible career paths that don’t punish researchers who take time to develop unconventional ideas. Track where our most creative researchers go when they leave academia—if we’re consistently losing them to industry or foreign institutions, that’s a failure signal we must heed.

Acknowledge the challenge ahead. These reforms require sustained political will across multiple administrations and consistent support from Congress. They demand patience—accepting that transformational breakthroughs can’t be scheduled or guaranteed. But the alternative is clear: we continue optimizing for incremental progress while the fundamental problems remain unsolved and our international competitors embrace the risk we’ve abandoned.

The choice before us is stark. We can optimize the current system for productivity—incremental papers, measurable progress—or we can create space for transformative discovery. We cannot have both with the same funding mechanisms.

The cost of inaction is clear: we will miss the next Einstein, fall further behind in fundamental discovery, watch science become a bureaucratic exercise, and lose what made American science into a powerhouse of discovery.

This requires action at every level. Scientists must advocate for reform and be willing to champion risky proposals. Program officers must have the courage to fund work that reviewers call too speculative. Policymakers must create new funding models and resist the temptation to demand near-term results. The public must understand that breakthrough science looks different from incremental progress—it’s messy, unpredictable, and often wrong before it’s right.

In 1905, Einstein changed our understanding of the universe while working in a patent office with no grant funding. Today, our funding system would never have let him try. We need to fix that.

The Replication Crisis Is a Market Failure (And We Designed It That Way)

Also published on my newsletter

The replication crisis isn’t a mystery. After presiding over the review for thousands of grants at NSF’s Biological Sciences Directorate, I can tell you exactly why science struggles to reproduce its own findings: we built incentives that reward novelty and punish verification.

A 2016 Nature survey found that over 70% of scientists have failed to reproduce another researcher’s experiments. But this isn’t about sloppy science or bad actors. It’s straightforward economics.

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The Researcher’s Optimization Problem

You have limited time and resources. You can either:

Pursue novel findings → potential Nature paper, grant funding, tenure
Replicate someone’s work → maybe a minor publication, minimal funding, colleagues questioning your creativity

The expected value calculation is obvious. Replication is a public good with privatized costs.

How NSF Review Panels Work

At NSF, I watched this play out in every review panel. Proposals to replicate existing work faced an uphill battle. Reviewers—themselves successful researchers who got there by publishing novel findings—naturally favor creative, untested ideas over verification work.

We tried various fixes. Some programs explicitly funded replication studies. Some review criteria emphasized robustness over novelty. But the core incentive remained: breakthrough science gets you the next grant; careful verification doesn’t.

The problem runs deeper than any single agency. Universities want prestigious publications. Journals want citations. Researchers want tenure. Nobody’s optimization function includes “produces reliable knowledge that someone else can build on.”

The Information Market Is Broken

Even when researchers try to replicate, they’re working with incomplete information. Methods sections in papers are sanitized versions of what actually happened in the lab. “Cells were cultured under standard conditions” means something different in every lab. One researcher’s gentle mixing is another’s vigorous shaking.

This information asymmetry makes replication attempts inherently inefficient. You’re trying to reproduce a result while missing critical details that the original researcher might not even realize mattered.

The Time Horizon Problem

NSF grants run 3-5 years. Tenure clocks run 6-7 years. But scientific truth emerges over decades. We’re optimizing for the wrong timescale.

During my time at NSF, I saw brilliant researchers make pragmatic choices: publish something surprising now (even if it might not hold up) rather than spend two years carefully verifying it. That’s not a moral failing—it’s responding rationally to the incentives we created.

What Would Actually Fix This

Make replication profitable:

Count verification studies equally with novel findings in grant review and tenure decisions
Fund researchers whose job is rigorous replication—make it a legitimate career path
Require data and detailed methods sharing as a funding condition, not an afterthought
Make failed replications as publishable as successful ones

The challenge isn’t technical. It’s institutional. We designed a market that overproduces flashy results and underproduces reliable knowledge. Until we fix the incentives, we’ll keep getting exactly what we’re paying for.

The Unsung Hero: Why Exploratory Science Deserves Equal Billing with Hypothesis-Driven Research

For decades, the scientific method taught in classrooms has followed a neat, linear path: observe, hypothesize, test, conclude. This hypothesis-driven approach has become so deeply embedded in our understanding of “real science” that research proposals without clear hypotheses often struggle to secure funding. Yet some of the most transformative discoveries in history emerged not from testing predictions, but from simply looking carefully at what nature had to show us.

It’s time we recognize exploratory science—sometimes called discovery science or descriptive science—as equally valuable to its hypothesis-testing counterpart.

What Makes Exploratory Science Different?

Hypothesis-driven science starts with a specific question and a predicted answer. You think protein X causes disease Y, so you design experiments to prove or disprove that relationship. It’s focused, efficient, and satisfyingly definitive when it works.

Exploratory science takes a different approach. It asks “what’s out there?” rather than “is this specific thing true?” Researchers might sequence every gene in an organism, catalog every species in an ecosystem, or map every neuron in a brain region. They’re generating data and looking for patterns without knowing exactly what they’ll find.

The Case for Exploration

The history of science is filled with examples where exploration led to revolutionary breakthroughs. One of my lab chiefs at NIH was Craig Venter, famous for his exploratory project: sequencing the human genome. The Human Genome Project didn’t test a hypothesis—it mapped our entire genetic code, creating a foundation for countless subsequent discoveries. Darwin’s theory of evolution emerged from years of cataloging specimens and observing patterns, not from testing a pre-formed hypothesis. The periodic table organized elements based on exploratory observations before anyone understood atomic structure.

More recently, large-scale exploratory efforts have transformed entire fields. The Sloan Digital Sky Survey mapped millions of galaxies, revealing unexpected structures in the universe. CRISPR technology was discovered through exploratory studies of bacterial immune systems, not because anyone was looking for a gene-editing tool. The explosive growth of machine learning has been fueled by massive exploratory datasets that revealed patterns no human could have hypothesized in advance.

Why Exploration Matters Now More Than Ever

We’re living in an era of unprecedented technological capability. We can sequence genomes for hundreds of dollars, image living brains in real time, and collect environmental data from every corner of the planet. These tools make exploration more powerful and more necessary than ever.

Exploratory science excels at revealing what we don’t know we don’t know. When you’re testing a hypothesis, you’re limited by your current understanding. You can only ask questions you’re smart enough to think of. Exploratory approaches let the data surprise you, pointing toward phenomena you never imagined.

This is particularly crucial in complex systems—ecosystems, brains, economies, climate—where interactions are so intricate that predicting specific outcomes is nearly impossible. In these domains, careful observation and pattern recognition often outperform narrow hypothesis testing.

The Complementary Relationship

None of this diminishes the importance of hypothesis-driven science. Testing specific predictions remains essential for establishing causation, validating mechanisms, and building reliable knowledge. The most powerful scientific progress often comes from the interplay between exploration and hypothesis testing.

Exploratory work generates observations and patterns that inspire hypotheses. Hypothesis testing validates or refutes these ideas, often raising new questions that require more exploration. It’s a virtuous cycle, not a competition.

Overcoming the Bias

Despite its value, exploratory science often faces skepticism. It’s sometimes dismissed as “fishing expeditions” or “stamp collecting”—mere data gathering without intellectual rigor. This bias shows up in grant reviews, promotion decisions, and journal publications.

This prejudice is both unfair and counterproductive. Good exploratory science requires tremendous rigor in experimental design, data quality, and analysis. It demands sophisticated statistical approaches to avoid false patterns and careful validation of findings. The difference isn’t in rigor but in starting point.

We need funding mechanisms that support high-quality exploratory work without forcing researchers to shoehorn discovery-oriented projects into hypothesis-testing frameworks. We need to train scientists who can move fluidly between both modes. And we need to celebrate exploratory breakthroughs with the same enthusiasm we reserve for hypothesis confirmation.

Looking Forward

As science tackles increasingly complex challenges—understanding consciousness, predicting climate change, curing cancer—we’ll need every tool in our methodological toolkit. Exploratory science helps us map unknown territory, revealing features of reality we didn’t know existed. Hypothesis-driven science helps us understand the mechanisms behind what we’ve discovered.

Both approaches are essential. Both require creativity, rigor, and insight. And both deserve recognition as legitimate, valuable paths to understanding our world.

The next time you hear about a massive dataset, a comprehensive catalog, or a systematic survey, don’t dismiss it as “just descriptive.” Remember that today’s exploration creates the foundation for tomorrow’s breakthroughs. In science, as in geography, you can’t know where you’re going until you know where you are.

Post lunch conversation with a colleague: trust in science

Yesterday, I had lunch with a colleague at a favorite BBQ spot in Arlington. Both of us work in science communication, so naturally our conversation drifted to the question that’s been nagging at many of us: why has public trust in scientific institutions declined in recent years? By the time we finished our, actually healthy food, we’d both come to the same conclusion—the current way scientists communicate with the public might be contributing to the problem.

From vaccine hesitancy to questions about research reliability, the relationship between science and society has grown more complex. To understand this dynamic, we need to examine not only what people think about science but also how different cultures approach the validation of knowledge itself.

Harvard scholar Sheila Jasanoff offers valuable insights through her concept of “civic epistemologies”—the cultural practices societies use to test and apply knowledge in public decision-making. These practices vary significantly across nations and help explain why scientific controversies unfold differently in different places.

American Approaches to Knowledge Validation

Jasanoff’s research identifies distinctive features of how Americans evaluate scientific claims:

Public Challenge: Americans tend to trust knowledge that has withstood open debate and questioning. This reflects legal traditions where competing arguments help reveal the truth.

Community Voice: There’s a strong expectation that affected groups should participate in discussions about scientific evidence that impacts them, particularly in policy contexts.

Open Access: Citizens expect transparency in how conclusions are reached, including access to underlying data and reasoning processes.

Multiple Perspectives: Rather than relying on single authoritative sources, Americans prefer hearing from various independent institutions and experts.

How This Shapes Science Communication

These cultural expectations help explain some recent communication challenges. When public health recommendations changed during the COVID-19 pandemic, this appeared to violate expectations for thorough prior testing of ideas. Similarly, when social platforms restricted specific discussions, this conflicted with preferences for open debate over gatekeeping.

In scientific fields like neuroscience, these dynamics have actually driven positive reforms. When research reliability issues emerged, the American response emphasized transparency solutions: open data sharing, study preregistration, and public peer review platforms. Major funding agencies now require data management plans that promote accountability.

Interestingly, other countries have addressed similar scientific quality concerns in different ways. European approaches have relied more on institutional reforms and expert committees, while American solutions have emphasized broader participation and transparent processes.

Digital Platforms and Knowledge

Online platforms have both satisfied and complicated American expectations. They provide the transparency and diverse voices people want, but the sheer volume of information makes careful evaluation difficult. Platforms like PubPeer enable post-publication scientific review that aligns with cultural preferences for ongoing scrutiny; however, the same openness can also amplify misleading information.

Building Better Science Communication

Understanding these cultural patterns suggests more effective approaches:

Acknowledge Uncertainty: Present science as an evolving process rather than a collection of final answers. This matches realistic expectations about how knowledge develops.

Create Meaningful Participation: Include affected communities in research priority-setting and policy discussions, following successful models in patient advocacy and environmental research.

Increase Transparency: Share reasoning processes and data openly. Open science practices align well with cultural expectations for accountability.

Recognize Broader Concerns: Understand that skepticism often reflects deeper questions about who participates in knowledge creation and whose interests are served.

Moving Forward

Public skepticism toward science isn’t simply a matter of misunderstanding—it often reflects tensions between scientific institutions and cultural expectations about legitimate authority. Rather than dismissing these expectations, we might develop communication approaches that honor both scientific rigor and democratic values.

The goal isn’t eliminating all skepticism, which serves essential functions in healthy societies. Instead, it channels critical thinking in ways that strengthen our collective ability to address complex challenges that require scientific insight.