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I was writing my PhD thesis and preparing for a defense when I first told my advisor that I was considering a jump into the science policy world. He said, “Interesting. Go for it!”. Looking back with the hindsight of a few years, I am so grateful for that support at a sensitive moment in life and career. Those conversations could easily have gone another way, taking on a different tone that is often present in our scientific communities: that a move away of academia is the end of all good career prospects; that there is nothing of substance to be achieved for a scientist in politics and policy; or worse still, that engaging the realm of policy and politics risks undermining that most precious asset of the scientist, credibility.
In my own experience, the migration from the lab to the science policy world has been enriching, challenging and fun. But the transition has also been tortuous, and not without frustration, terror, and apathy to offset the excitement and sense of mission and purpose that policy work can offer. And I know that my experience is not unique. I think much of the shared experience in the move from academic or bench science to policy-oriented problems is directly related to the cultural and philosophical differences between the two worlds. As physicists, the way we think about problems, frame solutions, interact with peers, and even define evidence are all very different than the methods of our cousins in law, economics, politics, and business—the dominant players in public policy, even science policy!
First, a bit of background on what I mean by “science policy”. I think about the problems of science policy as belonging to one of two categories: “policy for science”, and “science for policy”. This is not an original thought, but the distinction is helpful for putting some common conceptions about science polity into a more nuanced context.
Policy for Science
Policy for science is the exercise of planning, managing, supporting, and optimizing the scientific activities of an organization, region, or nation. How to design and run the funding agencies and their associated grant programs? How to manage and fund fellowships for students and early-career scientists? And at the political level, how to allocate resources among the universe of scientific pursuits, from basic R&D, to applied or commercialization efforts in domains ranging from clinical medicine, to ecology, to astronomy. How to even define these pursuits? Invariably this branch of science policy can be dominated by budget questions, specifically their size, their time derivatives, and their managers.
I think this is the most common notion of science policy among physicists—it certainly was mine. And I think it’s fair to say that lab budgeting and grant program structures are possibly the least motivating subject for most physicists. They’re in it for the science, not the accounting.
Budgets and funding for science are clearly a crucial matter, so I won’t bother trying to rationalize this side of science policy. Just think about what the relative increases or decreases in NSF program spending, or any other relevant funding body, have meant to your own work and career. Hammering out budgets may not be for everyone, but I think we’re all glad someone is working on it.
Science for Policy
In my case, the kernel of interest in science policy really blossomed when I became aware of the second category: science for policy. Science for policy is the exercise of using scientific approaches, data, and understanding to inform and support public decision-making. “Evidence-based decision making” might be the best current hallmark phrase of this activity.
If Not Us, Then Who?
Many systems of government have a long history of incorporating science directly into public policy, most notably in defense and the regulation of food and agriculture. But today, a growing number of pressing public policy questions involve science, technology, or engineering at their core, for instance: climate change and energy systems; digital and cybersecurity rights and privacy; ecological conservation; artificial intelligence; synthetic biology and reproductive rights. These areas all present incredibly complex public policy questions, but are firmly rooted in the science and technology of the 20th and 21st centuries. Physicists and scientists in general have a role to play to help our decision makers “Get the science right”. If not us, then who? But even more than a simple lesson on the difference between electrons and photons, RNA and DNA, or supplying data, maximizing the public good in tackling these problems calls for skill and approach to problem solving that is unique to physics and the other maths and sciences, to complement those already at the table from economics, law, business, and politics.
After discovering and understanding my interest in science and policy, the personal question became “now what?” How to get there from here? For me this was largely a process of trial and error, seeking mentors and feedback, and repeating. I describe it here in two ways, in hopes that it might offer guidance (or cautionary tales) to others of a similar mind, or at the very least to spark a good conversation.
I call the following description of my career the linear model for two reasons. First, it’s a straightforward chronological description of the steps (and mis-steps) I have taken, akin to a typical academic CV. Second, like most linear models, it has its uses, but misses much nuance which can be critical to deeper understanding.
Even as I pursued my experimental work in quantum optics and atomic physics as a graduate student in the Department of Physics at the University of Toronto, I began to more seriously explore careers in law, education, and politics. Mostly dabbling, and mostly out of curiosity and interest. I have always loved math and science and being creative with my hands in the lab, but I also loved teaching and science outreach and finding ways to connect science with the rest of my life. Without this basic feeling, I decided to seek spaces where a physicist could work on broader problems than those I had explored to date.
As I wrapped up my PhD work I began volunteering with the Canadian Science Policy Conference—at the time a grassroots organization of postdocs, students, and young professionals trying to pull together a critical mass of Canadian science policy geeks. This expanded my network outside of academic physics, and helped me begin to understand how professional scientists can and do fit into broader policy structures. After completing my PhD, I took a left turn by leaving academia and working as a quantitative analyst at a power utility, where I wrote code to analyze electricity markets. This was my formal introduction to the energy world, which remains my focus today. Energy, science and policy came together for me as a AAAS Science & Technology policy fellow, during which, with generous support of the SPIE / OSA Guenther Fellowship, I took up a position on staff of the U.S. Senate Energy & Natural Resources Committee in Washington, DC. Later on, back in Canada, I worked as a science policy consultant with the Council of Canadian Academies, and in fundraising and strategy of university / industry research partnerships in the Faculty of Applied Science & Engineering at the University of Toronto. Today, I manage the technical and operational aspects of global energy competition in CO2 conversion at XPRIZE (the NRG COSIA Carbon XPRIZE). The common threads of this linear model are physical science, energy technology and policy, government and industry interactions, and trying new things.
Like most academic CVs, the above description is simple retrospective narrative milestones, highlights, and formal education foundations. With a small number of exceptions, it is useless in practice for anyone other than its author who is trying to understand how and why and other subtleties of a move from physics to policy, and anything in between. So here is another description of my experience through and between these two worlds, which I jokingly call the “network model” because of it’s focus on interactions and skills learned and refined, rather than place and institution and title.
Physics Meets Policy, Regulation, and Governance
As an engineering and later physics student, I was well trained in the fundamentals of optics, atomic physics, materials science, and quantum mechanics. As my interest in energy grew, I realized that while I could do a great job explaining the thermodynamics of a power plant, or the optoelectronics of a photovoltaic cell, I had little or no practical understanding of the business, regulation, and operation of real energy systems. My odd decision to dive into work at a power utility (a culture shock in many ways, coming directly from academic experimental physics) was an attempt to fill in this blind spot. In exchange for technical depth, I developed breadth of understanding. Using technical tools (statistics, programming, data analysis) I worked on financial and business problems in a heavily regulated industry. For better and worse, I also got a taste of the 9-to-5 cubicle lifestyle.
As a Fellow in Washington, DC, I had a great opportunity to work on my networking, writing, and presentation skills, both formally and interpersonally. Networking can be a dirty word for scientists, but for me the exercise of networking has been a challenge to (a) meet new people in rapid succession and learn how not to seem like a freak scientist (b) describe my interests and projects in direct, concise ways, and communicate passion for my work in a way that (hopefully) makes my conversation partner want to keep chatting, rather than politely head for the bar, and (c) understand what other people from backgrounds and professional traditions wildly different from mine, are working on, how they see problems, and what they find interesting. As a gross oversimplification, physicists tend to elevate content, knowledge, and subject matter above all else, while our counterparts in policy, economics, business, etc. place a much higher premium on interpersonal relationships. It’s a hard habit to break; I still get funny looks every time I let slip “If we assume a spherical…” or “From first principles it seems obvious that…”. But refining my skills and learning to navigate this world—in conversation, oral presentation, or written communication—often through trial and error, was and continues to be a valuable resource for me in any project I have pursued.
Follow the Money
Finally, I point to my work in university fundraising, politics, and government consulting for helping me to understand money. By that I mean first gaining financial and economic literacy. Next, understanding the role of financial decision making and market forces in our basic research and funding frameworks, but also in broader policy conversations in science and related policy arenas.
In my lab career, as a graduate student and in industry, money essentially did not exist. This is a crazy thing to say, so let me explain. Money was never the driving force in daily lab routines, decision-making, and planning scientific work in the way that is in many other professions and aspects of life. I realize that my position as a junior scientist and not a group leader was privileged (any PI reading this is probably rolling their eyes in between grant applications and review committee work), but from my perspective it was always about the science. How to define the problem; what tools exist at our disposal for approaching solutions; experimental design, data collection, and analysis; understanding and presenting our progress to the broader community in clear and compelling ways. In my work outside of academia, in an equally simplistic view, it’s all about the money; discussion about problems, solutions, execution and efficacy are always immediately filtered through the lens of costs, affordability, rates of return, and fiscal management. By this I mean that economics and finance play a vital, immediate role in these conversations—and, in my view, they should in any discussion of public good and public resources—in a way that they simply did not in my experience in the lab.
For me that meant building at least a basic facility with the language of economics, finance, and budgeting in order to develop credibility among my peers and mentors. It helps that I have a natural interest in these topics. But learning this new language, like any new language, opened entirely new horizons and opportunities to engage. In science, technology and innovation policy in particular, an understanding of market forces in technology development, investor types and priorities (grants, philanthropy, angel and venture investors, institutional investors), budgets for basic research, university finances, government budget pressures, etc., is extremely helpful when discussing how best to support scientific communities, and use science to inform public discourse.
A DC mentor once said that “Politics is about who gets what”; in other words, decision-making in a zero-sum scenario. This dimension in no way should minimize the importance of purely scientific and technical input and wisdom, but instead can enrich and sharpen the communications and impact of our community’s voice.
I wrote earlier that my personal transition away from lab-based science and into policy and beyond has been difficult and can be a personal challenge. So why encourage this transition for those interested, and how to think about this migration? It is easy to write and talk about, but often difficult to accomplish. One reason is that the marked differences in style, personality, and approach between physicists and typical policy wonks can make person-to-person and institution-to-institution communication and cooperation difficult. These cultural gaps are clearly surmountable, but I close by noting them here because they have been key learnings for me along my path from the physics research to science policy work, and could be helpful for others curious about the transition.
Evidence and Judgment
If science is about truth, then imagine the shock for a scientist at approaching and discerning truth using means completely outside the scientific method. On one hand, we scientists collect data, look for patterns, form hypotheses, test, refine, repeat. Data and evidence are central, predictive power trumps past results (if demonstrated to be incorrect) and the role of individual actors is less important than that of the group of scientists past and present that make up the field. On the other hand, public policy and government decision making in liberal democracies has more in common with legal thinking that with the scientific method. To use another oversimplification, judgment and reason in law and policy trump data and evidence as methods for establishing facts and making decisions. Whereas scientists are conditioned to think of truths as absolute and objectively discernable using data and experiments, the legal influence on policy makers leads them to favor truths established through argument and reason, and decisions made by informed, wise individuals or small groups who consider many arguments.
Think of a courtroom. Defense attorneys may call an expert witness to testify on technical subject matter. Prosecution attorneys may call a separate expert witness. Neither witness is considered a fully reliable source of objective, unbiased technical information. Rather than an experiment to test one idea or explanation against another, as a scientist might suggest, courts use judges and juries who listen to both sides and make a decision using judgment.
This cartoon example of legal decision making is reflected in many high-profile public decisions. In my time in the US Senate, I observed the way my boss, the Senator, would accept advice from legal, economic, scientific, political, and social experts, and make a decision according to his own judgment and principles after considering all arguments. (I considered myself lucky to work in such an idealized environment most days, since decision making can be much less reasoned and much more biased.)
This approach can be difficult for scientists to understand or appreciate since we are used to thinking of scientific truths as THE truths. After all, the scientific method is a system of weeding out false theories, and then subjecting truths established by this method to continuous and open challenge in the face of new data. In my view, this culture clash informs part of the current thrust for “evidence based decision making” that is often advocated by scientific and other technical communities. In the best cases, this call for transparency and consideration of reliable evidence is proper and indispensable. But in its worst manifestations, it can degenerate into pleas to “do it my way” or “cite my work” and a failure to appreciate that legal, social, or political considerations can be just as important or even more important to a given policy question, even in the face of robust and clear scientific evidence.
Tell Me a Story
Another common culture clash centers on the role of individual and personal narratives in science, versus their place in the policy world. In science, while we celebrate and lionize singular genius, we generally understate the impact of individual contributions. We write papers using passive voice. Objectivity demands dispassion. And in a very practical way, science is a team sport—in local collaboration on individual projects, but also because each addition to knowledge and understanding necessarily builds on and incorporates the work of others.
To be crass, nobody ever won an election by minimizing their contributions and being self-deprecating. This is not a statement of personal preference, or meant to be an “us versus them” analysis. Rather, it’s a recognition that different communities place different value on the style and role of personal narratives in accomplishing their goals.
The ongoing U.S. Presidential election cycle is a great example. In some shape of form, each candidate defines and articulates their own personal narrative, including an origin story, a career arc, motivating passions, and sense of purpose. Political strategists and pundits routinely use words like “narrative”, “discourse”, “character”, and “values”. Figures such as Ronald Reagan “the great communicator”, and Bill Clinton “the great explainer” are celebrated for these qualities.
Electoral politics are different from policy making, and presidential politics are an even more extreme example. Still, the point is that a world influenced (if not dominated by) the power of personal narratives can be an uncomfortable one for those at home in scientific traditions. This is especially true on a personal, day-to-day level. In my case, I realized that I would sometimes understate my experience, skills, and abilities. Scientists are famous for declining to comment on issues outside of their direct, specific area of focus, even though in relative terms among peers in policy, they may very well be “experts” in those other areas. Of course over-reach and exaggeration are the other facets of this issue, but understanding the risks on all sides and broadening my actual expertise has made me a more effective team member and leader.
It’s fair to say that the physics community is not known for its storytelling. That may be changing somewhat, with recent productions such as Interstellar, Cosmos: A Space-time Odyssey, Particle Fever, and even the recent gravity waves announcement from LIGO, each tackling the physics storytelling challenge head-on. But for me, learning how to describe my work without shying away from my personal motivations, feelings, trails and tribulations—in other words, incorporating elements of classic storytelling—has helped me to get my better ideas across in a world of non-scientists.
A life in physics and science policy and the journey in between is not for everyone, but for me it continues to be a happy blend of my science brain and my desire to have broad impact and create positive change. Idealistic? Definitely. But if I’m honest, idealism is what attracted me to science in the first place, and still does.
Director of Technical Operations
Energy & Environment, XPRIZE