This grant provides ongoing support to the Institute of International Education (IIE)'s Scholar Rescue Fund, which offers fellowships for established scholars whose lives and work are threatened in their home countries. To date, the Scholar Rescue Fund has rescued and awarded academic fellowships to 1034 threatened scholars from 62 countries and relocated them to safety in 460 partner institutions across 54 countries. Grant funds will allow IIE to offer an additional 22 full fellowships to scientists, mathematicians, and engineers over the next three years, with each fellow receiving support in finding an academic placement, a $25,000 stipend, health insurance, funds for relocation, and professional development and language training.

Cognitive Economics studies how people make practical decisions in practice. Like Behavioral Economics, it acknowledges that choices do not always follow the rational patterns posited by traditional theorists. But whereas Behavioral Economists are often content to describe, classify, and try to 'nudge' away these anomalies, Cognitive Economists seek explanations for how, why, and when making mistakes might actually make sense.   PI Andrew Caplin has been pursuing this goal through the 'Sloan-NOMIS Program on the Attentional and Perceptual Foundations of Economic Behavior.' Under its auspices, an active community conducting research on decision-making has developed new concepts and approaches that now need testing in the real world. Caplin therefore plans to work with distinguished researchers in psychology, labor economics, and artificial intelligence on experiments that specifically address important workforce questions, too. Cognitive Economics starts with the notion that, in real life, there are costs that constrain decision-making other than monetary ones. Gathering, processing, storing, and analyzing information about the choices available are tasks that take time, effort, and attention. Though harder to measure, cognitive costs can be estimated and included in economic models to show why, how, and when it may make sense to make mistakes. This requires different kinds of analysis performed on different kinds of data. Specifically needed are datasets about what people would have done under other circumstances since the notion of a decision-making error can hardly even be defined otherwise. Having obtained such information about workforce decisions, and having obtained considerable conceptual progress with previous Sloan support, this project will test how well Cognitive Economics can actually answer practical research questions of significant current concern, including how cognitive costs affect individuals’ managerial skills, career trajectories, and ability to work with artificial intelligence. Besides producing novel research, Caplin’s team will organize workshops and conferences, develop new resources that make Cognitive Economics more accessible to students and researchers, and convene a broader Steering Committee to provide coordination among the many research groups and disciplines now affiliated with the field.

When Sloan helped launch an NBER Working Groups on Digitization in 2010 and another on Artificial Intelligence in 2017, there were only scattered researchers studying the economics of these topics. Since then, three developments are especially notable: Both groups have produced blockbuster research. Topics range from the gig economy to the surveillance economy, and from employee selection to employee displacement. While some has been done by distinguished senior faculty, also contributing fresh perspectives are the hundreds of junior faculty who participated in mentoring and training programs run by these two Working Groups for graduate students from departments that did not yet have inhouse expertise. The pace of advances in digital technology and artificial intelligence has only accelerated. So concepts, findings, and models that seemed to explain a great deal just a few years ago now have much more explaining to do. The need for creative and careful research in these areas has, despite significant progress, become even more urgent. Although they started as distinguishable topics, research on Digitization and on AI are converging. The people, problems, and principles associated with one subfield are increasingly the same as those associated with the other. Given that we cannot continue funding both communities indefinitely anyway, a plan was hatched to merge the two working groups going forward. Catherine Tucker of MIT, a leader of the Digitization group, and Avi Goldfarb from the University of Toronto, a leader of the AI Group, will form a unified program dedicated to 'Digital Economics and the Use of AI.' Over the next three years, the combined group will concentrate on (i) the impact of digital technologies on the nature of work, (ii) political economy and digital technology (including surveillance, media, and political protest), and (iii) the relationship between competition and innovation for digital technology. That work will be facilitated by activities that have proven successful to date, including workshops for PhD students, spring and fall meetings in San Francisco and Toronto, respectively, as well as a very popular session at the NBER Summer Institute. The Sloan Economics Program is always looking for ways to help grantees make the whole more than the sum of the parts. In this case, merging two successful Working Groups should result in even greater research on society’s most pressing questions about the economics of digital technologies.

Sloan support for the National Bureau of Economic Research (NBER) is nearly as old as the Foundation itself. A private non-profit, NBER is so strictly non-partisan that its charter forbids the making of policy recommendations. Papers released each week through its highly influential preprint series therefore speak instead about what either did or would happen under these or those circumstances. The best empirical economists from around the country are NBER members. Supported by grants from a variety of funders, they participate in working groups, conferences, and other research-enhancing activities. The highlight of the year for most empirical economists is the Summer Institute. NBER takes over a hotel in Cambridge, Massachusetts, for three weeks each July where 2,500 researchers gather in more than 50 distinct programs. About a dozen related conferences run in parallel. There are also lectures and networking opportunities for everyone, including meals, social events, and side meetings. The best papers of the year are typically workshopped and refined at the Summer Institute. Presentations are followed by spirited questioning, usually kicked off by pre-designated discussants. It is all very intense, and an invitation to speak is a rite of passage for aspiring economists. Even Nobel Laureate Paul Krugman has written about how the first paper he gave there in 1979 not only 'jump started his career' but was 'the best 90 minutes of his life.' Traditionally seen as quite an exclusive affair, the Summer Institute has been steadily earning a reputation for inclusivity as well. The pandemic helped, since virtual sessions were suddenly open to many more who could participate remotely. NBER now invites and funds top pre-doctoral students from the Summer Training Program designed by the American Economics Association to increase its diversity. More invitations, outreach, and travel grants are specifically targeting faculty members at Minority Serving Institutions generally and through programs NBER helps run at Historically Black Colleges and Universities. Inclusivity is also being enhanced by procedures to introduce, orient, and network participants who have not attended a Summer Institute before. After running studies comparing the climate for male and female speakers at its events, the percentage of women presenters has increased to nearly 50 percent in 2022 and NBER has pledged to collect even more data about the results of its DEI efforts generally. The PI, Janet Currie, is a co-organizer of the Summer Institute and the Henry Putnam Professor of Economics and Public Affairs at Princeton University. She is also president-elect of the American Economics Association. Its Committee on the Status of Women in the Economics Profession (CSWEP) previously honored Currie with its prestigious Carolyn Shaw Bell Award in recognition of her deep commitment to diversity.

The Sloan Foundation and other science funders naturally care about making basic research more productive, or, in other words, increasing the value of scholarly outputs per grant dollar we spend. Among many factors influencing that ratio, funder policies could be potentially important. How we attract applications, what we require applicants to do, and how we select among them all help shape the priorities and progress of the scientific enterprise. While funders like Sloan have broad control over our procedures, measuring the specific effects of changing those policies has traditionally been considered difficult at best. When it comes to evaluating policy implementations of any kind, the Abdul Latif Jameel Poverty Action Lab (J-PAL) is arguably the world’s premier organization dedicated to the design, execution, and interpretation of randomized controlled trials (RCTs). Based on J-PAL’s work since 2003 on international development, three of its founders shared the 2019 Nobel Prize in Economics. With Sloan support, J-PAL’s North America branch was launched in 2013 by Amy Finkelstein, who was named a MacArthur Fellow five years later. What does J-PAL’s mission have to do with scientific productivity? The role of new discoveries and innovations in economic growth is an old story among economists, of course. J-PAL North America has therefore concluded that policies for accelerating the progress of science should be among those it studies to help alleviate poverty. But there are two main rate-limiters for conducting RCTs in that area: first, not everyone knows how to design, carry out, and interpret a rigorous RCT; and second, those who have that expertise also need to partner with institutions willing to host such rigorous evaluations. J-PAL’s new Science for Progress Initiative (SfPI) aims to fix that. Co-led by MacArthur Fellow Heidi Williams from Stanford and economist, entrepreneur, and philanthropist Paul Niehaus from the University of San Diego, SfPI will not only train and support a community of researchers, but also match them to organizations with important questions to answer about making science more productive. As a step towards accomplishing this, SfPI leader Heidi Williams has helped organize a workshop at the National Academy of Science on 'Experimentation in Federal Funders.' Along with presentations from NSF, NIH, and other science agencies about what they want to learn from running policy evaluations, the U.S. Patent and Trade Office described how, inspired by what they already learned through randomized evaluations, a whole new office has been created there to run more RCTs in cooperation with academic researchers like the ones affiliated with SfPI.

Researchers and policymakers want to better understand the dynamics of the Care Economy, but reliable, timely, and comprehensive statistics on caregivers are severely lacking. Misty Heggeness, Professor and Co-Director of the Kansas Population Center at the University of Kansas, therefore proposes to create The Care Board, a user-friendly online dashboard containing high-quality statistics about the U.S. Care Economy. The idea is to cover all activities, paid or unpaid, that contribute to the development of human capital, including the maintenance of another human being’s ability to thrive. Measuring such work is a challenge for many reasons, not least because many caregivers hold multiple jobs. The Care Board will use standard definitions from sociology and economics that account for these nuances. To the extent possible, data will be disaggregated by age, gender, parental status, race, ethnicity, educational attainment, poverty status, income, geographic area, and household configuration. To begin, Heggeness’ team will publish statistics on care workers based on traditional publicly available data sources, such as the Bureau of Labor Statistics, the American Time Use Survey, and the Current Population Survey. Some examples include the number of hours and percent of individuals who spend time engaging in (paid or unpaid) care work as a primary, secondary, or supervisory activity; or the number of families receiving benefits from WIC. The indicators will be improved over time using state-of-the-art methods to link administrative records with traditional survey microdata that Heggeness helped develop as a research economist at the Census Bureau. The Care Board will also feature data on federal and local policies like parental leave that affect caregivers’ ability to work for pay. With over a dozen years of experience in the federal statistical system, Heggeness is prepared to meet these statistical, methodological, and organizational challenges. She also plans to disseminate Care Board data through conference presentations, seminars, and user trainings. Encouragingly, top leadership at the Census Bureau have taken an active interest in this pilot. If all goes well, it could help launch a major effort by the federal statistical system to begin officially measuring how caregivers underpin U.S. economic progress.

The Sloan Foundation and other science funders naturally care about making basic research more productive, or, in other words, increasing the value of scholarly outputs per grant dollar we spend. Among the many factors influencing that ratio, researchers’ ability to plan, organize, and lead projects could be potentially important. Management training, after all, demonstrably enhances team performance in industries that range from manufacturing to healthcare. But beyond anecdotes, no one knows much about how academics run their laboratories. This is particularly striking in comparison to other sectors of the economy whose management practices have been extensively studied and improved. Economist Daniela Scur is therefore launching the first large-scale and methodologically sophisticated survey of organizational practices in academic research. She brings years of practical experience that includes running parts of the vaunted World Management Survey. Her team has already begun testing questions and protocols that explore ethics, mentoring, inclusiveness, climate, and other factors of concern in academic settings. Biomedical research laboratories are the first target because their operations are relatively homogenous, identifiable, team-oriented, and expensive. Eventually the plan is to survey 2500 from around the country. To date, they have already made adjustments to their survey instruments and plans based on a pilot project at the Harvard Medical School. Even there, the preliminary findings are tantalizing. Compared with those actually running or working in their labs, the PIs contacted were often unaware of what management practices were in place and overestimated their quality in any case. The full survey will adhere to, and benefit from, standards developed by the World Management Survey. In addition to documenting current practices, the survey results will be used to design and test interventions for teaching leadership skills that scientists do not normally learn in graduate school. Mr. Sloan, the consummate executive and productivity expert who also loved both scientific research as well as management education, would likely take particular delight in this use of his funds. 

Neither in grade school nor in research seminars does anyone actually present every detail of a mathematical proof with complete rigor. Our faith in a given theorem is ultimately based on the belief that mathematicians could, if asked, fill in all the missing definitions and arguments rather than on the belief that they have already anticipated and answered every possible question. As proofs have grown more complicated and lengthy, even famous mathematicians sometimes admit that they are not quite sure whether their argument is entirely correct. Shouldn’t computers be able to help? Among open-source software projects designed to assist with formulating and checking mathematical proofs, one has emerged as the clear frontrunner. Launched by Leonardo de Moura when he was working at Microsoft Research in 2013, it is called 'The Lean Proof Assistant.' A Lean user inputs a theorem statement along with the steps of a purported proof in a special language. Based on the definitions, results, and logical rules it already knows, the program tries to check each step mechanically. In some cases, it can suggest shorter arguments. In others, it may interactively request more details. But once verified this way with super-human reliability, the theorem and its proof become part of the resources that Lean can call upon going forward. So far, educational resources based on Lean are helping students learn to construct proofs even when a skilled teacher is unavailable. Publications like Wired, Nature, and Quanta have hailed Lean, the last of these calling the Version 3 release 'one of 2020’s biggest breakthroughs in mathematics.' Even Fields Medalists like Peter Scholze praise Lean, which helped verify an important proof of his that was so long, subtle, and impenetrable that he described it as 'a thick jungle.' Despite all this excitement, Lean is still in its developmental stages with much hard work left to improve automation, scalability, usability, and sustainability. As Sloan found out years ago, usual academic structures and incentives are not well suited for such tasks. This is the motivation for launching a 'Focused Research Organization.' Known as a FRO, this is a new type of nonprofit start-up pioneered by PI Adam Marblestone. Each FRO takes on a specific challenge and works on it exclusively for a few years before disbanding. During this time, the FRO will be incubated at the new nonprofit called Convergent Research founded with core funding and backing from Schmidt Futures. Lean creator Leonardo da Moura will lead the FRO technical team, which, in addition to mathematical experts and software engineers, will also include product, project, and community managers. The sustainability plan calls for spinning out a nonprofit Lean Foundation as has been done successfully by other major open-source software projects supported by Sloan that have turned out to be totally transformational. In this case, Lean stands to accomplish nothing less than accelerating the use, discovery, and learning of mathematical truths.

Life as we know it is primarily chemistry: all living organisms are composed of carbon-based molecules such as proteins, carbohydrates, and nucleic acids that are the result of chemical reactions between different elements like carbon, hydrogen, oxygen, nitrogen, and sulfur. Understanding the basic principles underlying chemical reactions is important to understanding life. When thinking about reactions in a chemical context, scientists typically think about reactants interacting in a homogeneous solution that’s essentially the same in any given place. But the cytoplasm, the liquid in cells where biochemical reactions take place, is a biological context that’s quite different from the simplified models of chemistry textbooks. Cytoplasm is a heterogeneous fluid that looks and functions differently across the cell and so it’s not accurately represented by simplifying assumptions of homogeneity. Precisely how these assumptions are distorting our picture of cellular chemistry, and therefore our understanding of fundamental biochemistry, is not well understood. This grant supports Lauren Zarzar and Ayusman Sen at the Pennsylvania State University who will study how solution heterogeneity influences reactivity for three important classes of biochemical reactions. First, they will study autocatalytic reactions, in which one of the reaction products facilitates (i.e. is a catalyst for) the same, or a coupled, reaction. Autocatalysis is a mechanism for chemical self-replication and is considered a key aspect of the prebiotic chemistry that gave rise to life. Next, the team will study enzyme reaction cascades, a sequence of enzyme-catalyzed reactions whereby the product of one reaction is the reactant for the next reaction. They’ll focus on chemotaxis (chemical activity that leads to motion towards or away from a higher concentration of some substance) to study how solution structure affects enzyme cascades. Finally, they will study polymerization reactions and, in doing so, address an important question in prebiotic chemistry: how polymers with specific monomer sequences arise without a specific sequence-directing mechanism. Ultimately, this project will deepen our understanding of specific reactions central to cellular chemistry and shed light on the role solution heterogeneity plays in driving the chemistry of life.

This grant supports Ben Machta at Yale University who will use tools from information theory and statistical physics to explore how bacteria process signals from their environment, and how they use this information to drive behavior. Machta will use bacteria to study one aspect of information processing: how noise (spurious signal accompanying the information-carrying signal) limits bacterial behavior. Specifically, Machta  will investigate how bacteria navigate their local chemical environment through chemotaxis (movement along a concentration gradient of a substance) and how they communicate with one another through quorum sensing (chemical signaling that reflects the density of nearby bacteria). Grant funds will allow Machta to determine the theoretical limit on the rate at which E. coli acquire behaviorally-relevant information (the concentration of so-called attractant molecules) and to measure this information-acquisition rate, to provide the first direct measurement of whether any organism’s biochemical sensory system approaches the performance limits imposed by the laws of physics. Additionally, Machta and colleagues will study how E. coli amplify signals without introducing noise via experiments that will test whether equilibrium or non-equilibrium models do a better job of describing chemotactic signal amplification. Finally, the researchers will use V. cholerae bacteria as a model organism to study the fidelity of information transmission as multiple signals propagate through the quorum sensing signal processing pathway. Collectively, these experiments will provide an important demonstration of how the tools of  information theory and statistical physics can be used to gain mechanistic insight into the information processing that drives behavior in simple living systems.