Hu, A., & Tingley, D. (Forthcoming). : Regional Remediation Opportunities for a Job Driven Cleaner Environment. In. Publisher's Version
Ansolabehere, S., Araujo, K., He, Y., Hu, A., Karplus, V., Li, H., Thom, E., et al. (Forthcoming). A Low Carbon Energy Transition in Southwestern Pennsylvania. In. Publisher's Version
Chaudoin, S., Milner, H., & Tingley, D. (Forthcoming). 'America First' Meets Liberal Internationalism. In The Liberal Order Strikes Back? Donald Trump, Joe Biden, and the Future of International Politics . Columbia University Press. final_cmt_cup_volume_2022.pdf
Reddi, V., Plancher, B., Kennedy, S., Moroney, L., Warden, P., ManyGreatPeopleReadThePaper,, & Tingley, D. (Forthcoming). Widening Access to Applied Machine Learning with TinyML. Harvard Data Science Review. Publisher's VersionAbstract
Broadening access to both computational and educational resources is critical
to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.
Brutger, R., Kertzer, J., Renshon, J., Tingley, D., & Weiss, C. (Forthcoming). Abstraction and Detail in Experimental Design. American Journal of Political Science . Publisher's VersionAbstract
Experimentalists in political science often face the question of how abstract or concrete their experimental stimuli should be. Typically, this question is framed in terms of tradeoffs relating to experimental control and generalizability: the more context you introduce into your studies, the less control you have, and the more difficulty you have generalizing your results. Yet we have reasons to question this framing of the tradeoff, and there is relatively little systematic evidence experimenters can rely on when calibrating the degree of abstraction in their studies. We seek to make two contributions with this project. First, we provide a theoretical framework which identifies and considers the consequences of three dimensions of abstraction in experimental design: situational hypotheticality, actor identity, and contextual detail. Second, we replicate a range of classic vignette-based survey experiments from political science, varying these levels of abstraction. Our results suggest that apart from a specific set of conditions, there are fewer tradeoffs between abstraction and detail in survey experiment design than political scientists often assume.
Gaikwad, N., Genovese, F., & Tingley, D. (Forthcoming). Creating Climate Coalitions: Mass Preferences for Compensating Vulnerability in the World's Two Largest Democracies. American Political Science Review. compensationcoallitions.pdf
Romney, D., Jamal, A., Keohane, R., & Tingley, D. (Forthcoming). The Enemy of my Enemy is not my Friend: Arabic Twitter Sentiment Toward ISIS and the United States. International Studies Quarterly. enemyfriends.pdf
Tingley, D., & Tomz, M. (Forthcoming). The Effects of Naming and Shaming on Public Support for Compliance with International Agreements: An Experimental Analysis of the Paris Agreement. International Organization. Publisher's VersionAbstract

How does naming and shaming affect public support for compliance with international agreements? We investigated this question by conducting survey experiments about the Paris Agreement, which relies on social pressure for enforcement. Our experiments, administered to national samples in the United States, produced three sets of findings. First, shaming by foreign countries shifted domestic public opinion in favor of compliance, increasing the political incentive to honor the Paris Agreement. Second, the effects of shaming varied with the behavior of the target. Shaming was more effective against partial compliers than against targets that took no action or honored their obligations completely. Moreover, even partial compliers managed to reduce the effects of shaming through the strategic use of counter-rhetoric. Third, identity moderated responses to shaming. Shaming by allies was not significantly more effective than shaming by non-allies, but Democrats were more receptive to shaming than Republicans. Overall, our experiments expose both the power and the limits of shaming as a strategy for enforcing the Paris Agreement. At the same time, they advance our understanding of the most significant environmental problem facing the planet.

Kline, R., Mahajan, A., & Tingley, D. (Forthcoming). Collective Risk and Distributional Equity in Climate Change Bargaining. Journal of Conflict Resolution. distributionequity.pdf
Irvine, P., Burns, E., Caldeira, K., Keutch, F., Tingley, D., & Keith, D. (2021). Expert judgements on solar geoengineering research priorities and challenges. Earth Arxiv.
Tingley, D. (2021). Building on the Shoulders of Bears: Next Steps in Data Science Education. Harvard Data Science Review , 3 (2). Publisher's Version interleaving_computational_and_inferential_thinking-tingleyfollowup.pdf
Chaudoin, S., Milner, H. V., & Tingley, D. (2021). America First Meets Liberal Internationalism. In The H-Diplo/ISSF Policy Series: America and the World: The Effects of the Trump Presidency. americafirstliberalinternationalism.pdf
Dai, Z., Burns, E., Irvine, P., Xu, J., & Keith, D. (2021). US and Chinese climate experts share judgements on solar geoengineering. Humanities and Social Sciences Communications , 8 (18). Publisher's Version
Kizilcec, R. F., Reich, J., Yeomans, M., Dann, C., Brunskill, E., Lopez, G., Turkay, S., et al. (2020). Scaling Up Behavioral Science Interventions in Online Education. Proceedings of the National Academy of Sciences , 117 (26), 14900-14905. Publisher's Version
Tingley, D., & Tomz, M. (2020). International Commitments and Domestic Opinion: The Effect of the Paris Agreement on Public Support for Policies to Address Climate Change. Environmental Politics , 29 (7), 1135-1156. Publisher's Version parispledge.pdf
Guan, Y., Tingley, D., Romney, D., Jamal, A., & Keohane, R. (2020). Chinese views of the United States: evidence from Weibo. International Relations of the Asia-Pacific , 20 (1), 1-30. Publisher's Version
Chilton, A., Milner, H., & Tingley, D. (2020). Reciprocity and Public Opposition to Foreign Direct Investment. British Journal of Political Science , 50 (1), 129-153. Publisher's Version cmt-mergers.pdf
Tingley, D. (2019). Public Perceptions of Solar Geoengineering with Implications for Governance. In Governance of the deployment of solar geoengineering . Harvard Project on Climate Agreements. Publisher's Version
Mahajan, A., Tingley, D., & Wagner, G. (2019). Fast, cheap, and imperfect? U.S. public opinion about solar geoengineering. Environmental Politics , 28 (3), 523-543. fastcheapimperfect.pdf
Rushkin, I., Chuang, I., & Tingley, D. (2019). Modelling and using response times in online courses. Journal of Learning Analytics , 6 (3), 76-89. timestat.pdf