Umberto Mignozzetti is an Assistant Teaching Professor at the Department of Political Science and the Computational Social Sciences Program at the University of California, San Diego. He uses comparative politics and political economy tools, with a regional focus in Latin America, to investigate how to improve public goods provision in developing democracies. His research combines computational social sciences methods, experiments, and elite and popular surveys. His papers have contributed to understanding the nexus between legislature size and welfare, the failures in bottom-up accountability, the effects of elite capturing, and the elite preferences toward climate change mitigation agreements. Umberto’s research has been published in the American Journal of Political Science, British Journal of Political Science, Journal of Experimental Political Science, Research and Politics, and Global Environmental Politics. More recently, he has been working on applications of generative deep learning on computational social sciences issues.
Umberto Mignozzetti is an Assistant Teaching Professor at the Department of Political Science and the Computational Social Sciences Program at the University of California, San Diego. He uses comparative politics and political economy tools, with a regional focus in Latin America, to investigate how to improve public goods provision in developing democracies. His research combines computational social sciences methods, experiments, and elite and popular surveys. His papers have contributed to understanding the nexus between legislature size and welfare, the failures in bottom-up accountability, the effects of elite capturing, and the elite preferences toward climate change mitigation agreements. Umberto’s research has…