Nate Barlow, a graduate student in the UCLA economics department has won the Economic History Early Stage Dissertation Grant for his project on reparations paid to Japanese internees. The grant is awarded to the most promising dissertations in economic history.
UCLA Professors Simon Board and Moritz Meyer-ter-Vehn received the American Economic Journal (AEJ) Best Paper Award for their paper ‘A Reputational Theory of Firm Dynamics’ published in the American Economic Journal: Microeconomics in 2022. The annual AEJ Best Paper Award is given to the best paper published in each of the American Economic Journals: Applied Economics, Economic Policy, Macroeconomics, and Microeconomics over the last three years.
The announcement can be found here.
The paper here.
Professor Martha Bailey was elected a fellow of the cliometric society. Fellows must have published contributions to economic history that are markedly original and have significantly advanced the frontiers of knowledge.
A complete list of Fellows can be found here.
UCLA Professor Martha Bailey has been awarded an honorary doctorate from Lund University School of Economics and Management for her research on key issues in labor economics, demography and health in the US from a long-term historical perspective. Professor Bailey is one of the leading economic historians and economists, working at the intersection of labor markets, demography, health and inequality.
The announcement can be found here.
A paper by UCLA Professor Pierre-Olivier Weill studying bond trading was featured in the February issue of NBER Digest. The paper finds that, in addition to the characteristics of a bond trade request, the characteristics of the bond trader are important in determining the time to consummating a trade and the failure rate.
The February issue of NBER Digest can be found here.
The paper can be found here.
By Joao Guerreiro
Joao Guerreiro
The short stories in Isaac Asimov’s “I, Robot” discussed some of the dilemmas associated with artificial intelligence (AI). New developments in AI technology have brought these concerns from the pages of science-fiction stories to the forefront of policy discussions. In May 2023, a global AI consortium declared AI risks a priority, similar to pandemics and nuclear war. The G7 started the Hiroshima AI Process for global AI regulation. Both the European Union and the U.S. are discussing regulatory frameworks. Ideas include mandatory testing, holding developers accountable, and classifying AI into risk tiers. The critical issue is the uncertainty surrounding AI’s societal costs and benefits.
How should AI be regulated in the presence of uncertainty regarding the AI’s potential adverse external effects? Professor Joao Guerreiro, with Sergio Rebelo (Kellogg School of Management) and Pedro Teles (Banco de Portugal and Catolica-Lisbon School of Business and Economics), tackles this question in a recent paper: “Regulating Artificial Intelligence.” The paper evaluates regulatory approaches using a normative analysis. The authors explore two settings. In the first scenario, uncertainty is only resolved after the release of the AI. In the second scenario, it is possible to beta-test the algorithm to assess external effects.
In the absence of beta testing, there’s a mismatch between the optimal AI novelty level for society and what naturally arises in an unregulated market. The paper argues that the social optimum, or the ideal balance of AI novelty and safety, is generally more conservative than what the market would select without regulation. With beta testing, developers can learn the external effects of the AI by testing before release. This approach helps resolve uncertainties regarding potential negative externalities. Still, the social optimum requires a higher degree of conservativism both in testing and the algorithm’s release.
The authors evaluate three regulatory frameworks. First, they show that subjecting algorithms to regulatory approval is insufficient to implement the social optimum– since developers still have the incentive to go for too-risky algorithms. Second, simply holding developers liable for the external effects of the algorithms is also insufficient to implement the social optimum in the presence of limited liability. Finally, they show that mandating beta testing to assess the externalities and holding developers liable for the adverse effects of the algorithm can achieve the social optimum, even in the presence of limited liability.
Overall, the paper’s findings highlight the complexity of AI regulation and the need for nuanced approaches that balance innovation and safety. By considering various scenarios and regulatory frameworks, it provides valuable insights for policymakers and stakeholders in the AI field.
UCLA Professor Andres Santos has been appointed as coeditor of the American Economic Review, the leading journal in economics.
A paper by UCLA Professor Jay Lu and coauthor Annie Liang was featured in the Anderson Review. The paper studies the trade-off between accuracy and fairness across groups in designing algorithms — step-by-step instructions for accomplishing a task. The Review article can be found here.
The paper can be found here.
The UCLA’s team took third place in the annual Fed Challenge, a national competition that asks teams of undergraduate students to analyze the economy and present a monetary policy recommendation to judges from the Federal Reserve. This year, 107 schools took part in the competition, with 3 teams from each of the six regions chosen to advance to the semifinal Q&A round, and 6 finalists selected as the winners of each region. The other finalists were Harvard University (first place), Princeton University (second place), Columbia University, University of Chicago, and University of North Carolina Wilmington. The UCLA’s team was composed of Yohann Byun, Vivian Fan, Ryan Gonda, Laura Lu, and Sophie Simcox, with Casper Hsu, Carly King, and Jordan Lee serving as alternates and research support. Professor Chris Surro and graduate student Ali Haider Ismail advised the team. Our students registered for Econ 187 (The Fed Challenge) as part of their preparation for the Challenge. You can read more about the Fed Challenge here.
