Talking law and economics at ETH Zurich

ETH Center for Law & Economics

This podcast is brought to you by the ETH Zurich Center for Law & Economics. We discuss current topic in intellectual property law, the law of emerging technologies, experimental law & economics, law & tech, and machine learning.

  1. 3d ago

    Money and Social Exclusion in Networks — Maria Bigoni (University of Bologna)

    Globalization creates new opportunities for cooperation across vast networks of individuals who may never meet. Social exclusion and monetary exchange are institutions that in theory can incentivize cooperation. But what encourages cooperation most effectively – money or social exclusion? In this episode of the CLE Vlog & Podcast Series, Prof. Maria Bigoni (University of Bologna) and Maurizia Di Buono (ETH Zurich) discuss Prof. Bigoni's paper "Money and Social Exclusion in Networks" (joint with Gabriele Camera and Eoardo Gallo). In an experiment, the authors evaluate the relative performance and interaction of monetary exchange and social exclusion in anonymous networks of different sizes. The results suggest that monetary exchange and temporary social exclusion perform similarly well in small networks. In large networks, however, monetary exchange is the only institution that promotes full cooperation by crowding out ostracism and keeping the network complete. The study offers an important insight: in an increasingly globalized world, well-functioning monetary systems are more effective than social exclusion in fostering cooperation across large and diverse communities. Paper Reference: Maria Bigoni (University of Bologna) Gabriele Camera (Chapman University) Edoardo Gallo (University of Cambridge) Money and Social Exclusion in Networks https://ideas.repec.org/p/chu/wpaper/25-06.htmlAudio Credits for Trailer: AllttA by AllttA https://youtu.be/ZawLOcbQZ2w

  2. Mar 1

    Off-the-Shelf Large Language Models Are Unreliable Judges – Jonathan Choi (USC / WashU)

    With the rapid rise of artificial intelligence, large language models (LLMs) are increasingly being considered for tasks once thought to be uniquely human—including legal interpretation. The idea of “AI judges” suggests appealing possibilities: consistent, fast, and ostensibly unbiased answers to legal questions. But how reliable are these models? Can their judgments truly be trusted? And do they withstand careful empirical scrutiny? In this episode of the CLE Vlog Series, Prof. Jonathan Choi (University of Southern California & Washington University, St. Louis) joins Alessandro Tacconelli (ETH Zurich) to discuss his paper, “Off-the-Shelf Large Language Models Are Unreliable Judges.” Prof. Choi presents findings from a series of empirical experiments designed to test how well LLMs perform as legal interpreters. His results reveal that model judgments are highly sensitive to prompt phrasing, output processing methods, and training choices. Moreover, post-training adjustments in today’s most widely used models can push LLMs’ assessments far from empirically grounded predictions of language use. These insights raise serious questions about the credibility of LLMs in legal interpretation and cast doubt on their ability to capture the “ordinary meaning” of legal texts. Paper Reference: Jonathan Choi – University of Southern California / Washington University (St. Louis) Large Language Models Are Unreliable Judges https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5188865 Audio Credits for Trailer: AllttA by AllttA https://youtu.be/ZawLOcbQZ2w

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This podcast is brought to you by the ETH Zurich Center for Law & Economics. We discuss current topic in intellectual property law, the law of emerging technologies, experimental law & economics, law & tech, and machine learning.

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