12:45PM – 2:00PM
Stanford Law School, Room 180
This event is free and open to the public. Lunch will be provided.

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Scholars have increasingly investigated “crowdsourcing” as an alternative to expert-based judgment or purely data-driven approaches to predicting the future. Under certain conditions, scholars have found that crowdsourcing can outperform these other approaches. However, despite interest in the topic and a series of successful use cases, relatively few studies have applied empirical model thinking to evaluate the accuracy and robustness of crowdsourcing in real-world contexts. Our presenter, Daniel Martin Katz, reviews findings from a recent paper he co-authored with Michael Bommarito and Josh Blackman; to their knowledge, this dataset and analysis represent one of the largest explorations of recurring human prediction to date, providing additional empirical support for the use of crowdsourcing as a prediction method.

Speaker:

Daniel Martin Katz CodeX Affiliated Faculty; Associate Professor of Law and Director, The Law Lab, Illinois Tech – Chicago Kent College To view Daniel Martin Katz’s full bio, click here.