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Predicting Civil Jury Verdicts: How Attorneys Use (and Misuse) a Second Opinion

Authors


  • This research was supported by a grant from the Israeli Binational Science Foundation to Varda Liberman. The authors thank Lee Ross for his helpful comments, David Wenner for giving us access to a sample of experienced trial attorneys, and Janet Alexander and Jared Curhan for feedback on presentations and drafts.

Jonas Jacobson, Trial Behavior Consulting, 505 Sansome St., Ste. 1701, San Francisco, CA 94111; email: jacobson.jonas@gmail.com. Dobbs-Marsh is at Stanford University; Liberman is at the International Center Herzliya, Israel; Minson is at the Wharton School, University of Pennsylvania. Authors two through four are listed in alphabetical order.

Abstract

When predicting potential jury verdicts, trial attorneys often seek second opinions from other attorneys. But how much weight do they give to these opinions, and how optimally do they use them? In a four-round estimation task developed by Liberman et al. (under review), pairs of law students and pairs of experienced trial attorneys estimated actual jury verdicts. When participants were given access to a partner's estimates, participants' accuracy improved in both groups. However, participants in both groups underweighted their partners' estimates relative to their own, with experienced attorneys giving less weight to their partners' opinions than did law students. In doing so, participants failed to reap the full benefits of statistical aggregation. In both groups, requiring partners to reach agreement on a joint estimate improved accuracy. This benefit was then largely retained when participants gave final individual estimates. In a further analysis, we randomly sampled estimates of various-sized groups. The accuracy of mean estimates substantially increased as group size increased, with the largest relative benefit coming from the first additional estimate. We discuss the implications of these findings for the legal profession and for the study of individual versus collective estimation.

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