Computer Power and Legal Reasoning: A Case Study of Judicial Decision Prediction in Zoning Amendment Cases |
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| Authors: | Charles M Haar John P Sawyer Jr Stephen J Cummings |
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| Institution: | Charles M. Haar;is Louis D. Brandeis Professor of Law, Harvard University;Affiliated Scholar, American Bar Foundation. B.A. New York University;M.A. University of Wisconsin;LL.B. Harvard University. John P. Sawyer, Jr;., is Director, Bureau of Renewal, Neighborhood Improvement and Relocation, Massachusetts Department of Community Affairs. B.A. Yale University;M.C.P. Harvard Graduate School of Design;J.D. University of Kentucky School of Law. Stephen J. Cummings;is a systems planner and analyst for the Boston consulting firm Applied Resources Integration, Ltd.;B.S. Columbia University;M.C.P. Harvard Graduate School of Design. |
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| Abstract: | While social scientists have long advocated the use of statistical methodology in legal analysis, its practical application has not been tested. Statistical models based on social science theories have been used to predict judicial decisions and explain court behavior, but the legal profession has failed to develop statistical models based on traditional legal theories and using data familiar to the lawyer. This article seeks to demonstrate by practical application of statistical methodologies, coupled with traditional legal research methods, that such research can produce important insight into a court's decision making and provide a useful model for predicting the probability of a favorable decision. The zoning amendment decisions of the Connecticut Supreme Court are the data base of this study, which also provides a comprehensive explanation of zoning amendment law in Connecticut as a backdrop against which to evaluate the insights gained by statistical analysis. |
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