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dc.contributor.advisorHeidi Nepf.en_US
dc.contributor.authorKhoury, Basselen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2016-08-02T20:07:22Z
dc.date.available2016-08-02T20:07:22Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/103839
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 103-104).en_US
dc.description.abstractEmpirical legal analysis is an emerging discipline of statistical and legal scholarship. Despite its importance, however, there is a lack of empirical research on the affirmative defense of force majeure, which eliminates liability for a breach of contract in the case of an unforeseeable and uncontrollable event that renders performance impossible. This study seeks to fill the gap in the empirical literature by addressing some central questions on preparing to litigate a force majeure defense. Relying on methods from categorical data analysis, I examine the various criteria that a litigator would use in assessing the strength of a force majeure defense based on contractual terms. This study was supported by a database of manually collected lawsuits, with which I analyzed 291 state and federal force majeure cases from the 19th century to the present day. The results highlight the key elements in a force majeure provision that weigh most heavily in favor of the party raising the defense.en_US
dc.description.statementofresponsibilityby Bassel Khoury.en_US
dc.format.extent104 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleA poisson regression model for assessing force majeure claimsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.identifier.oclc953868751en_US


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