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Kvantitativ Modellering av förmögenhetsrättsliga dispositiva tvistemål
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2014 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Quantitative legal prediction : Modeling cases amenable to out-of-court Settlements (English)
Abstract [sv]

I den här uppsatsen beskrivs en ansats till att med hjälp av statistiska metoder förutse utfallet i förmögenhetsrättsliga dispositiva tvistemål. Logistiska- och multilogistiska regressionsmodeller skattades på data för 13299 tvistemål från 5 tingsrätter och användes  till att förutse utfallet för 1522 tvistemål från 3 andra tingsrätter.  

Modellerna presterade bättre än slumpen vilket ger stöd för slutsatsen att man kan använda statistiska metoder för att förutse utfallet i denna typ av tvistemål.

Abstract [en]

BACKROUND: The idea of legal automatization is a controversial topic that's been discussed for hundreds of years, in modern times in the context of Law & Artificial Intelligence. Strangely, real world applications are very rare. Assuming that the judicial system is like any system that transforms inputs into outputs one would think that we should be able measure it and and gain insight into its inner workings and ultimately use these measurements to make predictions about its output.

In this thesis, civil procedures on commercial matters amenable to out-of-court settlement (Förmögenhetsrättsliga Dispositiva Tvistemål) was devoted particular interest and the question was posed:

Can we predict the outcome of civil procedures using Statistical Methods?

METHOD: By analyzing procedural law and legal doctrin, the civil procedure was modeled in terms of a random variable with a discrete observable outcome. Some data for 14821 cases was extracted from eight different courts. Five of these courts (13299 cases) were used to train the models and three courts (1522 cases) were chosen randomly and kept untouched for validation. Most cases seemed to concern monetary claims (66%) and/or damages (12%). Binary- and Multinomial- logistic regression methods were used as classifiers.

RESULTS: The models where found to be uncalibrated but they clearly outperformed random score assignment at separating classes and at a preset threshold gave accuracies significantly higher (p<<0.001) than that of random guessing and in identifying settlements or the correct type of verdict performance was significantly better (p<<0.003) than consequently guessing the most common outcome.

CONCLUSION: Using data for cases from one set of courts can to some extent predict the outcomes of cases from another set of courts. The results from applying the models to new data concludes that the outcome in civil processes can be predicted using statistical methods.

Place, publisher, year, edition, pages
2014. , 63 p.
Keyword [en]
Statistics, legal prediction, civil procedures, quantitative legal prediction, jurimetrics, quantitative law, data driven law, empirical, jurisprudence, classification, machine learning, data mining, ai, mauc, auc, hosmer-lemeshew, confusion matrix
Keyword [sv]
statistik, jurimetrik, juridik, tvistemål, maskininlärning, empirisk, automatisering, Hosmer Lemeshew, AI, Klassificering, confusion matrix, AUC, MAUC, multilogit, logit
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-224516OAI: oai:DiVA.org:uu-224516DiVA: diva2:717028
Subject / course
Educational program
Ekonomie kandidatprogrammet
Available from: 2014-06-03 Created: 2014-05-13 Last updated: 2014-06-03Bibliographically approved

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