Varieties of Legal Probabilism : a Survey

• Autorzy: Małgorzata Stefaniak , Rafał Urbaniak

Warianty tytułu

Odmiany probabilizmu prawnego - przegląd

• Języki publikacji: EN

Abstrakty

EN

Legal Probabilism is the view that mathematics, and probability theory in particular, can be used to explicate the standard of legal decisions. While probabilistic tools are sometimes used in courtrooms, the construction of a general model of evidence evaluation remains a challenge. Conceptual diffi culties facing Legal Probabilism include the diffi culty about conjunction, the diffi culty about corroboration and the gatecrasher paradox. These problems need to be addressed before we construct a general model. In this survey we discuss the three diffi culties and present some theories proposed as their solutions. (original abstract)

Probabilizm prawny to pogląd, że teoria prawdopodobieństwa może być użyta do eksplikacji standardów decyzji prawnych dotyczących faktów. O ile narzędzia probabilistyczne są czasami używane w sądzie, to skonstruowanie ogólnego modelu, który pozwoliłby na ocenę dowodów, stanowi wyzwanie. Probabilizm prawny zmaga się z takimi trudnościami jak problem koniunkcji, problem dotyczący koroboracji czy paradoks widza bez biletu (gatecrasher paradox). Problemy te należy rozwiązać zanim przystąpimy do tworzenia ogólnego modelu. W niniejszym przeglądzie omawiamy wspomniane trudności oraz proponowane w literaturze sposoby ich rozwiązania. (abstrakt oryginalny)

Słowa kluczowe

EN

Decision making; Probability; Law

PL

Podejmowanie decyzji; Prawdopodobieństwo; Prawo

• Czasopismo: Decyzje
• Rocznik: 2018
• Numer: nr 30
• Strony: 33--52

Twórcy

autor

Małgorzata Stefaniak

• University of Gdansk, Poland

autor

Rafał Urbaniak

• University of Gdansk, Poland

Bibliografia

• Aitken, C., Roberts, P., Jackson, G. (2010). Fundamentals of Probability and Statistical Evidence in Criminal Proceedings (Practitioner Guide No. 1). Guidance for Judges, Lawyers, Forensic Scientists and Expert Witnesses. Royal Statistical Society's Working Group on Statistics and the Law.
• Aitken, C. , Taroni, F. (2004). Statistics and the evaluation of evidence for forensic scientists. Wiley Online Library.
• Allen, R., Pardo, M. (2007). The problematic value of mathematical models of evidence. The Journal of Legal Studies, 36(1),107-140.
• Ball, V.C. (1960). The moment of truth: probability theory and standards of proof. Vanderbilt Law Review, 14, 807-830.
• Bernoulli, J. (1713). Ars conjectandi, opus posthumum. Accedit Tractatus de seriebus infi nitis, et epistola gallicé scripta de ludo pilae reticularis. Bazylea: Thurneysen Brothers.
• Bolstad, W.M., Curran, J.M. (2016). Introduction to Bayesian statistics. John Wiley & Sons.
• Bovens, L., Hartmann, S. (2004). Bayesian epistemology. Oxford University Press.
• Bradley, D. (2015). A Critical Introduction to Formal Epistemology. Bloomsbury Publishing.
• Cheng, E. (2012). Reconceptualizing the burden of proof. Yale Law Journal, 122(5), 1254-1273.
• Cheng, E.K. (2009). A practical solution to the reference class problem. Columbia Law Review, 109, 2081-2103.
• Cohen, J. (1977). The probable and the provable. Oxford University Press.
• Cohen, L.J. (1981). Subjective probability and the paradox of the gatecrasher. Arizona State Law Journal, 2(2), 627-634.
• Colyvan, M. , Regan, H.M. (2007). Legal decisions and the reference class problem. The International Journal of Evidence and Proof, 11(4), 274-285.
• Colyvan, M., Regan, H.M., Ferson, S. (2001). Is it a crime to belong to a reference class. Journal of Political Philosophy, 9(2), 168-181.
• Cullison, A.D. (1969). Probability analysis of judicial fact-fi nding: A preliminary outline of the subjective approach. Toledo Law Review, 1, 538-598.
• Dant, M. (1988). Gambling on the truth: the use of purely statistical evidence as a basis for civil liability. Columbia Journal of Law and Social Problems, 22, 31-70.
• De Finetti, B. (1937). La prévision: ses lois logiques, ses sources subjectives. Annales de l'Institut Henri Poincaré, 7, 1-68. (translated as "Foresight: Its Logical Laws, Its Subjective Sources" in (Kyburg, 1964)).
• Enoch, D., Fisher, T. (2015). Sense and sensitivity: Epistemic and instrumental approaches to statistical evidence. Stanford Law Review, 67, 557.
• Finkelstein, M.O., Levin, B. (2001). Statistics for Lawyers. Springer.
• Franklin, J. (2010). Feature selection methods for solving the reference class problem: Comment on Edward K. Cheng, a practical solution to the reference class problem. Columbia Law Review Sidebar, 110, 12-23.
• Haack, S. (2014a). Evidence Matters: Science, Proof, and Truth in the Law. Cambridge University Press.
• Haack, S. (2014b). Legal probabilism: an epistemological dissent. In: Haack (2014a), 47-77.
• Ho, H.L. (2008). A philosophy of evidence law: Justice in the search for truth. Oxford University Press.
• Kaplan, J. (1968). Decision theory and the factfi nding process. Stanford Law Review, 20, 1065-1092.
• Kaplow, L. (2014). Likelihood ratio tests and legal decision rules. American Law and Economics Review, 16(1), 1-39.
• Kaye, D. (1979). The paradox of the gatecrasher and other stories. Arizona State Law Journal, 1033, 101-110.
• Kyburg, H.E. (1964). Studies in subjective probability. John Wiley & Sons.
• Lempert, R.O. (1977). Modeling relevance. Michigan Law Review, 75, 1021-1057.
• Lindley, D.V. (1970). Introduction to probability and statistics from a Bayesian viewpoint. Cambridge University Press.
• Lucy, D. (2013). Introduction to statistics for forensic scientists. John Wiley & Sons.
• Miller, D. (2018). Cohen's criticisms of the use of probability in the law. Lecture at the Sixth World Congress on Universal Logic, Vichy.
• Nesson, C.R. (1979). Reasonable doubt and permissive inferences: The value of complexity. Harvard Law Review, 92(6), 1187-1225.
• Pettigrew, R. (2011). Epistemic utility arguments for probabilism. Stanford Encyclopedia of Philosophy.
• Pettigrew, R. (2016). Accuracy and the Laws of Credence. Oxford University Press.
• Ramsey, F. (1978). Truth and probability. In: Mellor, D. H., editor, Foundations: Essays in Philosophy, Logic, Mathematics and Economics, 58-100. Routledge. [originally published in 1926].
• Resnik, M. (1987). Choices: An Introduction to Decision Theory. University of Minnesota Press.
• Robertson, B., Vignaux, G., Berger, C. (2016). Interpreting evidence: evaluating forensic science in the courtroom. John Wiley & Sons.
• Savchuk, V., Tsokos, C.P. (2011). Bayesian theory and methods with applications. Atlantis Studies in Probability and Statistics (Book 1), Atlantis Press.
• Simon, R.J., Mahan, L. (1970). Quantifying burdens of proof-a view from the bench, the jury, and the classroom. Law and Society Review, 5(3), 319-330.
• Smith, M. (2017). When does evidence suffi ce for conviction? Mind, 508(127), 1193-1218.
• Stein, A. (2005). Foundations of Evidence Law. Oxford University Press.
• Taroni, F., Biedermann, A., Bozza, S., Garbolino, P., Aitken, C. (2006). Bayesian networks for probabilistic inference and decision analysis in forensic science. John Wiley & Sons.
• Tillers, P., Green, E.D., editors (1988). Probability and Inference in the Law of Evidence. The Uses and Limits of Bayesianism. Boston Studies in the Philosophy and History of Science, volume 109. Springer Netherlands.
• Tribe, L.H. (1971a). A further critique of mathematical proof. Harvard Law Review, 84, 1810-1820.
• Tribe, L.H. (1971b). Trial by mathematics: Precision and ritual in the legal process. Harvard Law Review, 84(6), 1329-1393.
• Underwood, B.D. (1977). The thumb on the scale of justice: Burdens of persuasion in criminal cases. Yale Law Journal, 86(7), 1299-1348.
• Wasserman, D.T. (1991). The morality of statistical proof and the risk of mistaken liability. Cardozo Law Review, 935(13).
• Wells, G. (1992). Naked statistical evidence of liability: Is subjective probability enough? Journal of Personality and Social Psychology, 62(5), 739-752.

Identyfikatory

DOI

10.7206/DEC.1733-0092.112