Abstract
When a suspect appears in front of a criminal court, there is a high probability that he will be found guilty. In the USA, statistics for recent years show that the conviction rate in federal courts is roughly 90%, and in Japan reaches as high a rate as 99%.
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- 1.
On the conviction rate in US federal courts, see the statistical reports of the Offices of the United States Attorneys, available at www.justice.gov/usao/resources/annual-statistical-reports. Most of these convictions are guilty pleas, not convictions after trial. On Japan’s conviction rate, see White Paper on Crime 2014, Part 2, Chap. 3, Sect. 1, available at http://hakusyo1.moj.go.jp/en/63/nfm/mokuji.html.
- 2.
On the UK conviction rate, see Criminal Justice Statistics–March 2014, available at www.gov.uk/government/statistics. As in the US case, the rate include mostly guilty pleas. For the Netherlands, see CBS, the Dutch central bureau of statistics, publishing its data at www.cbs.nl.
- 3.
- 4.
- 5.
We use the terms “factually guilty” or simply “guilty” to express factual guilt, which is not the same as the legal verdict of guilt.
- 6.
At a rate of a dozen or more twin births per 1000 live births, identical twins are not that rare. Source https://en.wikipedia.org/wiki/Twin#Statistics.
- 7.
Bayes’ theorem can be derived using the definition of conditional probability. We have \(\Pr (E|H) = \Pr (H\wedge E)/\Pr (H)\). Here, we use logical conjunction \(\wedge \) to write the combined event H and E. Since \(\Pr (H\wedge E)=\Pr (E|H)\cdot \Pr (H)\), it follows that \(\Pr (H|E) = \Pr (H \wedge E)/\Pr (E) = \Pr (E|H)\cdot \Pr (H)/\Pr (E)\), proving Bayes’ theorem. Note that the theorem holds generally for probability functions and does not assume a temporal ordering of taking evidence into account, as instead suggested by the terminology of “prior” and “posterior” probability. This terminology is standard in the context of Bayesian updating.
- 8.
To see why, note that
$$\begin{aligned} \frac{\Pr (H|E)}{\Pr (\lnot H | E)} = \frac{\Pr (E | H)}{\Pr (E| \lnot H)}\cdot \frac{\Pr (H)}{\Pr (\lnot H)}, \end{aligned}$$which implies
$$\begin{aligned} \frac{\Pr (E|H)}{\Pr (E|\lnot H)}>1 \text { iff } \frac{\Pr (H|E)}{\Pr (\lnot H | E)} > \frac{\Pr (H)}{\Pr (\lnot H)}. \end{aligned}$$To prove the left-right direction of the equivalence in the text, if \(\Pr (H|E)> P(H)\), then \(1- \Pr (H|E)< 1- \Pr (H)\). This means that \(\frac{\Pr (H|E)}{1- \Pr (H | E)} > \frac{\Pr (H)}{1- \Pr (H)}\), and thus \(\frac{\Pr (H|E)}{\Pr (\lnot H | E)} > \frac{\Pr (H)}{\Pr (\lnot H)}\). So, by the equivalence above, \(\frac{\Pr (E|H)}{\Pr (E|\lnot H)}>1\). For the other direction, if \(\frac{\Pr (E|H)}{\Pr (E|\lnot H)}>1\), then \(\frac{\Pr (H|E)}{\Pr (\lnot H | E)} > \frac{\Pr (H)}{\Pr (\lnot H)}\), again by the equivalence above. The latter is the same as \(\frac{\Pr (H|E)}{1- \Pr (H | E)} > \frac{\Pr (H)}{1- \Pr (H)}\). To establish \(\Pr (H|E)> \Pr (H)\), suppose for contradiction that \(\Pr (H|E) \le \Pr (H)\), which implies \(1- \Pr (H|E) \ge 1- \Pr (H)\). This means that \(\frac{\Pr (H|E)}{1- \Pr (H | E)} \le \frac{\Pr (H)}{1- \Pr (H)}\). This contradicts \(\frac{\Pr (H|E)}{1- \Pr (H | E)} > \frac{\Pr (H)}{1- \Pr (H)}\), and thus \(\Pr (H|E)> \Pr (H)\).
- 9.
To derive the likelihood ratio formula, one first applies Bayes’ theorem to both H and \(\lnot H\). We get \(\Pr (H|E) = \Pr (E|H)\cdot \Pr (H)/\Pr (E)\) and \(\Pr (\lnot H|E) = \Pr (E|\lnot H)\cdot \Pr (\lnot H)/\Pr (E)\). Using these, we find:
$$\begin{aligned} \frac{\Pr (H|E)}{\Pr (\lnot H|E)} = \frac{\Pr (E|H)\cdot \Pr (H)/\Pr (E)}{\Pr (E|\lnot H)\cdot \Pr (\lnot H)/\Pr (E)} = \frac{\Pr (E|H)\cdot \Pr (H)}{\Pr (E|\lnot H)\cdot \Pr (\lnot H)}, \end{aligned}$$proving the likelihood ratio formula.
- 10.
\(\Pr (H|E)=\frac{\Pr (H|E)}{\Pr (H|E)+\Pr (\lnot H|E)}=\frac{\frac{\Pr (H|E)}{\Pr (\lnot H|E)}}{\frac{\Pr (H|E)+\Pr (\lnot H|E)}{\Pr (\lnot H|E)}}=\frac{\frac{\Pr (H|E)}{\Pr (\lnot H|E)}}{\frac{\Pr (H|E)}{\Pr (\lnot H|E)}+1}\).
- 11.
Since \(\frac{\Pr (\text {Guilt})}{\Pr (\lnot \text {Guilt})}=\frac{0.001}{0.999}\) and \(\frac{\Pr (\text {Camera} \vert \text {Guilt})}{\Pr (\text {Camera} \vert \lnot \text {Guilt})}=70\), by Bayes’ theorem, \(\frac{\Pr (\text {Guilt} \vert \text {Camera})}{\Pr (\lnot \text {Guilt} \vert \text {Camera})}=\frac{0.001}{0.999}\times 70 \approx 0.07\) and thus \(\Pr \)(Guilt\(\vert \)Camera)\( \approx \frac{0.07}{1+0.07}~\sim ~6.5\%\).
References
Allen, R.J. 2010. No plausible alternative to a plausible story of guilt as the rule of decision in criminal cases. In Prueba y Esandares de Prueba en el Derecho, ed. J. Cruz, and L. Laudan. Mexico: Instituto de Investigaciones Filosoficas-UNAM.
Allen, R.J., and M.S. Pardo. 2007. The problematic value of mathematical models of evidence. Journal of Legal Studies 36 (1): 107–140.
Allen, R.J., and A. Stein. 2013. Evidence, probability and the burden of proof. Arizona Law Journal 55: 557–602.
Allen, R.J., W.J. Stuntz, J.L. Hoffmann, D.A. Livingston, A.D. Leipold, and T.L. Meares. 2016. Comprehensive criminal procedure, 3rd ed. New York, N.Y.: Wolters Kluwer.
Anderson, T., D. Schum, and W. Twining. 2005. Analysis of Evidence, 2nd ed. Cambridge: Cambridge University Press.
Balding, D.J. 1999. When can a DNA profile be regarded as unique? Science & Justice 39.
Balding, D.J. 2005. Weight-of-evidence for forensic DNA profiles. West Sussex: Wiley.
Balding, D.J., and P. Donnely. 1996. Evaluating DNA profile evidence when the suspect is identified through a database search. Journal of Forensic Science 41: 603–607.
Bennett, W.L., and M.S. Feldman. 1981. Reconstructing reality in the courtroom. London: Tavistock Feldman.
Bernoulli, J. 1713. Ars Conjectandi.
Bex, F.J. 2011. Arguments, stories and criminal evidence: A formal hybrid theory. Berlin: Springer.
Bex, F.J., P.J. van Koppen, H. Prakken, and B. Verheij. 2010. A hybrid formal theory of arguments, stories and criminal evidence. Artificial Intelligence and Law 18: 1–30.
Bex, F.J., and B. Verheij. 2013. Legal stories and the process of proof. Artificial Intelligence and Law 21 (3): 253–278.
Biedermann, A., T. Hicks, F. Taroni, C. Champod, C. Aitken. On the use of the likelihood ratio for forensic evaluation: Response to Fenton, et al. 2014. Science and Justice 54 (4): 316–318.
Bondarenko, A., P.M. Dung, R.A. Kowalski, and F. Toni. 1997. An abstract, argumentation-theoretic approach to default reasoning. Artificial Intelligence 93: 63–101.
BonJour, L. 1985. The structure of empirical knowledge. Cambridge, MA: Harvard University Press.
Bovens, L., and S. Hartmann. 2003a. Bayesian Epistemology. Oxford: Oxford University Press.
Bovens, L., and S. Hartmann. 2003b. Solving the riddle of coherence. Mind 112: 601–633.
Carnap, R. 1950. Logical foundations of probability. Chicago, IL: University of Chicago Press.
Cheng, E. 2013. Reconceptualizing the burden of proof. Yale Law Journal 122 (5): 1104–1371.
Cohen, L.J. 1977. The probable and the provable. Oxford: Clarendon Press.
Cook, R., I.W. Evett, G. Jackson, P.J. Jones, and J.A. Lambert. 1998. A hierarchy of propositions: Deciding which level to address in casework. Science and Justice 38 (4): 231–239.
Crump, D. 2009. Eyewitness corroboration requirements as protections against wrongful conviction: The hidden questions. Ohio State Journal of Criminal Law 7 (1): 361–376.
Crupi, V. 2015. Confirmation. In Stanford encyclopedia of philosophy, ed. E.N. Zalta. Stanford University.
Darwiche, A. 2009. Modeling and reasoning with bayesian networks. Cambridge: Cambridge University Press.
Dawid, A.P. 1987. The difficulty about conjunction. Journal of the Royal Statistical Society. Series D (The Statistician) 36(2/3):91–92.
Dawid, A.P. 2002. Bayes’s theorem and weighing evidence by juries. In Bayes’s Theorem, vol. 113, 71–90, Oxford: Oxford University Press.
Dawid, A.P. 2010. Beware of the DAG! In JMLR workshop and conference proceedings: Volume 6. Causality: Objectives and assessment (NIPS 2008 workshop), eds. I. Guyon, D. Janzing, and B. Schölkopf, 59–86. http://www.jmlr.org/
Dawid, A.P., W. Twining, and M. Vasiliki (eds.). 2011. Evidence, inference and enquiry. Oxford: Oxford University Press.
Dung, P.M. 1995. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77: 321–357.
Evett, I., G. Jackson, J.A. Lambert, and S. McCrossan. 2000. The impact of the principles of evidence interpretation on the structure and content of statements. Science and Justice 40 (4): 233–239.
Fenton, N., D. Berger, D. Lagnado, M. Neil, and A. Hsu. 2014. When “neutral” evidence still has probative value (with implications from the barry george case). Science and Justice 54 (4): 274–287.
Fenton, N., M. Neil, and D. Berger. 2016. Bayes and the law. Annual Review of Statistics and Its Application 3.
Fenton, N.E. 2011. Science and law: Improve statistics in court. Nature 479: 36–37.
Fenton, N.E., and M.D. Neil. 2013. Risk assessment and decision analysis with Bayesian networks. Boca Raton, FL: CRC Press.
Fenton, N.E., M.D. Neil, and D.A. Lagnado. 2013. A general structure for legal arguments about evidence using Bayesian Networks. Cognitive Science 37: 61–102.
Finkelstein, M.O., and W.B. Fairley. 1970. A Bayesian approach to identification evidence. Harvard Law Review 83: 489–517.
Finkelstein, M.O., and B. Levin. 2001. Statistics for lawyers. Berlin: Springer.
Fisher, G. 2008. Evidence, 2nd ed. New York, N.Y.: Foundation Press.
Fitelson, B. 1999. The plurality of Bayesian measures of confirmation and the problem of measure sensitivity. Philosophy of Science 66: 362–378.
Freeman, J.B. 1991. Dialectics and the macrostructure of arguments. A theory of argument structure. Berlin: Foris.
Friedman, M. 1974. Explanation and scientific understanding. Journal of Philosophy 71: 5–19.
Friedman, R.D. 1987. Route analysis of credibility and hearsay. The Yale Law Journal 97 (4): 667–742.
Friedman, R.D. 2000. A presumption of innocence, not of even odds. Stanford Law Review 52: 873–887.
Frumkin, D., A. Wasserstrom, A. Davidson, and A. Grafit. 2009. Authentication of forensic DNA samples. Forensic Science International: Genetics 4 (2): 95–103.
Gabbay, D.M., C.J. Hogger, and J.A. Robinson (eds.). 1994. Handbook of logic in artificial intelligence and logic programming. Volume 3. Nonmonotonic reasoning and uncertain reasoning. Oxford: Clarendon Press.
Gastwirth, J.L. (ed.). 2012. Statistical Science in the Courtroom. Berlin: Springer.
Gordon, T.F., H. Prakken, and D.N. Walton. 2007. The Carneades model of argument and burden of proof. Artificial Intelligence 171 (10–15): 875–896.
Gordon, T.F., and D.N. Walton. 2009. Proof burdens and standards. In Argumentation in artificial intelligence, ed. I. Rahwan, and G.R. Simari, 239–258. Berlin: Springer.
Griffin, L.K. 2013. Narrative, truth, trial. Georgetown Law Journal 101: 281–335.
Haack, S. 2008. Warrant, causation, and the atomist of evidence law. Journal of Social Epistemology 5: 253–265.
Haack, S. 2014. Legal probabilism: An epistemological dissent. In Science, proof, and truth in the law, ed. Evidence Matters, 47–77. Cambridge: Cambridge University Press.
Hacking, I. 2001. An introduction to probability and inductive logic. Cambridge: Cambridge University Press.
Hage, J.C. 1997. Reasoning with rules. An essay on legal reasoning and its underlying logic. Dordrecht: Kluwer.
Hage, J.C. 2000. Dialectical models in artificial intelligence and law. Artificial Intelligence and Law 8: 137–172.
Hamer, D. 2004. Probabilistic standards of proof, their complements and the errors that are expected to flow from them. University of New England Law Journal 1 (1): 71–107.
Hempel, C., and P. Oppenheim. 1948. Studies in the logic of explanation. Philosophy of Science 15: 135–175.
Hepler, A.B., A.P. Dawid, and V. Leucari. 2007. Object-oriented graphical representations of complex patterns of evidence. Law, Probability and Risk 6 (1–4): 275–293.
Hicks, T., J. Buckleton, J.-A. Bright, and D. Taylor. 2016. A framework for interpreting evidence. In Forensic DNA Evidence Interpretation (second edition), ed. J. Buckleton, J.-A. Bright, and D. Taylor. Boca Raton, FL: CRC Press.
Ho, H.L. 2008. Philosophy of evidence law. Oxford: Oxford University Press.
Jensen, F.V., and T.D. Nielsen. 2007. Bayesian networks and decision graphs. Berlin: Springer.
Kadane, J.B., and D.A. Schum. 1996. A probabilistic analysis of the Sacco and Vanzetti evidence. Chichester: Wiley.
Kaplan, J. 1968. Decision theory and the fact-finding process. Stanford Law Review 20: 1065–1092.
Kaplow, L. 2012. Burden of proof. Yale Law Journal 121 (4): 738–1013.
Kaptein, H., H. Prakken, and B. Verheij (eds.). 2009. Legal evidence and proof: statistics, stories, logic (Applied legal philosophy series). Farnham: Ashgate.
Kaye, D.H. 1986. Do we need a calculus of weight to understand proof beyond a reasonable doubt? Boston University Law Review 66: 657–672.
Kaye, D.H. 1993. DNA evidence: Probability, population genetics and the courts. Harvard Journal of Law and Technology 7: 101–172.
Kaye, D.H. 1999. Clarifying the burden of persuasion: What Bayesian rules do and not do. International Commentary on Evidence 3: 1–28.
Kaye, D.H. 2010. The double helix and the law of evidence. Cambridge, Mass.: Harvard University Press.
Kaye, D.H. 2013. Beyond uniqueness: the birthday paradox, source attribution and individualization in forensic science. Law, Probability and Risk 12 (1): 3–11.
Kaye, D.H., and G.F. Sensabaugh. 2000. Reference guide on DNA evidence. In Reference manual on scientific evidence, 2nd ed., 576–585. Washington, D.C.: Federal Judicial Center.
Keppens, J. 2012. Argument diagram extraction from evidential Bayesian networks. Artificial Intelligence and Law 20: 109–143.
Keppens, J., and B. Schafer. 2006. Knowledge based crime scenario modelling. Expert Systems with Applications 30 (2): 203–222.
Kirschner, P.A., S.J.B. Shum, and C.S. Carr. 2003. Visualizing argumentation: Software tools for collaborative and educational sense-making. Berlin: Springer.
Koehler, J.J. 1993. Error and exaggeration in the presentation of DNA evidence in trial. Jurimetrics Journal 34: 21–39.
Koehler, J.J., and M.J. Saks. 2010. Individualization claims in forensic science: Still unwarranted. Brooklyn Law Review 75 (4): 1187–1208.
Laplace, P.-S. 1814. Essai Philosophique sur les Probabilités.
Laudan, L. 2006. Truth, error, and criminal law: An essay in legal epistemology. Cambridge: Cambridge University Press.
Lempert, R.O. 1977. Modeling relevance. Michigan Law Review 75 (5/6): 1021–1057.
Lipton, P. 1991. Inference to the best explanation. New York, N.Y.: Routledge.
Loftus, E.F. 1996. Eyewitness testimony (revised edition). Cambridge, MA: Harvard University Press.
Méndez, M.A. 2008. Evidence: The California code and the Federal rules, 4th ed. Eagan, MN: Thomson West.
Mortera, J., and P. Dawid. 2007. Probability and evidence. In Handbook of probability theory, ed. T. Rudas. Los Angeles, CA: Sage.
Nance, D.A. 2016. The burdens of proof: Discriminatory power, weight of evidence, and tenacity of belief. Cambridge: Cambridge University Press.
Nesson, C.R. 1979. Reasonable doubt and permissive inferences: The value of complexity. Harvard Law Review 92 (6): 1187–1225.
NRC. 1996. The evaluation of forensic DNA evidence. Washington, D.C.: National Academy Press.
Pardo, M.S., and R.J. Allen. 2008. Juridical proof and the best explanation. Law and Philosophy 27: 223–268.
Pearl, J. 1988. Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann.
Pearl, J. 2000/2009. Causality: Models, reasoning and inference, 2nd ed. Cambridge: Cambridge University Press.
Pennington, N., and R. Hastie. 1993a. Inside the juror, chap. The story model for juror decision making, 192–221. Cambridge: Cambridge University Press.
Pennington, N., and R. Hastie. 1993b. Reasoning in explanation-based decision making. Cognition 49 (1–2): 123–163.
Picinali, F. 2013. Two meanings of “reasonableness”: Dispelling the “floating” reasonable doubt. Modern Law Review 76 (5): 845–875.
Pollock, J.L. 1987. Defeasible reasoning. Cognitive Science 11 (4): 481–518.
Pollock, J.L. 1995. Cognitive Carpentry: A blueprint for how to build a person. Cambridge, MA: The MIT Press.
Prakken, H. 1997. Logical tools for modelling legal argument. A study of defeasible reasoning in law. Dordrecht: Kluwer.
Prakken, H. 2005. A study of accrual of arguments, with applications to evidential reasoning. In Proceedings of the tenth international conference on artificial intelligence and law, 85–94, New York (New York): ACM Press.
Prakken, H. 2010. An abstract framework for argumentation with structured arguments. Argument and Computation 1 (2): 93–124.
Prakken, H., and G. Sartor. 2007. Formalising arguments about the burden of persuasion. In Proceedings of the 11th international conference on artificial intelligence and law, 97–106, New York, N.Y.: ACM Press.
Prakken, H., and G. Sartor. 2009. A logical analysis of burdens of proof. In Legal evidence and proof: Statistics, stories, logic, chap. 9, ed. H. Kaptein, H. Prakken, and B. Verheij, 223–253, Farnham: Ashgate.
Prakken, H., and G.A.W. Vreeswijk. 2002. Logics for defeasible argumentation. In Handbook of philosophical logic, vol. 4, 2nd ed. D.M. Gabbay, and F. Guenthner, 218–319. Dordrecht: Kluwer Academic Publishers.
Redmayne, M. 2015. Character evidence in the criminal trial. Oxford: Oxford University Press.
Reed, C., and G. Rowe. 2004. Araucaria: Software for argument analysis, diagramming and representation. International Journal of AI Tools 14 (3–4): 961–980.
Robertson, B., and G.A. Vignaux. 1995. DNA evidence: Wrong answers or wrong questions? Genetica 96: 145–152.
Roberts, P., and A. Zuckerman. 2010. Criminal evidence, 2nd ed. Oxford: Oxford University Press.
Salmon, W. 1984. Scientific explanation and the causal structure of the world. Princeton, N.J.: Princeton University Press.
Schank, R., and R. Abelson. 1977. Scripts, plans, goals and understanding, an inquiry into human knowledge structures. Hillsdale: Lawrence Erlbaum.
Schneps, L., and C. Colmez. 2013. Math on trial: How numbers get used and abused in the courtroom. New York, N.Y.: Basic Books.
Schum, D.A. 1994. The evidential foundations of probabilistic reasoning. New York, N.Y.: Wiley.
Schum, D.A., and S. Starace. 2001. The evidential foundations of probabilistic reasoning. Evanston, Il.: Northwestern University Press.
Shapiro, B. 1991. Beyond reasonable doubt and probable cause: Historical perspectives on the Anglo-American law of evidence. Oakland, Calif.: University of California Press.
Simari, G.R., and R.P. Loui. 1992. A mathematical treatment of defeasible reasoning and its applications. Artificial Intelligence 53: 125–157.
Simons, D.J., and C.F. Chabris. 1999. Gorillas in our minds: Sustained inattention blindness for dynamic events. Perception 28: 1059–1074.
Skyrms, B. 2000. Choice and chance: An introduction to inductive logic, 4th ed. Belmont, CA: Wadsworth.
Stein, A. 2005. Foundations of evidence law. Oxford: Oxford University Press.
Swinburne, R. (ed.). 2002. Bayes’s theorem. Oxford: Oxford University Press.
Tanaka, J.W., and M.J. Farah. 1993. Parts and whole in face recognition. The Quarterly Journal of Experimental Psychology 46A (3): 225–245.
Taroni, F., A. Biedermann, S. Bozza, P. Garbolino, and C. Aitken. 2014. Statistics in practice. In Bayesian networks for probabilistic inference and decision analysis in forensic science, 2nd ed. Chichester: Wiley.
Taroni, F., C. Champod, and P. Margot. 1998. Forerunners of Bayesianism in early forensic science. Jurimetrics 38: 183–200.
Thagard, P. 1989. Explanatory coherence. Behavioral and Brain Sciences 12: 435–502.
Thagard, P. 2001. Coherence in thought and action. Cambridge, MA: The MIT Press.
Thompson, S.G. 2008. Beyond a reasonable doubt? reconsidering uncorroborated eyewitness identification testimony. UC Davis Law Review 41: 1487–1545.
Thompson, W.C., and E.L. Schumann. 1987. Interpretation of statistical evidence in criminal trials: The prosecutor’s fallacy and the defense attorney’s fallacy. Law and Human Behavior 11: 167–187.
Thompson, W.C., F. Taroni, and C.G.G. Aitken. 2003. How the probability of a false positive affects the value of DNA evidence. Journal of Forensic Science 48: 47–54.
Thomson, J.J. 1986. Liability and individualized evidence. Law and Contemporary Problems 49 (3): 199–219.
Thomson, P. 1980. Margaret Thatcher: A new illusion. Perception 9 (4): 483–484.
Tillers, P. 2011. Trial by mathematics-reconsidered. Law, Probability and Risk 10: 167–173.
Timmer, S.T., J.J. Meyer, H. Prakken, S. Renooij, and B. Verheij. 2017. A two-phase method for extracting explanatory arguments from Bayesian networks. International Journal of Approximate Reasoning 80: 475–494.
Toulmin, S.E. 1958. The uses of argument. Cambridge: Cambridge University Press.
Tribe, L. 1971. Trial by mathematics: Precision and ritual in the legal process. Harvard Law Review 84: 1329–1393.
van Eemeren, F.H., B. Garssen, E.C.W. Krabbe, A.F. Snoeck Henkemans, B. Verheij, and J.H.M. Wagemans. 2014a. Chapter 11: Argumentation in artificial intelligence. In Handbook of argumentation theory. Berlin: Springer.
van Eemeren, F.H., B. Garssen, E.C.W. Krabbe, A.F. Snoeck Henkemans, B. Verheij, and J.H.M. Wagemans. 2014b. Handbook of argumentation theory. Berlin: Springer.
van Gelder, T. 2003. Enhancing deliberation through computer supported argument visualization. In Visualizing argumentation: Software tools for collaborative and educational sense-making, ed. P.A. Kirschner, S.J.B. Shum, and C.S. Carr, 97–115. New York, N.Y.: Springer.
Velleman, D. 2003. Narrative explanation. The Philosophical Review 112 (1): 1–25.
Verheij, B. 1996. Rules, reasons, arguments. Formal studies of argumentation and defeat.. Maastricht: Dissertation Universiteit Maastricht.
Verheij, B. 2003. DefLog: on the logical interpretation of prima facie justified assumptions. Journal of Logic and Computation 13 (3): 319–346.
Verheij, B. 2005. Virtual arguments. On the design of argument assistants for lawyers and other arguers. The Hague: T.M.C. Asser Press.
Verheij, B. 2014. To catch a thief with and without numbers: Arguments, scenarios and probabilities in evidential reasoning. Law, Probability and Risk 13: 307–325.
Verheij, B. 2017. Proof with and without probabilities. correct evidential reasoning with presumptive arguments, coherent hypotheses and degrees of uncertainty. Artifical Intelligence and Law 25(1):127–154.
Verheij, B., F.J. Bex, S.T. Timmer, C.S. Vlek, J.J. Meyer, S. Renooij, and H. Prakken. 2016. Arguments, scenarios and probabilities: Connections between three normative frameworks for evidential reasoning. Law, Probability and Risk 15 (1): 35–70.
Vlek, C.S., H. Prakken, S. Renooij, and B. Verheij. 2014. Building Bayesian Networks for legal evidence with narratives: a case study evaluation. Artifical Intelligence and Law 22 (4): 375–421.
Vlek, C.S., H. Prakken, S. Renooij, and B. Verheij. 2016. A method for explaining Bayesian Networks for legal evidence with scenarios. Artifical Intelligence and Law 24 (3): 285–324.
Vreeswijk, G.A.W. 1997. Abstract argumentation systems. Artificial Intelligence 90: 225–279.
Vrij, A. 2008. Detecting lies and deceit: The psychology of lying and the implications for professional practice. Chichester: Wiley.
Wagenaar, W.A., P.J. van Koppen, and H.F.M. Crombag. 1993. Anchored narratives: The psychology of criminal evidence. London: Harvester Wheatsheaf.
Walton, D.N., and E. Krabbe. 1995. Commitment in dialogue. Basic concepts of interpersonal reasoning. Albany (New York): State University of New York Press.
Walton, D.N., C. Reed, and F. Macagno. 2008. Argumentation schemes. Cambridge: Cambridge University Press.
Wasserman, D. 2008. Forensic DNA typing. In A companion to genethics, ed. J. Burley, and J. Harris. Malden, MA: Blackwell.
Weir, B.S. 2007. The rarity of DNA profiles. The Annals of Applied Statistics 1: 358–370.
Wells, G.L. 1992. Naked statistical evidence of liability: Is subjective probability enough? Journal of Personality and Social Psychology 62: 793–752.
Wells, G.L., A. Memon, and S.D. Penrod. 2006. Eyewitness evidence: Improving its probative value. Psychological Science in the Public Interest 7 (2): 45–75.
Whitman, J.Q. 2008. The origins of reasonable doubt: Theological roots of the criminal trial. New Haven, CT: Yale University Press.
Wigmore, J.H. 1913. The principles of judicial proof as given by logic, psychology, and general experience, and illustrated in judicial trials. Second edition 1931, third edition “The science of judicial proof” 1937. Boston, MA: Little, Brown and Company.
Woodward, J. 2014. Scientific explanation. In The Stanford encyclopedia of philosophy, ed. E.N. Zalta. Stanford University.
Zabell, S.L. 2005. Fingerprint evidence. Journal of Law and Policy 13: 143–179.
Acknowledgements
This chapter has been developed in the context of the project “Designing and Understanding Forensic Bayesian Networks with Arguments and Scenarios,” funded in the NWO Forensic Science program (http://www.ai.rug.nl/~verheij/nwofs/). The first author would like to thank Infosys Limited which made possible his stay at the Institute for Advanced Study in Princeton for the academic year 2016–17 during which parts of this chapter were written. The second author would like to thank the Isaac Newton Institute for Mathematical Sciences at the University of Cambridge for its hospitality during the program “Probability and Statistics in Forensic Science” which was supported by EPSRC Grant Number EP/K032208/1. The authors would like to thank Ronald Allen, Alex Biedermann, Christian Dahlman, and Norman Fenton for helpful comments and suggestions on an earlier draft.
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Di Bello, M., Verheij, B. (2018). Evidential Reasoning. In: Bongiovanni, G., Postema, G., Rotolo, A., Sartor, G., Valentini, C., Walton, D. (eds) Handbook of Legal Reasoning and Argumentation. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9452-0_16
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