Author: Natalia Nikolova, Daniela Toneva, Snejana Ivanova, Kiril Tenekedjiev
Communicology. 2018. Vol.6. No.1
Natalia Nikolova, Australian Maritime College, University of Tasmania;
Daniela Toneva, Technical University of Varna;
Snejana Ivanova, Nikola Vaptsarov Maritime Academy;
Kiril Tenekedjiev, Nikola Vaptsarov Naval Academy. Address: V. Drumev Str., 9027 Varna, Bulgaria. Corresponding e-mail: kiril.tenekedjiev@utas.edu.au.
Abstract. The article focuses on the quantitative interpretation of trials, including its communicative inputs. Outlines some of the shortcomings of the process of identifying guilt and verdict. In the analysis of the legal solutions introduced subjective probabilities and some of the information and communication components. While the Bayesian inference is a common method for revision of beliefs, it requires precise prior probabilities and likelihoods, usually assessed in the form of intervals. Therefore this work comments upon procedures to introduce interval probabilities to statistical reasoning that support the analysis of evidence in court trials.
This work highlights the problems of judgment in legal trials and some of the communicative elements that are present here. It emphasizes the possibilities to improve the decision analysis process in trials by adopting subjective probability as a measure of uncertainty about the level of guilt of a defendant judged upon testimonies. Bayesian and other approaches can then serve to adapt beliefs. The key element of the discussion here is the introduction of interval probability estimates and the benefits they bring to legal decision making.
Keywords: legal decision making, classification system, subjective probability, interval probabilities
Text: PDF
For citation: Nikolova N., Toneva D., Ivanova S., Tenekedjiev K. Interval Probabilities in Juridical Practice and Its Communicative Inputs. Communicology (Russia). 2018. Vol. 6. No. 1. P. 192-198. DOI 10.21453 / 2311-3065-2018-6-1-192-198.
References
Coolen F. (1994) Bounds for Expected Loss in Bayesian Decision Theory with Imprecise Prior Probabilities. J. R. Stat. Soc. Vol. 43. P. 371-379.
De Finetti B. (1974). Theory of Probability. Vol. 1. John Wiley.
De Finetti B. (1975). Theory of Probability. Vol. 2. John Wiley.
French S., Insua D.R. (2000). Statistical Decision Theory. Arnold, London.
Goldman A. (1999). Knowledge in a Social World. Oxford University Press.
Goldman A. (2002). Quasi-objective Bayesianism and Legal Evidence. Jurimetrics Journal. Vol. 42. Ivanova S., Nikolova N.D., Tenekedjiev K. (2008). Court trial reasoning with interval probabilities.
Seventh International Scientific – Applied Conference “Strategic Trends in Business in 21st Century and the Quality of Higher Education. P. 381-385, 2-4 July, Varna, Bulgaria.
Pan Y., Klir G. (1997). Bayesian Inference Based on Interval-Valued Prior Distributions and Likelihoods. Journal of Intelligent and Fuzzy Systems. Vol. 5. P. 193-203.
Pratt J.W., Raiffa H., Schlaifer R. (2008). Introduction to Statistical Decision Theory. Cambridge, Massachusetts: MIT Press.
Redmayne M. (2003). Objective Probability and the Assessment of Evidence. Law, Probability and Risk. Vol. 2. P. 275-294.
Snow P. (1991). Improved Posterior Probability Estimates from Prior and Conditional Linear Constraint Systems. IEEE Trans. Systems, Man and Cybernetics. Vol. 21. P. 464-469.
Tenekedjiev K., Nikolova N.D., Toneva D. (2006). Laplace expected utility criterion for ranking fuzzy rational generalized lotteries of I type. Cybernetics and Information Technologies. No. 6 (3). P. 93-109.
Tenekedjiev K., Nikolova N.D. (2007). Decision Theory – Subjectivity, Reality and Fuzzy Rationality. CIELA, Sofia, Bulgaria (In Bulgarian).
Utkin L.V. (2007). Risk analysis under partial prior information and non-monotone utility functions. International Journal of Information Technology and Decision Making. Vol. 6. P. 625-647.
Walley P. (1991). Statistical Reasoning with Imprecise Probabilities. London: Chapman&Hall. White C. (1986). A Posterior Representation based on Linear Inequality Descriptions of A Priori
and Conditional Probabilities. IEEE Trans. Systems, Man and Cybernetics. Vol. 16. P. 570-573.
Communicology. 2018. Vol.6. No.1
Natalia Nikolova, Australian Maritime College, University of Tasmania;
Daniela Toneva, Technical University of Varna;
Snejana Ivanova, Nikola Vaptsarov Maritime Academy;
Kiril Tenekedjiev, Nikola Vaptsarov Naval Academy. Address: V. Drumev Str., 9027 Varna, Bulgaria. Corresponding e-mail: kiril.tenekedjiev@utas.edu.au.
Abstract. The article focuses on the quantitative interpretation of trials, including its communicative inputs. Outlines some of the shortcomings of the process of identifying guilt and verdict. In the analysis of the legal solutions introduced subjective probabilities and some of the information and communication components. While the Bayesian inference is a common method for revision of beliefs, it requires precise prior probabilities and likelihoods, usually assessed in the form of intervals. Therefore this work comments upon procedures to introduce interval probabilities to statistical reasoning that support the analysis of evidence in court trials.
This work highlights the problems of judgment in legal trials and some of the communicative elements that are present here. It emphasizes the possibilities to improve the decision analysis process in trials by adopting subjective probability as a measure of uncertainty about the level of guilt of a defendant judged upon testimonies. Bayesian and other approaches can then serve to adapt beliefs. The key element of the discussion here is the introduction of interval probability estimates and the benefits they bring to legal decision making.
Keywords: legal decision making, classification system, subjective probability, interval probabilities
Text: PDF
For citation: Nikolova N., Toneva D., Ivanova S., Tenekedjiev K. Interval Probabilities in Juridical Practice and Its Communicative Inputs. Communicology (Russia). 2018. Vol. 6. No. 1. P. 192-198. DOI 10.21453 / 2311-3065-2018-6-1-192-198.
References
Coolen F. (1994) Bounds for Expected Loss in Bayesian Decision Theory with Imprecise Prior Probabilities. J. R. Stat. Soc. Vol. 43. P. 371-379.
De Finetti B. (1974). Theory of Probability. Vol. 1. John Wiley.
De Finetti B. (1975). Theory of Probability. Vol. 2. John Wiley.
French S., Insua D.R. (2000). Statistical Decision Theory. Arnold, London.
Goldman A. (1999). Knowledge in a Social World. Oxford University Press.
Goldman A. (2002). Quasi-objective Bayesianism and Legal Evidence. Jurimetrics Journal. Vol. 42. Ivanova S., Nikolova N.D., Tenekedjiev K. (2008). Court trial reasoning with interval probabilities.
Seventh International Scientific – Applied Conference “Strategic Trends in Business in 21st Century and the Quality of Higher Education. P. 381-385, 2-4 July, Varna, Bulgaria.
Pan Y., Klir G. (1997). Bayesian Inference Based on Interval-Valued Prior Distributions and Likelihoods. Journal of Intelligent and Fuzzy Systems. Vol. 5. P. 193-203.
Pratt J.W., Raiffa H., Schlaifer R. (2008). Introduction to Statistical Decision Theory. Cambridge, Massachusetts: MIT Press.
Redmayne M. (2003). Objective Probability and the Assessment of Evidence. Law, Probability and Risk. Vol. 2. P. 275-294.
Snow P. (1991). Improved Posterior Probability Estimates from Prior and Conditional Linear Constraint Systems. IEEE Trans. Systems, Man and Cybernetics. Vol. 21. P. 464-469.
Tenekedjiev K., Nikolova N.D., Toneva D. (2006). Laplace expected utility criterion for ranking fuzzy rational generalized lotteries of I type. Cybernetics and Information Technologies. No. 6 (3). P. 93-109.
Tenekedjiev K., Nikolova N.D. (2007). Decision Theory – Subjectivity, Reality and Fuzzy Rationality. CIELA, Sofia, Bulgaria (In Bulgarian).
Utkin L.V. (2007). Risk analysis under partial prior information and non-monotone utility functions. International Journal of Information Technology and Decision Making. Vol. 6. P. 625-647.
Walley P. (1991). Statistical Reasoning with Imprecise Probabilities. London: Chapman&Hall. White C. (1986). A Posterior Representation based on Linear Inequality Descriptions of A Priori
and Conditional Probabilities. IEEE Trans. Systems, Man and Cybernetics. Vol. 16. P. 570-573.
