Parole Board Decision-Making using Adversarial Risk Analysis

References

• Banks, D. L., Rios, J., and Ríos Insua, D. (2015), Adversarial Risk Analysis, Boca Raton, FL: CRC Press.
   Google Scholar
• Brown, G., Carlyle, M., Salmerón, J., and Wood, K. (2006), “Defending Critical Infrastructure,” Interfaces, 36, 530–544. DOI: 10.1287/inte.1060.0252.
   Web of Science ®Google Scholar
• Buglar, S. (2016), “The ‘Focal Concerns’ of Parole Board Decision-Making: A Thematic Analysis,” Current Issues in Criminal Justice, 27, 285–302. DOI: 10.1080/10345329.2016.12036047.
   Web of Science ®Google Scholar
• Canada, P. (2019), Decision-Making Policy Manual for Board Members (2nd ed.), Parole Board of Canada.
   Google Scholar
• Cano, J., Pollini, A., Falciani, L., and Turhan, U. (2016a), “Modeling Current and Emerging Threats in the Airport Domain through Adversarial Risk Analysis,” Journal of Risk Research, 19, 894–912. DOI: 10.1080/13669877.2015.1057201.
   Web of Science ®Google Scholar
• Cano, J., Ríos Insua, D., Tedeschi, A., and Turhan, U. (2016b), “Security Economics: An Adversarial Risk Analysis Aproach to Airport Protection,” Annals of Operations Research, 245, 359–378. DOI: 10.1007/s10479-014-1690-7.
   Web of Science ®Google Scholar
• Caplan, J. (2007), “What Factors Affect Parole: A Review of Empirical Research,” Federal Probation, 71, 16–19.
   Google Scholar
• Ejaz, M., Joe, S., and Joshi, C. (2021), “Adversarial Risk Analysis for Auctions Using Mirror Equilibrium and Bayes Nash Equilibrium,” Decision Analysis, 18, 185–202. DOI: 10.1287/deca.2021.0425.
   Web of Science ®Google Scholar
• Ejaz, M., Joshi, C., and Joe, S. (2021), “Adversarial Risk Analysis for First-Price Sealed-Bid Auctions,” Australian and New Zealand Journal of Statistics, 63, 357–376. DOI: 10.1111/anzs.12315.
   Web of Science ®Google Scholar
• ———(2022), “Adversarial Risk Analysis for Auctions Using Non-strategic Play and Level-k Thinking: A General Case of n Bidders with Regret,” Communications in Statistics - Theory and Methods, 52, 7146–7164. DOI: 10.1080/03610926.2022.2042023.
   Web of Science ®Google Scholar
• Esteban, P. G., and Ríos Insua, D. (2014), “Supporting an Autonomous Social Agent Within a Competitive Environment,” Cybernetics and Systems, 45, 241–253. DOI: 10.1080/01969722.2014.894852.
   Web of Science ®Google Scholar
• Factsheet. (2018), “Percentage of Sentences Served,” Ministry of Justice, New Zealand Government. Available at https://www.justice.govt.nz/assets/Factsheet-Percentage-of-sentence-served.pdf.
   Google Scholar
• Falconer, J. R., Frank, E., Polaschek, D. L. L., and Joshi, C. (2022), “Methods for Eliciting Informative Prior Distributions: A Critical Review,” Decision Analysis, 19, 189–204. DOI: 10.1287/deca.2022.0451.
   Google Scholar
• ———(2023), “On Eliciting Expert Prior Distributions by Modelling Past Decision-Making,” Decision Analysis. DOI: 10.1287/deca.2023.0046.
   Google Scholar
• Gil, C., Ríos Insua, D., and Rios, J. (2016), “Adversarial Risk Analysis for Urban Security Resource Allocation,” Risk Analysis, 36, 727–741. DOI: 10.1111/risa.12580.
   PubMed Web of Science ®Google Scholar
• Gobeil, R., and Serin, R. C. (2009), “Preliminary Evidence of Adaptive Decision Making Techniques Used by Parole Board Members,” International Journal of Forensic Mental Health, 8, 97–104. DOI: 10.1080/14999010903199258.
   Google Scholar
• ———(2010), Parole Decision Making, Cambridge Handbooks in Psychology, pp. 251–258, Cambridge: Cambridge University Press. DOI: 10.1017/CBO9780511730290.032.
   Google Scholar
• González-Ortega, J., Radovic, V., and Ríos Insua, D. (2018), Utility Elicitation, Cham: Springer.
   Google Scholar
• González-Ortega, J., Insua, D. R., and Cano, J. (2019), “Adversarial Risk Analysis for Bi-agent Influence Diagrams: An Algorithmic Approach,” European Journal of Operational Research, 273. 1085–1096. DOI: 10.1016/j.ejor.2018.09.015.
   Web of Science ®Google Scholar
• Joshi, C., Aliaga, J. R., and Insua, D. R. (2020), “Insider Threat Modeling: An Adversarial Risk Analysis Approach,” IEEE Transactions on Information Forensics and Security, 16, 1131–1142. DOI: 10.1109/TIFS.2020.3029898.
   Web of Science ®Google Scholar
• McCarthy, B., and Chaudhary, A. (2014), Rational Choice Theory, pp. 4307–4315. New York: Springer.
   Google Scholar
• Montibeller, G., and von Winterfeldt, D. (2015), “Cognitive and Motivational Biases in Decision and Risk Analysis,” Risk Analysis, 35, 1230–1251. DOI: 10.1111/risa.12360.
   PubMed Web of Science ®Google Scholar
• ———(2018), Individual and Group Biases in Value and Uncertainty Judgments, pp. 377–392, Cham: Springer.
   Google Scholar
• Morton, A. (2018), Multiattribute Value Elicitation, pp. 287–311, Cham: Springer.
   Google Scholar
• Naveiro, R., Redondo, A., Ríos Insua, D., and Ruggeri, F. (2019), “Adversarial Classification: An Adversarial Risk Analysis Approach,” International Journal of Approximate Reasoning, 113, 133–148. DOI: 10.1016/j.ijar.2019.07.003.
   Web of Science ®Google Scholar
• Parnell, G. S., Smith, C. M., and Moxley, F. I. (2010), “Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model,” Risk Analysis, 30, 32–48. DOI: 10.1111/j.1539-6924.2009.01319.x.
   PubMed Web of Science ®Google Scholar
• Polaschek, D. L. L., and Yesberg, J. A. (2018), “High-Risk Violent Prisoners’ Patterns of Change on Parole on the Draor’s Dynamic Risk and Protective Factors,” Criminal Justice and Behavior, 45, 340–363. DOI: 10.1177/0093854817739928.
   Web of Science ®Google Scholar
• Polaschek, D. L. L., Yesberg, J. A., and Chauhan, P. (2018), “A Year Without a Conviction: An Integrated Examination of Potential Mechanisms for Successful Reentry in High-Risk Violent Prisoners,” Criminal Justice and Behavior, 45, 425–446. DOI: 10.1177/0093854817752757.
   Web of Science ®Google Scholar
• Rios, J., Ríos Insua, D. (2012), “Adversarial Risk Analysis for Counter-Terrorism Modeling,” Risk Analysis, 32, 894–915. DOI: 10.1111/j.1539-6924.2011.01713.x.
   PubMed Web of Science ®Google Scholar
• Ríos Insua, D., Rios, J., and Banks, D. (2009), “Adversarial Risk Analysis,” Journal of the American Statistical Association, 104, 841–854. DOI: 10.1198/jasa.2009.0155.
   Web of Science ®Google Scholar
• Rios Insua, D., Banks, D., and Rios, J. (2016), “Modeling Opponents in Adversarial Risk Analysis,” Risk Analysis, 36, 742–755. DOI: 10.1111/risa.12439.
   PubMed Web of Science ®Google Scholar
• Rios Insua, D., Couce-Vieira, A., Rubio, J. A., Pieters, W., Labunets, K., and Rasines, D. G. (2021), “An Adversarial Risk Analysis Framework for Cybersecurity,” Risk Analysis, 41, 16–36. DOI: 10.1111/risa.13331.
   PubMed Web of Science ®Google Scholar
• Roberts, J. (2009), “Listening to the Crime Victim: Evaluating Victim Input at Sentencing and Parole,” Crime and Justice, 38, 347–412. DOI: 10.1086/599203.
   Web of Science ®Google Scholar
• Roponen, J., Ríos Insua, D., Salo, A. (2020), “Adversarial Risk Analysis Under Partial Information,” European Journal of Operational Research, 287, 306–316. DOI: 10.1016/j.ejor.2020.04.037.
   Web of Science ®Google Scholar
• Ruhland, E. L. (2020), “Philosophies and Decision Making in Parole Board Members,” The Prison Journal, 100, 640–661. DOI: 10.1177/0032885520956566.
   Web of Science ®Google Scholar
• Schneider, P., van Hout, B., Heisen, M., Brazier, J., Devlin, N. (2022), “The Online Elicitation of Personal Utility Functions (OPUF) Tool: A New Method for Valuing Health States,” Wellcome Open Research, 7, 14. DOI: 10.12688/wellcomeopenres.17518.1.
   PubMedGoogle Scholar
• Serin, R., Gobeil, R., Lloyd, C., Chadwick, N., Wardrop, K., and Hanby, L. (2016), “Using Dynamic Risk to Enhance Conditional Release Decisions in Prisoners to Improve their Outcomes,” Behavioral Sciences & the Law, 34, 321–336. DOI: 10.1002/bsl.2213.
   PubMed Web of Science ®Google Scholar
• Serin, R. C., and Gobeil, R. (2014), “Analysis of the Use of the Structured Decision Making Framework in Three States,” US Department of Justice, National Institute of Corrections.
   Google Scholar
• Sevillano, J. C., Ríos Insua, D., and Rios, J. (2012), “Adversarial Risk Analysis: The Somali Pirates Case,” Decision Analysis, 9, 86–95. DOI: 10.1287/deca.1110.0225.
   Web of Science ®Google Scholar
• Shachter, R. D. (1986), “Evaluating Influence Diagrams,” Operations Research, 34, 871–882. DOI: 10.1287/opre.34.6.871.
   Web of Science ®Google Scholar
• Tollenaar, N., and van der Heijden, P. G. M. (2023), “Which Method Predicts Recidivism Best?: A Comparison of Statistical, Machine Learning and Data Mining Predictive Models,” Journal of the Royal Statistical Society, Series A, 176, 565–584. DOI: 10.1111/j.1467-985X.2012.01056.x.
   Google Scholar
• Vîlcicč, E. “Revisiting parole decision making: testing for the punitive hypothesis in a large U.S. jurisdiction,” International Journal of Offender Therapy and Comparative Criminology, 62, 1357–1383.
   PubMed Web of Science ®Google Scholar
• Wang, S., and Banks, D. (2011), “Network Routing for Insurgency: An Adversarial Risk Analysis Framework,” Naval Research Logistics (NRL), 58, 595–607. DOI: 10.1002/nav.20469.
   Web of Science ®Google Scholar