Expert Elicitation of Adversary Preferences Using Ordinal Judgments

References

• Abbas AE. Entropy method for adaptive utility elicitation. IEEE Trans. Systems, Sci. Cybernetics (2004) 32(2):169–178Crossref, Google Scholar
• Abbas AE. Maximum entropy utility. Oper. Res. (2006) 54(2):277–290Link, Google Scholar
• Abbas AE, Bell DE. One-switch independence for multiattribute utility functions. Oper. Res. (2011) 59(3):764–771Link, Google Scholar
• Antoniak CE. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Ann. Statist. (1974) 2:1152–1174Crossref, Google Scholar
• Baker J, Wool M, Smith A, Kahan J, Ansel C, Hammar P, McGarvey D, Phillips M, Lark R. Risk analysis and intelligence communities collaborative framework. (2009) . Report, Homeland Security Institute, Arlington, VA. Accessed January 4, 2013, http://www.homelanddefense.org/downloads/Risk-Intel%20Collaboration%20Final%20Report.pdfGoogle Scholar
• Barron FH, Barrett BE. The efficacy of SMARTER—Simple multi-attribute rating technique extended to ranking. Acta Psychologica (1996) 93:23–36Crossref, Google Scholar
• Barros CP, Proença I. Mixed logit estimation of radical Islamic terrorism in Europe and North America: A comparative study. J. Conflict Resolution (2005) 49(2):298–314Crossref, Google Scholar
• Bedford TJ, Cooke RM. Probabilistic Risk Analysis: Foundations and Methods (2001) (Cambridge University Press, New York) Crossref, Google Scholar
• Beitel GA, Gertman DI, Plum MM, Brebbia CA. Balanced scorecard method for predicting the probability of a terrorist attack. Risk Analysis IV (2004) (WIT Press, Southampton, UK) 581–592Google Scholar
• Bier VM, Abhichandani V, Haimes YY, Moser DA, Stakhiv EZ. Optimal allocation of resources for defense of simple series and parallel systems from determined adversaries. Risk-Based Decisionmaking in Water Resources X (2003) (American Society of Civil Engineers, Santa Barbara, CA) 59–76Crossref, Google Scholar
• Bier VM, Yi W. A Bayesian method for analyzing dependencies in precursor data. Internat. J. Forecasting (1995) 11(1):25–41Crossref, Google Scholar
• Bier VM, Bonorato JM, Wang C, Herrmann J. Achieving realistic levels of defensive hedging based on non-monotonic and multi-attribute terrorist utility functions. Handbook of Operations Research for Homeland Security (2013) (Springer, New York) 125–139Crossref, Google Scholar
• Bier VM, Nagaraj A, Abhichandani V. Optimal allocation of resources for defense of simple series and parallel systems from determined adversaries. Reliability Engrg. System Safety (2005) 87:313–323Crossref, Google Scholar
• Center for Risk and Economic Analysis of Terrorism Events Adaptive adversary modeling for terrorism risk analysis: Final report. (2011) . University of Southern California, Los AngelesGoogle Scholar
• Clemen RT, Reilly T. Correlations and copulas for decision and risk analysis. Management Sci. (1999) 45(2):208–224Link, Google Scholar
• Cooke RM. Experts in Uncertainty: Opinion and Subjective Probability in Science (1991) (Oxford University Press, Oxford, UK) Crossref, Google Scholar
• Cooke RM. Parameter fitting for uncertain models: Modeling uncertainty in small models. Reliability Engrg. System Safety (1994) 44:89–102Crossref, Google Scholar
• Csiszár I. I-divergence geometry of probability distributions and minimization problems. Ann. Probab. (1975) 3:146–158Crossref, Google Scholar
• Du C, Kurowicka D, Cooke RM. Techniques for generic probabilistic inversion. Comput. Statist. Data Anal. (2006) 50(1):1164–1187Crossref, Google Scholar
• Eckenrode RT. Weighting multiple criteria. Management Sci. (1965) 12(3):180–192Link, Google Scholar
• Edwards W. Behavioral decision theory. Annual Rev. Psych. (1961) 12:473–498Crossref, Google Scholar
• Edwards W. How to use multiattribute utility measurement for social decision making. IEEE Trans. Man, Systems, Cybernetics (1977) 7:326–340Crossref, Google Scholar
• Edwards W, Barron FH. SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Organ. Behav. Human Decision Processes (1994) 60:306–325Crossref, Google Scholar
• Enders W, Sandler T. Is transnational terrorism becoming more threatening: A time-series investigation. J. Conflict Resolution (2000) 44(3):307–332Crossref, Google Scholar
• Erkanli A, Stangl DK, Muller P. A Bayesian analysis of ordinal data. (1993) . ISDS Discussion Paper 93-A01, Duke University, Durham, NCGoogle Scholar
• Escobar MD, West M. Bayesian density estimation and inference using mixtures. J. Amer. Statist. Assoc. (1995) 90:577–588Crossref, Google Scholar
• Ferguson TS. A Bayesian analysis of some nonparametric problems. Ann. Statist. (1973) 1:209–230Crossref, Google Scholar
• Ferguson TS. Prior distributions on spaces of probability measures. Ann. Statist. (1974) 2:615–629Crossref, Google Scholar
• Ferguson TS, Rizvi MH, Rustagi J, Siegmund D. Bayesian density estimation by mixtures of normal distributions. Recent Advances in Statistics (1983) (Academic Press, New York) 287–409Crossref, Google Scholar
• Gelfand AE, Smith AFM, Lee TM. Bayesian analysis of constrained parameter and truncated data problems using Gibbs sampling. J. Amer. Statist. Assoc. (1992) 87:523–532Crossref, Google Scholar
• Geman S, Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Machine Intelligence (1984) 6:721–741Crossref, Google Scholar
• Gigerenzer G, Todd PM. Simple Heuristics That Make Us Smart (1999) (Oxford University Press, New York) . ABC Research GroupGoogle Scholar
• Green PE, Srinivasan V. Conjoint analysis in marketing: New developments with implications for research and practice. J. Marketing (1990) 54(4):3–19Crossref, Google Scholar
• Hanemann WM. Welfare evaluation in contingent valuation experiments with discrete responses. Amer. J. Agricultural Econom. (1984) 66:332–341Crossref, Google Scholar
• Hora S. An analytic method for evaluating the performance of aggregation rules for probability densities. Oper. Res. (2010) 58(5):1440–1449Link, Google Scholar
• Hora S, Jensen M. Expert Judgement Elicitation (2002) (Swedish Radiation Protection Authority, Stockholm) Google Scholar
• Jenelius E, Westin J, Holmgren ÅJ. Critical infrastructure protection under imperfect attacker perception. Internat. J. Critical Infrastructure Protection (2010) 3(1):16–26Crossref, Google Scholar
• Kadane JB, Dickey JM, Winkler RL, Smith WS, Peters SC. Interactive elicitation of opinion for a normal linear model. J. Amer. Statist. Assoc. (1980) 75(372):845–854Crossref, Google Scholar
• Keeney R, Raiffa H. Decisions with Multiple Objectives: Preferences and Value Trade-Offs (1976) (John Wiley & Sons, Inc., New York) Google Scholar
• Kraan B, Bedford T. Probabilistic inversion of expert judgments in the quantification of model uncertainty. Management Sci. (2005) 51(6):995–1006Link, Google Scholar
• Kurowicka D, Nauta M, Jozwiak K, Katarzyna J, Cooke RM. Updating parameters of the chicken processing line model. Risk Anal. (2010) 30(6):934–944Crossref, Google Scholar
• McFadden D. Contingent valuation and social choice. Amer. J. Agricultural Econom. (1994) 76:689–708Crossref, Google Scholar
• McFadden D, Train K. Mixed MNL models for discrete response. Appl. Econometrics (2000) 15:447–470Crossref, Google Scholar
• Mohtadi H, Murshid A. The risk of catastrophic terrorism: An extreme value approach. J. Appl. Econometrics (2009) 24:537–559Crossref, Google Scholar
• Neslo R, Micheli F, Kappel CV, Selkoe KA, Halpern BS, Cooke RM, Linkov I, Ferguson E, Magar V. Modeling stakeholder preferences with probabilistic inversion: Application to prioritizing marine ecosystem vulnerabilities. Real Time and Deliberative Decision Making: Application to Risk Assessment for Non-chemical Stressors (2011) (Springer, Amsterdam) 265–284Google Scholar
• Paté-Cornell ME, Guikema S. Probabilistic modeling of terrorist threats: A systems analysis approach to setting priorities among countermeasures. Military Oper. Res. (2002) 7(4):5–20Crossref, Google Scholar
• Revelt D, Train K. Mixed logit with repeated choices: Households choice of appliance efficiency level. Rev. Econom. Statist. (1998) LXXX(4):647–657Crossref, Google Scholar
• Rosoff H, John R. Decision analysis by proxy for the rational terrorist. Proc. Internat. Joint Conf. Artificial Intelligence (IJCAI-09), Quant. Risk Anal. Security Appl. (2009) . Accessed January 4, 2013, http://teamcore.usc.edu/QRASA-09/SubmissionsFinal/Paper_4.pdfGoogle Scholar
• Sándor Z, Wedel M. Profile construction in experimental choice designs for mixed logit models. Marketing Sci. (2002) 21(4):455–475Link, Google Scholar
• Seppäläinen T. Lecture Notes on Basics of Stochastic Analysis (2010) (Department of Mathematics, University of Wisconsin–Madison, Madison, WI) . Accessed January 4, 2013, http://www.math.wisc.edu/seppalai/sa-book/notes.pdfGoogle Scholar
• Shocker AD, Srinivasan V. Multiattribute approaches for product concept evaluation and generation: A critical review. J. Marketing Res. (1979) 16:159–180Crossref, Google Scholar
• Tierney L. Markov chains for exploring posterior distributions. Ann. Statist. (1994) 22:1701–1762Crossref, Google Scholar
• von Winterfeldt D, Edwards W. Decision Analysis and Behavioral Research (1986) (Cambridge University Press, New York) Google Scholar
• Wang C, Bier VM. Target-hardening decisions based on uncertain multiattribute terrorist utility. Decision Anal. (2011) 8(4):286–302Link, Google Scholar