Expert Elicitation of Adversary Preferences Using Ordinal Judgments

• 成果类型: Article
• 署名作者: Wang, Chen; Bier, Vicki M.
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2013.1159
• 发表日期: 2013
• 页码: 372-385
• 关键词: mixed logit; contingent valuation; bayesian-analysis; probability; distributions; TERRORISM; DECISION; parameter; smarter; models

摘要

We introduce a simple elicitation process where subject-matter experts provide only ordinal judgments of the attractiveness of potential targets, and the adversary utility of each target is assumed to involve multiple attributes. Probability distributions over the various attribute weights are then mathematically derived (using either probabilistic inversion or Bayesian density estimation). This elicitation process reduces the burden of time-consuming orientation and training in traditional methods of attribute weight elicitation, and explicitly captures the existing uncertainty and disagreement among experts, rather than attempts to achieve consensus by eliminating them. We identify the relationship between the two methods and conduct sensitivity analysis to elucidate how these methods handle expert consensus or disagreement. We also present a real-world application on elicitation of adversarial preferences over various attack scenarios to show the applicability of our proposed methods.