Benefit–cost analyses are critical to support U.S. agencies’ programmatic decision making. These analyses are particularly challenging when one of the benefits is adversary deterrence. This paper presents a framework for calculating the value of deterrence related to countermeasures implemented to mitigate an attack by an adaptive adversary. We offer an approach for partitioning the benefit of countermeasures into three components: (1) threat reduction (deterrence), (2) vulnerability reduction, and (3) consequence mitigation. The benefit of a countermeasure is measured by the expected value of countermeasure implementation (EVCI) attributable to a specific countermeasure. It is based on the concept of expected value of imperfect control, defined as the difference in the expected values of alternatives with and without countermeasures. The EVCI represents all the benefits of implementing the countermeasure and is derived from three sources: (1) changes in attack probability (threat reduction from deterrence), (2) changes in detection probability (vulnerability reduction), and (3) changes in the distribution of attack outcomes (consequence mitigation). We partition the EVCI and estimate the portion attributable to each of these three sources to quantify the unique benefit of each. We provide two applications of the partitioning methodology using examples from the published literature that examine countermeasures designed to protect commercial aircraft against man-portable air defense systems. The proposed framework provides an approach for explicitly accounting separately for deterrence, vulnerability reduction, and consequence mitigation in benefit–cost analyses. It provides quantifiable insights into how countermeasures reduce terrorism risk. Funding: This material is based upon work supported by the U.S. Department of Homeland Security under [Grant Award 22STESE00001-02-00]. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. This award was made to Northeastern University and the University of Southern California is a sub-awardee. This work was also supported by the National Science Foundation [Grant 2027296] awarded to Decision Research.
The success of extended warranties and buyer protection plans suggests that insurance against a small loss has high decision utility. We explore whether the behavioral insight that people are highly averse to small chances of loss can be used to create a powerful incentive that has very low expected value. We compare decisions of individuals offered fixed payments for healthy choices to those offered insurance in exchange for healthy choices. We test the prediction that aversion to small losses will result in very high rates of health behavior uptake in exchange for insurance. Three hundred participants endowed with a $2 bonus randomly received one of two incentives for completing a scheduled health risk assessment: (1) an insurance guarantee against the 1% risk of losing the $2 bonus or (2) a fixed payment at the expected value of the insurance. Relative to the fixed payment condition, participants in the insurance intervention were 70% more likely to meet their health risk assessment appointment (p < 0.01). Fixed payments of $2.59 were needed for every $1 spent on insurance to achieve the same behavioral effect. Loss aversion, probability weighting, and the certainty effect may account for this result. Incentive design may benefit from utilizing an insurance paradigm.
Based on subjective survival probability questions in the Health and Retirement Study HRS, we use an econometric model to estimate the determinants of individual-level uncertainty about personal longevity. This model is built around the modal response hypothesis MRH, a mathematical expression of the idea that survey responses of 0%, 50%, or 100% to probability questions indicate a high level of uncertainty about the relevant probability. We show that subjective survival expectations in 2002 line up very well with realized mortality of the HRS respondents between 2002 and 2010. We show that the MRH model performs better than typically used models in the literature of subjective probabilities. Our model gives more accurate estimates of low probability events and it is able to predict the unusually high fraction of focal 0%, 50%, and 100% answers observed in many data sets on subjective probabilities. We show that subjects place too much weight on parents' age at death when forming expectations about their own longevity, whereas other covariates such as demographics, cognition, personality, subjective health, and health behavior are underweighted. We also find that less educated people, smokers, and women have less certain beliefs, and recent health shocks increase uncertainty about survival, too.
Mammography is the most effective screening tool for early diagnosis of breast cancer. Based on the mammography findings, radiologists need to choose from one of the following three alternatives: 1) take immediate diagnostic actions including prompt biopsy to confirm breast cancer; 2) recommend a follow-up mammogram; 3) recommend routine annual mammography. There are no validated structured guidelines based on a decision-analytical framework to aid radiologists in making such patient management decisions. Surprisingly, only 15-45% of the breast biopsies and less than 1% of short-interval follow-up recommendations are found to be malignant, resulting in unnecessary tests and patient-anxiety. We develop a finite-horizon discrete-time Markov decision process (MDP) model that may help radiologists make patient-management decisions to maximize a patient's total expected quality-adjusted life years. We use clinical data to find the policies recommended by the MDP model and also compare them to decisions made by radiologists at a large mammography practice. We also derive the structural properties of the MDP model, including sufficiency conditions that ensure the existence of a double control-limit type policy.
