Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.     

Diesel Engine Exhaust and Lung Cancer Mortality: Time‐Related Factors in Exposure and Risk

To develop a quantitative exposure‐response relationship between concentrations and durations of inhaled diesel engine exhaust (DEE) and increases in lung cancer risks, we examined the role of temporal factors in modifying the estimated effects of exposure to DEE on lung cancer mortality and characterized risk by mine type in the Diesel Exhaust in Miners Study (DEMS) cohort, which followed 12,315 workers through December 1997. We analyzed the data using parametric functions based on concepts of multistage carcinogenesis to directly estimate the hazard functions associated with estimated exposure to a surrogate marker of DEE, respirable elemental carbon (REC). The REC‐associated risk of lung cancer mortality in DEMS is driven by increased risk in only one of four mine types (limestone), with statistically significant heterogeneity by mine type and no significant exposure‐response relationship after removal of the limestone mine workers. Temporal factors, such as duration of exposure, play an important role in determining the risk of lung cancer mortality following exposure to REC, and the relative risk declines after exposure to REC stops. There is evidence of effect modification of risk by attained age. The modifying impact of temporal factors and effect modification by age should be addressed in any quantitative risk assessment (QRA) of DEE. Until there is a better understanding of why the risk appears to be confined to a single mine type, data from DEMS cannot reliably be used for QRA.     

Reanalysis of the DEMS Nested Case‐Control Study of Lung Cancer and Diesel Exhaust: Suitability for Quantitative Risk Assessment

The International Agency for Research on Cancer (IARC) in 2012 upgraded its hazard characterization of diesel engine exhaust (DEE) to “carcinogenic to humans.” The Diesel Exhaust in Miners Study (DEMS) cohort and nested case‐control studies of lung cancer mortality in eight U.S. nonmetal mines were influential in IARC's determination. We conducted a reanalysis of the DEMS case‐control data to evaluate its suitability for quantitative risk assessment (QRA). Our reanalysis used conditional logistic regression and adjusted for cigarette smoking in a manner similar to the original DEMS analysis. However, we included additional estimates of DEE exposure and adjustment for radon exposure. In addition to applying three DEE exposure estimates developed by DEMS, we applied six alternative estimates. Without adjusting for radon, our results were similar to those in the original DEMS analysis: all but one of the nine DEE exposure estimates showed evidence of an association between DEE exposure and lung cancer mortality, with trend slopes differing only by about a factor of two. When exposure to radon was adjusted, the evidence for a DEE effect was greatly diminished, but was still present in some analyses that utilized the three original DEMS DEE exposure estimates. A DEE effect was not observed when the six alternative DEE exposure estimates were utilized and radon was adjusted. No consistent evidence of a DEE effect was found among miners who worked only underground. This article highlights some issues that should be addressed in any use of the DEMS data in developing a QRA for DEE.     

Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data

To protect and secure food resources for the United States, it is crucial to have a method to compare food systems’ criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank‐order of food and agriculture system criticality to a large degree.     

Are Economic Development and Education Improvement Associated with Participation in Transnational Terrorism?

Using transnational terrorism data from 1980 to 2000, this study empirically examines the relationships between frequency of participation in transnational terrorism acts and economic development and education improvement. We find an inverse U‐shaped association between the frequency of various nationals acting as perpetrators in transnational terrorism acts and per capita income in their respective home countries. As per capita incomes increase from relatively low levels, frequencies of participation in transnational terrorism increase. However, at sufficiently higher levels of per capita income, further increase in per capita income is negatively associated with the rate of participation in transnational terrorism. Education improvement from elementary to secondary is positively correlated with frequency of participation in transnational terrorism events, whereas further improvement from secondary to tertiary level is negatively correlated with participation in transnational terrorism. We also find that citizens of countries with greater openness to international trade, lower degree of income inequality, greater economic freedom, larger proportion of population with tertiary education, and less religious prevalence participate in transnational terrorism events less frequently.     

The Effects of Multitasking on Operations Scheduling

This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.     

Entry and Exit in OTC Derivatives Markets

We develop a parsimonious model to study the equilibrium and socially optimal decisions of banks to enter, trade in, and possibly exit, an OTC market. Although we endow all banks with the same trading technology, banks' optimal entry and trading decisions endogenously lead to a realistic market structure composed of dealers and customers with distinct trading patterns. We decompose banks' entry incentives into incentives to hedge risk and incentives to make intermediation profits. We show that dealer banks enter more than is socially optimal. In the face of large negative shocks, they may also exit more than is socially optimal when markets are not perfectly resilient.     

Further Beyond the Basic Background Check: Predicting Future Unethical Behavior

In this paper, we analyze the ethical issues of using honesty and integrity tests in employment screening. Our focus will be on the United States context: legal requirements related to applicant privacy differ in other countries, but we posit that our proposed balancing test is broadly applicable. We start by discussing why companies have ethical and legal obligations, based on a stakeholder analysis, to assess the integrity of potential employees. We then move to a consideration of how companies currently use background checks as a pre‐employment screening tool, noting their limitations. We then take up honesty and integrity testing, focusing particularly on the problems of false positives and due process. We offer a balancing test for the use of honesty and integrity testing that takes in three factors: (1) the potential harm posed by a dishonest employee in a particular job, (2) the linkage between the test and the assessment process, and (3) the accuracy and validity of the honesty and integrity test. We conclude with implications for practice and future research.     

On the inapproximability of the exemplar conserved interval distance problem of genomes

In this paper we present two main results about the inapproximability of the exemplar conserved interval distance problem of genomes. First, we prove that it is NP-complete to decide whether the exemplar conserved interval distance between any two genomes is zero or not. This result implies that the exemplar conserved interval distance problem does not admit any approximation in polynomial time, unless P=NP. In fact, this result holds, even when every gene appears in each of the given genomes at most three times. Second, we strengthen the first result under a weaker definition of approximation, called weak approximation. We show that the exemplar conserved interval distance problem does not admit any weak approximation within a super-linear factor of , where m is the maximal length of the given genomes. We also investigate polynomial time algorithms for solving the exemplar conserved interval distance problem when certain constrains are given. We prove that the zero exemplar conserved interval distance problem of two genomes is decidable in polynomial time when one genome is O(log n)-spanned. We also prove that one can solve the constant-sized exemplar conserved interval distance problem in polynomial time, provided that one genome is trivial.