Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure

Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector which captures well in a probabilistic sense the uncertainty. On the other hand, a problem-driven approach may be able to exploit the structure of a problem to provide a more concise representation of the uncertainty. In this paper we propose an analytic approach to problem-driven scenario generation. This approach applies to stochastic programs where a tail risk measure, such as conditional value-at-risk, is applied to a loss function. Since tail risk measures only depend on the upper tail of a distribution, standard methods of scenario generation, which typically spread their scenarios evenly across the support of the random vector, struggle to adequately represent tail risk. Our scenario generation approach works by targeting the construction of scenarios in areas of the distribution corresponding to the tails of the loss distributions. We provide conditions under which our approach is consistent with sampling, and as proof-of-concept demonstrate how our approach could be applied to two classes of problem, namely network design and portfolio selection. Numerical tests on the portfolio selection problem demonstrate that our approach yields better and more stable solutions compared to standard Monte Carlo sampling

Dioramas as a Place for Play and Early Science Learning: Exploring Teachers’ Perspectives and Experiences

In this qualitative study, we explore teachers’ perspectives and experiences with play and learning at dioramas as few studies on this topic exist. In a time when play is disappearing from classrooms (Nicolopoulou, 2010), scholars advocate for a return to play-based learning (Miller & Almon, 2009). Using grounded theory (Charmaz, 2006), we inquired: 1) How do teachers describe the ways in which children play and learn with dioramas during their classes?, 2) What do teachers perceive as the affordances and opportunities that dioramas provide for children’s play and learning?, and 3) What strategies and pedagogical decisions do teachers make to promote play and learning at diorama? We interviewed ten early childhood educators who teach at a large, urban museum. Nearly 30 unique examples of play and learning with dioramas were provided, nine referenced by multiple teachers. Findings suggest that play and learning at or inspired by dioramas looks different across classes and contexts, but is perceived as vital in sparking imagination and creativity for young children when integrated into experiences and affords unique opportunities. This study highlights how dioramas can be integral in play-based science learning—making museums that are not traditionally designed for children into places for play

The Perfect Storm in Higher Education

Higher education has always faced challenges, but what happens when colleges and universities are facing a ‘perfect storm?’ One of the victims of a pandemic, rising tuition costs, and less funding could be the traditional classroom or worse still a dramatic decrease in student enrollment. In this paper, we explore some of the elements that could make it more difficult to fulfill the American dream of attending a university for the campus life and what might lie in the future for students post COVID-19

Success in the Online Classroom: Lessons Learned

In the early 2000s, we embarked on research to study online education. At the time, online courses offered by traditional institutions was in its’ infancy. Through our research, we learned that increasing students’ intrinsic motivation could lead to more successful learning environments. Today’s online learning environments are afforded many more technological advances that were not available 20 years ago. In addition, the Covid19 Pandemic has forced the creation online learning environment. Therefore, we believe that revisiting the elements that lead to successful online learning is timely and necessary. Through this research, we affirm that technological advancements have led to more meaningful ways to enhance online learning environments

Problem-driven scenario generation:an analytical approach for stochastic programs with tail risk measure

Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector which captures well in a probabilistic sense the uncertainty. On the other hand, a problem-driven approach may be able to exploit the structure of a problem to provide a more concise representation of the uncertainty. There have been only a few problem-driven approaches proposed, and these have been heuristic in nature. In this paper we propose what is, as far as we are aware, the first analytic approach to problem-driven scenario generation. This approach applies to stochastic programs with a tail risk measure, such as conditional value-at-risk. Since tail risk measures only depend on the upper tail of a distribution, standard methods of scenario generation, which typically spread there scenarios evenly across the support of the solution, struggle to adequately represent tail risk well

Unintentional needlestick injuries in livestock production : a case series and review

Livestock producers and their employees sometimes experience unintentional needlestick injury (NSI) while vaccinating or injecting medications into animals. There is little published regarding the medical complications that can develop from this occupational exposure. The objectives of this study were to (1) perform a retrospective review of animal-related NSIs treated at a tertiary medical center of a rural state; and (2) review the risks of NSI and measures to decrease their occurrence. Medical records of patients with NSI related to animal injection were identified from the University of Iowa Hospitals and Clinics database from 2002 to 2008 and reviewed. Nine patients received medical care for NSI that occurred while vaccinating farm animals. Most common NSI site was the nondominant hand and most occurred while attempting to inject the animal. Soft tissue infection was common and all nine received oral and/or intravenous antibiotics. Two thirds required hospital admission. Three required surgery and one had a bedside incision and drainage procedure. One patient had a serious inflammatory reaction with necrosis in the leg due to the oil adjuvant in the animal vaccine. Another case had a probable mycetoma with osteomyelitis and soft tissue infection due to the bacteria Streptomyces, which is a NSI complication not previously reported. Although medical complications from farm-related NSIs do not appear to be common, this case series illustrates how these injuries can be debilitating, costly, and lead to loss of work time and productivity. Producers and employees who inject livestock need to be aware of the risks and utilize measures to decrease unintentional NSI. <br /

Investigating Disparities in High School Athletes’ Attitude Toward Concussion and Predictors of Continuing Play

Objective: Studies related to attitudes of concussion have been growing in athletic populations. While racial and socioeconomic disparities exist in knowledge and awareness of concussion, it remains unclear the effect of disparities on attitudes of concussion and reporting behaviors. The purpose of this study was to examine racial and socioeconomic disparities on attitudes towards concussion and the decision to remain in play with a suspected concussion. Design: This cross-sectional study included 577 athletes between the ages of 13-19 (16.0 ± 1.2) years from 14 high schools. Participants completed a knowledge and attitudes instrument assessing general attitudes of concussion using 7 Likert-scale attitude questions followed by 2 additional questions assessing the decision to continue play while symptomatic. Differences in attitudes of concussion between race and socioeconomic school type were examined using independent t-tests. A multivariable linear regression model was utilized to determine which demographic factors were associated with athletes’ attitude scores. Multivariable logistic regression models were utilized to determine what demographic variables were associated with athletes’ continuation of play in a practice or a game. Results: Differences in attitude scores between race were observed, with black athletes demonstrating lower scores than white athletes (pp=.04) and sex (OR: 0.59, 95% CI [0.36,0.96], p=.03) were, with females less likely to remain in a practice than males. Further, race and socioeconomic school type were not significantly associated with remaining in a game; however, attitude (OR: 0.97, 95% CI [0.95,0.99], p=.01) and sex (OR: 0.56 95% CI [0.35,0.90], p=.02) were, with females less likely to remain in a game than males. Conclusions: Disparities exist between race and socioeconomic school type on attitude of concussion. Black athletes and athletes attending Title I high schools had poorer attitude scores compared to white athletes and athletes attending non-Title I schools. Race was significantly associated with lower concussion attitude scores. The poorer, yet moderate concussion attitude scores suggest concussion education efforts be concentrated towards closing the disparity gap. Further, addressing concussion attitudes would likely also help to shift athletes’ decisions to remain in a practice or game while symptomatic

Scenario generation for single-period portfolio selection problems with tail risk measures:coping with high dimensions and integer variables

In this paper we propose a problem-driven scenario generation approach to the single-period portfolio selection problem which use tail risk measures such as conditional value-at-risk. Tail risk measures are useful for quantifying potential losses in worst cases. However, for scenario-based problems these are problematic: because the value of a tail risk measure only depends on a small subset of the support of the distribution of asset returns, traditional scenario based methods, which spread scenarios evenly across the whole support of the distribution, yield very unstable solutions unless we use a very large number of scenarios. The proposed approach works by prioritizing the construction of scenarios in the areas of a probability distribution which correspond to the tail losses of feasible portfolios. The proposed approach can be applied to difficult instances of the portfolio selection problem characterized by high-dimensions, non-elliptical distributions of asset returns, and the presence of integer variables. It is also observed that the methodology works better as the feasible set of portfolios becomes more constrained. Based on this fact, a heuristic algorithm based on the sample average approximation method is proposed. This algorithm works by adding artificial constraints to the problem which are gradually tightened, allowing one to telescope onto high quality solutions

Problem-driven scenario generation:an analytical approach for stochastic programs with tail risk measure

Scenario generation is the construction of a discrete random vector to represent parameters of uncertain values in a stochastic program. Most approaches to scenario generation are distribution-driven, that is, they attempt to construct a random vector which captures well in a probabilistic sense the uncertainty. On the other hand, a problem-driven approach may be able to exploit the structure of a problem to provide a more concise representation of the uncertainty. In this paper we propose an analytic approach to problem-driven scenario generation. This approach applies to stochastic programs where a tail risk measure, such as conditional value-at-risk, is applied to a loss function. Since tail risk measures only depend on the upper tail of a distribution, standard methods of scenario generation, which typically spread their scenarios evenly across the support of the random vector, struggle to adequately represent tail risk. Our scenario generation approach works by targeting the construction of scenarios in areas of the distribution corresponding to the tails of the loss distributions. We provide conditions under which our approach is consistent with sampling, and as proof-of-concept demonstrate how our approach could be applied to two classes of problem, namely network design and portfolio selection. Numerical tests on the portfolio selection problem demonstrate that our approach yields better and more stable solutions compared to standard Monte Carlo sampling