Economic Analysis of Simulation Selection Problems

Ranking and selection procedures are standard methods for selecting the best of a finite number of simulated design alternatives based on a desired level of statistical evidence for correct selection. But the link between statistical significance and financial significance is indirect, and there has been little or no research into it. This paper presents a new approach to the simulation selection problem, one that maximizes the expected net present value of decisions made when using stochastic simulation. We provide a framework for answering these managerial questions: When does a proposed system design, whose performance is unknown, merit the time and money needed to develop a simulation to infer its performance? For how long should the simulation analysis continue before a design is approved or rejected? We frame the simulation selection problem as a “stoppable” version of a Bayesian bandit problem that treats the ability to simulate as a real option prior to project implementation. For a single proposed system, we solve a free boundary problem for a heat equation that approximates the solution to a dynamic program that finds optimal simulation project stopping times and that answers the managerial questions. For multiple proposed systems, we extend previous Bayesian selection procedures to account for discounting and simulation-tool development costs

New Data and Tools for Integrating Discrete and Continuous Population Modeling Strategies

Realistic population models have interactions between individuals. Such interactions cause populations to behave as systems with nonlinear dynamics. Much population data analysis is done using linear models assuming no interactions between individuals. Such analyses miss strong influences on population behavior and can lead to serious errors—especially for infectious diseases. To promote more effective population system analyses, we present a flexible and intuitive modeling framework for infection transmission systems. This framework will help population scientists gain insight into population dynamics, develop theory about population processes, better analyze and interpret population data, design more powerful and informative studies, and better inform policy decisions. Our framework uses a hierarchy of infection transmission system models. Four levels are presented here: deterministic compartmental models using ordinary differential equations (DE); stochastic compartmental (SC) models that relax assumptions about population size and include stochastic effects; individual event history models (IEH) that relax the SC compartmental structure assumptions by allowing each individual to be unique. IEH models also track each individual's history, and thus, allow the simulation of field studies. Finally, dynamic network (DNW) models relax the assumption of the previous models that contacts between individuals are instantaneous events that do not affect subsequent contacts. Eventually it should be possible to transit between these model forms at the click of a mouse. An example is presented dealing with Cryptosporidium . It illustrates how transiting model forms helps assess water contamination effects, evaluate control options, and design studies of infection transmission systems using nucleotide sequences of infectious agents.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75616/1/j.1749-6632.2001.tb02756.x.pd

Simulations to Evaluate HIV Vaccine Trial Designs

Many HIV vaccine trials have been proposed to evaluate susceptibility of individuals. However, vac cines may also affect an epidemic's course at the population level by altering the infectiousness of vaccinated individuals who become infected. A vac cine trial design that does not estimate both suscep tibility and infectiousness might reject a proposed vaccine that is capable of halting the HIV epidemic. We describe a vaccine trial design called the Retro spective Partner Trial (RPT), which can quantify vaccine effects on both susceptibility and infectious ness. We describe HIVSIM, a simulation environ ment that generates simulated populations and al lows for empirical evaluation of the statistical power of the RPT. HIVSIM explicitly models a number of factors which influence transmission and preva lence, and which have proven difficult to model us ing standard models. These factors include the infec tion stage of infected individuals, partnership selec tion, the duration of partnerships and concurrence, and transmission of HIV. The simulation analysis indicates that the RPT design has substantially greater statistical power for identifying vaccines which, in spite of exhibiting poor protection against infection, are nonetheless capable of halting the HIV epidemic by substantially reducing the infectious ness of vaccinated individuals who become infected.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68501/2/10.1177_003754979807100403.pd

Giving more detailed information about health insurance encourages consumers to choose compromise options

IntroductionTo investigate how the provision of additional information about the health events and procedures covered by a healthcare plan affect the level of coverage chosen by young adults taking their first full time job.MethodsUniversity students were recruited for a study at two behavioral laboratories (one located at the University of Toronto and the other located at INSEAD-Sorbonne University in Paris) in which they imagine they are making choices about the healthcare coverage associated with the taking a new job in Chicago, Illinois. Every participant made choices in four categories: Physician Care, Clinical Care, Hospital Care, and Dental Care. Participants were randomly assigned to one of two conditions: Low Detail or High Detail coverage information and they chose between three levels of coverage: Basic, Enhanced, and Superior. The study took place in March 2017 with 120 students in Toronto and 121 students in Paris.ResultsThe provision of more detailed information about the health events and procedures covered by a healthcare plan leads to a compromise effect in which participants shift their choices significantly towards Enhanced (moderate coverage) from Basic (low coverage) and Superior (high coverage). The compromise effect was observed at both locations; however, Paris participants choose significantly higher levels of coverage than Toronto participants.DiscussionProviding more detail to employees about the health events and procedures covered by a healthcare plan will increase the fraction of employees who choose the intermediate level of coverage. It is beyond the scope of this study to conclude whether this is good or bad; however, in a context where employees gravitate to either insufficient or excessive coverage, providing additional detail may reduce these tendencies

Effect of Concurrent Partnerships and Sex-Act Rate on Gonorrhea Prevalence

The disease gonorrhea (GC) is a major public health problem in the United States, and the dynamics of the spread of GC through popula tions are complicated and not well understood. Studies have drawn attention to the effect of concurrent sexual partnerships as an influen tial factor for determining disease prevalence. However, little has been done to date to quantify the combined effects of concurrency and within-partnership sex-act rates on the prevalence of GC. This simulation study examines this issue with a simplified model of GC transmission in closed human populations that include concurrent partnerships. Two models of within-partnership sex-act rate are compared; one is a fixed sex-act rate per partnership, and the other is perhaps more realistic in that the rate depends on the number of concurrent partners. After controlling for total number of sex acts, pseudo-equilibrium prevalence is higher with the fixed sex-act rate than under the concurrency-adjusted rate in all the modeled partnership formation conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68414/2/10.1177_003754979807100404.pd

Simulation Budget Allocation for Further Enhancing the Efficiency of Ordinal Optimization

Ordinal Optimization has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. In this paper, we present a new approach that can further enhance the efficiency of ordinal optimization. Our approach determines a highly efficient number of simulation replications or samples and significantly reduces the total simulation cost. We also compare several different allocation procedures, including a popular two-stage procedure in simulation literature. Numerical testing shows that our approach is much more efficient than all compared methods. The results further indicate that our approach can obtain a speedup factor of higher than 20 above and beyond the speedup achieved by the use of ordinal optimization for a 210-design example.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45045/1/10626_2004_Article_264696.pd

A descriptive multi-attribute model for reconfigurable machining system selection that examines buyer-supplier relationships

http://deepblue.lib.umich.edu/bitstream/2027.42/35496/2/b2013435.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/35496/1/b2013435.0001.001.tx

A Bayesian account of uncertainty for discrete-event dynamic simulation : selection of input distributions

http://deepblue.lib.umich.edu/bitstream/2027.42/4161/5/ban4513.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/4161/4/ban4513.0001.001.tx