{"title":"Utilization of Machine Learning to Simulate the Implementation of Instant Runoff Voting","authors":"Nicholas J. Joyner","doi":"10.1137/18s016709","DOIUrl":"https://doi.org/10.1137/18s016709","url":null,"abstract":"In election years when the popular vote winner and Electoral College winner differ, such as in the 2016 presidential election, there tends to be an increase in discussions about alternative voting strategies. Ranked choice voting is a strategy that has been discussed and is currently used in approximately fourteen cities across the United States and in six states for special elections and overseas ballots. Ranked choice voting (RCV), sometimes called Instant Runoff Voting (IRV), is a system of voting in which voters are allowed to rank the candidates. If no candidate wins over fifty percent of the vote, the election automatically goes to another round. The candidate with the least support is eliminated and their votes are redistributed to the voters’ next choice. This process of elimination and redistribution continues until a candidate receives a majority of the vote. In this paper, we use predictive modeling strategies and simulation to investigate the potential implications of employing ranked choice voting in a presidential election using the 2016 presidential election as a case study.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64310334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Parameter and Uncertainty Estimation for a Model of Atmospheric CO2 Observations","authors":"A. Maurais","doi":"10.1137/18s017533","DOIUrl":"https://doi.org/10.1137/18s017533","url":null,"abstract":"","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64310409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Limitations of Richardson Extrapolation for Kernel Density Estimation","authors":"R. Ascoli","doi":"10.1137/18S017570","DOIUrl":"https://doi.org/10.1137/18S017570","url":null,"abstract":"This paper develops the process of using Richardson Extrapolation to improve the Kernel Density Estimation method, resulting in a more accurate (lower Mean Squared Error) estimate of a probability density function for a distribution of data in $R_d$ given a set of data from the distribution. The method of Richardson Extrapolation is explained, showing how to fix conditioning issues that arise with higher-order extrapolations. Then, it is shown why higher-order estimators do not always provide the best estimate, and it is discussed how to choose the optimal order of the estimate. It is shown that given n one-dimensional data points, it is possible to estimate the probability density function with a mean squared error value on the order of only $n^{-1}sqrt{ln(n)}$. Finally, this paper introduces a possible direction of future research that could further minimize the mean squared error.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43002143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Global Solution to a Non-linear Wave Equation of Liquid Crystal in the Constant Electric Field","authors":"Lin-jun Huang","doi":"10.1137/18S017557","DOIUrl":"https://doi.org/10.1137/18S017557","url":null,"abstract":"We construct a global conservative weak solution to the Cauchy problem for the non-linear variational wave equation $v_{tt} - c(v)(c(v)v_x)_x + frac{1}{2}(v+v^3)= 0$ where $c(cdot)$ is any smooth function with uniformly positive bounded value. This wave equation is derived from a wave system modelling nematic liquid crystals in a constant electric field.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46356118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Kim, Eun Kyung Kwon, Qian Sha, B. Junker, T. Sweet
{"title":"CID Models on Real-world Social Networks and Goodness of Fit Measurements","authors":"J. Kim, Eun Kyung Kwon, Qian Sha, B. Junker, T. Sweet","doi":"10.1137/18s017260","DOIUrl":"https://doi.org/10.1137/18s017260","url":null,"abstract":"Assessing the model fit quality of statistical models for network data is an ongoing and under-examined topic in statistical network analysis. Traditional metrics for evaluating model fit on tabular data such as the Bayesian Information Criterion are not suitable for models specialized for network data. We propose a novel self-developed goodness of fit (GOF) measure, the `stratified-sampling cross-validation' (SCV) metric, that uses a procedure similar to traditional cross-validation via stratified-sampling to select dyads in the network's adjacency matrix to be removed. SCV is capable of intuitively expressing different models' ability to predict on missing dyads. Using SCV on real-world social networks, we identify the appropriate statistical models for different network structures and generalize such patterns. In particular, we focus on conditionally independent dyad (CID) models such as the Erdos Renyi model, the stochastic block model, the sender-receiver model, and the latent space model.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46985513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Computing shape DNA using the closest point method","authors":"Rachel Han","doi":"10.1137/18s016801","DOIUrl":"https://doi.org/10.1137/18s016801","url":null,"abstract":"We demonstrate an application of the closest point method where the truncated spectrum of the Laplace--Beltrami operator of an object is used to identify the object. The effectiveness of the method is analyzed as well as the default algorithm, `eigs', in MATLAB which computes the eigenvalues of a given matrix. We also cluster \"similar\" objects via multi-dimensional scaling algorithm and empirically measure its effectiveness.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45984628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A sample size calculator for <i>SMART</i> pilot studies.","authors":"Hwanwoo Kim, Edward Ionides, Daniel Almirall","doi":"10.1137/15s014058","DOIUrl":"10.1137/15s014058","url":null,"abstract":"<p><p>In clinical practice, as well as in other areas where interventions are provided, a sequential individualized approach to treatment is often necessary, whereby each treatment is adapted based on the object's response. An <i>adaptive intervention</i> is a sequence of decision rules which formalizes the provision of treatment at critical decision points in the care of an individual. In order to inform the development of an <i>adaptive intervention</i>, scientists are increasingly interested in the use of <i>sequential multiple assignment randomized trials (SMART</i>), which is a type of multi-stage randomized trial where individuals are randomized repeatedly at critical decision points to a set treatment options. While there is great interest in the use of <i>SMART</i> and in the development of <i>adaptive interventions</i>, both are relatively new to the medical and behavioral sciences. As a result, many clinical researchers will first implement a <i>SMART</i> pilot study (i.e., a small-scale version of a <i>SMART</i>) to examine feasibility and acceptability considerations prior to conducting a full-scale <i>SMART</i> study. A primary aim of this paper is to introduce a new methodology to calculate minimal sample size necessary for conducting a <i>SMART</i> pilot.</p>","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"9 ","pages":"229-250"},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8528343/pdf/nihms-1057459.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39539397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"COVID-19, Crowdedness, and CMC Dining: An Agent-Based Model Approach to Reducing the Spread of COVID-19","authors":"Reia Li, Ruth Efe, Zintan Mwinila-Yuori","doi":"10.1137/22s1483633","DOIUrl":"https://doi.org/10.1137/22s1483633","url":null,"abstract":". To make a COVID-safe return to in-person learning in the fall 2021 semester, Claremont McKenna College (CMC) created a new outdoor dining option to decrease the number of students inside the dining hall at one time: food trucks. However, crowding often occurs at both the inside and outside dining options. And so, we constructed an Agent-Based Model (ABM) to simulate the flow of students to the dining options over the lunch time hours. We use our ABM to investigate when over the lunch period crowding occurs and how often both or either option is crowded. Our analysis examines three different student behaviors namely, the ability to stick to an initial preference, the ability to sense crowding, and the ability to be influenced by other students. We find that the behavior that influences the level of crowding in the dining areas the most is having a strong preference for one of the dining areas. We also explore two different control measures that CMC could take to reduce crowding: adding another outdoor food option or increasing the amount of grab-and-go options. We find that adding another dining area is more effective in reducing crowding.","PeriodicalId":93373,"journal":{"name":"SIAM undergraduate research online","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64316975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}