Psychological research often involves understanding psychological constructs through conducting factor analysis on data collected by a questionnaire, which can comprise hundreds of questions. Without interactive systems for interpreting factor models, researchers are frequently exposed to subjectivity, potentially leading to misinterpretations or overlooked crucial information. This paper introduces FAVis, a novel interactive visualization tool designed to aid researchers in interpreting and evaluating factor analysis results. FAVis enhances the understanding of relationships between variables and factors by supporting multiple views for visualizing factor loadings and correlations, allowing users to analyze information from various perspectives. The primary feature of FAVis is to enable users to set optimal thresholds for factor loadings to balance clarity and information retention. FAVis also allows users to assign tags to variables, enhancing the understanding of factors by linking them to their associated psychological constructs. Our user study demonstrates the utility of FAVis in various tasks.
{"title":"FAVis: Visual Analytics of Factor Analysis for Psychological Research","authors":"Yikai Lu, Chaoli Wang","doi":"arxiv-2407.14072","DOIUrl":"https://doi.org/arxiv-2407.14072","url":null,"abstract":"Psychological research often involves understanding psychological constructs\u0000through conducting factor analysis on data collected by a questionnaire, which\u0000can comprise hundreds of questions. Without interactive systems for\u0000interpreting factor models, researchers are frequently exposed to subjectivity,\u0000potentially leading to misinterpretations or overlooked crucial information.\u0000This paper introduces FAVis, a novel interactive visualization tool designed to\u0000aid researchers in interpreting and evaluating factor analysis results. FAVis\u0000enhances the understanding of relationships between variables and factors by\u0000supporting multiple views for visualizing factor loadings and correlations,\u0000allowing users to analyze information from various perspectives. The primary\u0000feature of FAVis is to enable users to set optimal thresholds for factor\u0000loadings to balance clarity and information retention. FAVis also allows users\u0000to assign tags to variables, enhancing the understanding of factors by linking\u0000them to their associated psychological constructs. Our user study demonstrates\u0000the utility of FAVis in various tasks.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744941","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}
By conducting a bibliometric analysis on 4,869 publications in Current Psychology from 2013 to 2022, this paper examined the annual publications and annual citations, as well as the leading institutions, countries, and keywords. CiteSpace, VOSviewer and SCImago Graphica were utilized for visualization analysis. On one hand, this paper analyzed the academic influence of Current Psychology over the past decade. On the other hand, it explored the research hotspots and future development trends within the field of international psychology. The results revealed that the three main research areas covered in the publications of Current Psychology were: the psychological well-being of young people, the negative emotions of adults, and self-awareness and management. The latest research hotspots highlighted in the journal include negative emotions, personality, and mental health. The three main development trends of Current Psychology are: 1) exploring the personality psychology of both adolescents and adults, 2) promoting the interdisciplinary research to study social psychological issues through the use of diversified research methods, and 3) emphasizing the emotional psychology of individuals and their interaction with social reality, from a people-oriented perspective.
{"title":"Identifying Research Hotspots and Future Development Trends in Current Psychology: A Bibliometric Analysis of the Past Decade's Publications","authors":"Shen Liu, Yan Yang","doi":"arxiv-2407.13495","DOIUrl":"https://doi.org/arxiv-2407.13495","url":null,"abstract":"By conducting a bibliometric analysis on 4,869 publications in Current\u0000Psychology from 2013 to 2022, this paper examined the annual publications and\u0000annual citations, as well as the leading institutions, countries, and keywords.\u0000CiteSpace, VOSviewer and SCImago Graphica were utilized for visualization\u0000analysis. On one hand, this paper analyzed the academic influence of Current\u0000Psychology over the past decade. On the other hand, it explored the research\u0000hotspots and future development trends within the field of international\u0000psychology. The results revealed that the three main research areas covered in\u0000the publications of Current Psychology were: the psychological well-being of\u0000young people, the negative emotions of adults, and self-awareness and\u0000management. The latest research hotspots highlighted in the journal include\u0000negative emotions, personality, and mental health. The three main development\u0000trends of Current Psychology are: 1) exploring the personality psychology of\u0000both adolescents and adults, 2) promoting the interdisciplinary research to\u0000study social psychological issues through the use of diversified research\u0000methods, and 3) emphasizing the emotional psychology of individuals and their\u0000interaction with social reality, from a people-oriented perspective.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744934","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}
The definition of Data Science is a hotly debated topic. For many, the definition is a simple shortcut to Artificial Intelligence or Machine Learning. However, there is far more depth and nuance to the field of Data Science than a simple shortcut can provide. The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science. This model is based on identifying a unified understanding of the data work done across all areas of Data Science. It represents a generational leap forward in how we understand and teach Data Science. In this paper we will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI. From this foundation we will present our Undergraduate Major curriculum in Data Science and demonstrate how it prepares students to be well-rounded Data Science team members and leaders. The paper will conclude with an in-depth overview of the Foundations of Data Science course designed to introduce students to the field while also implementing proven STEM oriented pedagogical methods. These include, for example, specifications grading, active learning lectures, guest lectures from industry experts and weekly gamification labs.
{"title":"The Future of Data Science Education","authors":"Brian Wright, Peter Alonzi, Ali Riveria","doi":"arxiv-2407.11824","DOIUrl":"https://doi.org/arxiv-2407.11824","url":null,"abstract":"The definition of Data Science is a hotly debated topic. For many, the\u0000definition is a simple shortcut to Artificial Intelligence or Machine Learning.\u0000However, there is far more depth and nuance to the field of Data Science than a\u0000simple shortcut can provide. The School of Data Science at the University of\u0000Virginia has developed a novel model for the definition of Data Science. This\u0000model is based on identifying a unified understanding of the data work done\u0000across all areas of Data Science. It represents a generational leap forward in\u0000how we understand and teach Data Science. In this paper we will present the\u0000core features of the model and explain how it unifies various concepts going\u0000far beyond the analytics component of AI. From this foundation we will present\u0000our Undergraduate Major curriculum in Data Science and demonstrate how it\u0000prepares students to be well-rounded Data Science team members and leaders. The\u0000paper will conclude with an in-depth overview of the Foundations of Data\u0000Science course designed to introduce students to the field while also\u0000implementing proven STEM oriented pedagogical methods. These include, for\u0000example, specifications grading, active learning lectures, guest lectures from\u0000industry experts and weekly gamification labs.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721117","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}
In this paper, we present a new ensemble-based filter method by reconstructing the analysis step of the particle filter through a transport map, which directly transports prior particles to posterior particles. The transport map is constructed through an optimization problem described by the Maximum Mean Discrepancy loss function, which matches the expectation information of the approximated posterior and reference posterior. The proposed method inherits the accurate estimation of the posterior distribution from particle filtering. To improve the robustness of Maximum Mean Discrepancy, a variance penalty term is used to guide the optimization. It prioritizes minimizing the discrepancy between the expectations of highly informative statistics for the approximated and reference posteriors. The penalty term significantly enhances the robustness of the proposed method and leads to a better approximation of the posterior. A few numerical examples are presented to illustrate the advantage of the proposed method over the ensemble Kalman filter.
在本文中，我们提出了一种新的基于集合的滤波方法，它通过一个传输图（transportmap）来重新构建粒子滤波的分析步骤，直接将先验粒子传输到后验粒子。传输图是通过最大均差损失函数（Maximum Mean Discrepancy loss function）描述的优化问题构建的，它匹配了近似后验和参考后验的期望信息。所提出的方法继承了粒子滤波法对后验分布的精确估计。为了提高最大均差法的鲁棒性，使用了方差惩罚项来指导优化。它优先最小化近似后验和参考后验的高信息量统计期望之间的差异。惩罚项显著增强了所提方法的鲁棒性，并使后验的近似度更高。本文列举了几个数值示例来说明所提方法相对于集合卡尔曼滤波器的优势。
{"title":"Ensemble Transport Filter via Optimized Maximum Mean Discrepancy","authors":"Dengfei Zeng, Lijian Jiang","doi":"arxiv-2407.11518","DOIUrl":"https://doi.org/arxiv-2407.11518","url":null,"abstract":"In this paper, we present a new ensemble-based filter method by\u0000reconstructing the analysis step of the particle filter through a transport\u0000map, which directly transports prior particles to posterior particles. The\u0000transport map is constructed through an optimization problem described by the\u0000Maximum Mean Discrepancy loss function, which matches the expectation\u0000information of the approximated posterior and reference posterior. The proposed\u0000method inherits the accurate estimation of the posterior distribution from\u0000particle filtering. To improve the robustness of Maximum Mean Discrepancy, a\u0000variance penalty term is used to guide the optimization. It prioritizes\u0000minimizing the discrepancy between the expectations of highly informative\u0000statistics for the approximated and reference posteriors. The penalty term\u0000significantly enhances the robustness of the proposed method and leads to a\u0000better approximation of the posterior. A few numerical examples are presented\u0000to illustrate the advantage of the proposed method over the ensemble Kalman\u0000filter.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721121","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}
For a sequence of $n$ random variables taking values $0$ or $1$, the hot hand statistic of streak length $k$ counts what fraction of the streaks of length $k$, that is, $k$ consecutive variables taking the value $1$, among the $n$ variables are followed by another $1$. Since this statistic does not use the expected value of how many streaks of length $k$ are observed, but instead uses the realization of the number of streaks present in the data, it may be a biased estimator of the conditional probability of a fixed random variable taking value $1$ if it is preceded by a streak of length $k$, as was first studied and observed explicitly in [Miller and Sanjurjo, 2018]. In this short note, we suggest an alternative proof for an explicit formula of the expectation of the hot hand statistic for the case of streak length one. This formula was obtained through a different argument in [Miller and Sanjurjo, 2018] and [Rinott and Bar-Hillel, 2015].
对于取值为 $0$ 或 $1$的 $n$ 随机变量序列，长度为 $k$ 的条纹长度热手统计量（hot handstatistic of streak length $k$）计算的是在 $n$ 变量中，长度为 $k$ 的条纹（即取值为 $1$的 $k$ 连续变量）中，有多少个是在另一个 $1$ 变量之后出现的。由于该统计量并不使用观察到的长度为$k$的条纹数量的预期值，而是使用数据中存在的条纹数量的实现值，因此它可能是固定随机变量取值$1$的条件概率的无偏估计值，如果它前面有长度为$k$的条纹，这在[Miller and Sanjurjo, 2018]中得到了首次研究和明确观察。在本短文中，我们提出了另一种证明方法，即在条纹长度为 1 的情况下，热手统计量期望值的明确公式。这个公式在 [Miller and Sanjurjo, 2018] 和 [Rinott and Bar-Hillel, 2015] 中通过不同的论证得到。
{"title":"Alternative proof for the bias of the hot hand statistic of streak length one","authors":"Maximilian Janisch","doi":"arxiv-2407.10577","DOIUrl":"https://doi.org/arxiv-2407.10577","url":null,"abstract":"For a sequence of $n$ random variables taking values $0$ or $1$, the hot hand\u0000statistic of streak length $k$ counts what fraction of the streaks of length\u0000$k$, that is, $k$ consecutive variables taking the value $1$, among the $n$\u0000variables are followed by another $1$. Since this statistic does not use the\u0000expected value of how many streaks of length $k$ are observed, but instead uses\u0000the realization of the number of streaks present in the data, it may be a\u0000biased estimator of the conditional probability of a fixed random variable\u0000taking value $1$ if it is preceded by a streak of length $k$, as was first\u0000studied and observed explicitly in [Miller and Sanjurjo, 2018]. In this short\u0000note, we suggest an alternative proof for an explicit formula of the\u0000expectation of the hot hand statistic for the case of streak length one. This\u0000formula was obtained through a different argument in [Miller and Sanjurjo,\u00002018] and [Rinott and Bar-Hillel, 2015].","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721118","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}
In a decision problem, observations are said to be material if they must be taken into account to perform optimally. Decision problems have an underlying (graphical) causal structure, which may sometimes be used to evaluate certain observations as immaterial. For soluble graphs - ones where important past observations are remembered - there is a complete graphical criterion; one that rules out materiality whenever this can be done on the basis of the graphical structure alone. In this work, we analyse a proposed criterion for insoluble graphs. In particular, we prove that some of the conditions used to prove immateriality are necessary; when they are not satisfied, materiality is possible. We discuss possible avenues and obstacles to proving necessity of the remaining conditions.
{"title":"Toward a Complete Criterion for Value of Information in Insoluble Decision Problems","authors":"Ryan Carey, Sanghack Lee, Robin J. Evans","doi":"arxiv-2407.09883","DOIUrl":"https://doi.org/arxiv-2407.09883","url":null,"abstract":"In a decision problem, observations are said to be material if they must be\u0000taken into account to perform optimally. Decision problems have an underlying\u0000(graphical) causal structure, which may sometimes be used to evaluate certain\u0000observations as immaterial. For soluble graphs - ones where important past\u0000observations are remembered - there is a complete graphical criterion; one that\u0000rules out materiality whenever this can be done on the basis of the graphical\u0000structure alone. In this work, we analyse a proposed criterion for insoluble\u0000graphs. In particular, we prove that some of the conditions used to prove\u0000immateriality are necessary; when they are not satisfied, materiality is\u0000possible. We discuss possible avenues and obstacles to proving necessity of the\u0000remaining conditions.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721173","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}
This paper introduces a novel course design in the Master Program in Biostatistics at the University of Zurich that integrates computing skills, effective communication, reproducibility, and scientific integrity within one course. Utilizing a flipped classroom model, the course aims to equip students with the necessary competencies to handle real-world data analysis challenges and effective statistical practice in general. The curriculum includes practical tools such as version control with Git, dynamic reporting, unit testing and containerization to foster reproducibility, and integrity in statistical practice. Feedback gathered from both staff and students post-implementation indicates that the course significantly enhances student readiness for professional and academic environments, demonstrating the effectiveness of this educational approach.
{"title":"More than Formulas -- Integrity, Communication, Computing and Reproducibility in Statistics Education","authors":"Eva Furrer, Annina Cincera, Reinhard Furrer","doi":"arxiv-2407.08835","DOIUrl":"https://doi.org/arxiv-2407.08835","url":null,"abstract":"This paper introduces a novel course design in the Master Program in\u0000Biostatistics at the University of Zurich that integrates computing skills,\u0000effective communication, reproducibility, and scientific integrity within one\u0000course. Utilizing a flipped classroom model, the course aims to equip students\u0000with the necessary competencies to handle real-world data analysis challenges\u0000and effective statistical practice in general. The curriculum includes\u0000practical tools such as version control with Git, dynamic reporting, unit\u0000testing and containerization to foster reproducibility, and integrity in\u0000statistical practice. Feedback gathered from both staff and students\u0000post-implementation indicates that the course significantly enhances student\u0000readiness for professional and academic environments, demonstrating the\u0000effectiveness of this educational approach.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721119","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}
Peter K. Enns, Colleen L. Barry, James N. Druckman, Sergio Garcia-Rios, David C. Wilson, Jonathon P. Schuldt
As survey methods adapt to technological and societal changes, a growing body of research seeks to understand the tradeoffs associated with various sampling methods and administration modes. We show how the NSF-funded 2022 Collaborative Midterm Survey (CMS) can be used as a dynamic and transparent framework for evaluating which sampling approaches - or combination of approaches - are best suited for various research goals. The CMS is ideally suited for this purpose because it includes almost 20,000 respondents interviewed using two administration modes (phone and online) and data drawn from random digit dialing, random address-based sampling, a probability-based panel, two nonprobability panels, and two nonprobability marketplaces. The analysis considers three types of population benchmarks (election data, administrative records, and large government surveys) and focuses on the national-level estimates as well as oversamples in three states (California, Florida, and Wisconsin). In addition to documenting how each of the survey strategies performed, we develop a strategy to assess how different combinations of approaches compare to different population benchmarks in order to guide researchers combining sampling methods and sources. We conclude by providing specific recommendations to public opinion and election survey researchers and demonstrating how our approach could be applied to a large government survey conducted at regular intervals to provide ongoing guidance to researchers, government, businesses, and nonprofits regarding the most appropriate survey sampling and administration methods.
{"title":"The Need for a Recurring Large-Scale Benchmarking Survey to Continually Evaluate Sampling Methods and Administration Modes: Lessons from the 2022 Collaborative Midterm Survey","authors":"Peter K. Enns, Colleen L. Barry, James N. Druckman, Sergio Garcia-Rios, David C. Wilson, Jonathon P. Schuldt","doi":"arxiv-2407.06090","DOIUrl":"https://doi.org/arxiv-2407.06090","url":null,"abstract":"As survey methods adapt to technological and societal changes, a growing body\u0000of research seeks to understand the tradeoffs associated with various sampling\u0000methods and administration modes. We show how the NSF-funded 2022 Collaborative\u0000Midterm Survey (CMS) can be used as a dynamic and transparent framework for\u0000evaluating which sampling approaches - or combination of approaches - are best\u0000suited for various research goals. The CMS is ideally suited for this purpose\u0000because it includes almost 20,000 respondents interviewed using two\u0000administration modes (phone and online) and data drawn from random digit\u0000dialing, random address-based sampling, a probability-based panel, two\u0000nonprobability panels, and two nonprobability marketplaces. The analysis\u0000considers three types of population benchmarks (election data, administrative\u0000records, and large government surveys) and focuses on the national-level\u0000estimates as well as oversamples in three states (California, Florida, and\u0000Wisconsin). In addition to documenting how each of the survey strategies\u0000performed, we develop a strategy to assess how different combinations of\u0000approaches compare to different population benchmarks in order to guide\u0000researchers combining sampling methods and sources. We conclude by providing\u0000specific recommendations to public opinion and election survey researchers and\u0000demonstrating how our approach could be applied to a large government survey\u0000conducted at regular intervals to provide ongoing guidance to researchers,\u0000government, businesses, and nonprofits regarding the most appropriate survey\u0000sampling and administration methods.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141577853","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}
Serious traffic congestion and emission by excessive usage of private cars are crucial issues in our modern society. As one solution for these, a concept of Park-and-Ride (PnR) where people stop their private cars (i.e. single-occupancy vehicles) at stations and ride on public vehicles (i.e. mass transportation) are receiving wide attention recently. In this paper, we propose a comprehensive mathematical model which can evaluate waiting times and traveling times of customers, and the total emission of vehicles for various usage ratio of PnR and operation policies of public transportation. Using a system of queues integrated with an emissions model we perform a case-study of Tsukuba city, in Japan. We indicate an intriguing trade-off between the waiting time of customers for the PnR and the long traveling time due to the traffic congestion (leading to high emissions) caused by private cars depending on the usage ratio through some numerical experiments. Moreover, we study the total cost to society caused by total trip times and pollution, in which the decision variables are the capacities and frequencies of the public transportation for the PnR system. Our numerical results showed a significant reduction in the total social cost under the optimal transit policy for the current high usage rate of single-occupancy vehicles. Furthermore, we show that further reduction in the total social cost can be revealed by considering the reduction on the use of private cars compared to the current state implying the social importance of promoting car-free movement.
{"title":"Reducing Total Trip Time and Vehicle Emission through Park-and-Ride -- methods and case-study","authors":"Ayane Nakamura, Fabiana Ferracina, Naoki Sakata, Takahiro Noguchi, Hiroyasu Ando","doi":"arxiv-2407.05572","DOIUrl":"https://doi.org/arxiv-2407.05572","url":null,"abstract":"Serious traffic congestion and emission by excessive usage of private cars\u0000are crucial issues in our modern society. As one solution for these, a concept\u0000of Park-and-Ride (PnR) where people stop their private cars (i.e.\u0000single-occupancy vehicles) at stations and ride on public vehicles (i.e. mass\u0000transportation) are receiving wide attention recently. In this paper, we\u0000propose a comprehensive mathematical model which can evaluate waiting times and\u0000traveling times of customers, and the total emission of vehicles for various\u0000usage ratio of PnR and operation policies of public transportation. Using a\u0000system of queues integrated with an emissions model we perform a case-study of\u0000Tsukuba city, in Japan. We indicate an intriguing trade-off between the waiting\u0000time of customers for the PnR and the long traveling time due to the traffic\u0000congestion (leading to high emissions) caused by private cars depending on the\u0000usage ratio through some numerical experiments. Moreover, we study the total\u0000cost to society caused by total trip times and pollution, in which the decision\u0000variables are the capacities and frequencies of the public transportation for\u0000the PnR system. Our numerical results showed a significant reduction in the\u0000total social cost under the optimal transit policy for the current high usage\u0000rate of single-occupancy vehicles. Furthermore, we show that further reduction\u0000in the total social cost can be revealed by considering the reduction on the\u0000use of private cars compared to the current state implying the social\u0000importance of promoting car-free movement.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575205","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}
Social network analysis (SNA) helps us understand the relationships and interactions between individuals, groups, organisations, or other social entities. In SNA, ties are generally binary or weighted based on their strength. Nonetheless, when actors are individuals, the relationships between actors are often imprecise and identifying them with simple scalars leads to information loss. Social relationships are often vague in real life. Despite many classical social network techniques contemplate the use of weighted links, these approaches do not align with the original philosophy of fuzzy logic, which instead aims to preserve the vagueness inherent in human language and real life. Dealing with imprecise ties and introducing fuzziness in the definition of relationships requires an extension of social network analysis to fuzzy numbers instead of crisp values. The mathematical formalisation for this generalisation needs to extend classical centrality indices and operations to fuzzy numbers. For this reason, this paper proposes a generalisation of the so-called Fuzzy Social Network Analysis (FSNA) to the context of imprecise relationships among actors. The article shows the theory and application of real data collected through a fascinating mouse tracking technique to study the fuzzy relationships in a collaboration network among the members of a University department.
社会网络分析（SNA）有助于我们了解个人、团体、组织或其他社会实体之间的关系和互动。在 SNA 中，联系通常是二元的，或根据其强度加权。然而，当行动者是个人时，行动者之间的关系往往是不精确的，用简单的标量来识别会导致信息丢失。在现实生活中，社会关系往往是模糊的。尽管许多经典的社会网络技术都考虑使用加权链接，但这些方法并不符合模糊逻辑的最初理念，而模糊逻辑的目标是保留人类语言和现实生活中固有的模糊性。要处理不精确的联系并在关系定义中引入模糊性，就需要将社会网络分析扩展到模糊数而不是清晰值。这种扩展的数学形式化需要将经典的中心度指数和运算扩展到模糊数。为此，本文提出了将所谓的模糊社会网络分析（FSNA）推广到行动者之间不精确关系的环境中。文章展示了通过引人入胜的鼠标跟踪技术收集到的真实数据的理论和应用，以研究大学某系成员之间合作网络中的模糊关系。
{"title":"Fuzzy Social Network Analysis: Theory and Application in a University Department's Collaboration Network","authors":"Annamaria Porreca, Fabrizio Maturo, Viviana Ventre","doi":"arxiv-2407.02401","DOIUrl":"https://doi.org/arxiv-2407.02401","url":null,"abstract":"Social network analysis (SNA) helps us understand the relationships and\u0000interactions between individuals, groups, organisations, or other social\u0000entities. In SNA, ties are generally binary or weighted based on their\u0000strength. Nonetheless, when actors are individuals, the relationships between\u0000actors are often imprecise and identifying them with simple scalars leads to\u0000information loss. Social relationships are often vague in real life. Despite\u0000many classical social network techniques contemplate the use of weighted links,\u0000these approaches do not align with the original philosophy of fuzzy logic,\u0000which instead aims to preserve the vagueness inherent in human language and\u0000real life. Dealing with imprecise ties and introducing fuzziness in the\u0000definition of relationships requires an extension of social network analysis to\u0000fuzzy numbers instead of crisp values. The mathematical formalisation for this\u0000generalisation needs to extend classical centrality indices and operations to\u0000fuzzy numbers. For this reason, this paper proposes a generalisation of the\u0000so-called Fuzzy Social Network Analysis (FSNA) to the context of imprecise\u0000relationships among actors. The article shows the theory and application of\u0000real data collected through a fascinating mouse tracking technique to study the\u0000fuzzy relationships in a collaboration network among the members of a\u0000University department.","PeriodicalId":501323,"journal":{"name":"arXiv - STAT - Other Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513511","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}
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