Introduction to Modeling and Simulation
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Reporting Guidelines for Simulation-Based Research in Social Sciences
Reporting Guidelines for Simulation-based Research in Social Sciences Hazhir Rahmandad John D. Sterman Grado Department of Industrial and Sloan School of Management Systems Engineering Massachusetts Institute of Technology Virginia Tech [email protected] [email protected] Abstract Reproducibility of research is critical for the healthy growth and accumulation of reliable knowledge, and simulation-based research is no exception. However, studies show many simulation-based studies in the social sciences are not reproducible. Better standards for documenting simulation models and reporting results are needed to enhance the reproducibility of simulation-based research in the social sciences. We provide an initial set of Reporting Guidelines for Simulation-based Research (RGSR) in the social sciences, with a focus on common scenarios in system dynamics research. We discuss these guidelines separately for reporting models, reporting simulation experiments, and reporting optimization results. The guidelines are further divided into minimum and preferred requirements, distinguishing between factors that are indispensable for reproduction of research and those that enhance transparency. We also provide a few guidelines for improved visualization of research to reduce the costs of reproduction. Suggestions for enhancing the adoption of these guidelines are discussed at the end. Acknowledgments: We would like to thank Mohammad Jalali for providing excellent research assistance for this paper. We thank David Lane for his helpful suggestions. 1. Introduction and Motivation Reproducibility of research is central to the progress of science. Only when research results are independently reproducible can different research projects build on each other, verify the results reported by other researchers, and convince the public of the reliability of their results (Laine, Goodman et al. -
Serious Games, Debriefing, and Simulation/Gaming As a Discipline
41610.1177/104687811 0390784CrookallSimulation & Gaming © 2010 SAGE Publications Reprints and permission: http://www. sagepub.com/journalsPermissions.nav Editorial Simulation & Gaming 41(6) 898 –920 Serious Games, © 2010 SAGE Publications Reprints and permission: http://www. Debriefing, and sagepub.com/journalsPermissions.nav DOI: 10.1177/1046878110390784 Simulation/Gaming http://sg.sagepub.com as a Discipline David Crookall Abstract At the close of the 40th Anniversary Symposium of S&G, this editorial offers some thoughts on a few important themes related to simulation/gaming. These are develop- ment of the field, the notion of serious games, the importance of debriefing, the need for research, and the emergence of a discipline. I suggest that the serious gaming com- munity has much to offer the discipline of simulation/gaming and that debriefing is vital both for learning and for establishing simulation/gaming as a discipline. Keywords academic programs, acceptability, after action review, 40th anniversary, computers, debriefing, definitions, design, discipline, engagement, events, experiential learning, feedback, games, interdisciplinarity, journals, learning, periodicals, philosophy of simulation, practice, processing experience, professional associations, publication, research, review articles, scholarship, serious games, S&G, simulation, simulation/ gaming, terminology, theory, training The field of simulation/gaming is, to be sure, rather fuzzy, and sits uneasily in many areas. The 40th Anniversary Symposium was an opportunity -
Operations Research and Simulation in Master's
Proceedings of the 2013 Winter Simulation Conference R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds OPERATIONS RESEARCH AND SIMULATION IN MASTER’S DEGREES: A CASE STUDY REGARDING DIFFERENT UNIVERSITIES IN SPAIN Alex Grasas Angel A. Juan Helena Ramalhinho Dept. of Economics and Business Dept. of Computer Science Universitat Pompeu Fabra / IN3 – Open University of Catalonia / Barcelona GSE Autonomous University of Barcelona Barcelona, 08005, SPAIN Barcelona, 08018, SPAIN ABSTRACT This paper presents several experiences regarding Operations Research (OR) and Simulation education activities in three master programs, each of them offered at a different university. The paper discusses the importance of teaching these contents in most managerial and engineering masters. After a brief overview of existing related work, the paper provides some recommendations –based on our own teaching experi- ences– that instructors should keep in mind when designing OR/Simulation courses, either in traditional face-to-face as well as in pure online learning models. The case studies exposed here include students from business management, computer science, and aeronautical management degrees, respectively. For each type of student, different OR/Simulation tools are employed in the courses, ranging from easy-to-use optimization and simulation software to simulation-based algorithms developed from scratch using a pro- gramming language. 1 INTRODUCTION Operations Research (OR) can be defined as the application of advanced analytical methods to support complex decision-making processes. Among others, some of these analytical methods are: simulation, da- ta analysis, mathematical optimization, and metaheuristics. Thus, OR is an interdisciplinary area which borrows methods and techniques from many fields, including: mathematics, computer science, statistics, and business management. -
Module 1 (Computer Modeling and Simulation) Introduction
Module 1: Modeling and Simulation MODULE 1 (COMPUTER MODELING AND SIMULATION) INTRODUCTION Module Name: Introduction to Computer Modeling and Simulation Content of this Introduction: 1. Overview of the Module 2. Prerequisite knowledge and assumptions encompassed by the Module 3. Standards covered by the Module 4. Materials needed for the Module 5. Pacing Guides for 6 Lessons, including Learning Objectives and Assessment Questions 1. Overview of the Module This module introduces basic concepts in modeling complex systems through hands-on activities and participatory simulations. A scaffolded series of highly-engaging design and build activities guide students through developing their first computer model in StarLogo Nova, a modeling and simulation environment developed at Massachusetts Institute of Technology. Students practice designing and running experiments using a computer model as a virtual test bed. 2. Prerequisite knowledge and assumptions encompassed by the Module There are no prerequisites for Module 1. The module was designed to be an introduction to computer modeling and simulation for students with no prior background in the topic. It is necessary to complete this module prior to commencing the Earth, Life or Physical Science module. 3. Standards covered by the Module Please see the Standards Document for a detailed description of Standards covered by this Module, Lesson by Lesson. 4. Materials needed for this Module You will need the following materials to teach this module: • Computer and projector • What is a CAS? document -
Vbotz: a Pedagogical Cross-Disciplinary, Multi- Academic Level Manufacturing Corporate Simulation
Developments in Business Simulation and Experiential Learning, Volume 29, 2002 VBOTZ: A PEDAGOGICAL CROSS-DISCIPLINARY, MULTI- ACADEMIC LEVEL MANUFACTURING CORPORATE SIMULATION Kuvshinikov, Joseph Kent State University Ashtabula ABSTRACT their own discipline. Finally, students often fail to understand the language and needs of other disciplines. For VBOTZ is a pedagogical structured cross-disciplinary example, Accounting Technology students need to be able business simulation that sells a dynamic product. All to communicate with Engineering Technology students in concepts taught in participating disciplines are applied to order to work toward individual and shared goals. planning and operation of VBOTZ. Faculty serve as the Understanding the need to address these issues, School of board of directors and use the hands-on simulation to Technology faculty at Kent State University Ashtabula created enhance class content. Classes spend varying amounts of the cross-disciplinary virtual corporation simulation. their time in the classroom with their instructor and the remainder in VBOTZ meetings applying concepts learned. SYNOPSIS OF CORPORATE SIMULATION This virtual classroom is designed to employ students from all disciplines needed to create a real business, and to The cross-disciplinary virtual corporate simulation (VBOTZ) progress students up the corporate ladder at a pace that is a structured time-lined manufacturing simulation that sells parallels their curriculum. robotic teaching aids to other schools and institutions. Students enrolled in Accounting, Business Management, INTRODUCTION: Computer, Electrical, or Mechanical Technologies at the Ashtabula Campus are involved in creating and operating the THEORETICAL GROUNDING AND simulated business. All concepts currently taught in RELEVANT CONSTRUCTS respective discipline classes are applied to strategic planning and day-to-day operation of the corporation. -
Nursing Students' Perceptions of Satisfaction and Self-Confidence
Journal of Education and Practice www.iiste.org ISSN 2222-1735 (Paper) ISSN 2222-288X (Online) Vol.7, No.5, 2016 Nursing Students’ Perceptions of Satisfaction and Self-Confidence with Clinical Simulation Experience Tagwa Omer College of Nursing-Jeddah, King Saud bin Abdulaziz University for Health Sciences, Saudi Arabia Abstract Nursing and other health professionals are increasingly using simulation as a strategy and a tool for teaching and learning at all levels that need clinical training. Nursing education for decades used simulation as an integral part of nursing education. Recent studies indicated that simulation improves nursing knowledge, clinical practice, critical thinking, communication skills, improve self-confidence and satisfaction as well as clinical decision making.The aim of this study is to explore the perception of 117 nursing students on their satisfaction and self- confidence after clinical simulation experience using a survey method.This study was carried out at College of Nursing- Jeddah, King Saud bin Abdul Aziz University for Health Sciences. Where the nursing program consist of up to 30% clinical simulation.Data analyzed using descriptive method. Results indicated an overall satisfaction with simulation clinical experience. (Mean is 3.76 to 4.0) and indicated that their self-confidence is built after clinical simulation experience. (Mean is 3.11 to 4.14). The highest satisfaction items mean indicating participants agree that the teaching methods and strategies used in the simulation were effective, clinical instructors/ -
Lecture 9 – Modeling, Simulation, and Systems Engineering
Lecture 9 – Modeling, Simulation, and Systems Engineering • Development steps • Model-based control engineering • Modeling and simulation • Systems platform: hardware, systems software. EE392m - Spring 2005 Control Engineering 9-1 Gorinevsky Control Engineering Technology • Science – abstraction – concepts – simplified models • Engineering – building new things – constrained resources: time, money, • Technology – repeatable processes • Control platform technology • Control engineering technology EE392m - Spring 2005 Control Engineering 9-2 Gorinevsky Controls development cycle • Analysis and modeling – Control algorithm design using a simplified model – System trade study - defines overall system design • Simulation – Detailed model: physics, or empirical, or data driven – Design validation using detailed performance model • System development – Control application software – Real-time software platform – Hardware platform • Validation and verification – Performance against initial specs – Software verification – Certification/commissioning EE392m - Spring 2005 Control Engineering 9-3 Gorinevsky Algorithms/Analysis Much more than real-time control feedback computations • modeling • identification • tuning • optimization • feedforward • feedback • estimation and navigation • user interface • diagnostics and system self-test • system level logic, mode change EE392m - Spring 2005 Control Engineering 9-4 Gorinevsky Model-based Control Development Conceptual Control design model: Conceptual control sis algorithm: y Analysis x(t+1) = x(t) + -
Review of Artificial Intelligence, Simulation, and Modeling
Book Reviews AI Magazine Volume 12 Number 1 (1991) (© AAAI) BookReviews Artificial Intelligence, Numeric simulation is used for what- applied in modeling and simulation Simulation, and Modeling if questions, but diagnosis and expla- as the quantitative approach (for nation by symbolic reasoning are example, minimizing an objective Mark E. Lacy used for why questions. Execution on function that describes how well a computing machinery can be differ- model fits the available data). Typi- As a system scientist doing modeling ent as well: AI tools do not lend cally, the systems challenging us are and simulation, I have been interested themselves to the advantages of par- complex, and we are lucky if we can for some time in ways that modeling allel architectures as well as mathe- make any qualitative statements and simulation and AI could be of value matical tools do, and systems that about the behavior of a system other to each other. After all, both areas have attempt to integrate AI and mathe- than, perhaps, statements regarding their roots in putting knowledge into matical approaches pose a real chal- stability or long-term steady-state useful representations. I have specu- lenge to the development of fast behavior. Only the simplest of systems lated (AI Magazine, summer 1989, pp. software and hardware. can be analyzed in terms of its quali- 4348) that the scientist of the future, There is great potential, however, tative behavior. This reason is one of in applying computing to his(her) for AI and simulation to take advantage the main ones for performing simu- work, could benefit from a virtual of each other’s strengths. -
5 Modeling and Simulation for Systems of Systems Engineering Saurabh Mittal, Ph.D
System of Systems – Innovations for the 21 st Center, Edited by [Mo Jamshidi]. ISBN 0-471-XXXXX-X Copyright © 2008 Wiley[Imprint], Inc. 5 Modeling and Simulation for Systems of Systems Engineering Saurabh Mittal, Ph.D. Bernard P. Zeigler, Ph.D. Arizona Center for Integrative Modeling and Simulation Electrical and Computer Engineering, University of Arizona Tucson, AZ José L. Risco Martín, Ph.D. Departamento de Arquitectura de Computadores y Automática Facultad de Informática Universidad Complutense de Madrid Madrid, Spain Ferat Sahin, Ph.D. Multi Agent Bio-Robotics Laboratory Electrical Engineering Rochester Institute of Technology Rochester, NY Mo Jamshidi, Ph.D. Lutcher Brown Endowed Chair Electrical and Computer Engineering University of Texas San Antonio San Antonio, TX Wiley STM / Mo Jamshidi: System of Systems - Innovation for the 21st Century page 2 Chapter 5 / Mittal, Zeigler, Martin, Sahin, Jamshidi / filename: ch5.doc Abstract: A critical aspect and differentiator of a System of Systems (SoS) versus a single monolithic system is interoperability among the constituent disparate systems. A major application of Modeling and Simulation (M&S) to SoS Engineering is to facilitate system integration in a manner that helps to cope with such interoperability problems. A case in point is the integration infrastructure offered by the DoD Global Information Grid (GIG) and its Service Oriented Architecture (SOA). In this chapter, we discuss a process called DEVS Unified Process (DUNIP) that uses the Discrete Event System Specification (DEVS) formalism as a basis for integrated system engineering and testing called the Bifurcated Model- Continuity life-cycle development methodology. DUNIP uses an XML-based DEVS Modeling Language (DEVSML) framework that provides the capability to compose models that may be expressed in a variety of DEVS implementation languages. -
The Use of Role-Play Simulations to Foster Interdisciplinary/Global Learning
Journal of Instructional Pedagogies Volume 16, July, 2015 Confronting pedagogy in “Confronting Globalization”: The use of role-play simulations to foster interdisciplinary/global learning Lesley A. DeNardis Sacred Heart University ABSTRACT With the increasing emphasis on global learning as part of the redesigned institutional mission of American higher education, there will arguably be a need for a variety of global learning experiences across the undergraduate curriculum. Efforts to incorporate global learning in course content at home by globalizing or internationalizing the curricula are already underway at many institutions of higher education. This article offers a set of recommendations for educators wishing to globalize their courses by adopting an interdisciplinary approach to global learning specifically through the use of role-play simulations. As a problem-based pedagogy, role-play simulations are uniquely equipped to deliver interdisciplinary and global learning outcomes since both fields are explicitly geared towards practical problem-solving. It will be argued that an interdisciplinary approach to global learning through the use of role-play simulations offers a number of pedagogical advantages to traditional teaching techniques. Keywords: interdisciplinary, global learning, role-play simulations Copyright statement: Authors retain the copyright to the manuscripts published in AABRI journals. Please see the AABRI Copyright Policy at http://www.aabri.com/copyright.html Confronting pedagogy, Page 1 Journal of Instructional Pedagogies Volume 16, July, 2015 INTRODUCTION The “Confronting Globalization” simulation created by University of Maryland Technology Accelerators Project, presents an excellent opportunity to advance global learning through the adoption of an interdisciplinary approach. This article will present a critical examination of global learning through the use of classroom simulations. -
Department of Computational Modeling and Simulation Engineering 4
decision support. Visualization of these is also a significant part of these Department of areas. Collaborative Autonomous Systems Laboratory Computational Modeling The Collaborative Autonomous Systems Laboratory supports instructional and multidisciplinary research activities related to autonomous systems. and Simulation This forth laboratory area is shared with the mechanical and aerospace department. CMSE maintains 4 PC workstations and 10 various types of robotic systems. The lab contains an area dedicated to cyber security Engineering research as related to collaborative autonomous systems. Web Site: http://www.odu.edu/cmsv (http://www.odu.edu/cmsv/) The CAVE (CAVE Automated Virtual Environment) 1300 Engineering and Computational Sciences Building The CAVE (Cave Automated Virtual Environment) Virtual Reality 757-683-3720 laboratory area contains several 3D visualization systems. The CAVE is Yuzhong Shen, Chair a high-resolution projection-screen virtual reality system. The screens are Masha Sosonkina, Graduate Program Director arranged in a 10 foot cube with computer-generated images projected on three walls and a floor. The CAVE lab also contains a 3 meter Vision Dome Department Description projection system and an Immersa-Desk virtual reality display. Two 3D printers are also placed in the CAVE Lab. The CMSE Department offers an undergraduate four-year degree program leading to the Bachelor of Science in Modeling and Simulation Engineering Associated Centers: (BS-M&SE). The department also offers programs of graduate study leading to the degrees Master of Engineering, Master of Science, Doctor A significant resource to the department is the Virginia Modeling, Analysis of Engineering, and Doctor of Philosophy with a major in Modeling and and Simulation Center located adjacent to the University's Tri-Cities Higher Simulation. -
Modeling and Simulation Technologies Can Help U.S
OUR ADVANCED SIMULATION CAPABILITIES MEAN YOU CAN TRAIN FROM ANYWHERE WITHOUT SACRIFICING TACTICAL REALISM. MODELING & SIMULATION Real-World Environments to Help You Train Like You Fight Success on the modern battlefield is a complex and rapidly evolving pursuit, as new enemies and technologies continue to emerge. The only way to ensure readiness in the face of this shifting landscape is for warfighters to train using the same scenarios, obstacles, and teamwork they’ll use on the field of battle, no matter how it changes. AN ENRICHED TRAINING APPROACH technology. By creating environments that mimic real-world Basic, stand-alone classroom training is not enough to build situations, we provide an opportunity for warfighters to a force that is ready to meet any challenge. But training at prepare in ways that would otherwise be difficult, dangerous, scale has traditionally been expensive and involved extensive and expensive. manual tuning. Fortunately, today’s modeling and simulation technologies can help U.S. defense and national security Our secure, comprehensive solutions encompass software, leaders enrich training for refined strategies, more predictable hardware, and networks to bring together live, virtual, outcomes, and optimal results. and constructive (LVC) assets in a fully-integrated, unified training battlespace. We are enhancing LVC training and Through distributed, highly-scalable, integrated training that simulation with machine learning, to test and calibrate remote incorporates live, virtual, computer-to-computer, and gaming system elements, while using deep learning to automate elements, U.S. warfighters can learn and reinforce their skills in content creation. The result is the development of training real-world environments.