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Annual modeling and simulation Conference (ANNSIM)

作者： Belfrage, Michael Lorig, Fabian Davidsson, Paul Malmo Univ Dept Comp Sci & Media Technol Malmo Sweden Malmo Univ Internet Things & People Res Ctr Malmo Sweden

ISBN: (纸本)9798350350562;9781713899310

Social phenomena emerge from agent-environment interactions, rendering many statistical models unsuitable. agent-based Models (ABMs) offer a viable alternative for exploring policy implications. While recent crises like the COVID-19 pandemic may have increased ABM awareness, their use in policy-making has a long history. To better understand the potential challenges and opportunities of using ABMs to inform policy-making, we conducted a systematic literature review and identified 34 articles describing the use of ABMs involving policymakers. This review revealed that ABMs have been implemented to support policymakers across a range of policy areas, but also identified low levels of model traceability and formal communication. Moreover, the review showed that the model's purpose and type tend to influence how validation is performed. The review concludes that models that have undergone little validation and lack proper documentation, while being informally communicated, may hinder policymakers from effectively motivating their decision-making.

关键词： agent-based modeling and simulation policy-modeling policy-making policy support

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Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder

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Heliyon 2025年第2期11卷 e42119页

作者： Saadati, Sina Sepahvand, Abdolah Razzazi, Mohammadreza Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just the affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy muscles from abnormal ones. Existing analysis applications, designed for other purposes, often lack essential software engineering features such as a user-friendly interface, infrastructure independence, usability and learning ability, cloud computing capabilities, and AI-based assistance. This research proposes a computer-based methodology to analyze human motion and differentiate between healthy and unhealthy muscles. First, an IoT-based approach is proposed to digitize human motion using smartphones instead of hardly accessible wearable sensors and markers. The motion data is then simulated to analyze the neuromusculoskeletal system. An agent-driven modeling method ensures the naturalness, accuracy, and interpretability of the simulation, incorporating neuromuscular details such as Henneman's size principle, action potentials, motor units, and biomechanical principles. The results are then provided to medical and clinical experts to aid in differentiating between healthy and unhealthy muscles and for further investigation. Additionally, a deep learning-based ensemble framework is proposed to assist in the analysis of the simulation results, offering both accuracy and interpretability. A user-friendly graphical interface enhances the application's usability. Being fully cloud-based, the application is infrastructure-independent and can be accessed on smartphones, PCs, and other devices without installation. This strategy not only addresses the current challenges in treating motion disorders but also paves the way for other clinical simulations by considering both scientific and com

关键词： agent-based modeling and simulation agent-driven simulation Cloud and IoT based simulation Human gait Muscles disorder

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Model-driven agent-based simulation development: A modeling language and empirical evaluation in the adaptive traffic signal control domain

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simulation MODELLING PRACTICE AND THEORY 2018年 83卷 162-187页

作者： Santos, Fernando Nunes, Ingrid Bazzan, Ana L. C. Univ Fed Rio Grande do Sul Inst Informat Ave Bento Goncalves 9500 BR-91501970 Porto Alegre RS Brazil Univ Estado Santa Catarina UDESC Ibirama Brazil TU Dortmund Dortmund Germany

Model-driven development (MDD) is an approach for supporting the development of software systems, in which high-level modeling artifacts drive the production of time and effort-consuming low-level artifacts, such as the source code. Previous studies of the MDD effectiveness showed that it significantly increases development productivity, because the development effort is focused on the business domain rather than technical issues. However, MDD was exploited in the context of agent-based development in a limited way, and most of the existing proposals demonstrated the effectiveness of using MDD in this context by argumentation or examples, lacking disciplined empirical analyses. In this paper, we explore the use of MDD for agent-based modeling and simulation in the adaptive traffic signal control (ATSC) domain, in which autonomous agents are in charge of managing traffic light indicators to optimize traffic flow. We propose an MDD approach, composed of a modeling language and model-to-code transformations for producing runnable simulations automatically. In order to analyze the productivity gains of our MDD approach, we compared the amount of design and implementation artifacts produced using our approach and traditional simulation platforms. Results indicate that our approach reduces the workload to develop agent-based simulations in the ATSC domain. (C) 2017 Elsevier B.V. All rights reserved.

关键词： agent-based modeling and simulation Model-driven development Development effort Traffic signal control

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An integrative agent-based vertical evacuation risk assessment model for near-field tsunami hazards

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RISK ANALYSIS 2022年第12期42卷 2720-2734页

作者： Chen, Chen Mostafizi, Alireza Wang, Haizhong Cox, Dan Chand, Cadell Oregon State Univ Sch Civil & Construct Engn Corvallis OR 97331 USA Washington Cty Dept Land Use & Transportat Hillsboro OR USA

This study couples FN-curves with agent-based modeling and simulation (ABMS) to assess risk for tsunamis with various recurrence intervals . By considering both expected number of casualties and the likelihood of tsunami events, multiple series of simulations and in-depth analyses determine (1) how vertical evacuation structure (VES) placement impacts mortality rate;(2) what the best evacuation strategies VES locations are;and (3) where evacuees are likely to be caught by tsunami waves. The results from utilizing FN-curves to conduct disaggregative analyses based on six tsunami scenarios indicate that choosing one tsunami scenario or averaging the risk of different scenarios may not fully articulate VES impacts due to the "levee effect," which potentially leads to false positives. Findings show that placing VESs close to shorelines saves nearby at-risk populations, but also results in two risk increasing phenomena: "exposure to risk" (i.e., evacuees being attracted to high risk roads by a VES when evacuating) and "blind zones" (i.e., locations near a VES where evacuees increase their risk by evacuating to that VES). When limited to one VES, placement near a population's centroid results in the lowest mortality rate. More than one VES may lower mortality rate further if VESs are spreading out according to community's topography. In addition to the analysis of tsunamis, the approach of coupling FN-curves with ABMS can be used by local authorities and engineers to determine tailored hard-adaptive measures and evacuation strategies, which helps to avoid maladaptive actions in different hazardous events.

关键词： agent-based modeling and simulation FN-curve risk analysis tsunami evacuation

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modeling and simulation to Assess the Role of Culture for Successful Lean Transformations 31st

Modeling and Simulation to Assess the Role of Culture for Su...

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31st International Conference on Flexible Automation and Intelligent Manufacturing

作者： Najarzadeh, Amir Badurdeen, Fazleena Univ Kentucky Dept Mech Engn Lexington KY 40506 USA Inst Sustainable Mfg Lexington KY 40506 USA

ISBN: (纸本)9783031176289;9783031176296

Lean manufacturing has been embraced by many companies as the business model to promote operational excellence. However, a high percentage of lean implementation attempts have failed. This failure is often attributed to the sole focus on the hard side (tools) of lean, at the expense of the soft side (culture). Most of the research investigating the culture-related aspects of lean either attempt to raise awareness about culture or merely promote a conceptual approach. This paper proposes a simulation-based method that can be used to identify and assess the role of various factors essential to forming an organizational culture conducive to successful lean transformations. The culture of an organization emerges from interaction among members, interaction with the environment, and personal attributes. agent-based modeling is an effective technique for evaluating dynamic systems of interacting agents and therefore is potent for modeling complexities that arise from interactions of the system components. However, due to blurred indicators which exist in defining culture, many measures are described subjectively by linguistic terms. Therefore, the use of fuzzy logic is proposed to emulate subjective human decision making by team members during interactions. Preliminary efforts for fuzzy-integrated, agent-based modeling and simulation are presented in an attempt to demonstrate the potential of using the method to identify and assess factors influencing organizational culture for lean transformations.

关键词： Lean Culture agent-based modeling and simulation Fuzzy logic

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OSIRIS: Organization simulation in Response to Intrusion Strategies 15th

OSIRIS: Organization Simulation in Response to Intrusion Str...

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15th International Conference on Social Computing, Behavioral- Cultural modeling and Prediction (SBP) / Behavior Representation in modeling and simulation Conference (BRiMS)

作者： Shin, Jeongkeun Dobson, Geoffrey B. Carley, Kathleen M. Carley, L. Richard Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9783031171147;9783031171130

OSIRIS, Organization simulation In Response to Intrusion Strategies, is an agent-based simulation framework that models virtual organization composed of end user agents with complex and realistic behavior patterns. The purpose of OSIRIS is to predict and analyze the scale of cyberattack damage on the organization once targeted by cybercriminals with a consideration of organization members' properties, behavior patterns, and social relations. In this paper, we detail how we reflect real world organization environments and cyberattack scenarios to OSIRIS by illustrating our organization and cybercriminal design.

关键词： agent-based modeling and simulation Human behavior modeling Organization studies

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Optimal agent-based Pickup and Delivery with Time Windows and Electric Vehicles

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APPLIED SCIENCES-BASEL 2024年第17期14卷 7528页

作者： Muraretu, Ionut Badica, Costin Univ Craiova Fac Automat Comp & Elect Craiova 200440 Romania

The traditional methods of transporting goods and people in urban areas using vehicles powered by internal combustion engines are major contributors to pollution. As a result, an increasing number of logistics companies are transitioning to electric vehicles (EVs) for daily operations, replacing traditional engines. This shift opens research avenues regarding the integration of EVs into delivery workflows and how this can contribute to greener cities. This study tackles the EV routing problem, focusing on balancing battery constraints and optimizing routes. We formulated the problem as a pickup and delivery with time windows, incorporating electric energy consumption constraints, and utilized consensus mechanisms in an agent-based simulation context. Our evaluation used 15 scenarios, capturing variations in vehicle configurations, order generation rates, and battery and freight capacities. We compared two order allocation strategies: "Closest Allocation" and "Negotiation" consensus-based allocation. The results confirmed that the consensus-based strategy outperformed the "Closest Allocation" in metrics such as remaining orders, orders not handled in time, total distance traveled, total recharging cost, and total number of recharges. These findings have significant implications for urban planners, logistic companies, and policymakers, demonstrating that an agent-based simulation context for electric vehicles using consensus-based strategies can enhance delivery efficiency and promote sustainability.

关键词： consensus mechanism agent-based modeling and simulation electric vehicle pickup delivery with time window

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Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2024年 191卷 104899页

作者： Bhattacharya, Parantapa Machi, Dustin Chen, Jiangzhuo Hoops, Stefan Lewis, Bryan Mortveit, Henning Venkatramanan, Srinivasan Wilson, Mandy L. Marathe, Achla Porebski, Przemyslaw Klahn, Brian Outten, Joseph Vullikanti, Anil Xie, Dawen Adiga, Abhijin Brown, Shawn Barrett, Christopher Marathe, Madhav Univ Virginia Biocomplex Inst Charlottesville VA 22904 USA Univ Virginia Dept Publ Hlth Sci Charlottesville VA USA Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA Univ Virginia Dept Syst Engn Charlottesville VA 22903 USA Hewlett Packard Corp San Francisco CA USA

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10 K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4 K to 28.2 K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4%

关键词： High performance computing workflow agent-based modeling and simulation Covid-19 vaccine acceptance

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A Framework for the Design and Development of Adaptive agent-based simulations to Explore Student Thinking and Performance in K-20 Science 1

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4th International Conference on Adaptive Instructional Systems Held as Part of the 24th International Conference on Human-Computer Interaction (HCII)

作者： Kinner, Tyler Whitaker, Elizabeth T. Georgia Tech Res Inst Atlanta GA 30332 USA

ISBN: (数字)9783031058875

ISBN: (纸本)9783031058875;9783031058868

In this paper, we describe the design and development of an agent-based modeling and simulation framework to support adaptive science learning based on modeling and simulation using NetLogo. This exploration is informed by the need for new curricular resources aligned with the modern vision of science teaching and learning, as put forth by the National Research Council's Framework for K12 Science Education [1]. This paper seeks to support the development of interactive, adaptive resources for science education that meet the 3-Dimensional expectations of science education described in the National Research Council's Framework. In addition to the primary focus on the student learning experience, the Framework for agent-based Adaptive Learning in Science (FABALS) considers both the developer and practitioner perspectives. As such, the modeling and simulation development framework and modular procedures for facilitating the adaptive training were developed with technological, pedagogical, and curricular perspectives in mind.

关键词： Science learning modeling and simulation agent-based modeling and simulation Curriculum Intelligent tutoring Representations Science and engineering practices NGSS Develop and use models

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A methodology for selecting a performance-convenient ABMS development framework on HPC platforms

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simulation MODELLING PRACTICE AND THEORY 2023年第1期128卷

作者： Moreno, Andreu Jorba, Josep Peralta, Cristina Cesar, Eduardo Sikora, Anna Hanzich, Mauricio Univ Autonoma Barcelona Cerdanyola Del Valles Spain Escola Univ Salesiana Sarria EUSS Barcelona Spain Univ Oberta Catalunya Barcelona Spain Mitiga Solut Barcelona Spain

agent-based modeling and simulation (ABMS) is an approach for simulating the actions and interactions of autonomous agents. Such interactions occur within a defined environment to assess their effects on a system as a whole. Depending on the complexity of the model and the number of simulated agents, an ABMS application may require a significant amount of computational resources. It makes them good candidates to be parallelized on HPC systems. However, most developers of ABMS simulators are experts in the specific simulation domain, but they lack the expertise to develop parallel applications. Consequently, several frameworks for generating HPC ABMS applications have been developed, and it may now be challenging for these non-expert users to choose which of these frameworks would provide the best performing simulator for a particular model. This paper presents a methodology that uses a benchmark to help non-expert users to select the most suitable framework to generate the best performing parallel implementation for a given ABMS model. Such a benchmark considers the common characteristics of parallel ABMS applications and includes parameters for influencing their relevant performance aspects. The methodology is based on defining a set of problem classes that represent the majority of known ABMS models and systematically conducting a series of experiments to determine which framework offers the best performance for each class. Then, users only need to identify the class that closely aligns with their model to make an informed decision regarding the appropriate development framework. The methodology is used to assess well-known ABMS parallel development frameworks (FLAME, RepastHPC, and DMASON) on real HPC platforms. The obtained results are validated using a real application for infection and contact tracing modeling.

关键词： agent-based modeling and simulation Parallel applications Performance Benchmark HPC

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