There has been a large increase in the number of days per year with numerous EF1-EF5 tornadoes.Given the significant damage incurred by tornadoes upon communities,community resilience analyses for tornado-stricken com...There has been a large increase in the number of days per year with numerous EF1-EF5 tornadoes.Given the significant damage incurred by tornadoes upon communities,community resilience analyses for tornado-stricken communities have been gaining momentum.As the community resilience analysis aims to guide how to lay out effective hazard mitigation strategies to decrease damage and improve recovery,a comprehensive and accurate approach is necessary.Agent-based modeling,an analysis approach in which different types of agents are created with their properties and behavior clearly defined to simulate the processes of those agents in an external environ-ment,is the most comprehensive and accurate approach so far to conducting community resilience simulations and investigating the decision-making for mitigation and recovery under natural hazards.In this paper,agent-based models(ABMs)are created to simulate the recovery process of a virtual testbed based on the real-world community in Joplin City,MO.The tornado path associated with the real-world tornado event that occurred in May 2011 is adopted in the tornado hazard modeling for the Joplin testbed.In addition,agent-based models are created for another virtual community in the Midwest United States named Centerville using an assumed tornado scenario of the same EF-scale as that in Joplin.The effects of hazard mitigation strategies on the two communities are also explored.A comparison between the analysis results of these two testbeds can indicate the influence of the characteristics of a tornado-prone community on the resilience of the community as well as on the effects of hazard mitigation strategies.It is observed that a community’s level of development significantly impacts the tornado resilience.In addition,the effects of a specific type of hazard mitigation strategy on the recovery process are contingent upon testbed characteristics.展开更多
A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions.The effect of the hazard geomet...A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions.The effect of the hazard geometry(center and angle of tornado path as well as the tornado width)is studied herein on how it influences the recovery of physical and social systems within the community.Given that pre-disaster preparedness including mitigation strategies(e.g.,retrofits)and policies(e.g.,insurance)is crucial for increasing the resilience of the community and facilitating a faster recovery process,in this study,the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties.The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling(ABM)approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies.The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems(lifeline networks,schools,healthcare,businesses,and households).The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.展开更多
A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study...A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.展开更多
Agent-based modeling （ABM） is an emerging modeling approach. In the past two decades, agent-based models have been increasingly adapted by social scientists, especially scientists in urban and geospatial studies, as...Agent-based modeling （ABM） is an emerging modeling approach. In the past two decades, agent-based models have been increasingly adapted by social scientists, especially scientists in urban and geospatial studies, as an effective paradigm for framing the underlying problems of complex and dynamic processes. As a result, the literature of ABM research is growing rapidly, covering a diverse range of topics. This paper presents a systematic literature review of ABM research, and discusses both theoretical issues such as ABM definition and architecture, and practical issues such as ABM applications and development platforms. A comprehensive and up-to-date bibliography is presented.展开更多
Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential custo...Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market situations.For those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive way.In this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)approach.The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)procedure.The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions.Retailers and GenCos optimize their bidding strategies via the RL procedure.The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions.The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)conditions.Based on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households.Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides.The models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.展开更多
Although intragroup conflict has both multilevel and dynamic natures,less attention has been paid to establishing a holistic model of intragroup conflict that emerges across levels and unfolds over time.To address thi...Although intragroup conflict has both multilevel and dynamic natures,less attention has been paid to establishing a holistic model of intragroup conflict that emerges across levels and unfolds over time.To address this research gap,we extend the multilevel view of intragroup conflict(Korsgaard et al.2008)to develop a multilevel and dynamic model of intragroup conflict that explicitly includes(1)the role of time and(2)the feedback loop to encompass the dynamic aspect of intragroup conflict.We further instantiate the extended model in the context of team decision-making.To achieve this and systematically examine the complex relationships,we use agentbased modeling and simulation(ABMS).We directly investigate how two types of intragroup conflict—task and relationship conflict—interplay with cross-level antecedences,interrelate and develop over time,and affect team outcomes.This study adds to the intragroup conflict research by extending the field with multilevel and dynamic views.展开更多
Artificial Intelligence (AI) is playing an increasingly pivotal role in New Product Development (NPD) project management.We propose a comprehensive framework to explore the impact of human–AI collaboration on organiz...Artificial Intelligence (AI) is playing an increasingly pivotal role in New Product Development (NPD) project management.We propose a comprehensive framework to explore the impact of human–AI collaboration on organizational knowledge diffusion.First,we develop a knowledge diffusion model based on continuous human–AI interactions,and we use the Agent-Based Modeling (ABM) method to simulate the diffusion process within the collaborative team and assess diffusion rates and efficiency based on knowledge levels.Second,we examine the interdependencies among members under different roles of AI,integrating AI cognitive capabilities,human–AI cognitive trust,and task interdependencies,and build a tie strength measurement model from the Social Network Analysis (SNA) perspective.Third,an entropy-based model is introduced to measure AI’s cognitive capability,accounting for project complexity and AI-generated solution uncertainty.We also establish a dynamic cognitive trust model that incorporates both the dynamic nature of trust in human–AI interactions and AI’s cognitive capability.Task interdependencies are assessed through a multi-dimensional activity network,and visualized by the Dependency Structure Matrix (DSM) method.Finally,an industrial example is provided to demonstrate the proposed model.Results show that organizational knowledge diffusion performs best when AI acts both as a collaborator and a tool.Moreover,this paper provides new insights,including how trust and task interdependencies significantly impact knowledge diffusion in human–AI collaborative organizations.展开更多
Due to the growing number of emergency accidents occurring around students, evacuation issues have become significantly important for both school officials and architects. Simply following construction codes cannot en...Due to the growing number of emergency accidents occurring around students, evacuation issues have become significantly important for both school officials and architects. Simply following construction codes cannot ensure that a building＇s layout is suitable for evacuation behaviors; therefore, to discover the suitable planning schemes, we have introduced an agentbased simulation model via Netlogo to investigate the interrelationships between evacuation efficiency and classroom layouts. Before conducting modeling experiments, both the simulation structure and the sensitivity to its parameter settings are examined by validation research and sensitivity analysis. Furthermore, to demonstrate the importance of conducting fire drills with students, two different types of behavior rules are designed to reflect the distinctive characteristics of students evacuating without instructions and students evacuating in good order. The general comparison results show us that the classroom layout with two exits shortens students＇ evacuation time, and the premeditated behavior rules, meaning that students who follow preset instructions to arrange their activities, not only escape faster but also have some advantages in ensuring their safety during the evacuation process. Moreover, at the end of this paper, several methods of improving this simulation model are proposed for more complex research in the future.展开更多
Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system’s individual elements.This paper presents current resear...Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system’s individual elements.This paper presents current research across the literature promoting cyber security as a complex adaptive system.We introduce complex systems concepts and fields of study,and deliver historical context,main themes,and current research relevant to cyber operations.Examples of cyber operations research leveraging agent-based modeling demonstrate the power of computational modeling grounded in complex systems principles.We discuss cyber operations as a scientific field,define current shortfalls for scientific rigor,and provide examples of how a complexity science foundation can further research and practice across a variety of cyber-based efforts.We propose standard definitions applicable to complex systems for cyber professionals and conclude with recommendations for future cyber operations research.展开更多
基金Financial support for this work was provided by the US Department of Commerce,the National Institute of Standards and Technology(NIST)under the Financial Assistance Award Number#70NANB20H008the US National Science Foundation(NSF)under Award Number 2052930.
文摘There has been a large increase in the number of days per year with numerous EF1-EF5 tornadoes.Given the significant damage incurred by tornadoes upon communities,community resilience analyses for tornado-stricken communities have been gaining momentum.As the community resilience analysis aims to guide how to lay out effective hazard mitigation strategies to decrease damage and improve recovery,a comprehensive and accurate approach is necessary.Agent-based modeling,an analysis approach in which different types of agents are created with their properties and behavior clearly defined to simulate the processes of those agents in an external environ-ment,is the most comprehensive and accurate approach so far to conducting community resilience simulations and investigating the decision-making for mitigation and recovery under natural hazards.In this paper,agent-based models(ABMs)are created to simulate the recovery process of a virtual testbed based on the real-world community in Joplin City,MO.The tornado path associated with the real-world tornado event that occurred in May 2011 is adopted in the tornado hazard modeling for the Joplin testbed.In addition,agent-based models are created for another virtual community in the Midwest United States named Centerville using an assumed tornado scenario of the same EF-scale as that in Joplin.The effects of hazard mitigation strategies on the two communities are also explored.A comparison between the analysis results of these two testbeds can indicate the influence of the characteristics of a tornado-prone community on the resilience of the community as well as on the effects of hazard mitigation strategies.It is observed that a community’s level of development significantly impacts the tornado resilience.In addition,the effects of a specific type of hazard mitigation strategy on the recovery process are contingent upon testbed characteristics.
基金Financial support for this work was provided by the US Department of Commerce,National Institute of Standards and Technology(NIST)under the Financial Assistance Award Number(FAIN)#70NANB20H008.
文摘A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions.The effect of the hazard geometry(center and angle of tornado path as well as the tornado width)is studied herein on how it influences the recovery of physical and social systems within the community.Given that pre-disaster preparedness including mitigation strategies(e.g.,retrofits)and policies(e.g.,insurance)is crucial for increasing the resilience of the community and facilitating a faster recovery process,in this study,the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties.The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling(ABM)approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies.The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems(lifeline networks,schools,healthcare,businesses,and households).The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.
基金supported by the National Natural Science Foundation of China(No.71971162)Key Research Project from Shanxi Transportation Holdings Group(No.20-JKKJ-1).
文摘A low utilization rate of public chargers and unmatched deployment of public charging sta-tions(CSs)are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation.This study proposes an optimization methodology for public CS deployment,considering real charging behavior and interactions between battery elec-tric vehicle(BEV)users and CSs.Realistic charging choice behavior is modeled based on surveys,and a dynamic charging decision chain is simulated,allowing interactions between BEV users and CSs through an agent-based modeling(ABM)approach.The charging-related activities are triggered by state of charge(SOC)levels randomly generated from distributions derived from real BEV operating data,including the random SOC levels at the start of a trip,the SOC level that prompts the user to charge the BEV,and the SOC level at which the user stops charging the BEV.A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs,to determine the location and the capacity of new CSs.The objective is to minimize the total time cost per BEV user,including travel time,charging time and waiting time in the queue.An application is conducted,for the deployment of fast CSs in Washington State,USA.The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems.The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs,leading to a more balanced utilization among CSs.The proposed approach is expected to contribute to better planning and deployment of public CSs,satisfaction of the booming charging demand,and increased utilization of pub-lic CSs.
文摘Agent-based modeling （ABM） is an emerging modeling approach. In the past two decades, agent-based models have been increasingly adapted by social scientists, especially scientists in urban and geospatial studies, as an effective paradigm for framing the underlying problems of complex and dynamic processes. As a result, the literature of ABM research is growing rapidly, covering a diverse range of topics. This paper presents a systematic literature review of ABM research, and discusses both theoretical issues such as ABM definition and architecture, and practical issues such as ABM applications and development platforms. A comprehensive and up-to-date bibliography is presented.
基金supported in part by the National Key Research and Development Program of China(2016YFB0901100)the National Natural Science Foundation of China(U1766203)+1 种基金the Science and Technology Project of State Grid Corporation of China(Friendly interaction system of supply-demand between urban electric power customers and power grid)the China Scholarship Council(CSC).
文摘Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market situations.For those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive way.In this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)approach.The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)procedure.The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions.Retailers and GenCos optimize their bidding strategies via the RL procedure.The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions.The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)conditions.Based on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households.Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides.The models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.
文摘Although intragroup conflict has both multilevel and dynamic natures,less attention has been paid to establishing a holistic model of intragroup conflict that emerges across levels and unfolds over time.To address this research gap,we extend the multilevel view of intragroup conflict(Korsgaard et al.2008)to develop a multilevel and dynamic model of intragroup conflict that explicitly includes(1)the role of time and(2)the feedback loop to encompass the dynamic aspect of intragroup conflict.We further instantiate the extended model in the context of team decision-making.To achieve this and systematically examine the complex relationships,we use agentbased modeling and simulation(ABMS).We directly investigate how two types of intragroup conflict—task and relationship conflict—interplay with cross-level antecedences,interrelate and develop over time,and affect team outcomes.This study adds to the intragroup conflict research by extending the field with multilevel and dynamic views.
基金supported by the National Natural Science Foundation of China(Grant Nos.W2441021 and 72271022).
文摘Artificial Intelligence (AI) is playing an increasingly pivotal role in New Product Development (NPD) project management.We propose a comprehensive framework to explore the impact of human–AI collaboration on organizational knowledge diffusion.First,we develop a knowledge diffusion model based on continuous human–AI interactions,and we use the Agent-Based Modeling (ABM) method to simulate the diffusion process within the collaborative team and assess diffusion rates and efficiency based on knowledge levels.Second,we examine the interdependencies among members under different roles of AI,integrating AI cognitive capabilities,human–AI cognitive trust,and task interdependencies,and build a tie strength measurement model from the Social Network Analysis (SNA) perspective.Third,an entropy-based model is introduced to measure AI’s cognitive capability,accounting for project complexity and AI-generated solution uncertainty.We also establish a dynamic cognitive trust model that incorporates both the dynamic nature of trust in human–AI interactions and AI’s cognitive capability.Task interdependencies are assessed through a multi-dimensional activity network,and visualized by the Dependency Structure Matrix (DSM) method.Finally,an industrial example is provided to demonstrate the proposed model.Results show that organizational knowledge diffusion performs best when AI acts both as a collaborator and a tool.Moreover,this paper provides new insights,including how trust and task interdependencies significantly impact knowledge diffusion in human–AI collaborative organizations.
文摘Due to the growing number of emergency accidents occurring around students, evacuation issues have become significantly important for both school officials and architects. Simply following construction codes cannot ensure that a building＇s layout is suitable for evacuation behaviors; therefore, to discover the suitable planning schemes, we have introduced an agentbased simulation model via Netlogo to investigate the interrelationships between evacuation efficiency and classroom layouts. Before conducting modeling experiments, both the simulation structure and the sensitivity to its parameter settings are examined by validation research and sensitivity analysis. Furthermore, to demonstrate the importance of conducting fire drills with students, two different types of behavior rules are designed to reflect the distinctive characteristics of students evacuating without instructions and students evacuating in good order. The general comparison results show us that the classroom layout with two exits shortens students＇ evacuation time, and the premeditated behavior rules, meaning that students who follow preset instructions to arrange their activities, not only escape faster but also have some advantages in ensuring their safety during the evacuation process. Moreover, at the end of this paper, several methods of improving this simulation model are proposed for more complex research in the future.
文摘Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system’s individual elements.This paper presents current research across the literature promoting cyber security as a complex adaptive system.We introduce complex systems concepts and fields of study,and deliver historical context,main themes,and current research relevant to cyber operations.Examples of cyber operations research leveraging agent-based modeling demonstrate the power of computational modeling grounded in complex systems principles.We discuss cyber operations as a scientific field,define current shortfalls for scientific rigor,and provide examples of how a complexity science foundation can further research and practice across a variety of cyber-based efforts.We propose standard definitions applicable to complex systems for cyber professionals and conclude with recommendations for future cyber operations research.
