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
Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to i...Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.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
Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises.This study developed a universal dynamic agent-based supply cha...Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises.This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sus-tainability.The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks.It includes production,con-sumption,and transportation agents,attributing economic features to supply chain components and cap-turing their interactions.It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors.By coupling high spatial-temporal resolution firm-level data from 18916 chemical enterprises,this study investigates the economic and environmental impacts of an environmen-tal policy resulting in the closure of 1800 chemical enterprises over three years.The results revealed a significant economic loss of 25.8 billion USD,ranging from 23.8 billion to 31.8 billion USD.Notably,over 80%of this loss was attributed to supply chain propagation.Counterfactual analyses indicated that imple-menting a staggered shutdown strategy prevented 18.8%of supply chain losses,highlighting the impor-tance of a gradual policy implementation to prevent abrupt supply chain disruptions.Furthermore,the study highlights the effectiveness of a multi-objective policy design in reducing economic losses(about 29%)and environmental risks(about 40%),substantially enhancing the efficiency of the environmental policy.The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.展开更多
Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (AB...Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (ABM) combines the strength of human crowd behavior description from classical social force models with discrete dynamics expression from cellular automaton models by extending the conception of floor field. Several important factors which may influence the results of decision-making of pedestrians are taken into consideration, such as the location of sign, the attraction of exit, and the interaction among pedestrians. To compare the effect of information on the pedestrians, we construct three decision-making mechanisms with different assumptions. To validate these three simulation models, we compare the numerical results from different perspectives with rational range in the case study where the Tampere Theater evacuation was carried out. The ABM framework is open for rules modification and could be applied to different building plans and has implication for architectural design of gates and signs in order to increase the evacuation efficiency.展开更多
Computer programs have been categorized as a useful tool to evaluate the complexity of systems. In fact, agent-based modeling (ABM) is considered a new method to model complex systems characterized by the role of inde...Computer programs have been categorized as a useful tool to evaluate the complexity of systems. In fact, agent-based modeling (ABM) is considered a new method to model complex systems characterized by the role of independent and interrelating agents. Simulations contribute in estimating and comprehending emerging behaviors that require the development of new regulations for local agents that would make improvements to the system. This paper offers an example of a methodology and a process utilized to develop a simulation model named Befergyonet, an ABM used to conduct computer simulations within a spatio-intertemporal environment. The methodology discussed in this paper is intended solely to stimulate the use of innovative computer programs to simulate complex systems as an approach to represent real world events and may be a methodological guide for readers interested in developing their own ABM.展开更多
This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionar...This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.展开更多
Evaluating the resilience of the innovation ecosystem to maintain its performance,in the sense of resistance to disruption and recovery after it,has recently received more attention.Several studies have been conducted...Evaluating the resilience of the innovation ecosystem to maintain its performance,in the sense of resistance to disruption and recovery after it,has recently received more attention.Several studies have been conducted to model different ecosystems and evaluate their resilience.However,modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention.This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps.In the first step,a case study related to the innovation ecosystem of Iran's Ministry of Energy,called the Power Innovation Ecosystem,is modeled by combining system dynamics and agent-based modeling.Upon validating the model in the second step,the disruption of the loss of experts is investigated in the third step,and all possible actions to recover each actor are analyzed.In the fourth step,the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps.Finally,resilience is calculated in two different ways in the fifth step.Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level.This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model.By applying strategic changes to this model,they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem.展开更多
This study presents a new holistic framework for modeling farmer decision-making by integrating both top-down and bottom-up approaches.It uses three interlinked subsystems to evaluate how changes in water policies imp...This study presents a new holistic framework for modeling farmer decision-making by integrating both top-down and bottom-up approaches.It uses three interlinked subsystems to evaluate how changes in water policies impact farmer decisions and profits:the first model simulates water balance,the second simulates farmer behavior,and the third assesses farmer profits.Two scenarios are explored:Scenario I introduces penalties for groundwater overexploitation,and Scenario Il implements awareness raising and training to encourage using modern irrigation systems.The results show that penalties lead to reductions in water requests exceeding limits by 8%,45%,and 68%for fines of 1000,5000,and 10,000 IRRm-3,with corresponding net profit decreases of 1.3%,8.0%,and 11.6%.The ranges of farmer cooperation for groundwater management vary from 20%to 50%over the 10-year simulation period.In Scenario Il,increasing the radius of awareness from 0.5 to 2 km substantially increases the adoption of modern irrigation from 1457 to 2057 farmers.These findings highlight how different policy measures impact various types of farmer based on their specific characteristics and preferences.展开更多
In the face of sudden pandemics,it becomes crucial for project managers to quickly adapt and make informed decisions that anticipate the consequences of their actions.This highlights the need for proactive management ...In the face of sudden pandemics,it becomes crucial for project managers to quickly adapt and make informed decisions that anticipate the consequences of their actions.This highlights the need for proactive management strategies to enhance epidemic response efforts.However,current research mainly emphasizes the negative impacts of pandemics,often neglecting the development of adaptable management approaches for construction sites.This study aims to fill this research void by developing strategies tailored to managing pandemics at construction sites.Using agent-based modeling,the study simulates the movement patterns of workers and the consequent spread of an epidemic under different risk scenarios and management tactics.The results indicate that measures such as wearing masks,managing group activities,and enforcing entry controls can significantly reduce epidemic spread on construction sites,with entry controls showing the greatest effectiveness.展开更多
Estimating potential casualties from a significant earthquake and tsunami event is crucial to enhance disaster preparedness and response.Although various approaches exist to assess potential casualties,few studies hav...Estimating potential casualties from a significant earthquake and tsunami event is crucial to enhance disaster preparedness and response.Although various approaches exist to assess potential casualties,few studies have made direct comparisons between them.The present study aimed to clarify the differences in the estimation of casualties between an agent-based model(ABM),which can capture detailed evacuation behavior but demands significant computational resources,and a simplified approach at less computational cost by assuming that evacuees would move along a straight line from their initial location to the closest evacuation destination.These different approaches were applied to three coastal cities in Japan—Mihama,Kushimoto,and Shingu in Wakayama Prefecture—revealing significant differences in the estimated results between the ABM and the simplified approach.Notably,when the effects of building collapse due to an earthquake were considered,the mortality rates estimated by the ABM were higher than those estimated by the simplified approach in the three cities.There were also significant differences in the spatial distribution of the estimated mortality rates between the ABM and the simplified approach.The findings suggest that while the simplified approach can yield results more quickly,casualty estimates derived from such models should be interpreted with caution.展开更多
Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subs...Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.展开更多
This research investigates retrofitting strategies for multifunctional spaces within educational buildings,employing agent-based and performance-based modeling to support decision-making.An experimental matrix was dev...This research investigates retrofitting strategies for multifunctional spaces within educational buildings,employing agent-based and performance-based modeling to support decision-making.An experimental matrix was developed,reflecting three usage scenarios(reading,exhibition,lecture)across four retrofitting schemes.An agent-based model was developed to delineate intricate human behaviors in space and examined the self-organizing behaviors of 30 agents for each scheme in every scenario,evaluating six metrics on spatial efficiency and visual experience.Calibrated models,derived from real data and processed through DesignBuilder software,evaluated three metrics:energy use,thermal comfort,and visual comfort.The research then incorporated metrics from the agent-based model and performance simulation to develop a method for discussing the decision-making process in retrofit strategies.The findings indicate that the optimal retrofitting solution for multifunctional spaces is heavily influenced by the distribution of usage scenarios.Given the substantial influence of space metrics on selecting the optimal retrofit scheme,the proposed framework effectively facilitates decision-making for building retrofits by providing a holistic evaluation of both spatial and energy criteria.展开更多
Earthquake is a disastrous natural hazard that threatens numerous cities worldwide.The interval between the foreshock and the main event can sometimes last several minutes.Meanwhile,crowd emergency evacuation and find...Earthquake is a disastrous natural hazard that threatens numerous cities worldwide.The interval between the foreshock and the main event can sometimes last several minutes.Meanwhile,crowd emergency evacuation and finding shelter are vital for search and rescue managers.At the same time,many unpredicted challenges,such as the sudden increase in travel demand,shifts in public behavior,and the change in the regular transport supply,may arise due to evacuation conditions,which lead to different situations.This paper aims to introduce an approach for quick decision-making and timely evacuation response required by establishing a situation-aware system to minimize these risks and ensure the success of the evacuation plans,to support and predict current and future actions within the dynamic space of the crisis.The main contribution is innovating a Situation-Aware Emergency Evacuation(SAEE)model to enable crisis managers and evacuees to make the right decisions by providing timely and reliable information about the situation.This method is utilized in two situations:designing the emergency evacuation plan and finding the shortest/safest routes to reduce travel time for evacuees.Therefore,a hybrid approach is introduced,which involves a Fuzzy Inference System(FIS)and Deep Long Short-Term Memory(DLSTM)algorithm to identify,infer,and extract the existing situation at different levels(e.g.people,vehicles,and surroundings)after a foreshock using multi-agent-based simulation.The method proposed was simulated in the traffic network of District 6 of Tehran,the capital of Iran.The model results show that the evacuees'spatial knowledge and perception,as well as awareness of the situation of other agents and their surroundings,led to a significant(40%)reduction in the complete evacuation time.This time is considered the most pivotal factor in saving human lives and their arrival in safer areas.The role of situation awareness systems and increasing human cognition and perception can significantly help in this matter.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
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
Evacuation modeling is a promising measure to support decision making in scenarios such as flooding,explosion,terrorist attack and other emergency incidents.Given the special attention to the terrorist attack,we build...Evacuation modeling is a promising measure to support decision making in scenarios such as flooding,explosion,terrorist attack and other emergency incidents.Given the special attention to the terrorist attack,we build up an agent-based evacuation model in a railway station square under sarin terrorist attack to analyze such incident.Sarin dispersion process is described by Gaussian puff model.Due to sarin’s special properties of being colorless and odorless,we focus more on the modeling of agents’perceiving and reasoning process and use a Belief,Desire,Intention(BDI)architecture to solve the problem.Another contribution of our work is that we put forward a path planning algorithm which not only take distance but also comfort and threat factors into consideration.A series of simulation experiments demonstrate the ability of the proposed model and examine some crucial factors in sarin terrorist attack evacuation.Though far from perfect,the proposed model could serve to support decision making.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
Mobility,pollution,and other barriers against sustainable goods movement are pushing local administrators to seek innovations in urban transportation infrastructure.The urban underground logistics system(ULS)has been ...Mobility,pollution,and other barriers against sustainable goods movement are pushing local administrators to seek innovations in urban transportation infrastructure.The urban underground logistics system(ULS)has been recognized as a prospective alternative to realize large-scale automated freight distribution within and around megacities.This paper proposes an integrated approach combing system dynamics and agent-based modeling to evaluate the long-term development and operating status of a city-wide ULS project.The project boundaries regarding underground network expansion,stakeholders’attributes,and social-environmental benefit metrics were structured as eight highly-interacted agent modules.Critical decision variables of agents in terms of supply-demand equilibrium,investment plan,pricing-to-market and willingness-to-pay were incorporated into three formulized subsystem models.From empirical perspective,the urban territory of Beijing,China,was taken as a case to simulate the development footprints of ULS project under different funding options and market acceptance degrees.Results show that ULS has significant competence with respect to service capacity and profitability,while enabling billions of dollars of external cost-saving annually.Moreover,the comprehensive performance of ULS project regarding economic incomes,benefits,market demand,and construction schedule can reach satisfactory trade-offs through adaptively adjusting the funding policies,incentives and pricing portfolios during project development.展开更多
This paper uses Covasim,an agent-based model(ABM)of COVID-19,to evaluate and scenarios of epidemic spread in New York State(USA)and the UK.Epidemiological parameters such as contagiousness(virus transmission rate),ini...This paper uses Covasim,an agent-based model(ABM)of COVID-19,to evaluate and scenarios of epidemic spread in New York State(USA)and the UK.Epidemiological parameters such as contagiousness(virus transmission rate),initial number of infected people,and probability of being tested depend on the region's demographic and geographical features,the containment measures introduced;they are calibrated to data about COVID-19 spread in the region of interest.At the first stage of our study,epidemiological data(numbers of people tested,diagnoses,critical cases,hospitalizations,and deaths)for each of the mentioned regions were analyzed.The data were characterized in terms of seasonality,stationarity,and dependency spaces,and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model.At the second stage,the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters.The model was validated with the historical data of 2020.The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved,the number of positive cases in New York State remain the same during March of 2021,while in the UK it will reduce.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supported by the Shanghai Philosophy and Social Science Foundation(2022ECK004)Shanghai Soft Science Research Project(23692123400)。
文摘Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.
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(52200228 and 72022004)the China Postdoctoral Science Foundation(2022M721817)the National Key Scientific Research Project(2021YFC3200200).
文摘Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises.This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sus-tainability.The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks.It includes production,con-sumption,and transportation agents,attributing economic features to supply chain components and cap-turing their interactions.It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors.By coupling high spatial-temporal resolution firm-level data from 18916 chemical enterprises,this study investigates the economic and environmental impacts of an environmen-tal policy resulting in the closure of 1800 chemical enterprises over three years.The results revealed a significant economic loss of 25.8 billion USD,ranging from 23.8 billion to 31.8 billion USD.Notably,over 80%of this loss was attributed to supply chain propagation.Counterfactual analyses indicated that imple-menting a staggered shutdown strategy prevented 18.8%of supply chain losses,highlighting the impor-tance of a gradual policy implementation to prevent abrupt supply chain disruptions.Furthermore,the study highlights the effectiveness of a multi-objective policy design in reducing economic losses(about 29%)and environmental risks(about 40%),substantially enhancing the efficiency of the environmental policy.The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.
基金the Natural Science Foundation of Shanghai (No. 18ZR1420200)the National Natural Science Foundation of China (No. 61603253)the China Postdoctoral Science Foundation Funded Project (No. 2016M601598)。
文摘Crowd evacuation in different situations is an important topic in the research field of safety. This paper presents a hybrid model for heterogeneous pedestrian evacuation simulation. Our adaptive agent-based model (ABM) combines the strength of human crowd behavior description from classical social force models with discrete dynamics expression from cellular automaton models by extending the conception of floor field. Several important factors which may influence the results of decision-making of pedestrians are taken into consideration, such as the location of sign, the attraction of exit, and the interaction among pedestrians. To compare the effect of information on the pedestrians, we construct three decision-making mechanisms with different assumptions. To validate these three simulation models, we compare the numerical results from different perspectives with rational range in the case study where the Tampere Theater evacuation was carried out. The ABM framework is open for rules modification and could be applied to different building plans and has implication for architectural design of gates and signs in order to increase the evacuation efficiency.
文摘Computer programs have been categorized as a useful tool to evaluate the complexity of systems. In fact, agent-based modeling (ABM) is considered a new method to model complex systems characterized by the role of independent and interrelating agents. Simulations contribute in estimating and comprehending emerging behaviors that require the development of new regulations for local agents that would make improvements to the system. This paper offers an example of a methodology and a process utilized to develop a simulation model named Befergyonet, an ABM used to conduct computer simulations within a spatio-intertemporal environment. The methodology discussed in this paper is intended solely to stimulate the use of innovative computer programs to simulate complex systems as an approach to represent real world events and may be a methodological guide for readers interested in developing their own ABM.
文摘This review explores the use of agent-based modeling(ABM)within the framework of study human emotion and cognition in the context of its ability to simulate complex social interactions,adaptive changes,and evolutionary processes.By representing agents and their defined environments with probabilistic interactions,ABM allows the assessment of the effects of individual behavior at the micro level on the greater social phenomena at the macro level.The review looks into the applications of ABM in portraying some of the key components of emotions and cognition-empathy,cooperation,decision making,and emotional transmission-and analyzes the problems including scalability,empirical validation,and description of sensitive emotional states.The most important conclusion is that merging ABM with information neurobiological data and artificial intelligence(AI)techniques would allow for deepening the interactions within the system and enhancing its responsiveness to stimuli.This review highlights approaches that aim to exploit the ABM methodology more fully and integrates methods from biology,neuroscience,and engineering.This integration could contribute to our understanding of the human behavior evolution and adaptation within systems relevant to policymaking,healthcare,and education.
文摘Evaluating the resilience of the innovation ecosystem to maintain its performance,in the sense of resistance to disruption and recovery after it,has recently received more attention.Several studies have been conducted to model different ecosystems and evaluate their resilience.However,modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention.This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps.In the first step,a case study related to the innovation ecosystem of Iran's Ministry of Energy,called the Power Innovation Ecosystem,is modeled by combining system dynamics and agent-based modeling.Upon validating the model in the second step,the disruption of the loss of experts is investigated in the third step,and all possible actions to recover each actor are analyzed.In the fourth step,the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps.Finally,resilience is calculated in two different ways in the fifth step.Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level.This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model.By applying strategic changes to this model,they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem.
文摘This study presents a new holistic framework for modeling farmer decision-making by integrating both top-down and bottom-up approaches.It uses three interlinked subsystems to evaluate how changes in water policies impact farmer decisions and profits:the first model simulates water balance,the second simulates farmer behavior,and the third assesses farmer profits.Two scenarios are explored:Scenario I introduces penalties for groundwater overexploitation,and Scenario Il implements awareness raising and training to encourage using modern irrigation systems.The results show that penalties lead to reductions in water requests exceeding limits by 8%,45%,and 68%for fines of 1000,5000,and 10,000 IRRm-3,with corresponding net profit decreases of 1.3%,8.0%,and 11.6%.The ranges of farmer cooperation for groundwater management vary from 20%to 50%over the 10-year simulation period.In Scenario Il,increasing the radius of awareness from 0.5 to 2 km substantially increases the adoption of modern irrigation from 1457 to 2057 farmers.These findings highlight how different policy measures impact various types of farmer based on their specific characteristics and preferences.
基金supported by the National Natural Science Foundation of China(Grant Nos.72201095,72101275,and U21A20151)the National Natural Science Foundation of Hunan Province(Grant Nos.2023JJ40189and2022JJ40645).
文摘In the face of sudden pandemics,it becomes crucial for project managers to quickly adapt and make informed decisions that anticipate the consequences of their actions.This highlights the need for proactive management strategies to enhance epidemic response efforts.However,current research mainly emphasizes the negative impacts of pandemics,often neglecting the development of adaptable management approaches for construction sites.This study aims to fill this research void by developing strategies tailored to managing pandemics at construction sites.Using agent-based modeling,the study simulates the movement patterns of workers and the consequent spread of an epidemic under different risk scenarios and management tactics.The results indicate that measures such as wearing masks,managing group activities,and enforcing entry controls can significantly reduce epidemic spread on construction sites,with entry controls showing the greatest effectiveness.
基金supported by the Kurata Grants by the Hitachi Global Foundationthe Kindai University Research Enhancement Grant(Faculty Assistance and Development Research Grant)。
文摘Estimating potential casualties from a significant earthquake and tsunami event is crucial to enhance disaster preparedness and response.Although various approaches exist to assess potential casualties,few studies have made direct comparisons between them.The present study aimed to clarify the differences in the estimation of casualties between an agent-based model(ABM),which can capture detailed evacuation behavior but demands significant computational resources,and a simplified approach at less computational cost by assuming that evacuees would move along a straight line from their initial location to the closest evacuation destination.These different approaches were applied to three coastal cities in Japan—Mihama,Kushimoto,and Shingu in Wakayama Prefecture—revealing significant differences in the estimated results between the ABM and the simplified approach.Notably,when the effects of building collapse due to an earthquake were considered,the mortality rates estimated by the ABM were higher than those estimated by the simplified approach in the three cities.There were also significant differences in the spatial distribution of the estimated mortality rates between the ABM and the simplified approach.The findings suggest that while the simplified approach can yield results more quickly,casualty estimates derived from such models should be interpreted with caution.
基金supported by National Natural Science Foundation of China(72288101,72361137002,and 72101018)the Dutch Research Council(NWO Grant 482.22.01).
文摘Vehicle electrification,an important method for reducing carbon emissions from road transport,has been promoted globally.In this study,we analyze how individuals adapt to this transition in transportation and its subsequent impact on urban structure.Considering the varying travel costs associated with electric and fuel vehicles,we analyze the dynamic choices of households concerning house locations and vehicle types in a two-dimensional monocentric city.A spatial equilibrium is developed to model the interactions between urban density,vehicle age and vehicle type.An agent-based microeconomic residential choice model dynamically coupled with a house rent market is developed to analyze household choices of home locations and vehicle energy types,considering vehicle ages and competition for public charging piles.Key findings from our proposed models show that the proportion of electric vehicles(EVs)peaks at over 50%by the end of the first scrappage period,accompanied by more than a 40%increase in commuting distance and time compared to the scenario with only fuel vehicles.Simulation experiments on a theoretical grid indicate that heterogeneity-induced residential segregation can lead to urban sprawl and congestion.Furthermore,households with EVs tend to be located farther from the city center,and an increase in EV ownership contributes to urban expansion.Our study provides insights into how individuals adapt to EV transitions and the resulting impacts on home locations and land use changes.It offers a novel perspective on the dynamic interactions between EV adoption and urban development.
基金sponsored by the National Science and Foundation of China(No.52208011)the Natural Science and Foundation of China(NSFC No.52208010)the China Postdoctoral Science Foundation(No.2022M720716).
文摘This research investigates retrofitting strategies for multifunctional spaces within educational buildings,employing agent-based and performance-based modeling to support decision-making.An experimental matrix was developed,reflecting three usage scenarios(reading,exhibition,lecture)across four retrofitting schemes.An agent-based model was developed to delineate intricate human behaviors in space and examined the self-organizing behaviors of 30 agents for each scheme in every scenario,evaluating six metrics on spatial efficiency and visual experience.Calibrated models,derived from real data and processed through DesignBuilder software,evaluated three metrics:energy use,thermal comfort,and visual comfort.The research then incorporated metrics from the agent-based model and performance simulation to develop a method for discussing the decision-making process in retrofit strategies.The findings indicate that the optimal retrofitting solution for multifunctional spaces is heavily influenced by the distribution of usage scenarios.Given the substantial influence of space metrics on selecting the optimal retrofit scheme,the proposed framework effectively facilitates decision-making for building retrofits by providing a holistic evaluation of both spatial and energy criteria.
文摘Earthquake is a disastrous natural hazard that threatens numerous cities worldwide.The interval between the foreshock and the main event can sometimes last several minutes.Meanwhile,crowd emergency evacuation and finding shelter are vital for search and rescue managers.At the same time,many unpredicted challenges,such as the sudden increase in travel demand,shifts in public behavior,and the change in the regular transport supply,may arise due to evacuation conditions,which lead to different situations.This paper aims to introduce an approach for quick decision-making and timely evacuation response required by establishing a situation-aware system to minimize these risks and ensure the success of the evacuation plans,to support and predict current and future actions within the dynamic space of the crisis.The main contribution is innovating a Situation-Aware Emergency Evacuation(SAEE)model to enable crisis managers and evacuees to make the right decisions by providing timely and reliable information about the situation.This method is utilized in two situations:designing the emergency evacuation plan and finding the shortest/safest routes to reduce travel time for evacuees.Therefore,a hybrid approach is introduced,which involves a Fuzzy Inference System(FIS)and Deep Long Short-Term Memory(DLSTM)algorithm to identify,infer,and extract the existing situation at different levels(e.g.people,vehicles,and surroundings)after a foreshock using multi-agent-based simulation.The method proposed was simulated in the traffic network of District 6 of Tehran,the capital of Iran.The model results show that the evacuees'spatial knowledge and perception,as well as awareness of the situation of other agents and their surroundings,led to a significant(40%)reduction in the complete evacuation time.This time is considered the most pivotal factor in saving human lives and their arrival in safer areas.The role of situation awareness systems and increasing human cognition and perception can significantly help in this matter.
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the National Natural Science Foundation of China under Grant Nos.71303252,61403402,61503402 and 71673292.
文摘Evacuation modeling is a promising measure to support decision making in scenarios such as flooding,explosion,terrorist attack and other emergency incidents.Given the special attention to the terrorist attack,we build up an agent-based evacuation model in a railway station square under sarin terrorist attack to analyze such incident.Sarin dispersion process is described by Gaussian puff model.Due to sarin’s special properties of being colorless and odorless,we focus more on the modeling of agents’perceiving and reasoning process and use a Belief,Desire,Intention(BDI)architecture to solve the problem.Another contribution of our work is that we put forward a path planning algorithm which not only take distance but also comfort and threat factors into consideration.A series of simulation experiments demonstrate the ability of the proposed model and examine some crucial factors in sarin terrorist attack evacuation.Though far from perfect,the proposed model could serve to support decision making.
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 bythe National Natural Science Foundationof China(grants No.71631007 and 71971214)。
文摘Mobility,pollution,and other barriers against sustainable goods movement are pushing local administrators to seek innovations in urban transportation infrastructure.The urban underground logistics system(ULS)has been recognized as a prospective alternative to realize large-scale automated freight distribution within and around megacities.This paper proposes an integrated approach combing system dynamics and agent-based modeling to evaluate the long-term development and operating status of a city-wide ULS project.The project boundaries regarding underground network expansion,stakeholders’attributes,and social-environmental benefit metrics were structured as eight highly-interacted agent modules.Critical decision variables of agents in terms of supply-demand equilibrium,investment plan,pricing-to-market and willingness-to-pay were incorporated into three formulized subsystem models.From empirical perspective,the urban territory of Beijing,China,was taken as a case to simulate the development footprints of ULS project under different funding options and market acceptance degrees.Results show that ULS has significant competence with respect to service capacity and profitability,while enabling billions of dollars of external cost-saving annually.Moreover,the comprehensive performance of ULS project regarding economic incomes,benefits,market demand,and construction schedule can reach satisfactory trade-offs through adaptively adjusting the funding policies,incentives and pricing portfolios during project development.
基金supported by the Russian Foundation for Basic Research and Royal Society(project no.21-51-10003)The agent-based mathematical model construction and analysis of numerical results(sections 3,4,5)+1 种基金supported by the Russian Science Foundation(project no.18-71-10044)the Royal Society IECyR2y202020 e International Exchanges 2020 Cost Share between UK and Russia.
文摘This paper uses Covasim,an agent-based model(ABM)of COVID-19,to evaluate and scenarios of epidemic spread in New York State(USA)and the UK.Epidemiological parameters such as contagiousness(virus transmission rate),initial number of infected people,and probability of being tested depend on the region's demographic and geographical features,the containment measures introduced;they are calibrated to data about COVID-19 spread in the region of interest.At the first stage of our study,epidemiological data(numbers of people tested,diagnoses,critical cases,hospitalizations,and deaths)for each of the mentioned regions were analyzed.The data were characterized in terms of seasonality,stationarity,and dependency spaces,and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model.At the second stage,the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters.The model was validated with the historical data of 2020.The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved,the number of positive cases in New York State remain the same during March of 2021,while in the UK it will reduce.
