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
This paper builds an agent-based model to study the impact of analyst competition on analyst optimism.Two strategies(a catering strategy and a pressure strategy)are used to model analysts conflicts of interest between...This paper builds an agent-based model to study the impact of analyst competition on analyst optimism.Two strategies(a catering strategy and a pressure strategy)are used to model analysts conflicts of interest between listed corporations and institutional clients.The finding suggests that the relationship between competition and analyst optimism is nonlinear.Low-level competition generates more analyst unbiased forecasts.However,the condition of no competition or high-level competition generates more analyst optimistic forecasts.The empirical test also confirms that analysts issue less biased earnings forecasts under the condition of low-level competition.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
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
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
Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochast...Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.展开更多
Typically,tsunami evacuation routes are marked using signs in the transportation network and the evacuation map is made to educate people on how to follow the evacuation route.However,tsunami evacuation routes are usu...Typically,tsunami evacuation routes are marked using signs in the transportation network and the evacuation map is made to educate people on how to follow the evacuation route.However,tsunami evacuation routes are usually identified without the support of evacuation simulation,and the route effectiveness in the reduction of evacuation risk is typically unknown quantitatively.This study proposes a simulation-based and risk-informed framework for quantitative evaluation of the effectiveness of evacuation routes in reducing evacuation risk.An agentbased model is used to simulate the tsunami evacuation,which is then used in a simulation-based risk assessment framework to evaluate the evacuation risk.The route effectiveness in reducing the evacuation risk is evaluated by investigating how the evacuation risk varies with the proportion of the evacuees that use the evacuation route.The impacts of critical risk factors such as evacuation mode(for example,on foot or by car)and population size and distribution on the route effectiveness are also investigated.The evacuation risks under different cases are efficiently calculated using the augmented sample-based approach.The proposed approach is applied to the riskinformed evaluation of the route effectiveness for tsunami evacuation in Seaside,Oregon.The evaluation results show that the route usage is overall effective in reducing the evacuation risk in the study area.The results can be used for evacuation preparedness education and hence effective evacuation.展开更多
As the COVID-19 vaccination has been quickly rolling out around the globe,the evaluation of the effects of vaccinating populations for the safe reopening of schools has become a focal point for educators,decision-make...As the COVID-19 vaccination has been quickly rolling out around the globe,the evaluation of the effects of vaccinating populations for the safe reopening of schools has become a focal point for educators,decision-makers,and the general public.Within this context,we develop a contact network agent-based model(CN-ABM)to simulate on-campus disease transmission scenarios.The CN-ABM establishes contact networks for agents based on their daily activity patterns,evaluates the agents’health status change in different activity environments,and then simulates the epidemic curve.By applying the model to a real-world campus environment,we identify how different community risk levels,teaching modalities,and vaccination rates would shape the epidemic curve.The results show that without vaccination,retaining under 50%of on-campus students can largely flatten the curve,and having 25%on-campus students can achieve the best result(peak value<1%).With vaccination,having a maximum of 75%on-campus students and at least a 45%vaccination rate can suppress the curve,and a 65%vaccination rate can achieve the best result.The developed CN-ABM can be employed to assist local government and school officials with developing proactive intervention strategies to safely reopen schools.展开更多
Spatial optimization as part of spatial modeling has been facilitated significantly by integration with GIS techniques. However, for certain research topics, applying standard GIS techniques may create problems which ...Spatial optimization as part of spatial modeling has been facilitated significantly by integration with GIS techniques. However, for certain research topics, applying standard GIS techniques may create problems which require attention. This paper serves as a cautionary note to demonstrate two problems associated with applying GIS in spatial optimization, using a capacitated p-median facility location optimization problem as an example. The first problem involves errors in interpolating spatial variations of travel costs from using kriging, a common set of techniques for raster files. The second problem is inaccuracy in routing performed on a graph directly created from polyline shapefiles, a common vector file type. While revealing these problems, the paper also suggests remedies. Specifically, interpolation errors can be eliminated by using agent-based spatial modeling while the inaccuracy in routing can be improved through altering the graph topology by splitting the long edges of the shapefile. These issues suggest the need for caution in applying GIS in spatial optimization study.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
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
This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network.The proposed model use theories and tools from the psychology of consumer behavi...This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network.The proposed model use theories and tools from the psychology of consumer behavior,social networks and complex dynamical systems like the Consumat framework and fuzzy logic.The model has been adjusted using real data,tested with the automobile market and it can recreate trends like those described in the world market.展开更多
Mathematical and computational models are useful tools for virtual policy experiments on infectious disease con-trol.Most models fail to provide flexible and rapid simulation of various epidemic scenarios for policy a...Mathematical and computational models are useful tools for virtual policy experiments on infectious disease con-trol.Most models fail to provide flexible and rapid simulation of various epidemic scenarios for policy assessment.This paper establishes a multi-scale agent-based model to investigate the infectious disease propagation between cities and within a city using the knowledge from person-to-person transmission.In the model,the contact and infection of individuals at the micro scale where an agent represents a person provide insights for the interactions of agents at the meso scale where an agent refers to hundreds of individuals.Four cities with frequent population movements in China are taken as an example and actual data on traffic patterns and demographic parameters are adopted.The scenarios for dynamic propagation of infectious disease with no external measures are compared versus the scenarios with vaccination and non-pharmaceutical interventions.The model predicts that the peak of infections will decline by 67.37%with 80%vaccination rate,compared to a drop of 89.56%when isolation and quarantine measures are also in place.The results highlight the importance of controlling the source of infection by isolation and quarantine throughout the epidemic.We also study the effect when cities implement inconsis-tent public health interventions,which is common in practical situations.Based on our results,the model can be applied to COVID-19 and other infectious diseases according to the various needs of government agencies.展开更多
This article examines the impact of partial/full reopening of school/college campuses on the spread of a pandemic using COVID-19 as a case study.The study uses an agent-based simulation model that replicates community...This article examines the impact of partial/full reopening of school/college campuses on the spread of a pandemic using COVID-19 as a case study.The study uses an agent-based simulation model that replicates community spread in an urban region of U.S.A.via daily social mixing of susceptible and infected individuals.Data representing population demographics,SARS-CoV-2 epidemiology,and social interventions guides the model's behavior,which is calibrated and validated using data reported by the government.The model indicates a modest but significant increase(8.15%)in the total number of reported cases in the region for a complete(100%)reopening compared to keeping schools and colleges fully virtual.For partial returns of 75%and 50%,the percent increases in the number of reported cases are shown to be small(2.87%and 1.26%,respectively)and statistically insignificant.The AB model also predicts that relaxing the stringency of the school safety protocol for sanitizing,use of mask,social distancing,testing,and quarantining and thus allowing the school transmission coefficient to double may result in a small increase in the number of reported infected cases(2.14%).Hence for pandemic outbreaks from viruses with similar characteristics as for SARS-CoV-2,keeping the schools and colleges open with a modest campus safety protocol and in-person attendance below a certain threshold may be advisable.展开更多
This paper presents an assessment of land use changes and their impacts on the ecosystem in the Montado, a traditional agricultural landscape of Portugal in response to global environmental change. The assessment uses...This paper presents an assessment of land use changes and their impacts on the ecosystem in the Montado, a traditional agricultural landscape of Portugal in response to global environmental change. The assessment uses an agent-based model (ABM) of the adaptive decisions of farmers to simulate the influence on future land use patterns of socio-economic attributes such as social relationships and farmer reliance on subsidies and biophysical constraints. The application and development of the ABM are supported empirically using three categories of input data: 1) farmer types based on a cluster analysis of socio-economic attributes;2) agricultural suitability based on regression analysis of historical land use maps and biophysical attributes;and 3) future trends in the economic and climatic environments based on the A1fi scenario of the Intergovernmental Panel on Climate Change. Model sensitivity and uncertainty analyses are carried out prior to the scenario analysis in order to verify the absence of systematic errors in the model structure. The results of the scenario analysis show that the area of Montado declines significantly by 2050, but it remains the dominant land use in the case study area, indicating some resilience to change. An important policy challenge arising from this assessment is how to encourage next generation of innovative farmers to conserve this traditional landscape for social and ecological values.展开更多
Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavi...Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.展开更多
A system of systems(SoS)composes a set of independent constituent systems(CSs),where the degree of authority to control the independence of CSs varies,depending on different SoS types.Key researchers describe four SoS...A system of systems(SoS)composes a set of independent constituent systems(CSs),where the degree of authority to control the independence of CSs varies,depending on different SoS types.Key researchers describe four SoS types with descending levels of central authority:directed,acknowledged,collaborative and virtual.Although the definitions have been recognized in SoS engineering,what is challenging is the difficulty of translating these definitions into models and simulation environments.Thus,we provide a goal-based method including a mathematical baseline to translate these definitions into more effective agent-based modeling and simulations.First,we construct the theoretical models of CS and SoS.Based on the theoretical models,we analyze the degree of authority influenced by SoS characteristics.Next,we propose a definition of SoS types by quantitatively explaining the degree of authority.Finally,we recognize the differences between acknowledged SoS and collaborative SoS using a migrating waterfowl flock by an agentbased model(ABM)simulation.This paper contributes to the SoS body of knowledge by increasing our understanding of the degree of authority in an SoS,so we may identify suitable SoS types to achieve SoS goals by modeling and simulation.展开更多
Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a nove...Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.展开更多
We propose an empirical behavioral order-driven(EBOD)model with price limit rules,which consists of an order placement process and an order cancellation process.All the ingredients of the model are determined based on...We propose an empirical behavioral order-driven(EBOD)model with price limit rules,which consists of an order placement process and an order cancellation process.All the ingredients of the model are determined based on the empirical microscopic regularities in the order flows of stocks traded on the Shenzhen Stock Exchange.The model can reproduce the main stylized facts in real markets.Computational experiments unveil that asymmetric setting of price limits will cause the stock price to diverge exponentially when the up price limit is higher than the down price limit and to vanish vice versa.We also find that asymmetric price limits have little influence on the correlation structure of the return series and the volatility series,but cause remarkable changes in the average returns and the tail exponents of returns.Our EBOD model provides a suitable computational experiment platform for academics,market participants,and policy makers.展开更多
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the National Natural Science Foundation of China under Grant Nos.7187115771790594 and 71532009the Major project of Tianjin Education Commission under Grant No.2018JWZD47。
文摘This paper builds an agent-based model to study the impact of analyst competition on analyst optimism.Two strategies(a catering strategy and a pressure strategy)are used to model analysts conflicts of interest between listed corporations and institutional clients.The finding suggests that the relationship between competition and analyst optimism is nonlinear.Low-level competition generates more analyst unbiased forecasts.However,the condition of no competition or high-level competition generates more analyst optimistic forecasts.The empirical test also confirms that analysts issue less biased earnings forecasts under the condition of low-level competition.
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.
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 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 National Natural Science Foundation of China(Grant Nos.82173620 to Yang Zhao and 82041024 to Feng Chen)partially supported by the Bill&Melinda Gates Foundation(Grant No.INV-006371 to Feng Chen)Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.
文摘Typically,tsunami evacuation routes are marked using signs in the transportation network and the evacuation map is made to educate people on how to follow the evacuation route.However,tsunami evacuation routes are usually identified without the support of evacuation simulation,and the route effectiveness in the reduction of evacuation risk is typically unknown quantitatively.This study proposes a simulation-based and risk-informed framework for quantitative evaluation of the effectiveness of evacuation routes in reducing evacuation risk.An agentbased model is used to simulate the tsunami evacuation,which is then used in a simulation-based risk assessment framework to evaluate the evacuation risk.The route effectiveness in reducing the evacuation risk is evaluated by investigating how the evacuation risk varies with the proportion of the evacuees that use the evacuation route.The impacts of critical risk factors such as evacuation mode(for example,on foot or by car)and population size and distribution on the route effectiveness are also investigated.The evacuation risks under different cases are efficiently calculated using the augmented sample-based approach.The proposed approach is applied to the riskinformed evaluation of the route effectiveness for tsunami evacuation in Seaside,Oregon.The evaluation results show that the route usage is overall effective in reducing the evacuation risk in the study area.The results can be used for evacuation preparedness education and hence effective evacuation.
基金supported by the National Natural Science Foundation of China(grant number 41971372)in part by the Natural Science Foundation of Guangdong Province(grant number 2020A1515010680).
文摘As the COVID-19 vaccination has been quickly rolling out around the globe,the evaluation of the effects of vaccinating populations for the safe reopening of schools has become a focal point for educators,decision-makers,and the general public.Within this context,we develop a contact network agent-based model(CN-ABM)to simulate on-campus disease transmission scenarios.The CN-ABM establishes contact networks for agents based on their daily activity patterns,evaluates the agents’health status change in different activity environments,and then simulates the epidemic curve.By applying the model to a real-world campus environment,we identify how different community risk levels,teaching modalities,and vaccination rates would shape the epidemic curve.The results show that without vaccination,retaining under 50%of on-campus students can largely flatten the curve,and having 25%on-campus students can achieve the best result(peak value<1%).With vaccination,having a maximum of 75%on-campus students and at least a 45%vaccination rate can suppress the curve,and a 65%vaccination rate can achieve the best result.The developed CN-ABM can be employed to assist local government and school officials with developing proactive intervention strategies to safely reopen schools.
文摘Spatial optimization as part of spatial modeling has been facilitated significantly by integration with GIS techniques. However, for certain research topics, applying standard GIS techniques may create problems which require attention. This paper serves as a cautionary note to demonstrate two problems associated with applying GIS in spatial optimization, using a capacitated p-median facility location optimization problem as an example. The first problem involves errors in interpolating spatial variations of travel costs from using kriging, a common set of techniques for raster files. The second problem is inaccuracy in routing performed on a graph directly created from polyline shapefiles, a common vector file type. While revealing these problems, the paper also suggests remedies. Specifically, interpolation errors can be eliminated by using agent-based spatial modeling while the inaccuracy in routing can be improved through altering the graph topology by splitting the long edges of the shapefile. These issues suggest the need for caution in applying GIS in spatial optimization study.
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.
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This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network.The proposed model use theories and tools from the psychology of consumer behavior,social networks and complex dynamical systems like the Consumat framework and fuzzy logic.The model has been adjusted using real data,tested with the automobile market and it can recreate trends like those described in the world market.
基金National Key R&D Program of China(No.2020YFA0714500)National Science Foundation of China(Grant nos.72174099,72042010)High-tech Discipline Construction Fundings for Universities in Beijing(Safety Science and Engineering).
文摘Mathematical and computational models are useful tools for virtual policy experiments on infectious disease con-trol.Most models fail to provide flexible and rapid simulation of various epidemic scenarios for policy assessment.This paper establishes a multi-scale agent-based model to investigate the infectious disease propagation between cities and within a city using the knowledge from person-to-person transmission.In the model,the contact and infection of individuals at the micro scale where an agent represents a person provide insights for the interactions of agents at the meso scale where an agent refers to hundreds of individuals.Four cities with frequent population movements in China are taken as an example and actual data on traffic patterns and demographic parameters are adopted.The scenarios for dynamic propagation of infectious disease with no external measures are compared versus the scenarios with vaccination and non-pharmaceutical interventions.The model predicts that the peak of infections will decline by 67.37%with 80%vaccination rate,compared to a drop of 89.56%when isolation and quarantine measures are also in place.The results highlight the importance of controlling the source of infection by isolation and quarantine throughout the epidemic.We also study the effect when cities implement inconsis-tent public health interventions,which is common in practical situations.Based on our results,the model can be applied to COVID-19 and other infectious diseases according to the various needs of government agencies.
文摘This article examines the impact of partial/full reopening of school/college campuses on the spread of a pandemic using COVID-19 as a case study.The study uses an agent-based simulation model that replicates community spread in an urban region of U.S.A.via daily social mixing of susceptible and infected individuals.Data representing population demographics,SARS-CoV-2 epidemiology,and social interventions guides the model's behavior,which is calibrated and validated using data reported by the government.The model indicates a modest but significant increase(8.15%)in the total number of reported cases in the region for a complete(100%)reopening compared to keeping schools and colleges fully virtual.For partial returns of 75%and 50%,the percent increases in the number of reported cases are shown to be small(2.87%and 1.26%,respectively)and statistically insignificant.The AB model also predicts that relaxing the stringency of the school safety protocol for sanitizing,use of mask,social distancing,testing,and quarantining and thus allowing the school transmission coefficient to double may result in a small increase in the number of reported infected cases(2.14%).Hence for pandemic outbreaks from viruses with similar characteristics as for SARS-CoV-2,keeping the schools and colleges open with a modest campus safety protocol and in-person attendance below a certain threshold may be advisable.
基金funded through the VISTA Project that was carried out by the authors at the Département de Géologie et de Géographie,Universite catholique de Louvain,BelgiumVISTA was funded within the 5th Framework Programme of the European Commission.
文摘This paper presents an assessment of land use changes and their impacts on the ecosystem in the Montado, a traditional agricultural landscape of Portugal in response to global environmental change. The assessment uses an agent-based model (ABM) of the adaptive decisions of farmers to simulate the influence on future land use patterns of socio-economic attributes such as social relationships and farmer reliance on subsidies and biophysical constraints. The application and development of the ABM are supported empirically using three categories of input data: 1) farmer types based on a cluster analysis of socio-economic attributes;2) agricultural suitability based on regression analysis of historical land use maps and biophysical attributes;and 3) future trends in the economic and climatic environments based on the A1fi scenario of the Intergovernmental Panel on Climate Change. Model sensitivity and uncertainty analyses are carried out prior to the scenario analysis in order to verify the absence of systematic errors in the model structure. The results of the scenario analysis show that the area of Montado declines significantly by 2050, but it remains the dominant land use in the case study area, indicating some resilience to change. An important policy challenge arising from this assessment is how to encourage next generation of innovative farmers to conserve this traditional landscape for social and ecological values.
基金provided by Marie Sklodowska-Curie ITN Horizon 2020-funded project INSIGHTS(call H2020-MSCA-ITN-2017,grant agreement n.765710)NWO—Nederlandse Organisatie voor Wetenschappelijk Onderzoek(Award Number:KIVI.2019.006 HUMAINER AI project)。
文摘Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market,both directly influencing the price of an asset or indirectly by misin-forming other market participants.Such behavior can be a source of systemic risk and increasing distrust for the market participants,consequences that call for viable countermeasures.Building on the foundations provided by the extant literature,this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018.The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies,including a fraudulent agent,initialized from empirical data and who performs market manipulation.The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume.Simulation results provide a satisfactory fit to historical data.Several price dips and volume anomalies are explained by the actions of the fraudulent trader,completing the known body of evidence extracted from blockchain activity.The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period;without this agent,it would have been very unlikely that the price had reached the heights as it did in late 2017.The insights gained from the model,especially the connection between liquidity and manipulation efficiency,unfold a discussion on how to prevent illicit behavior.
基金supported by the National Key Research and Development Program of China(61873236)the Natural Science Foundation of Zhejiang Province(LZ21F020003,LY18F030001)the Civil Aerospace Pre-research Project(D020101).
文摘A system of systems(SoS)composes a set of independent constituent systems(CSs),where the degree of authority to control the independence of CSs varies,depending on different SoS types.Key researchers describe four SoS types with descending levels of central authority:directed,acknowledged,collaborative and virtual.Although the definitions have been recognized in SoS engineering,what is challenging is the difficulty of translating these definitions into models and simulation environments.Thus,we provide a goal-based method including a mathematical baseline to translate these definitions into more effective agent-based modeling and simulations.First,we construct the theoretical models of CS and SoS.Based on the theoretical models,we analyze the degree of authority influenced by SoS characteristics.Next,we propose a definition of SoS types by quantitatively explaining the degree of authority.Finally,we recognize the differences between acknowledged SoS and collaborative SoS using a migrating waterfowl flock by an agentbased model(ABM)simulation.This paper contributes to the SoS body of knowledge by increasing our understanding of the degree of authority in an SoS,so we may identify suitable SoS types to achieve SoS goals by modeling and simulation.
基金the National Key Research and Development Program of China(2021YFF0900800)the National Natural Science Foundation of China(61972276,62206116,62032016)+2 种基金the New Liberal Arts Reform and Practice Project of National Ministry of Education(2021170002)the Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems(20210101)Tianjin University Talent Innovation Reward Program for Literature and Science Graduate Student(C1-2022-010)。
文摘Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.
基金This work was supported by the National Natural Science Foundation of China(Grants Nos.U1811462,71671066,and 71532009)the Fundamental Research Funds for the Central Universities.
文摘We propose an empirical behavioral order-driven(EBOD)model with price limit rules,which consists of an order placement process and an order cancellation process.All the ingredients of the model are determined based on the empirical microscopic regularities in the order flows of stocks traded on the Shenzhen Stock Exchange.The model can reproduce the main stylized facts in real markets.Computational experiments unveil that asymmetric setting of price limits will cause the stock price to diverge exponentially when the up price limit is higher than the down price limit and to vanish vice versa.We also find that asymmetric price limits have little influence on the correlation structure of the return series and the volatility series,but cause remarkable changes in the average returns and the tail exponents of returns.Our EBOD model provides a suitable computational experiment platform for academics,market participants,and policy makers.
