Complex adaptive system theory,agent-based modeling,and simulation in dominant technology formation

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.

Agent-Based Modeling: An Application to Natural Resource Management

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.

Investigation of effects of hazard geometry and mitigation strategies on community resilience under tornado hazards using an Agent-based modeling approach

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.

Enhancing the Efficiency of Enterprise Shutdowns for Environmental Protection:An Agent-Based Modeling Approach with High Spatial–Temporal Resolution Data

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.

Adaptive pandemic management strategies for construction sites:An agent-based modeling approach

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.

Evaluating innovation ecosystem resiliency using agent-based modeling and systems dynamics

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.

Integrated framework for space-and energy-efficient retrofitting in multifunctional buildings:A synergy of agent-based modeling and performance-based modeling

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.

Agent-based Modeling and Simulation for the Electricity Market with Residential Demand Response

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.

Agent-based modeling in urban and architectura research： A brief literature review

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.

Agent-based modeling of emergency evacuation in a railway station square under sarin terrorist attack

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.

A multilevel and dynamic model of intragroup conflict and decision making:application of agent-based modeling

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.

Agent-based modeling approach for evaluating underground logistics system benefits and long-term development in megacities

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.

Agent-based modeling of COVID-19 outbreaks for New York state and UK:Parameter identification algorithm

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.

An agent-based model of agricultural land expansion in the mountain forest of Timor Island,Indonesia

The Mutis-Timau Forest Complex,located on Timor Island,Indonesia,is a mountainous tropical forest area that gradually decreases due to deforestation and forest degradation.Previous modelling studies based on patterns indicate that deforestation primarily occurs at lower elevations and near the boundaries of forests and settlements,often associated with shifting cultivation by local farmers.This study adopts a process-based modelling approach,specifically the agent-based model,to simulate land changes,particularly farmers'expansion of agricultural land around the Mutis mountain forest.The underlying concept of this agent-based approach is the interaction between the human and environmental systems.Farmers,representing the human system,interact with the land,which represents the environmental system,through land use decision-making mechanisms.The research was conducted in the Community Forest of the Timor Tengah Utara District,one of the sites within the Mutis-Timau Forest Complex with the highest deforestation rate.Land use change simulations were performed using agent-based modelling from 1999 to 2030,considering the socio-economic conditions of farmers,spatial preferences,land use decisions,and natural transitions.The results revealed that the agricultural area increased by 14%under the Business as Usual scenario and 5%under the Reducing Emission from Deforestation and Forest Degradation scenario,compared to the initial agricultural area of 245 hectares.The probability of farmers deciding to extend agricultural activities was positively associated with the number of livestock maintained by farmers and the size of the village area.Conversely,the likelihood of farmers opting for agricultural extensification decreased with an increase in the area of private land and the farmer's age.These findings are crucial for the managers of the Mutis-Timau Forest Complex and other relevant stakeholders,as they aid in arranging actions to combat deforestation,designing proper forest-related policies,and providing support for initiatives such as reducing emissions from deforestation and forest degradation programs or further incentive schemes.

Investigating impacts of different building contents on the post-earthquake evacuation time using an agent-based model with considering turning behavior

Accurate assessment of crowd evacuation inside the post-earthquake environment is critical from many perspectives,but this issue receives much less attention compared to the seismic losses of structural and non-structural components.This could be attributed to the fact that post-earthquake evacuation analysis is complex due to the interaction between human behavior and the actual built environment induced by different building contents.This study attempts to tackle this problem by investigating the impacts of different building contents on post-earthquake evacuation time by using an agent-based model that considers turning behavior.To this end,the agent-based model is first described,including:properties of the agent-based model with turning behavior,key aspects in its formulation considering different evacuation stages,and influence of different building contents(namely,debris from partition walls and ceiling systems,and various types of equipment)on the agent’s behavior.Subsequently,a school building is used as a benchmark problem to validate the model without earthquake,and the findings indicate that the agent-based model can match the real safety drill results reasonably well.After the validation,the school building is subsequently designed in accordance with modern seismic design codes,and the influence of debris and equipment on post-earthquake evacuation time is quantitatively studied using a suite of pulse-type ground motions as input.Based on this case study,recommendations are made for structural and architectural designers in an effort to reduce the potential evacuation time.Specifically,debris induced by partition walls or ceiling systems should be controlled as it has the greatest impact on the total evacuation time.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

Agent-Based Network Modeling Study of Immune Responses in Progression of Ulcerative Colitis

Ulcerative colitis, an inflammatory bowel disease, is a chronic inflammatory disorder that results in ulcers of the colon and rectum without known etiology. Ulcerative colitis causes a huge public health care burden particularly in developed countries. Many studies suggest that ulcerative colitis results from an abnormal immune response against components of cornrnensal rnicrobiota in genetically susceptible individuals. However, understanding of the disease mechanisms at cellular and molecular levels remains largely elusive. In this paper, a network model is developed based on our previous study and computer simulations are perforrned using an agent-based network modeling to elucidate the dynamics of immune response in ulcerative colitis progression. Our modeling study identifies several important positive feedback loops as a driving force for ulcerative colitis initiation and progression. The results demonstrate that although immune response in ulcerative colitis patients is dominated by anti-inflarnrnatory/regulatory cells such as alternatively activated rnacrophages and type II natural killer T cells, proinflarnrnatory cells including classically activated rnacrophages, T helper 1 and T helper 17 cells, and their secreted cytokines tumor necrosis factor-α, interleukin-12, interleukin-23, interleukin-17 and interferon-γ remain at certain levels （lower than those in Crohn＇s disease, another inflammatory bowel disease）. Long-terrn exposure to these proinflarnrnatory components, causes rnucosal tissue damage persistently, leading to ulcerative colitis. Our simulation results are qualitatively in agreement with clinical and laboratory measurements, offering novel insight into the disease mechanisms.

Simulation-Based and Risk-Informed Assessment of the Effectiveness of Tsunami Evacuation Routes Using Agent-Based Modeling:A Case Study of Seaside,Oregon

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.

Adaptive Agent-Based Modeling Framework for Collective Decision-Making in Crowd Building Evacuation

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.

Modeling of Agile Manufacturing Execution Systems with an Agent-based Approach

Agile manufacturing execution systems (AMES) are used to help manufacturers optimize shop floor production in an agile way. And the modeling of AMES is the key issue of realizing AMES. This paper presents an agent-based approach to AMES modeling. Firstly, the characteristics of AMES and its requirements on modeling are discussed. Secondly, a comparative analysis of modeling methods is carried out, and AMES modeling using an agent-based approach is put forward. Agent-based modeling method not only inherit the favorable features of traditional object-oriented modeling method such as data encapsulation, modularity and so on, but also has the ability to construct intelligent, rational and autonomous agent which can cooperate together to realize the goal of agile operation. A general agent architecture used in AMES modeling is described. Under this architecture, an agent can be divided into domain-independent components and domain-specific components which helps solve problems such as information overload, incomplete information handling and soft decision-making. Furthermore, an AMES model using four types of agents, i.e., interface agent, information agent, resource agent and management agent, is established. Thirdly, a snapshot of AMES model is provided in the case study. Especially, an agent-based cooperating process of task scheduling in AMES is illustrated in detail. Finally, the advantages and disadvantages of this modeling approach are discussed as well.