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.

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.

技术创新难度对高科技企业最优创新投入的影响——基于Agent-Based Model的分析

通过Agent-Based Model模拟了高技术企业的发展环境,分析了不同技术创新门槛下企业技术的发展状况。研究发现,高技术创新门槛阻碍了市场最高技术水平的发展,但能提升市场平均技术水平及公司数目。同时,企业研发活动存在能实现最大技术增长的最优创新研发投入比例,并且随着技术创新门槛的提升,企业的研发政策向模仿性投入倾斜。由此可知,针对有高技术创新门槛的高科技企业,政府应提升专利保护强度以影响企业的最优研发政策,保证高新技术产业的健康发展。

Agent-Based Model: A Surging Tool to Simulate Infectious Diseases in the Immune System

Agent-based models (ABMs) are capable of constructing individual system components at different levels of representation to describe non-linear relationships between those components. Compared to a traditional mathematical modeling approach, agent-based models have an inherent spatial component with which they can easily describe local interactions and environmental heterogeneity. Furthermore, agent-based model maps interactions among agents inherently to the biological phenomenon by embedding the stochastic nature and dynamics transitions, thereby demonstrating suitability for the development of complex biological processes. Recently, an abundance of literature has presented application of agent-based modeling in the biological system. This review focuses on application of agent-based modeling to progression in simulation of infectious disease in the human immune system and discusses advantages and disadvantages of agent-based modeling application. Finally, potential implementation of agent-based modeling in relation to infectious disease modeling in future research is explored.

Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression

An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately reproduce some essential findings that were previously reported for a rather complex model of diabetes progression. Our models are translations of basicelements of this previously reported system dynamics model of diabetes. The system dynamics model, which mimics diabetes progression over an aggregated US population, was disaggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. The four estimated models attempted to replicate stock counts representing disease states in the system dynamics model while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent’s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. All three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model, although behavioral factors appeared to contribute more than the elderliness factor. The results illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.

银行部门与实体经济间的风险反馈效应——基于Agent-Based Model的研究

本文通过企业部门的信贷需求与银行部门的信贷供给,建立起实体经济与金融部门之间的信贷联系,从而构建了银行—企业间Agent-Based Model。企业的信贷需求和银行的信贷供给构成了风险传染与经济波动内生联系的微观基础。在此模型基础上,本文研究银行与企业部门之间风险传染的内生性演化规律。并模拟了系统性冲击下,利率政策、坏账剥离和资本注入等政策手段对控制风险、维持金融系统稳定的影响效果。研究发现:外生冲击会破坏经济系统的稳定,导致市场的自我调节机制失效。同时,冲击结束后系统需要花费相当长的时间来"自我修复"。而利率政策、资本注入和坏账剥离等政策手段能够有效加速系统修复。相比而言,利率政策的短期效果更加明显,资本注入的长期效果更加显著。

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.

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.

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.

An agent-based model with antibody dynamics information in COVID-19 epidemic simulation

Accurate prediction of the temporal and spatial characteristics of COVID-19 infection is of paramount importance for effective epidemic prevention and control.In order to accomplish this objective,we incorporated individual antibody dynamics into an agent-based model and devised a methodology that encompasses the dynamic behaviors of each individual,thereby explicitly capturing the count and spatial distribution of infected individuals with varying symptoms at distinct time points.Our model also permits the evaluation of diverse prevention and control measures.Based on our findings,the widespread employment of nucleic acid testing and the implementation of quarantine measures for positive cases and their close contacts in China have yielded remarkable outcomes in curtailing a less transmissible yet more virulent strain;however,they may prove inadequate against highly transmissible and less virulent variants.Additionally,our model excels in its ability to trace back to the initial infected case(patient zero)through early epidemic patterns.Ultimately,our model extends the frontiers of traditional epidemiological simulation methodologies and offers an alternative approach to epidemic modeling.

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.

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 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.

The Impact of Competition on Analysts’Forecasts:A Simple Agent-Based Model

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.

The organization of societal conflicts by pavement ants Tetramorium caespitum： an agent-based model of amine-mediated decision making

Ant colonies self-organize to solve complex problems despite the simplicity of an individual ant＇s brain. Pavement ant Tetramorium caespitum colonies must solve the problem of defending the ter- ritory that they patrol in search of energetically rich forage. When members of 2 colonies randomly interact at the territory boundary a decision to fight occurs when： 1） there is a mismatch in nest- mate recognition cues and 2） each ant has a recent history of high interaction rates with nestmate ants. Instead of fighting, some ants will decide to recruit more workers from the nest to the fighting location, and in this way a positive feedback mediates the development of colony wide wars. In ants, the monoamines serotonin （5-HT） and octopamine （OA） modulate many behaviors associated with colony organization and in particular behaviors associated with nestmate recognition and ag- gression. In this article, we develop and explore an agent-based model that conceptualizes how in- dividual changes in brain concentrations of 5-HT and OA, paired with a simple threshold-based de- cision rule, can lead to the development of colony wide warfare. Model simulations do lead to the development of warfare with 91% of ants fighting at the end of 1 h. When conducting a sensitivity analysis, we determined that uncertainty in monoamine concentration signal decay influences the behavior of the model more than uncertainty in the decision-making rule or density. We conclude that pavement ant behavior is consistent with the detection of interaction rate through a single timed interval rather than integration of multiple interactions.

Reopen schools safely:simulating COVID-19 transmission on campus with a contact network agent-based model

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.

Agent-based model of land system:Theory,application and modelling framework

Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems.As a process-oriented modelling approach,agent based model(ABM)plays an important role in revealing the driving forces of land change and understanding the process of land change.This paper starts from three aspects:The theory,application and modeling framework of ABM.First,we summarize the theoretical basis of ABM and introduce some related concepts.Then we expound the application and development of ABM in both urban land systems and agricultural land systems,and further introduce the case study of a model on Grain for Green Program in Hengduan Mountainous region,China.On the basis of combing the ABM modeling protocol,we propose the land system ABM modeling framework and process from the perspective of agents.In terms of urban land use,ABM research initially focused on the study of urban expansion based on landscape,then expanded to issues like urban residential separation,planning and zoning,ecological functions,etc.In terms of agricultural land use,ABM application presents more diverse and individualized features.Research topics include farmers’behavior,farmers’decision-making,planting systems,agricultural policy,etc.Compared to traditional models,ABM is more complex and difficult to generalize beyond specific context since it relies on local knowledge and data.However,due to its unique bottom-up model structure,ABM has an indispensable role in exploring the driving forces of land change and also the impact of human behavior on the environment.