Agent-based Modeling Approach Research Articles

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.

Tackling school segregation with transportation network interventions: an agent-based modelling approach

We address the emerging challenge of school segregation within the context of free school choice systems. Households take into account both proximity and demographic composition when deciding on which schools to send their children to, potentially exacerbating residential segregation. This raises an important question: can we strategically intervene in transportation networks to enhance school access and mitigate segregation? In this paper, we propose a novel, network agent-based model to explore this question. Through simulations in both synthetic and real-world networks, we demonstrate that enhancing school accessibility via transportation network interventions can lead to a reduction in school segregation, under specific conditions. We introduce group-based network centrality measures and show that increasing the centrality of certain neighborhood nodes with respect to a transportation network can be an effective strategy for strategic interventions. We conduct experiments in two synthetic network environments, as well as in an environment based on real-world data from Amsterdam, the Netherlands. In both cases, we simulate a population of representative agents emulating real citizens’ schooling preferences, and we assume that agents belong to two different groups (e.g., based on migration background). We show that, under specific homophily regimes in the population, school segregation can be reduced by up to 35%. Our proposed framework provides the foundation to explore how citizens’ preferences, school capacity, and public transportation can shape patterns of urban segregation.

Using an epidemiological model to explore the interplay between sharing and advertising in viral videos

How to exploit social networks to make internet content spread rapidly and consistently is an interesting question in marketing management. Although epidemic models have been employed to comprehend the spread dynamics of internet content, such as viral videos, the effects of advertising and individual sharing on information dissemination are difficult to distinguish. This gap forbids us to evaluate the efficiency of marketing strategies. In this paper, we modify a classic mean-field SIR (susceptible–infected–recovered) model, incorporating the influences of sharing and advertising in viral videos. We mathematically analyze the global stability of the system and propose an agent-based modeling approach to evaluate the efficiency of sharing and advertising. We further provide a case study of music videos on YouTube to show the validity of our model.

Subtractive proteomics-guided vaccine targets identification and designing of multi-epitopes vaccine for immune response instigation against Burkholderia pseudomallei

In this study, a methodical workflow using subtractive proteomics, vaccine designing, molecular simulation, and agent-based modeling approaches were used to annotate the whole proteome of Burkholderia pseudomallei (strain K96243) for vaccine designing. Among the total 5717 proteins in the whole proteome, 505 were observed to be essential for the pathogen's survival and pathogenesis predicted by the Database of Essential Genes. Among these, 23 vaccine targets were identified, of which fimbrial assembly chaperone (Q63UH5), Outer membrane protein (Q63UH1), and Hemolysin-like protein (Q63UE4) were selected for the subsequent analysis based on the systematic approaches. Using immunoinformatic approaches CTL (cytotoxic T lymphocytes), HTL (helper T lymphocytes), IFN-positive, and B cell epitopes were predicted for these targets. A total of 9 CTL epitopes were added using the GSS linker, 6 HTL epitopes using the GPGPG linker, and 6 B cell epitopes using the KK linker. An adjuvant was added for enhanced antigenicity, an HIV-TAT peptide for improved delivery, and a PADRE sequence was added to form a 466 amino acids long vaccine construct. The construct was classified as non-allergenic, highly antigenic, and experimentally feasible. Molecular docking results validated the robust interaction of MEVC with immune receptors such as TLR2/4. Furthermore, molecular simulation revealed stable dynamics and compact nature of the complexes. The binding free energy results further validated the robust binding. In silico cloning, results revealed GC contents of 50.73 % and a CIA value of 0.978 which shows proper downstream processing. Immune simulation results reported that after the three injections of the vaccine a robust secondary immune response, improved antigen clearance, and effective immune memory generation were observed highlighting its potential for effective and sustained immunity. Future directions should encompass experimental validations, animal model studies, and clinical trials to substantiate the vaccine's efficacy, safety, and immunogenicity.

Unraveling the interplay of human decisions and flood risk: An agent-based modeling approach

Flooding is a frequent and highly destructive natural disaster that requires effective flood management policies. This paper addresses the limitations of traditional flood risk assessment methods, focusing on the interplay between human decisions and flood risk using an integrated approach. Agent-Based Modeling (ABM) is employed to capture the social behavior of government, households, and farmers in three flood management scenarios. The study is conducted in the Fall River watershed, Idaho, USA, over a 30-year period. The results highlight the important influence of crop selection decisions on flood damage outcomes, with alfalfa emerging as the preferred crop due to its profitability and lower damage potential (around 67 % crop loss compared to more than 81 % for crops like corn, barley, and wheat). The levee scenario is identified as the most effective strategy, significantly reducing flood damage by more than 37 % and generating the highest average annual profit for farmers. The study emphasizes the need for comprehensive flood risk management strategies that address both frequent and rare flood occurrences in mitigating flood damage and promoting community resilience. By integrating human-flood interactions into flood risk assessment, this study offers valuable insights for Policymakers and stakeholders can utilize the research implications to develop effective strategies that mitigate damage and enhance community resilience.

Assessing urban water demand-side management policies before their implementation: An agent-based model approach

In the context of climate change and increasing water scarcity, adopting water demand-side management (DSM) policies has become necessary. This study advocates for utilizing agent-based modelling (ABM) as a robust simulation tool to assess the impact of nonprice (nudges) and price (changes in increasing block tariff) measures on urban water use. Overcoming challenges posed by insufficient high-quality data, the research integrates four sociocognitive profiles and diverse household income levels to reflect the variability in DSM policy effectiveness based on socioeconomic characteristics. Through 125 simulated scenarios combining various increasing block tariffs with nonpricing measures, the study reveals an average monthly demand reduction ranging from 8.1% to 15.6%. Significantly, nonprice measures prove more effective in curbing water use than pricing measures, attributed to the prioritization of environmental concerns in conservation efforts. Higher-income households exhibit less-pronounced reductions in water consumption. Emphasizing the reliability of ABM for ex ante evaluations of DSM policies, this research underscores the importance of a balanced approach, incorporating both nonprice and price-based measures, to effectively address water scarcity challenges.

Modeling the implications of policy reforms on pesticide risk for Switzerland

Growing public awareness of the negative effects of pesticides on the environment, ecosystems, and human health has led governments to set targets for reducing pesticide risk. Switzerland introduced in 2023 two new policy measures to reduce pesticide risk by 50 % by 2027: (1) voluntary direct payment programs supporting pesticide-reduced and pesticide-free but non-organic cropping systems for most crops on arable land, and (2) restrictions of harmful pesticides for farmers managing under Swiss cross-compliance standards. This study aims to (1) develop a method to assess pesticide risk on a national scale and (2) carry out an ex-ante impact assessment to predict whether these policies can effectively reduce pesticide risks in Switzerland. Therefore, we introduced crop-specific pesticide quantities and pesticide risk scores into a sample of 1907 bio-economic farm optimization models. The models were used to predict farmers' adoption decisions regarding voluntary direct payment programs from 2019 to 2030. By combining the bio-economic farm optimization models with an agent-based modeling approach, we assessed the evolution of pesticide-related risks at the national level. Simulations for pesticide risk from 2019 to 2022 reflected the observed pesticide risk monitored by the Swiss government. In surface waters and semi-natural habitats, achieving the target depends on reducing pyrethroids, a class of insecticides with high-risk potential. Further, we highlight significant uncertainty in projecting the risk potential for surface waters and semi-natural habitats due to uncertainty about the amounts of pyrethroid used for different crops. The results underline the need for comprehensive datasets on pesticide use in Switzerland.

The impact of climate change on the agriculture and the economy of Southern Gaul: New perspectives of agent-based modelling.

What impact did the Roman Climate Optimum (RCO) and the Late Antique Little Ice Age (LALIA) have on the rise and fall of the Roman Empire? Our article presents an agent-based modelling (ABM) approach developed to evaluate the impact of climate change on the profitability of vineyards, olive groves, and grain farms in Southern Gaul, which were the main source of wealth in the roman period. This ABM simulates an agroecosystem model which processes potential agricultural yield values from paleoclimatic data. The model calculates the revenues made by agricultural exploitations from the sale of crops whose annual volumes vary according to climate and market prices. The potential profits made by the different agricultural exploitations are calculated by deducting from the income the operating and transportation costs. We conclude that the warm and wet climate of the Roman period may have had an extremely beneficial effect on the profitability of wine and olive farms between the 2nd century BCE and the 3rd century CE, but a more modest effect on grain production. Subsequently, there is a significant decrease in the potential profitability of farms during the Late Antique Little Ice Age (4th-7th century CE). Comparing the results of our model with archaeological data enables us to discuss the impact of these climatic fluctuations on the agricultural and economic growth, and then their subsequent recession in Southern Gaul from the beginning to the end of antiquity.

Economic and labour market impacts of migration in Austria: an agent-based modelling approach

This study examines the potential economic and labour market impacts of a hypothetical but plausible migration scenario of 250,000 new migrants inspired by Austria’s experience in 2015. Using the agent-based macroeconomic model developed by Poledna et al. (Eur Econ Rev, 151:104306, 2023. 10.1016/j.euroecorev.2022.104306, the study explores the detailed labour market outcomes for different groups in Austria’s population and the macroeconomic effects of the migration scenario. The analysis suggests that Austria’s economy and labour market have the potential to be resilient to the simulated migration influx. The results indicate a positive impact on GDP due to increased aggregate consumption and investment. The labour market experiences an increase in the unemployment rates of natives and previous migrants. In some industries, the increase in the unemployment rates is more significant, potentially indicating competition among different groups of migrants. This research provides insights for policymakers and stakeholders in Austria and other countries that may face the challenge of managing large-scale migration in the near future.

An agent-based resilience model of oil tank farms exposed to earthquakes

Frequent unpredictable earthquake disasters such as the Turkey Earthquake in 2023 pose an increasing threat to oil tank farms since they may trigger major accidents and domino effects, resulting in casualties, economic losses, and environmental pollution. Unpredictable earthquakes are definitely difficult to prevent and thus resilience strategies such as emergency response should be applied to reduce losses. However, little attention has been paid to the quantitative resilience modeling of oil tank farms, resulting in difficulties in decision-making on resilience assessment and management. Therefore, this study proposes a quantitative seismic resilience model of oil storage tanks by using a dynamic agent-based modeling approach. This approach models the storage tank, active fault, and the environment as three independent agents with their attributes and behaviors. The interaction between agents can also be modeled through disaster evolution rules, and the consequences of interactions can be adjusted through adaptation and recovery strategies. The dynamic propagation of earthquake accidents and the evolution of potential domino effects can be quantified from a bottom-up perspective, thereby quantifying the seismic resilience of oil tank farms. A case study is carried out to illustrate the application of the developed model in oil tank farms and to analyze the sensitivity of different model parameters. The results show that the developed model can dynamically characterize the evolution of earthquake-induced domino effects as well as the emergency and restoration processes, supporting the decision-making on the allocation of resilience measures.

Interactions between dynamic team composition and coordination: an agent-based modeling approach

AbstractThis paper examines the interactions between selected coordination modes and dynamic team composition, and their joint effects on task performance under different task complexity and individual learning conditions. Prior research often treats dynamic team composition as a consequence of suboptimal organizational design choices. The emergence of new organizational forms that consciously employ teams that change their composition periodically challenges this perspective. In this paper, we follow the contingency theory and characterize dynamic team composition as a design choice that interacts with other choices such as the coordination mode, and with additional contextual factors such as individual learning and task complexity. We employ an agent-based modeling approach based on the NK framework, which includes a reinforcement learning mechanism, a recurring team formation mechanism based on signaling, and three different coordination modes. Our results suggest that by implementing lateral communication or sequential decision-making, teams may exploit the benefits of dynamic composition more than if decision-making is fully autonomous. The choice of a proper coordination mode, however, is partly moderated by the task complexity and individual learning. Additionally, we show that only a coordination mode based on lateral communication may prevent the negative effects of individual learning.

In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

How individuals’ opinions influence society’s resistance to epidemics: an agent-based model approach

BackgroundProtecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration.MethodsWe propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis.ResultsOur results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor.ConclusionsOur results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach

Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households’ electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model.

Bladder cancer is an increasingly prevalent global disease that continues to cause morbidity and mortality despite recent advances in treatment. Immune checkpoint inhibitors (ICI) and fibroblast growth factor receptor (FGFR)-targeted therapeutics have had modest success in bladder cancer when used as monotherapy. Emerging data suggests that the combination of these two therapies could lead to improved clinical outcomes, but the optimal strategy for combining these agents remains uncertain. Mathematical models, specifically agent-based models (ABMs), have shown recent successes in uncovering the multiscale dynamics that shape the trajectory of cancer. They have enabled the optimization of treatment methods and the identification of novel therapeutic strategies. To assess the combined effects of anti-PD-1 and anti-FGFR3 small molecule inhibitors (SMI) on tumor growth and the immune response, we built an ABM that captures key facets of tumor heterogeneity and CD8+ T cell phenotypes, their spatial interactions, and their response to therapeutic pressures. Our model quantifies how tumor antigenicity and FGFR3 activating mutations impact disease trajectory and response to anti-PD-1 antibodies and anti-FGFR3 SMI. We find that even a small population of weakly antigenic tumor cells bearing an FGFR3 mutation can render the tumor resistant to combination therapy. However, highly antigenic tumors can overcome therapeutic resistance mediated by FGFR3 mutation. The optimal therapy depends on the strength of the FGFR3 signaling pathway. Under certain conditions, ICI alone is optimal; in others, ICI followed by anti-FGFR3 therapy is best. These results indicate the need to quantify FGFR3 signaling and the fitness advantage conferred on bladder cancer cells harboring this mutation. This ABM approach may enable rationally designed treatment plans to improve clinical outcomes.

An Agent-Based Modeling Approach for the Diffusion Analysis of Electric Vehicles With Two-Stage Purchase Choice Modeling

Abstract Diffusion research of innovative technologies is crucial for new product positioning and strategic planning in product design. As a versatile system simulation method, agent-based modeling (ABM) has been used in many previous studies on the diffusion analysis of electric vehicles (EVs). In these simulations, modeling consumers' purchase decisions is a significant step. Previous studies often adopt simple rule-based decision criteria in this step, while an accurate purchase decision model can contribute to a more reasonable diffusion analysis of EVs. To fill this gap, this brief presents an agent-based modeling approach for the diffusion analysis of electric vehicles with two-stage choice modeling. The core idea is to separate consumers' decision-making process for purchasing cars into two stages. Consumers first form a small choice set from the whole auto market. Then, they make the final choice from the choice set built in the first stage. In addition, the word-of-mouth (WOM) effect and consumers' social networks are also considered in the ABM, which can further improve the accuracy of the diffusion analysis. A case study using data collected from Shanghai, China, is presented to demonstrate the proposed approach. Our approach outperforms other ablation models as well as traditional statistical models in the prediction accuracy of EV's market share. The influence of factors such as government policy and technological improvement on the diffusion of EVs is also discussed. These insights can assist automakers in improving their product design and enhancing their market competitiveness.

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 sustainability. 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, consumption, and transportation agents, attributing economic features to supply chain components and capturing 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 18 916 chemical enterprises, this study investigates the economic and environmental impacts of an environmental policy resulting in the closure of 1 800 chemical enterprises over three years. The results revealed a significant economic loss of 25.8 billion USD, ranging from 23.8 USD to 31.8 billion USD. Notably, over 80% of this loss was attributed to supply chain propagation. Counterfactual analyses indicated that implementing a staggered shutdown strategy prevented 18.8% of supply chain losses, highlighting the importance 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.

Engineering Optimal Cooperation Levels with Prosocial Autonomous Agents in Hybrid Human-Agent Populations: An Agent-Based Modeling Approach

Engineering Optimal Cooperation Levels with Prosocial Autonomous Agents in Hybrid Human-Agent Populations: An Agent-Based Modeling Approach

Agent-Based Principal Strips Modeling for Freeform Surfaces in Architecture

The principal curvature (PC) of a freeform surface, as an important indicator of its fundamental features, is frequently used to guide their rationalization in the field of architectural geometry. The division of a surface using its PC lines into principal strips (PSs) is an innovative way to break down a freeform surface for construction. However, the application of PC networks in architectural design is hindered by the difficulty to generate them and flexibly control their density. This paper introduces a method for PS-based reconstruction of freeform surfaces with different umbilical conditions in the early stages of design. An agent-based modeling approach is developed to find the umbilics and increase the degree of control over the spacing of PC lines. This research can effectively expand the application range of PS-based surface reconstruction methods for freeform architectures.

Quantitative characterization of steady states for systems of microtubules: A stochastic agent-based modeling approach

Quantitative characterization of steady states for systems of microtubules: A stochastic agent-based modeling approach