Agent-based Modeling Approach Research Articles

A WEAP-assisted agent-based modeling approach for watershed planning in a coupled human and nature system

ABSTRACT Coupled human and nature systems exhibit dynamic water demands due to diverse consumer socio-economic characteristics, resulting in inherent complexities. Modeling such systems effectively entails adopting an agent-based methodology. To enhance comprehension of water resource management in the Zarrineh-Roud Basin, northwest of Iran, a comprehensive hydrological-behavioral model has been utilized. This model consists of two primary components: An agent-based model (ABM) for simulating farmers’ decision-making processes and a water evaluation and planning (WEAP) model for simulating catchment area, cultivated area, and agricultural water consumption. Validation outcomes of the simulation model demonstrate precise estimation, particularly in the runoff estimation of the Nezam Abad hydrometric station, with statistically favorable correlation coefficient (R2) and Nash–Sutcliffe index (NS) values of 0.78 and 0.75, respectively. Analysis of different management scenarios indicates that with ample government funds, simultaneous implementation of ‘Education and increasing environmental awareness’ and ‘Alternative employment’ policies can significantly improve water resources conditions and income. In scenarios with limited financial resources, a combined implementation of ‘Water pricing,’ ‘Alternative employment,’ and ‘Education and raising environmental awareness’ emerges as the most effective strategy for managing water resources, albeit with a slight reduction in farmers’ income.

Financial Revolution through Agent-based Artificial Simulation Computational Models for Predicting Market Behavior

The fundamental theory of the Efficient market hypothesis (EMH), which states that market participants are rational, has received a lot of criticism. The complexity of behavior in the capital market is still a black box, especially when psychological biases influence aggressively on decision-making amid uncertainty. Experimental research on finance and capital markets in the form of AI using machine learning seeks to predict the results of more complex interactions. This multidisciplinary approach offers efforts to explain social phenomena from the micro level to macro descriptions which are built artificially through the computational world. The processing modeling approach is preferred because it includes the complexes that emerge from the behavior and interactions of individuals in the real world. Agent Based Model (ABM) is an AI approach in the form of computational simulation that performs a bottom-up approach by combining irrational–rational agent interactions through networks in microenvironments. Using the ABM approach through Netlogo computing, this study proves that AI can be used to analyze investor behavior in the capital market. Keywords: Agent Based Model, artificial intelligence, investor behavior

Predicting transit ridership using an agent-based modeling approach

Accurate ridership estimation is pivotal in the advancement of sustainable transit systems, be it for proposed or existing transit networks. A multitude of methods, including travel demand models, direct ridership models, and regression models, have been employed by practitioners and researchers to estimate ridership at both station and network levels. However, travel demand models, frequently utilized for new transit lines, exhibit intrinsic limitations due to their aggregate nature and complexity based on their types. Researchers have also identified deficiencies, such as the incapacity to capture small spatial resolutions and specific station characteristics, as these models are predominantly designed for large-scale analyses.This study aims to overcome these limitations by introducing a novel approach that utilizes three microscopic agent-based models to develop a travel demand modeling suite, providing a policy-sensitive forecasting tool. The suite comprises three agent-based models: SILO-MITO-MATSim. Validation of the model against previous year data is conducted, and projections are made for future years. The model is applied to estimate network-level ridership for the proposed ‘Purple Line,’ a light rail transit line planned by MDOT, MTA, Maryland, which will integrate with the Washington D.C. Metro, the fourth largest transit system in the USA, boasting an average daily ridership of half a million. The study’s findings indicate an anticipated ridership of approximately 31,230 passengers in the inaugural year of 2027. The proposed model offers a robust and policy-sensitive solution empowering decision-makers to make informed choices to support a sustainable transportation system.

Modeling a game to shift peak water demands: WaterTime leaderboard

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.

Diffusion mechanism of green building in industrial clusters: An agent-based modeling approach

Industrial clusters are conducive to accelerating the adoption of green technologies and green buildings, but the underlying mechanism of cluster-based adoption among different stakeholders is seldom examined. The paper aims to explore the diffusion mechanism of green building among stakeholders in industrial clusters based on an agent-based modeling (ABM) approach. Factors influencing stakeholders’ adoption of green buildings were identified in five dimensions: economic benefit, green responsibility, peer effect, market demand, and technological maturity. An ABM model was developed for simulating green building diffusion among stakeholders, and validated with a Shenzhen case. The results indicate an S-shape trend of the adoption rate within the cluster. Developers show a relatively laggard adoption compared to others. Mandatory policy and financial subsidies could effectively boost the adoption rate, while technological progress and market demand accelerate the diffusion progress. The study provides useful insights into the practice and policy-making of green buildings within industrial clusters.

An agent-based modelling approach to wave-like diversification of language families

Abstract In contrast to phylogenetic tree inference, wave model approaches are often regarded as difficult to computationally implement for inference of language relatedness. This paper proposes a basic framework for the computational modelling of wave-like diversification in language families and explains the model type of agent-based models for linguistic data. The approach is based on agent-based simulations which allow for the detailed simulation of speaker interactions within speech communities. The proposed framework operates by simulating a large number of possible diversification situations with different parameter settings and selecting those runs that yield a good fit to the linguistic data of the geographical spread of different languages. The model can be fed with the geographical extent of languages and their known innovations in order to computationally reconstruct the most likely diversification scenarios of these languages under the wave model.

Evaluating the Interaction of Emerging Diseases on White-Tailed Deer Populations Using an Agent-Based Modeling Approach.

Disease co-occurrence in wildlife populations is common yet understudied. In the case of disease-caused mortality, the mortality attributed to one disease has the potential to buffer populations against subsequent alternative disease outbreaks by reducing populations and thus contacts needed to sustain disease transmission. However, substantial disease-driven population declines may also prevent populations from recovering, leading to localized extinctions. Hemorrhagic disease (HD), a vector-transmitted, viral disease in white-tailed deer (WTD), similar to chronic wasting disease (CWD), a prion disease, has increased in frequency and distribution in the United States. However, unlike CWD, which progresses slowly, HD can cause mortality only days after infection. Hemorrhagic disease outbreaks can result in substantial localized mortality events in WTD near vector habitats such as wetlands and may reduce local deer densities and consequent CWD transmission. The objective of our study was to evaluate the potential for HD outbreaks to buffer CWD risk where the diseases co-occur. Using an agent-based modeling approach, we found that frequent, intense HD outbreaks have the potential to mitigate CWD risk, especially if those outbreaks occur shortly after CWD introduction. However, HD outbreaks that do not result in substantial WTD mortality are unlikely to impact CWD or WTD population dynamics. Severe HD outbreaks may reduce CWD cases and could present an opportunity for managers to boost CWD control initiatives in a post-HD outbreak year.

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

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

Using electric vehicles to enhance power outage resilience – An agent-based modeling approach

Power outages can cause severe disruption to critical infrastructure. With the predicted increase in the electrification of the transport sector, society will become even more vulnerable to the effects of power outages. While increased electric vehicle (EV) adoption will contribute to the electrification process, EVs can also offer capabilities to provide services during an outage. This paper studied the use of a fleet of EVs during the aftermath of a disaster to provide disaster relief by donating power to a shelter, delivering critical supplies and people in need, and providing transport for personnel or performing inspections. While the bulk of the past work has focused on using EVs to increase the resilience of the distribution grid, or individual buildings, to a power outage, this paper was novel in its use of an agent-based model to study EVs that are performing functions to increase the resilience of a community to an outage. The fleet of EVs were provided access to a microgrid with a solar array, one or two EV fast chargers, and three possible sizes of a storage. Useful outputs were produced and studied for such features as daily energy donated to a shelter, daily energy used at the microgrid, and the length of outages that can be supported before the energy is depleted at the microgrid. Results showed that increasing storage size at the microgrid led to substantial increases in the outage length that could be support. Additionally, it was found that focusing a fleet on delivery and transport tasks, as opposed to energy donation, could also increase the length of outages that could be supported.

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.

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.

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.

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.

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.

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.