Agent-based Modeling Research Articles

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Deployment of Public Charging Stations for BEVs Using an Agent-Based Modeling Approach

A low utilization rate of public chargers and unmatched deployment of public charging stations (CSs) are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation. This study proposes an optimization methodology for public CS deployment, considering real charging behavior and interactions between battery electric vehicle (BEV) users and CSs. Realistic charging choice behavior is modeled based on surveys, and a dynamic charging decision chain is simulated, allowing interactions between BEV users and CSs through an agent-based modeling (ABM) approach. The charging-related activities are triggered by state of charge (SOC) levels randomly generated from distributions derived from real BEV operating data, including the random SOC levels at the start of a trip, the SOC level that prompts the user to charge the BEV, and the SOC level at which the user stops charging the BEV. A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs, to determine the location and the capacity of new CSs. The objective is to minimize the total time cost per BEV user, including travel time, charging time and waiting time in the queue. An application is conducted, for the deployment of fast CSs in Washington State, USA. The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems. The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs and leads to a more balanced utilization among CSs. The proposed approach is expected to contribute to better planning and deployment of public CSs, satisfaction of the booming charging demand and increased utilization of public CSs.

Key factors influencing sustainable population growth: A DEMATEL-ANP combined approach

This study highlights the importance of sustainable social demographic growth. It uses a model that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) with the Analytic Network Process (ANP) to examine key factors affecting this growth and their interactions. The analysis focuses on six critical factors: economic development, education and gender equality, health services, environmental sustainability, immigration policies, and technological advancement. Experiments using government and international organization databases include comparative experiments with deep learning prediction models, ensemble learning models, Causal Inference Models, complex network analysis models, and agent-based models. Comparison metrics cover accuracy, precision, recall, and F1 score. The results indicate that, with a data volume of 4000, the optimized model achieves an accuracy of 0.973, precision of 0.981, recall of 0.969, and an F1 score of 0.89, demonstrating the model's superior performance. The DEMATEL method analyzes the direct relationships among the factors. The results show that economic development and technological advancement have impact scores of 3.91 and 3.43, respectively, indicating their strong influence on other factors and their role in promoting sustainable demographic growth. Education and gender equality, health services, and technological advancement each have impact scores of 3.39, meaning they are significantly affected by other factors and are sensitive in the growth process. Finally, the ANP method is used to calculate the weights of each factor, determining their relative importance in sustainable social demographic growth. The results highlight that economic development level and technological advancement and innovation are core factors influencing sustainable social demographic growth, with significant direct and indirect impacts on other factors and a crucial role in the overall system. These findings provide a scientific basis for formulating relevant policies and interventions, particularly in prioritizing and strengthening economic and technological development strategies. This study offers valuable insights for research in demography, sustainable development, and social policy formulation.

Simulation of migration paths using agent-based modeling: The case of Syrian refugees en route to Turkey

The decade-long Syrian civil war has triggered a significant migration wave in the Middle East, with Turkey hosting the largest number of Syrian refugees. Our study introduces an agent-based model (ABM) designed to simulate and predict migration paths in potential future refugee crises. The primary goal is to support aid organizations in planning the delivery of essential aid services during migration movements, offering insights that can be applied to various geographical areas and migration scenarios. While we use the Syrian refugee movement to Turkey as a case study, the model is intended as a flexible tool for analyzing migration patterns in future crises. The proposed ABM considers two characteristics of refugee groups: level of risk sensitivity and level of information. To enhance the model’s functionality, we have extended the A* algorithm with a cost metric to calculate the weighted average of distance and risk to a destination point. Our case study examines the crisis in southern Idlib through six scenarios, offering insights into refugee numbers, migration paths, camp occupancy rates, and heat maps of densely populated regions for each scenario. Validation is performed by comparing model outcomes with situation reports and official statements from the relevant period, demonstrating the proposed ABM’s potential for adaptation to other migration instances and further analysis under different parameters.

Feasibility of vehicle-to-grid (V2G) implementation in Japan: A regional analysis of the electricity supply and demand adjustment market

Vehicle-to-Grid (V2G) technology forms a critical bridge between electric vehicles (EVs) and renewable energy, playing a transformative role in the global shift towards a low-carbon future. Despite its promise, the impact of regional differences on V2G potential, particularly within specific application scenarios, remains largely unexplored. This study employs an agent-based modeling approach to evaluate V2G potential across Japan's 47 prefectures, with a focus on its integration into an energy reserve service business model. Our analysis reveals that EV ownership is the dominant factor influencing regional variability, with the Tokyo Metropolitan Area, Aichi, and Kanagawa emerging as top candidates for V2G deployment. Interestingly, when EV ownership is excluded from consideration, regions like Okinawa, Kagoshima, and Ibaraki show leadership in V2G potential. These findings highlight the existence of regional priorities for V2G application. Moreover, a ‘High Demand Disparity’ is observed in the power supply and demand adjustment market, where V2G systems are often either underutilized or overwhelmed, leaving many idle. The study underscores the need for region-specific strategies and the integration of diverse business models to fully capitalize on V2G's potential in various demand adjustment scenarios.

Effect of automation of routine and non-routine tasks on labour demand and wages

Artificial intelligence and associated technology advances have progressively diffused from routine tasks to non-routine tasks, causing disruptions in the labour market. In this work, we study the effect of automation on the labour market outcomes for low-skilled and high-skilled workers. We use the agent-based modelling approach to model firms and workers as rational agents with defined objective functions, endowments, and interactions. Using extensive simulations, we analyse the emergent phenomenon of employment levels and wage inequality in the labour market under varying scenarios. The key findings of our simulations indicate that automation of routine tasks increases wage inequality, whereas automation of non-routine tasks reduces it. Based on our results, we propose policy prescriptions regarding the job categories in which automation can be introduced for societal benefits, the skill enhancement programme needed for the workers, and guidelines on the redeployment of labour displaced through automation.

Profit Maximization in Online Influencer Marketing From the Perspective of Modified Agent-Based Modeling

Profit Maximization in Online Influencer Marketing From the Perspective of Modified Agent-Based Modeling

The Future Museum: Integrating Augmented Reality (AR) and Virtual-text with AI-enhanced Information Systems

This study examines how AR, virtual text, and AI in future museums affect visitor engagement, knowledge acquisition, emotional response, and social interaction. The study uses econometrics equations, text analysis, thematic qualitative data analysis, and agent-based modelling. The economic models show that virtual text type, depth of knowledge, personalization, accessibility options, and AR museum experiences increase visitor engagement, knowledge, emotional response, and social interaction. Thematic analysis displays visitor viewpoints such as AR curiosity, personalization, and cultural significance, while correlation matrices indicate their relationships. Bayesian econometrics, text analysis, thematic qualitative data analysis, and agent-based modelling are used to explore museum visitors' interactions with augmented reality, virtual text, and AI-enhanced information systems. These devices improve visitor engagement, social connection, emotional response, and information collection. Self-reported data biases, context biases, and rapid technological advancement limit the study. Museums can benefit from user-centric technology, AR, personalization, and cultural knowledge. The main findings reveal that these technologies significantly increase museum visitors, emphasizing that future museum administration and design must carefully adopt new technology. The findings may affect museum staff, designers, and decision-makers. Learning important technology factors improves AR, virtual text, and AI-enhanced information system design and deployment decisions. UX design prioritizes visitor choices, while immersive and integrated displays connect museum experiences. These practical implications force museums to actively and practically integrate technology to stay relevant. The project's findings, practical insights, and visitor experiences will improve future technological and cultural organisation research. The whole process contributes to the discussion on how emerging technologies affect museum experiences and innovation and decision-making in the ever-changing cultural heritage context.

Assessment of an Agent’s Wayfinding of the Urban Environment Through Reinforcement Learning

This simulation study explores wayfinding motivated behavioral patterns in the city through agent-based modelling. Agents were trained using Unity’s ML-Agents toolkit with reinforcement learning. The study uses the Sultan Ahmet Mosque and its surrounding boundary as a model environment for the training of an agent’s wayfinding. Agents are trained to locate the Sultan Ahmet Mosque target. The behaviors of agents trained with two different methods, “Complex” and “Simple” learning, comparing their navigation quests at various difficulty levels featuring respawn points. After the training of the agents, the alternative routes produced while attaining the target during the wayfinding process were analyzed. As an outcome of the analysis, it was observed that the agents were prone to go off-route, navigate to different locations they perceived in the urban space, and then would reach the target. This occurrence is justified as an agent’s curiosity trained through reinforcement learning. This study differs from the literature in a way that it attempts to understand the navigational behavior of agents that were trained with reinforcement learning. Moreover, this research discusses the perception of wayfinding through curiosity and aims to make a comprehension of the perception of the city, which is one of the key ideas in neurourbanism. The study contributes to the literature by showing that wayfinding behaviors acquired from agents’ curiosity-driven explorations and past experiences can be an input for neurourbanism, supporting urban design. It informs urban enhancements that are user-centric and rich in urban perception using the reinforcement learning method.

Simulating the Effects of Internal Marketing on Patient Satisfaction Development in Hospitals of Ardabil Province, Iran

Background: To gain and maintain stable competitive advantages, hospitals are currently seeking novel strategies for Patient Satisfaction Development (PSD). To this end, hospitals are required to establish strategies for improvement and provide basic platforms; one of these strategies is Internal Marketing (IM). This type of marketing typically demands customer-oriented behavior and thinking by all hospital staff in terms of dealing with patients. Accordingly, the current research recommends a simulation pattern for PSD within hospitals in Ardabil Province, Iran, considering the predominant factors affecting IM. Methods: This study, utilizing a qualitative-quantitative research design, was conducted on a statistical population of 17 theoretical (the university professors) and experimental (hospital managers) experts. The sampling technique was of the non-probability type and purposive, and the data collection tools were interviews and questionnaires. In the qualitative phase, the main themes and sub-themes of IM in the hospitals of Ardabil Province, Iran, were initially identified by thematic analysis. Then, in the quantitative phase, the major factors were prioritized via the fuzzy Delphi method, and finally, the effects of IM on PSD were simulated through agent-based modeling. Data analysis in the qualitative phase was done with MaxQDA 20 software, and Microsoft Excel and AnyLogic 8.9 were operated in the quantitative one. Results: Based on the study results, 35 primary codes, 12 sub-themes, and three main themes (organizational, occupational, and individual) were obtained. In light of this, the organizational factors were introduced as the most effective ones. Besides, the study results suggested that the PSD prospect in the hospitals of Ardabil Province, Iran, reached 70 % at the end of the simulation. Conclusion: According to the study results, it is suggested that hospital managers devote much attention to the effective factors in IM, specially the organizational ones, as they play a leading role in PSD.

Distributed Space Team Coordination: An Agent-based Modeling and Simulation Approach

Distributed Space Team Coordination: An Agent-based Modeling and Simulation Approach

Agent-based digital traffic model generation for regions facing data scarcity using aggregated cellphone data: a case study for Brussels

ABSTRACT The adoption of agent-based models (ABMs) in transport research has highlighted their potential in digitally simulating travel behaviour from a bottom-up perspective. However, generating ABMs remains a complex multi-step process, often suffering from the lack of model representation or reliance on proprietary data, thereby limiting ABMs' transferability, especially in regions facing data scarcity. This research presents a systematic ABM generation algorithm for Brussels, Belgium, that generates high-quality ABMs more representative of real-life travel patterns, including incorporating external travel demand and allowing multiple destinations for the same activity types in activity chaining. Additionally, our algorithm's input data only contains social-spatial data and aggregated cellphone matrices. Due to the ubiquitous nature of such data worldwide, our algorithm demonstrates significant transferability to other regions for ABM generation while preserving individual privacy. We demonstrate the input data and the proposed algorithm step by step. The ABM generated using MATSim is validated against real-world data regarding mode share, distance share, travel time distribution and traffic counts. Overall, this research would serve as valuable guidance for ABM modellers in data collection and specific modelling setups, lowering the entry barrier of ABM research towards more efficient, representative and reproducible ABMs.

Между «ratio» и «irratio»: в поисках оптимальной стратегии изучения доверия в политическом пространстве

The literature on political trust currently continues to be dominated by the cognitive approach, whose adherents perceive trust as a function of political knowledge and social experience. In turn, the psycho-emotional foundations of political trust (personality traits, psychological needs and emotions) are still in the shadow of the cognitive revolution and remain poorly understood. The article attempts to justify the need to study political trust not only as a rational-evaluative construct, but also as a complex psycho-emotional phenomenon that arises at the intersection of cognitive and affective processes occurring at the conscious and subconscious levels. Having consistently analyzed three approaches to understanding the nature of political trust — as knowledge, as heuristics and as affect — the author elaborates his definition of this phenomenon, describing it as a specific psycho-emotional state that accumulates knowledge about objects of political space, emotionally and symbolically significant images of politicians and institutions formed in the process of socialization, as well as the confidence that the expectations arising on this basis will be met to one degree or another in the future. Moving from the conceptual to the empirical level, he examines survey and experimental strategies for distinguishing between cognitive and affective foundations of trust, revealing their possibilities and limitations. The final part of the article outlines potential further directions for research in the field of political trust. According to the author, agent-based modeling is extremely promising in this regard, as it allows looking at the cognitive and affective bases of political trust as a single whole, where cognitive and affective processes are closely interconnected and capable of moving from one state to another.

How do smallholder farmers run towards agricultural modernization under multiple stressors in China's coastal zones: an agent-based modeling approach

How do smallholder farmers run towards agricultural modernization under multiple stressors in China's coastal zones: an agent-based modeling approach

Forecasting and Predicting Stochastic Agent-Based Model Data with Biologically-Informed Neural Networks.

Collective migration is an important component of many biological processes, including wound healing, tumorigenesis, and embryo development. Spatial agent-based models (ABMs) are often used to model collective migration, but it is challenging to thoroughly predict these models' behavior throughout parameter space due to their random and computationally intensive nature. Modelers often coarse-grain ABM rules into mean-field differential equation (DE) models. While these DE models are fast to simulate, they suffer from poor (or even ill-posed) ABM predictions in some regions of parameter space. In this work, we describe how biologically-informed neural networks (BINNs) can be trained to learn interpretable BINN-guided DE models capable of accurately predicting ABM behavior. In particular, we show that BINN-guided partial DE (PDE) simulations can (1) forecast future spatial ABM data not seen during model training, and (2) predict ABM data at previously-unexplored parameter values. This latter task is achieved by combining BINN-guided PDE simulations with multivariate interpolation. We demonstrate our approach using three case study ABMs of collective migration that imitate cell biology experiments and find that BINN-guided PDEs accurately forecast and predict ABM data with a one-compartment PDE when the mean-field PDE is ill-posed or requires two compartments. This work suggests that BINN-guided PDEs allow modelers to efficiently explore parameter space, which may enable data-driven tasks for ABMs, such as estimating parameters from experimental data. All code and data from our study is available at https://github.com/johnnardini/Forecasting_predicting_ABMs .

Simulating the emergence of institutions that reverse freshwater salinization: An agent-based modeling approach

Salt concentration in global freshwater supplies has increased steadily, leading to the Freshwater Salinization Syndrome (FSS). To curb the FSS, stakeholders can self-organize to develop institutions, or a set of rules that limit salt emissions. This research develops an agent-based modeling framework to explore how institutions reverse the FSS. Property owners are represented as agents that apply rules of behavior to apply salt to deice pavement in response to winter weather, vote on institutions, and comply with or defect from institutions. Salt enters the soil-groundwater system through infiltration, which is modeled using a transit time distribution approach. Results demonstrate that stable institutions lead to positive economic outcomes for stakeholders, based on their ability to apply salt during winter events and access high-quality drinking water. Simulations are analyzed to explore institutions, or limits to the application of salt, that emerge based on the interactions of stakeholders as they agree on salt application limits, the intensity of monitoring for defectors, and sanctions. Institutions that emerge effectively limit the concentration of salt in drinking water. The emergence of stable institutions low rates of innovation among stakeholders, and the concentration of salt in groundwater exceeds standards due to high rates of defection among stakeholders. This research demonstrates how self-organized institutions can lead to sustainable application strategies that reverse the FSS.

Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers.

The assessment of theallergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand-protein and protein-protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS-an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol's ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.

Building multiscale models with PhysiBoSS, an agent-based modeling tool.

Multiscale models provide a unique tool for analyzing complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. These models aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms-coupled with a graphical interface-is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to construct these models more easily, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as Supplementary Material and all models are provided at https://physiboss.github.io/tutorial/.

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

Agent-based modelling of evacuation scenarios for a landslide-generated tsunami in Milford Sound, New Zealand

Agent-based modelling is a useful tool for evacuation planning as it can increase understanding of the factors affecting potential evacuation outcomes. In New Zealand, Milford Sound has been shown to have a high risk from landslide-generated tsunami, with an estimated 1-in-1000-year wave runup of ∼17 m arriving on shore within 2–7 min. With an annual average of >1500 people visiting a day, there is potential for widespread loss of life. However, the number of people present varies substantially with time of day and season, yet how this affects the ability to evacuate remains unknown. This research developed an agent-based model to understand how many people can be safely evacuated in Milford Sound and explored how the number of people initially exposed affected the evacuation outcome alongside the effect of potential changes to evacuation messaging. Assuming a 17 m wave, the results suggest that currently no one can safely evacuate before the shortest wave arrival time regardless of the number of people present. Altering evacuation messaging results in minimal gains, with only ∼5 % of the exposed population reaching safety in time. This work demonstrates the importance of evacuation modelling for understanding risk in isolated tourism destinations where the population exposure can fluctuate dramatically across multiple timescales. Accounting for changing population exposure is essential to understand whether evacuation is a suitable risk treatment and can provide valuable information for determining safe levels of population exposure in locations with high hazard but limited evacuation options.