Search Results (219)

This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article

The rapid proliferation of electronic waste, driven by the short lifecycle of smartphones and planned obsolescence strategies, presents escalating global environmental challenges. To address these issues from a systems perspective, this study develops an agent-based modeling (ABM) framework that simulates consumer decisions and stakeholder interactions within the smartphone reuse and recycling ecosystem. The model incorporates key behavioral drivers—privacy concerns, moral norms, and financial incentives—to examine how social and economic factors shape consumer behavior. Four primary agent types—consumers, manufacturers, recyclers, and second-hand retailers—are modeled to capture complex feedback and market dynamics. Calibrated using empirical data from Jiangsu Province, China, the simulation reveals a dominant consumer tendency to store obsolete smartphones rather than engage in reuse or formal recycling. However, the introduction of government subsidies significantly shifts behavior, doubling participation in second-hand markets and markedly improving recycling rates. These results highlight the value of integrating behavioral insights into environmental modeling to inform circular economy strategies. By offering a flexible and behaviorally grounded simulation tool, this study supports the design of more effective policies for promoting responsible smartphone disposal and lifecycle extension. Full article

Amid global environmental changes, urbanisation erodes nature connectedness, an important driver of pro-environmental behaviours and human well-being, exacerbating human-made risks like biodiversity loss and climate change. This study introduces a novel hybrid agent-based model (ABM), calibrated with historical urbanisation data, to explore how urbanisation, opportunity and orientation to engage with nature, and intergenerational transmission have shaped nature connectedness over time. The model simulates historical trends (1800–2020) against target data, with projections extending to 2125. The ABM revealed a significant nature connectedness decline with excellent fit to the target data, derived from nature word use in cultural products. Although a lifetime ‘extinction of experience’ mechanism refined the fit, intergenerational transmission emerged as the dominant driver—supporting a socio-ecological tipping point in human–nature disconnection. Even with transformative interventions like dramatic urban greening and enhanced nature engagement, projections suggest a persistent disconnection from nature through to 2050, highlighting locked-in risks to environmental stewardship. After 2050, the most transformative interventions trigger a self-sustaining recovery, highlighting the need for sustained, systemic policies that embed nature connectedness into urban planning and education. Full article

As part of the post-conflict reconstruction and recovery, the development of Kabul New City aims to bring relief to the existing capital city, Kabul, which has experienced exponential population growth, putting heavy pressure on its existing resources. Kabul New City is divided into four subsectors, and each of them is being developed and is expected to reach a target population by 2025, as defined by the master plan. The study’s objective is to determine which of the four zones are in demand and need to be prioritized for development, as per the model results. The data collection involves an online questionnaire, and the responses are collected from residents of Kabul and Herat. Agent-based modeling (ABM) is an emerging method of simulating urban dynamics. Cities are evolving continuously and are forming unique spatial patterns that result from the movement of residents in search of new locations that accommodate their needs and preferences. An agent-based model is developed using the weighted random selection process based on household size and income levels. The agents are the residents of Kabul and Herat, and the environment is the land use classification image using the Sentinel 2 image of Kabul New City. The barren class is treated as the developable area and is divided into four sub-sectors. The model simulates three alternative growth rate scenarios, i.e., ambitious, moderate, and steady. The results of the simulation reveal that the sub-sector Dehsabz South, being closer to Kabul city, is in higher demand. Barikab is another sub-sector high in demand, which has connectivity through the highway and is an upcoming industrial hub. Full article

Inefficient inland repositioning of empty containers between depots remains a persistent challenge in container logistics, contributing significantly to unnecessary truck movements, elevated operational costs, and increased CO₂ emissions. Acknowledging the importance of this problem, a large amount of relevant literature has appeared. The objective of this paper is to track the empty container flow between ports, empty depots, inland terminals, and customer premises. Additionally, it aims to simulate and assess CO₂ emissions, capturing the dynamic interactions between different agents. In this study, agent-based modeling (ABM) was proposed to simulate the empty container movements with an emphasis on inland transportation. ABM is an emerging approach that is increasingly used to simulate complex economic systems and artificial market behaviours. NetLogo was used to incorporate real-world geographic data and quantify CO₂ emissions based on truckload status and to evaluate the other operational aspects. Behavior Space was also utilized to systematically conduct multiple simulation experiments, varying parameters to analyze different scenarios. The results of the study show that customer demand frequency plays a crucial role in system efficiency, affecting container availability and logistical tension. Full article

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

Water distribution systems (WDSs) exhibit intricate, nonlinear behaviors shaped by both internal dynamics and external influences. The incorporation of additional models, such as contamination or population models, further increases their complexity. This study investigated WDSs under various uncertainty scenarios to enhance system stability, robustness, and control. In particular, we built upon prior research by exploring an Agent-Based Modeling (ABM) framework integrated within a WDS, focusing on three types of uncertainties: (1) adjustments to existing probabilistic parameters, (2) variations in agent movement across network nodes, and (3) changes in agent distributions across different node types. We conducted our analysis using the virtual city of Micropolis as a testbed. Our findings indicate that while the system remains resilient to uncertainties in predefined probabilistic parameters, substantial and often nonlinear effects arise when uncertainties are introduced in agent mobility and distribution patterns. These results emphasize the significance of understanding how WDSs respond to external behavioral dynamics, which is essential for managing real-world challenges, such as pandemics or shifts in urban behavior. This study underscores the necessity for further research into broader uncertainty categories and emergent effects to enhance WDS modeling and inform decision-making. Full article

The spread of infectious diseases is inherently linked to human social behavior, characterized by complexity, diversity, and openness. Intelligent agents in computer science provide a powerful framework for capturing such dynamics, enabling complex epidemic patterns to emerge from simple local rules. These agents exhibit self-organization, adaptability, and self-optimization, making them well suited for individual-level modeling. Agent-based models (ABMs) have shown promising results in epidemic simulation and policy evaluation. However, current implementations often suffer from simplistic behavioral assumptions and rigid interaction mechanisms, limiting their realism and flexibility. This paper first reviews the current landscape of epidemic modeling approaches. It then analyzes the underlying mechanisms of advanced intelligent agents, highlighting their modeling capabilities. The study focuses on four key advantages of intelligent agent-based modeling and elaborates on three critical roles these agents play in evaluating and optimizing intervention strategies. Full article

Historical districts face pressing disaster preparedness challenges due to their special spatial properties—risks compounded by static approaches that overlook dynamic fire–pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, ≥28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east–west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0–10 min), congestion-driven delays on critical roads (11–30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the “fast alert-fast congestion” paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments. Full article

Understanding the dynamics of public opinion formation in digital environments is crucial for managing technological communications effectively. This study investigates stance interactions and opinion reversal phenomena in technical discourse through analysis of the Manus AI controversy that generated approximately 36,932 social media interactions during March 2025. Employing an integrated methodology combining Large Language Model (LLM)-enhanced stance detection with agent-based modeling (ABM), we reveal distinctive patterns challenging traditional public opinion theories. Our cross-correlation analysis identifies significant lagged interaction effects between skeptical and supportive stances, demonstrating how critical expressions trigger amplified counter-responses rather than inducing silence. Unlike prior conceptualizations of counter-silencing that emphasize ideological resistance or echo chambers, our notion of the “counter-spiral of silence” specifically highlights lagged emotional responses and reactive amplification triggered by minority expressions in digital technical discourse. We delineate its boundary conditions as arising under high emotional salience, asymmetrical expertise, and platform structures that enable real-time feedback. The agent-based simulation reproduces empirical patterns, revealing how emotional contagion and network clustering mechanisms generate “counter-spiral of silence” phenomena where challenges to dominant positions ultimately strengthen rather than weaken those positions. These findings illuminate how cognitive asymmetries between public expectations and industry realities create distinctive discourse patterns in technical contexts, offering insights for managing technology communication and predicting public response trajectories in rapidly evolving digital environments. Full article

Understanding consumer heterogeneity is crucial for analysing attitude formation and its role in innovation diffusion. Traditional top-down models struggle to reflect the nuanced characteristics and activities of the consumer population, while bottom-up approaches like agent-based modelling (ABM) offer the ability to simulate individual decision-making in social networks. However, current ABM applications often lack a strong theoretical foundation. This study introduces a novel, theory-driven ABM framework to examine the heterogeneity of consumer attitude formation, focusing on electric vehicle (EV) adoption across consumer segments. The model incorporates non-linear decision-making rules grounded in established consumer theories, incorporating Rogers’s Diffusion of Innovations, Social Influence Theory, and Theory of Planned Behaviour. The consumer agents are characterised using UK empirical data, and are segmented into early adopters, early majority, late majority, and laggards. Social interactions and attitude formation are simulated, micro-validated, and optimised using supervised machine learning (SML) approaches. The results reveal that early adopters and early majority are highly responsive to social influences, environmental beliefs, and external events such as the pandemic and the war conflict in performing pro-EV attitudes. In contrast, late majority and laggards show more stable or delayed responses. These findings provide actionable insights for targeting segments to enhance EV adoption strategies. Full article

The building energy performance gap (BEPG) remains a significant challenge, undermining the accuracy of energy simulations and complicating efforts to design energy-efficient buildings. This study addresses this issue by developing an adaptive occupant behaviour framework for office buildings, integrating agent-based modelling (ABM) with a building performance simulation (BPS) platform. Conventional BPS models often rely on deterministic assumptions and overlook the dynamic, stochastic nature of occupant interactions, such as window and blind operations. By incorporating occupant-driven behaviours, this research enhances the realism of energy predictions and provides insights into reducing the BEPG. Focusing on a multi-functional office building at the University of Nottingham, the study used empirical data to validate the model. The ABM framework simulated occupant behaviours influenced by factors like indoor and outdoor temperatures, solar radiation, clothing levels, and metabolic rates. Profiles generated by the ABM were integrated into the energy model, creating an Adjust model compared against a Base model with deterministic settings. Validation against measured boiler energy use showed that the Baseline model over-predicted consumption by roughly 45 %, whereas the behaviour-informed Adjust model cut the deviation to about 26 %, albeit under-predicting the total load. Statistical analyses revealed improvements in mean squared error (MSE) and root mean squared error (RMSE), although hourly energy predictions remained a challenge. Additionally, the Adjust model provided a more realistic representation of thermal comfort, reducing variability in the predicted mean vote (PMV) index from extreme values in the Base model to a more stable range in the Adjust model. However, the Adjust model also predicted higher indoor CO₂ concentrations, particularly in individual offices, due to reduced ventilation associated with occupant actions. This study demonstrates the potential of integrating ABM with BPS models to address modelling discrepancies by capturing detailed and dynamic occupant interactions, emphasising the importance of adaptive behaviours in improving prediction accuracy and occupant well-being. Full article

Agent-based modelling (ABM) has revolutionised the simulation of complex systems, finding applications in diverse fields such as economic markets and traffic management. By modelling individuals as autonomous agents within a dynamic environment, ABM enables the exploration of system behaviours and the evaluation of interventions at various spatiotemporal resolutions. However, the computational intensity of ABM, particularly in large-scale simulations, remains a significant hurdle. This paper presents a novel approach to addressing these challenges through the development of a GPU-accelerated transport model, specifically applied to a road network. Utilising the FLAME-GPU framework, the proposed model demonstrates enhanced scalability and efficiency compared with traditional CPU-based simulations, such as Simulation of Urban MObility (SUMO). Through rigorous comparative analysis, this study highlights significant improvements in simulation speed and the capacity to manage larger vehicle populations. The research underscores the transformative potential of GPU acceleration in mitigating computational constraints within ABM, offering a practical framework for simulating transport systems with greater precision and depth. Extensive experimentation validates the model’s ability to realistically simulate the vehicle population of the Isle of Wight, achieving a balance between computational efficiency and the accurate representation of complex traffic dynamics. Full article

Mass shootings result in significant casualties. Due to the complexity of buildings, capturing crowd dynamics during mass shooting incidents is particularly challenging. Therefore, it is necessary to study crowd dynamics and the key mechanisms of mass shooting incidents and explore optimal building design solutions to mitigate the damage caused by terrorist attacks and enhance urban safety. In this study, we focused on the Bataclan Shooting (13 November 2015) as the target case. We used an agent-based model (ABM) to model both the attacking force (shooting) and counterforce (anti-terrorism response). According to the real situation, the dynamic behavior of three types of agents (civilians, police, and shooters) during the shooting accident was modeled to explore the key mechanism of individual behavior. Taking civilian casualties, police deaths, and shooter deaths as the real target values, we obtained combinations for optimal solutions fitting the target values. Under the optimal solutions, we verified the effectiveness and robustness of the model. We also used artificial neural networks (ANNs) to detect the predictive stability of the ABM model’s parameters. In addition, we studied the counterfactual situation to explore the impact of police anti-terrorism strategies and building exits on public safety evacuation. The results show that for the real cases, the optimal anti-terrorism size was four police and the optimal response time was 40 ticks. For double-layer buildings, it was necessary to set exits on each floor, and the uniform distribution of exits was conducive to evacuation under emergencies. These findings can improve police patrol routes and the location of police stations and promote the creation of public safety structures, enhancing the urban emergency response capacity and the level of public safety governance. Full article

This study conducts a comprehensive bibliometric analysis of the use of agent-based modeling (ABM) in finance and banking, aiming to uncover how this methodology has evolved over the past two decades. It addresses the following overarching question: How has ABM contributed to the development of financial research in terms of trends, key contributors, and thematic directions? The relevance of this topic is based on the growing complexity of financial systems and the limitations of traditional models in capturing dynamic interactions, contagion effects, and systemic risks. Using a refined dataset of 489 articles from the Web of Science (2000–2024), selected through a multi-step keyword and relevance screening process, we apply bibliometric techniques using R Studio (version 2024.12.1+563) and Bibliometrix (version 4.3.3). The analysis reveals stable publication growth, strong international collaborations (notably Italy, USA, and China), and core thematic areas such as risk management, market simulation, financial stability, and policy evaluation. The findings highlight both well-established and emerging research fronts, with agent-based models increasingly used to simulate real-world financial phenomena and support regulatory strategies. By mapping the intellectual structure of the field, this paper provides a solid foundation for future interdisciplinary research and practical insights for policymakers seeking innovative tools for financial supervision and decision making. Full article