Agent-based Modeling Research Articles

Spatial interactions modulate tumor growth and immune infiltration.

Lenia, a cellular automata framework used in artificial life, provides a natural setting to implement mathematical models of cancer incorporating features such as morphogenesis, homeostasis, motility, reproduction, growth, stimuli response, evolvability, and adaptation. Historically, agent-based models of cancer progression have been constructed with rules that govern birth, death and migration, with attempts to map local rules to emergent global growth dynamics. In contrast, Lenia provides a flexible framework for considering a spectrum of local (cell-scale) to global (tumor-scale) dynamics by defining an interaction kernel governing density-dependent growth dynamics. Lenia can recapitulate a range of cancer model classifications including local or global, deterministic or stochastic, non-spatial or spatial, single or multi-population, and off or on-lattice. Lenia is subsequently used to develop data-informed models of 1) single-population growth dynamics, 2) multi-population cell-cell competition models, and 3) cell migration or chemotaxis. Mathematical modeling provides important mechanistic insights. First, short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects. Next, we find that asymmetric interaction tumor-immune kernels lead to poor immune response. Finally, modeling recapitulates immune-ECM interactions where patterns of collagen formation provide immune protection, indicated by an emergent inverse relationship between disease stage and immune coverage.

Theory for and from agent-based modelling: Insights from a virtual special issue and a vision

The Virtual Special Issue "Agents for Theory" discusses theory development using agent-based models (ABMs). The six contributions focus on how the word "theory" is used in the ABM literature, how reviews of ABMs should be conducted to gain general insights, how even the conceptualisation of ABMs can help to transform heuristic theories into scientific ones, how the context-dependent choice of decision models can be better justified, how reusable building blocks (RBBs) of ABMs could support theory development, and how a modular framework of RBBs can be used to identify general solutions. Overall, theories of decision making that go into ABMs and theories of system dynamics that come out of ABMs are interrelated, so the micro-macro perspective and the attempt to identify and reproduce patterns at both levels simultaneously is a way forward. Theory development requires clearer communication using a common language, reference to patterns, and more detailed model analysis and testing.

COVID-19 outbreaks in residential aged care facilities: an agent-based modeling study.

A disproportionate number of COVID-19 deaths occur in Residential Aged Care Facilities (RACFs), where better evidence is needed to target COVID-19 interventions to prevent mortality. This study used an agent-based model to assess the role of community prevalence, vaccination strategies, and non-pharmaceutical interventions (NPIs) on COVID-19 outcomes in RACFs in Victoria, Australia. The model simulated outbreaks in RACFs over time, and was calibrated to distributions for outbreak size, outbreak duration, and case fatality rate in Victorian RACFs over 2022. The number of incursions to RACFs per day were estimated to fit total deaths and diagnoses over time and community prevalence.Total infections, diagnoses, and deaths in RACFs were estimated over July 2023-June 2024 under scenarios of different: community epidemic wave assumptions (magnitude and frequency); RACF vaccination strategies (6-monthly, 12-monthly, no further vaccines); additional non-pharmaceutical interventions (10, 25, 50% efficacy); and reduction in incursions (30% or 60%). Total RACF outcomes were proportional to cumulative community infections and incursion rates, suggesting potential for strategic visitation/staff policies or community-based interventions to reduce deaths. Recency of vaccination when epidemic waves occurred was critical; compared with 6-monthly boosters, 12-monthly boosters had approximately 1.2 times more deaths and no further boosters had approximately 1.6 times more deaths over July 2023-June 2024. Additional NPIs, even with only 10-25% efficacy, could lead to a 13-31% reduction in deaths in RACFs. Future community epidemic wave patterns are unknown but will be major drivers of outcomes in RACFs. Maintaining high coverage of recent vaccination, minimizing incursions, and increasing NPIs can have a major impact on cumulative infections and deaths.

Understanding Tsunami Evacuation via a Social Force Model While Considering Stress Levels Using Agent-Based Modelling

Given massive events, such as demonstrations in coastal cities exposed to tsunamigenic earthquakes, it is essential to explore pedestrian motion methods to help at-risk coastal communities and stakeholders understand the current issues they face to enhance disaster preparedness. This research targets SDG 11 Sustainable Cities and Communities. It strengthens resilience in coastal areas by implementing a social force model using a microscopic agent-based model to assess the impact of human behaviour on evacuation performance by introducing evacuation stress levels due to a tsunami triggered in central Chile. Two scenarios with two environments and three crowd sizes are implemented in NetLogo. In Scenario 1, pedestrians walk at a relaxed velocity. In Scenario 2, tsunami evacuation stress is incorporated, resulting in pedestrians walking at a running velocity, taking, on average, four times less time to evacuate. We explored more realistic settings by considering the internal susceptibility of each agent to spread tsunami evacuation stress among other evacuees. Results from Scenario 2 show that internal susceptibility effects almost double the mean evacuation time for 200 agents. Findings suggest a trade-off between realism and the minimization of evacuation time. This research is considered a first step toward including stress in tsunami evacuations for sustainable evacuation planning.

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.

Exploring Education as a Complex System: Computational Educational Research with Multi-Level Agent-Based Modeling

Our study employs multi-level agent-based modeling and computational techniques to explore education as a complex system. With an underlying focus that education should be underpinned by a scientific understanding of student learning, we created computational models that simulated learning dynamics in classrooms, integrating both quantitative and qualitative insights. Through these models, we conducted experiments aligned with real classroom data to address key questions, such as “How can we effectively support the academic progress of underperforming students, who are disproportionately from low socio-economic status (SES) backgrounds, to close their multi-year achievement gap in mathematics?” Our study analyzes various instructional approaches for mathematical learning, and our findings highlight the potential effectiveness of Productive Failure as an instructional approach. Considerations of the broader applicability of computational methods in advancing educational research are also provided.

Dynamic multi-layer walkability model for transit-oriented movement: Nodes and Routes optimization method

This paper presents a novel approach to optimizing movement in urban areas through a dynamic multi-layer walkability model. This research uncovers new facets of walkability modeling within transit-oriented movement, aligning pedestrian pathways (Routes) with urban architecture, public spaces, and green spaces (Nodes). The dynamic multi-layer (DML) approach involves optimizing both the Nodes and Routes of the transit system using a multi-objective optimization method. This method improves accessibility and connectivity by aggregating the results of agent-based modeling for route simulations and considers multiple criteria, including greenness, distance to transit, and destination accessibility. Experimentation with a case study produced several findings that underscore the value of multi-layered models for transit movement and the power of computational methods in optimizing both Nodes and Routes. This discovery offers valuable insights into the DML process and its potential applications in the field of urban design and architecture.

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.

Agent-based modeling of the COVID-19 pandemic in Florida

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like “Should we lockdown?” During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands.

ФРАКЦИОНИРОВАНИЕ ПЛАНКТОНА С ПОМОЩЬЮ ПОСЛЕДОВАТЕЛЬНОЙ ФИЛЬТРАЦИИ И ПОСТРОЕНИЕ ПИРАМИД БИОРАЗНООБРАЗИЯ

Lotka-Volterra predator-prey models are used to study community ecology, but their ability to generate ecological pyramids compared to field data has not been investigated in detail. In this paper, agent-based modeling (ABM) was used instead of systems of ordinary differential equations (ODE). It was shown that the two-component producer-consumer system is unstable, whereas the three-component system with consumers of the 1st and 2nd order is stable under prolonged simulation. Time slices as the program progresses can generate both ecological pyramids and cascades. Simulation results are consistent with experiments on separation of the Black Sea plankton from the area of Cape Fiolent (Crimea) into fractions ranging in size from 2 mm to 2 microns. Although biodiversity in individual samples at different points in time as well as abundance vary widely, both predictably decline with rising trophic levels in cases where the number of tests increases over time.

Making evidence go further: Advancing synergy between agent-based modeling and randomized control trials

Making evidence go further: Advancing synergy between agent-based modeling and randomized control trials

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PurposeStandard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo. MethodsWe describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach. ResultsUsing two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption. ConclusionsMDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

Agent-based modeling of nuclear chromosome ensembles identifies determinants of homolog pairing during meiosis.

During meiosis, pairing of homologous chromosomes (homologs) ensures the formation of haploid gametes from diploid precursor cells, a prerequisite for sexual reproduction. Pairing during meiotic prophase I facilitates crossover recombination and homolog segregation during the ensuing reductional cell division. Mechanisms that ensure stable homolog alignment in the presence of an excess of non-homologous chromosomes have remained elusive, but rapid chromosome movements appear to play a role in the process. Apart from homolog attraction, provided by early intermediates of homologous recombination, dissociation of non-homologous associations also appears to contribute to homolog pairing, as suggested by the detection of stable non-homologous chromosome associations in pairing-defective mutants. Here, we have developed an agent-based model for homolog pairing derived from the dynamics of a naturally occurring chromosome ensemble. The model simulates unidirectional chromosome movements, as well as collision dynamics determined by attractive and repulsive forces arising from close-range physical interactions. Chromosome number and size as well as movement velocity and repulsive forces are identified as key factors in the kinetics and efficiency of homologous pairing in addition to homolog attraction. Dissociation of interactions between non-homologous chromosomes may contribute to pairing by crowding homologs into a limited nuclear area thus creating preconditions for close-range homolog attraction. Incorporating natural chromosome lengths, the model accurately recapitulates efficiency and kinetics of homolog pairing observed for wild-type and mutant meiosis in budding yeast, and can be adapted to nuclear dimensions and chromosome sets of other organisms.

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative AI. I examine how bias in the data used to train these tools can negatively impact social science research—as well as a range of other challenges related to ethics, replication, environmental impact, and the proliferation of low-quality research. I conclude by arguing that social scientists can address many of these limitations by creating open-source infrastructure for research on human behavior. Such infrastructure is not only necessary to ensure broad access to high-quality research tools, I argue, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.

Understanding visitor flow and behaviour in developing tourism-service-oriented villages by space syntax methodologies: a case study of Tabian Rural Section of Qingshan Village, Hangzhou

ABSTRACT In the built environment (BE) of tourism-service-oriented villages, understanding visitor flow volumes and behaviours is crucial for guiding space revitalization and tourism management. This research uncovers quantitative relationships between visitor flow volume, spatial visual exposure, and visitors’ behaviour at 12 sites in the Tabian Rural Section of Qingshan Village, a typical tourism service-oriented village in Hangzhou. The methodologies employed in this study are based on two space syntax algorithms: 3-D isovist analysis and agent-based models (ABMs). Visual Exposure Rates (VERs) in 3-D isovists along visitors’ routes were calculated using parametric programming, and visitor flow simulation was conducted using two ABMs. Real-world visitor volumes (RWFVs) and visitors’ behaviors were also recorded during an in-situ survey. A comprehensive correlation analysis revealed multiple associations. The results demonstrated that combined simulations from ABM I, and VERs explained 50.5% of the overall variability in RWFVs. Notably, VERs were found to be associated with visitors’ behaviours of photography and seating, while the outputs from ABM I showed a moderate correlation with RWFVs. This study innovatively combines space syntax models for extended application in the research of rural BEs, providing guidance for optimizing strategies to improve spatial configuration in homogeneous villages.

Micromobility systems and disease transmission: a spatially explicit agent-based approach

We propose a novel micro-level and spatially explicit agent-based modeling framework dubbed TranEpiSim to model the spread of infectious diseases through micromobility systems and a baseline population in an urban area. A case study is conducted on the Chicago City public bikesharing system to demonstrate the ability of the model to isolate the role of transportation as a vector and assess the efficacy of the model. Results show that the emergence of viral disease through micromobility transportation is possible, but the overall impact of the system on the disease dynamics in a worst-case scenario, especially with the current size of the system, is rather small. The proposed model offers a comprehensive approach to estimate the impact of micromobility (and more generally, specific transportation modes) on disease spread by considering infections that occur both at destinations and in/on transportation vehicles and infrastructure. The spatial pattern for the risk of exposure shows a higher risk in the central business district and north of it, where most of the shared bike transportation occurs. Because of its intrinsic features, the proposed framework is uniquely placed to assess the efficacy of interventions and make trade-offs between competing scenarios when dealing with epidemics.

Utilizing Amphibious AGVs to Optimize Container Transshipment for Deep Sea and Hinterland Operations

Transshipment is a key component of modern-day shipping logistics. Container supply chains rely on tran-shipment hubs to access remote locations. With globalisation driving growth in container trade, maritime congestion is rising at container terminals in ports worldwide. This is expected to worsen as demand con-tinues to grow. This research explores novel maritime equipment designs that can contribute to solving problems in the trans-shipment chain. One such idea is that of the Amphibious Automated Guided Vehicle, an innovative concept that travels on both land and sea. Envisioned as a tool to minimise the rehandling of containers, the Amphibious AGV forms the heart of the new changes that this research proposes for the fu-ture of trans-shipment. Complementing swifter trans-shipment, the research also proposes complementary design concepts such as floating terminals to add more flexibility for container ships and Amphibious AGVs applied to exchange containers offshore. To validate these ideas, an agent-based modelling methodology was used and replicated in the environment of the Hong Kong- Pearl River Delta. This work, therefore, opens up an intriguing future scope for maritime transshipment that is both sustainable and adaptable while also discussing limitations and concerns that need to be carefully considered.

Utilizing Agent-Based Modeling for Optimization of Wayfinding in Hospital: A Case Study.

This research aims to propose a novel methodology for analyzing and optimizing wayfinding in complex environments by examining their spatial configurations. Wayfinding difficulties often lead to disorientation and hinder users' ability to locate destinations. Although architectural design can aid in simplifying user access, existing approaches lack a specific focus on wayfinding optimization despite its significant impact on users' navigational abilities. In this study, an agent-based model was employed to assess the efficacy of wayfinding in a multistory hospital. Subsequently, the layouts were optimized, leading to the creation of a new space distribution diagram. The simulation was then repeated to examine the potential improvement in wayfinding. Data collection encompassed user types, workflow scenarios, population distribution, and user speed. Comparative analysis of the agent-based simulation findings before and after layout optimization revealed a decrease in total distance and time spent on the modified floor plans for all users when compared to the existing layout. This suggests that the optimized layout holds significant potential for enhancing wayfinding performance. Given the positive outcomes observed for users, this approach is particularly well suited for preliminary design stages of complex environments, where designations among user groups are less crucial or flexibility is desired. Additional advantages include the ability to generate a comprehensive simulation of users' daily workflow, which is integrated into the optimization process and considers specific requirements regarding spatial adjacency.

Agent-based modelling and simulation of elderly residents’ dynamic usage patterns in neighbourhood squares during summer

Understanding how elderly residents use neighbourhood squares is helpful for designing these public areas to maximize their usage and minimize heat exposure risks, especially given the global increase in heatwaves and initiatives for healthy aging in place. This study aims to explore the dynamic use of a neighbourhood square with multiple shade configurations by its elderly residents. In-situ observations and measurements in three squares in public housing estates in Hong Kong were conducted in hot summer days to learn the relationship between elderly users' usage behaviours and multiple driving factors, including outdoor thermal environment, level of social interaction, and different shade types. The relationship was then used to develop an agent-based model (ABM) to simulate the dynamic usage pattern in a square over a day. The result indicates that the dynamic usage pattern from 9:00 to 18:00 in a square emerges from interactions between elderly users’ behaviours and shade configurations, which can be reasonably estimated by the developed ABM. Based on the ABM simulation, shade configurations that cover 60 % of the square and create microclimates with a maximum mPET not exceeding 34.0 °C during summer days encourage square usage and mitigate heat-stress risks of elderly users. The prototype ABM enables the understanding of the complex usage pattern in a neighbourhood square and can potentially assist planning and optimization of shade configurations at the design/renewal stage.