Agent-based Research Articles (Page 1)

In a socially tense environment with rising emotional pressure, understanding the spread patterns of group emotions-particularly negative emotions-is crucial for identifying social risks. Extensive research has explored emotion contagion, often using propagation models where node state transitions rely on preset probabilities. However, these methods introduce randomness, making them less reflective of real-world dynamics by failing to capture individual node behaviors and interactions in emotional networks. To address this, our study introduces a novel approach integrating text-based emotion recognition with propagation models, reconstructing emotion contagion at an individual level. This model enhances traditional nodes with multihop agents driven by text emotion analysis, where agents record and respond to neighbors' emotional states. As a result, emotion spread becomes a deterministic process, with individualized infection rates reflecting node variability. We categorized nodes based on emotional states, creating corresponding agent types to form the dynamic agent-based emotion model (AEmo). Tests on real-world and scale-free networks show this method effectively predicts group negative emotion spread and provides insight into individual emotion evolution, validating the model's effectiveness.

In vertebrate embryogenesis, cranial neural crest cells (CNCCs) migrate along discrete pathways. Analyses in the chick have identified key molecular candidates for the confinement of CNCC migration to stereotypical pathways as Colec12, Trail, and Dan. The effects of these factors on CNCCs in vitro are known, but how they confine migration to discrete streams in vivo remains poorly understood. Here, we propose and test several hypothetical mechanisms by which these factors confine cell streams and maintain coherent migration, simulating an expanded agent-based model for collective CNCC migration. Model simulations suggest that Trail enhances adhesion between CNCCs, facilitating movement towards stereotypical migratory pathways, whereas Colec12 confines CNCCs by inducing longer, branched filopodia that facilitate movement down Colec12 gradients and re-connections with streams. Moreover, we find that Trail and Colec12 facilitate the exchange of CNCCs and the formation of CNCC bridges between adjacent streams that are observed in vivo but poorly understood mechanistically. Finally, we predict that Dan increases the coherence of streams by modulating the speed of CNCCs at the leading edge of collectives to prevent escape. Our work highlights the importance of Trail, Colec12, and Dan in CNCC migration and predicts novel mechanisms for the confinement of CNCCs to stereotypical pathways in vivo.

The 2022 outbreak of mpox affected more than 80,000 individuals worldwide, most of whom were gay, bisexual, and other men who have sex with men (GBMSM) who likely contracted the disease through close contact during sex. Given the unprecedented number of mpox infections and the new route of infection, there was substantial uncertainty about how best to manage the outbreak. We implemented a dynamic agent-based network model to simulate the spread of mpox in a United States-based GBMSM population. This model allowed us to implement data-informed dynamic network evolution to simulate realistic disease spreading and behavioral adaptations. We found that behavior change, the reduction in one-time partnerships, and widespread vaccination are effective in preventing the transmission of mpox, and that earlier vaccination and behavior adaptation has a greater effect, even when only a high-activity portion of the population participates. With no vaccination or behavior adaptation, 16% of the population was infected (25th percentile, 75th percentiles of simulations: 15.3%, 16.6%). With vaccination and behavior change in only the 25% of GBMSM most likely to have a one-time partner, cumulative infections were reduced by 30%, or a total reduction in nearly 500 infections (mean: 11.3%, [Formula: see text] and [Formula: see text]: 9.4%, 13.4%). Earlier vaccination and behavior adaptation further reduce cumulative infections; beginning vaccination a year before the outbreak results in only 2.6% of GBMSM being infected, averting 1300 infections or nearly 13% of the total population in our model. We also show that sustained partnerships drive the early outbreak, while one-time partnerships drive transmission after the first initial weeks. The median effective reproductive number, [Formula: see text], at [Formula: see text] days is 1.40 for casual partnerships, 1.00 for main, and 0.35 for one-time. By [Formula: see text], the median [Formula: see text] for one-time partnerships more than tripled to 1.47, while it decreases for casual and main partnerships: 0.37 and 0.19, respectively. With the ability to model individuals' behavior, mechanistic networks are particularly well suited to studying sexually transmitted infections, the spread and control of which are often governed by individual-level action. Our results contribute valuable insights into the role of different outbreak mitigation strategies and relationship types in mpox transmission dynamics.

Abstract Agriculture is often in conflict with biodiversity conservation, while at the same time depending on the provided ecosystem services (ESS) biodiversity provides. Many of these ESS have a regional scale that extends beyond the local scale of individual farms. This leads to spatial externalities so that the preservation of ESS on one farm benefits neighbouring farms. At the same time, ESS preservation often incurs local costs, creating a trade-off between local costs and regional benefits. In these types of common-resource-management problems, the observed level of biodiversity and ESS is generally below the economically efficient level. A generic spatially explicit agent-based model from literature that focuses on the ESS of pollination is extended to analyse the impact of spatial coordination payments on the land-use dynamics and the regional level of ESS preservation. The economically efficient design that maximises the return (in terms of agricultural profit) on investment (in terms of expenditure for the payments) is determined and analysed as a function of the ecological and economic conditions in the model region.

This paper displays a new model for Muti-Level Direct Selling Marketing. It's based on the CALISTA model proposed in Fanti [Cellular automata modelling of direct selling multilevel marketing dynamics. Int J Parallel Emergent Distrib Syst. 2024;1–21. doi:10.1080/17445760.2024.2447265], which is a cellular-automata-based model with six types of populations. In the new model, referred to as CALISTA-MM, several key modifications are made: (1) the transition rules are modified in order to appear as explicit functions of decision variables originating in a dedicated Marketing-Mix model, leading to the definition of a model with direct usability for decision makers since it provides a mathematically rigorous framework to interpret empirical data and (2) an agent-based model is introduced for distributors, which includes an original Net Economic Return model. Three interacting marketing-mix variables are retained (price, quality and quality-price ratio), along with an advanced model of distributor behaviour.

We consider a dynamic pricing problem in a double-lane system consisting of one general purpose lane and one wireless charging lane (WCL). The electricity price is dynamically adjusted to affect the lane-choice behaviors of incoming electric vehicles (EVs), thereby regulating the traffic assignment between the two lanes with both traffic operation efficiency and charging service efficiency considered in the control objective. We first establish an agent-based dynamic double-lane traffic system model, whereby each EV acts as an agent with distinct behavioral and operational characteristics. Then, a deep Q-learning algorithm is proposed to derive the optimal pricing decisions. A regression tree (CART) algorithm is also designed for benchmarking. The simulation results reveal that the deep Q-learning algorithm demonstrates superior capability in optimizing dynamic pricing strategies compared to CART by more effectively leveraging system dynamics and future traffic demand information, and both outperform the static pricing strategy. This study serves as a pioneering work to explore dynamic pricing issues for WCLs.

ABSTRACT STI policies are key drivers of innovation but require context-specific approaches. While combining technology-push, market-pull, and systemic policies is widely advocated, empirical evidence on their effectiveness in agricultural innovation systems remains limited. This study applies the Adaptive Innovation System Model (AdaptISM), an Agent-Based Model grounded in prior theory, to analyse innovation in Antioquia, Colombia’s coffee and avocado production chains – two sectors with distinct innovation patterns. Using empirical data for model validation, we test how different policy mixes affect innovation performance and economic outcomes. The analysis reveals trade-offs and synergies across policy instruments, offering practical insights for STI design. Our findings demonstrate how established innovation models can guide evidence-based policymaking in developing economies, particularly within diverse agri-food systems. This computational approach supports more targeted resource allocation and strategy formulation, grounded in local dynamics.

Arteriovenous fistula (AVF) failure is a frequent clinical problem among end stage renal patients seeking durable long term dialysis access. The most common histological in vivo observation of AVF failure is endothelial injury at the juxta-anastomosis area (JAA) followed by thrombus deposition and subsequent neointimal hyperplasia (NH). While hemodynamic factors have been postulated to affect AVF remodeling and failure, the spatial correlations between changes in hemodynamics post AVF creation and in vivo physiologic observations remain poorly understood. In this work, we developed a novel computational fluid dynamics (CFD) model of an AVF using a pre-established aortocaval mouse model and integrated it with agent-based modeling for NH. The CFD simulation was performed using an animal-specific aortocaval fistula geometry derived from in vivo CTA images with prescribed boundary conditions obtained from in vivo ultrasound measurements. CFD results were validated against in vivo ultrasound velocity measurements at the level of the fistula. CFD allowed quantification of turbulence intensities throughout the fluid domain of the AVF. Turbulence was significantly elevated at the JAA and in regions of venous outflow stenosis. Turbulence intensity served as an input parameter for a simple two-rule agent-based model to test the hypothesis that non-homeostatic hemodynamic changes resulting from AVF creation drive spatial gradients in endothelial damage and proliferation of vascular smooth muscle cells (VSMC) leading to an increase in venous thickness or NH. Our findings show that increased velocity and turbulence in the JAA parallels in vivo NH formation, and that further from the JAA (both cranial and caudal) velocity and turbulence decrease incrementally. The results corroborate that perturbed hemodynamics in the JAA are potential triggers for NH and the source of thickness gradients observed in AVFs.

Introduction Life’s macroevolutionary patterns—rapid post-extinction recoveries and bursts of novelty—are not fully explained by mutation and vertical descent alone. I introduce Biological Recycling Theory (BRT), a cyclic, network-based framework. Methods An agent-based model compared four scenarios (classical, cryptic-only, HGT-only, full BRT), with extinction pulses and explicit constraints on DNA uptake/compatibility; code and runs are archived. Results Under 50% extinction, BRT restored ~90% of pre-event diversity in ~⅓ fewer generations than classical models and yielded ~3,600 novel genotype combinations (vs. ~2,800 cryptic-only; ~700 HGT-only; ~0 mutation-only). Longer eDNA half-life increased diversity retention and innovation. Discussion BRT integrates balancing selection, cryptic genetic variation, and genetic recycling via HGT/eDNA to expand the effective genetic search space across time, offering a testable framework for macroevolutionary resilience. Conclusion Evolution is better modeled as a cyclic network, where alleles circulate across populations, environments, and time, complementing Darwinian microevolution.

PurposeWhen using stochastic models for cost-effectiveness analysis (CEA), run-to-run outcome variability arising from model stochasticity can sometimes exceed the change in outcomes resulting from an intervention, especially when individual-level efficacy is small, leading to counterintuitive results. This issue is compounded for probabilistic sensitivity analyses (PSAs), in which stochastic noise can obscure the influence of parameter uncertainty. This study evaluates meta-modeling as a variance-reduction technique to mitigate stochastic noise while preserving parameter uncertainty in PSAs.MethodsWe applied meta-modeling to 2 simulation models: 1) a 4-state Sick-Sicker model and 2) an agent-based HIV transmission model among men who have sex with men (MSM). We conducted a PSA and applied 3 meta-modeling techniques-linear regression, generalized additive models, and artificial neural networks-to reduce stochastic noise. Model performance was assessed using R2 and root mean squared error (RMSE) values on a validation dataset. We compared PSA results by examining scatter plots of incremental costs and quality-adjusted life-years (QALYs), cost-effectiveness acceptability curves (CEACs), and the occurrence of unintuitive results, such as interventions appearing to reduce QALYs due to stochastic noise.ResultsIn the Sick-Sicker model, stochastic noise increased variance in incremental costs and QALYs. Applying meta-modeling techniques substantially reduced this variance and nearly eliminated unintuitive results, with R2 and RMSE values indicating good model fit. In the HIV agent-based model, all 3 meta-models effectively reduced outcome variability while retaining parameter uncertainty, yielding more informative CEACs with higher probabilities of being cost-effective for the optimal strategy.ConclusionsMeta-modeling effectively reduces stochastic noise in simulation models while maintaining parameter uncertainty in PSA, enhancing the reliability of CEA results without requiring an impractical number of simulations.HighlightsWhen using complex stochastic models for cost-effectiveness analysis (CEA), stochastic noise can overwhelm intervention effects and obscure the impact of parameter uncertainty on CEA outcomes in probabilistic sensitivity analysis (PSA).Meta-modeling offers a solution by effectively reducing stochastic noise in complex stochastic simulation models without increasing computational burden, thereby improving the interpretability of PSA results.

Urbanization and the growing scarcity of surface land resources have highlighted the strategic importance of underground space as a critical component of sustainable urban infrastructure. This study presents a multi-objective optimization framework for underground infrastructure planning around transit hubs, aligning with the principles of Transit-Oriented Development (TOD). By integrating an agent-based model (ABM) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and incorporating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the framework forms a unified evaluation and optimization tool that accounts for user behavior while addressing competing objectives, including minimizing evacuation time and functional conflicts, maximizing functional efficiency, and reducing layout deviations. Using Qingdaobei Railway Station in China as a case study, the method yields notable improvements: a 15% reduction in evacuation time, a 16% increase in development benefits, and a more balanced spatial configuration. Beyond technical gains, the study also discusses station planning and governance under the TOD policy context, highlighting how integrated layouts can alleviate congestion, strengthen functional synergy, and support sustainable urban development.

Diffusion of tracer particles in early growing biofilms a computer simulation study.

Agent-based collaborative model for forecasting large-scale intermittent spare parts in smart manufacturing industry

Bias in transect counts of forest birds: An agent-based simulation model and an empirical assessment

Assessing accessibility to quiet and green areas at the city scale using an agent-based transport model

An agent-based model to simulate pedestrians' affective experiences and activities for evaluating urban public space design

Evaluating the impact of NPC1 single nucleotide polymorphisms on entry efficiency of filoviruses in vitro: Agent-based model approach

Simulating tertiary educational decision dynamics: an agent-based model for the Netherlands

Introduction. The COVID-19 pandemic has revealed a whole complex of problems related to mathematical modeling of the epidemic process and assessing the effect of preventive and anti-epidemic measures in modern complex societies. Along with this, the accumulation of significant factual data has spurred the active development of agent-based models, in which each agent (a hypothetical person) has a unique set of characteristics and interaction methods determined based on real sociological and demographic data. Aim and objectives. Development and demonstration of the capabilities of the epidemiological agent-based model POEM platform (POpulation Epidemiological Model). Materials and methods. The POEM platform is developed based on the source code of one of the most widely used agent-based models worldwide, Covasim, taking into account the demographic and organizational-administrative conditions specific to the Russian Federation. Results. Computational experiments have shown that due to individual variability in the dynamics of infection development and the specifics of disease registration, even mass events, while leading to an actual increase in the number of infected individuals, do not have a significant impact on the shape of the curve of registered disease incidence. It has been shown that intercity traffic flows at a level of 0.1% of the population per day have a minimal effect on the dynamics of the epidemic's development, while the effect of a 1% population outflow per day sharply reduces the effect of strict anti-epidemic measures implemented in only one particular city. Using the example of the Voronezh region, the transition from the Delta variant to Omicron in early 2022 was modeled, and a high degree of correlation was shown between the model dynamics and the actual ratio of virus variants observed. Conclusion. The model is fully implemented within the Russian system on the server of the Research Institute for System Biology and Medicine of Rospotrebnadzor and can be used to conduct digital epidemiological experiments to predict the effectiveness of proposed anti-epidemic measures.

Although oral pre-exposure prophylaxis (PrEP) has been instrumental in decreasing HIV incidence, its daily dosing regimen poses adherence challenges. Using an agent-based network model informed by empirical data, we simulate the impact of introducing long-acting injectable (LAI) PrEP among young Black men who have sex with men (YBMSM) in Los Angeles County, a group disproportionately affected by HIV. Computer simulations using an agent-based network model (ABNM). We modeled HIV transmission among YBMSM over 10 years under scenarios varying the proportion of PrEP users opting for LAI instead of oral medications and adherence levels to LAI retention. The model was calibrated with empirical data and included dynamic sexual networks, HIV progression, and biomedical interventions. Modeling showed that LAI PrEP substantially reduced HIV incidence and prevalence over 10 years compared to oral PrEP alone. Scenarios with LAI retention (i.e., continued use across bimonthly dosing cycles) rates of 60% or higher resulted in reductions comparable to or exceeding those achieved by oral PrEP, with up to a 45% decrease in HIV incidence observed when all PrEP users switched to LAI and retention reached 85%. Long-acting injectable PrEP offers significant potential to advance HIV prevention efforts among YBMSM by addressing adherence challenges inherent to oral PrEP. Integrating LAI into public health initiatives may yield substantial reductions in HIV incidence, contributing to ending the HIV epidemic among this high-priority population.