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We introduce a novel agent‐based model for simulating interactions between migrating barchan dunes. A new two‐flank representation of barchans allows modeling of bedform asymmetries that are intrinsic to collision dynamics but have not been explored before. Although simple compared with real‐world barchans or those in continuum and cellular automata simulations, all known barchan behaviors emerge from the rules of our model. In particular, the two mechanisms for asymmetry growth in bimodal winds are observed and qualitatively agree with existing theories. We also reproduce the emergence of calving and all types of collisions that have been reported in reductionist models, water‐tank experiments, and field observations. The computational efficiency of the new model, compared with continuum simulations, enables the simulation of large swarms of dunes while maintaining the complex phenomenology of these bedforms, some of which has been lacking in previous agent‐based models. Plain Language Summary Barchans are naturally occurring sand dunes found in regions where the wind direction is near‐constant, and the overall supply of sand is low. Because of these conditions, barchans migrate very quickly resulting in collisions between the dunes. Interactions between dunes also occur as sand streams off upwind dunes and is absorbed into downwind barchans. In this work, we present an agent‐based model which treats the dunes themselves as the elementary objects, rather than the sand and airflow. Such models are capable of simulating large populations of dunes. We model barchans as comprising two flanks which can grow semi‐independently. With this structure, we can replicate complex phenomena, including dune asymmetry due to varying winds, restoration of symmetry under a constant wind, and the spontaneous breakup of dunes due to strong winds in directions close to 90° from the usual. These phenomena were inaccessible to previous agent‐based models of barchans. We are also able to reproduce all of the different types of collision which have been observed in lab experiments and more computationally intensive models. The new model, therefore, represents an improvement on previous agent‐based models while remaining computationally cheap enough to simulate large populations. Key Points We present a new agent‐based model for simulating barchan dune dynamics in terms of their two flanks, able to capture dune asymmetry With one fundamental principle of overlapping flanks, the model reproduces all known dune collision types, asymmetry phenomena, and calving We have mapped the phase‐space of collision types as a function of initial conditions and a key lateral‐flux parameter

In recent years, flood risk in urban areas has been rapidly increasing due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Flood risk assessment should realistically take into account the complex interactions between human and natural systems to better inform risk management and improve resilience. In this study, we propose a novel Coupled Human And Natural Systems (CHANS) modeling framework to capture the intricate interactive human behaviors and flooding process at a high spatial resolution. The new CHANS modeling framework integrates a high‐performance hydrodynamic model with an agent‐based model to simulate the complex responses of individual households to the evolving flood conditions, leveraging the computing power of graphics processing units (GPUs) to achieve real‐time simulation. The framework is applied to reproduce the 2015 Desmond flood in the 2,500 km2 Eden Catchment in England, demonstrating its ability to predict interactive flood‐human dynamics and assess flood impact at the household‐level. The study also further explores the effectiveness of different flood risk management strategies, including the provision of early warning and distribution of sandbags, in mitigating flood impact. The new CHANS model potentially provides a useful tool for understanding short‐term human behaviors and their impact on flood risk during a flood event, which is important for the development of effective disaster risk management plans. Key Points A new Coupled Human And Natural Systems (CHANS) framework is developed by integrating hydrodynamic and agent‐based models for simulation of flood dynamics and human responses The CHANS model is implemented on high‐performance GPUs to enable high‐resolution simulation across a large city or region Model performance is demonstrated by reproducing an extreme flood event and predicting financial loss influenced by household activities in a 2,500 km2 catchment

In the individualized treatment of a patient with Coarctation of the Aorta (CoA), a non-severe case which initially exhibits no symptoms, and thus requires no treatment, could potentially become severe over time. This progression can be attributed to insufficient growth at the coarctation site relative to the overall growth of the child. Therefore, an agent-based model (ABM) to predict the aortic growth of a CoA patient is introduced. The multi-scale approach combines Computational Fluid Dynamics (CFD) and ABM to study systems that are influenced by both mechanical stimuli and biochemical responses characteristic of growth. Our focus is on ABM development; thus, CFD insights were applied solely to enhance the ABM framework. Comparative medicine was leveraged to develop a species-specific ABM by considering the rat and porcine species commonly used in cardiovascular research together with data from healthy human toddlers. The ABM luminal radius prediction accuracy was observed to be 79% for rat, above 95% for porcine and 91. 6% for the healthy toddler; while that observed for the growth rate was 38.7%, 90% and 64.3% respectively. Given its performance, the ABM was adapted to a 2.5-year-old patient-specific CoA. Subsequently, the model predicted that by age 3, the condition would worsen, marked by persistent CoA enhanced by the predicted least growth compared to growth predicted in the rest of the aorta, hypertension, and increased turbulent flow; thus, increased vessel injury risk. The findings advise for incorporating vascular remodelling into the ABM to enhance its predictive capability for intervention planning.

There are many barriers to fieldwork including cost, time, and physical ability. Unfortunately, these barriers disproportionately affect minority communities and create a disparity in access to fieldwork in the natural sciences. Travel restrictions, concerns about our carbon footprint, and the global lockdown have extended this barrier to fieldwork across the community and led to increased anxiety about gaps in productivity, especially among graduate students and early‐career researchers. In this paper, we discuss agent‐based modeling as an open‐source, accessible, and inclusive resource to substitute for lost fieldwork during COVID‐19 and for future scenarios of travel restrictions such as climate change and economic downturn. We describe the benefits of Agent‐Based models as a teaching and training resource for students across education levels. We discuss how and why educators and research scientists can implement them with examples from the literature on how agent‐based models can be applied broadly across life science research. We aim to amplify awareness and adoption of this technique to broaden the diversity and size of the agent‐based modeling community in ecology and evolutionary research. Finally, we discuss the challenges facing agent‐based modeling and discuss how quantitative ecology can work in tandem with traditional field ecology to improve both methods. The COVID‐19 global lockdown may be an early harbinger for future disruptions to fieldwork under climate change scenarios. Agent‐Based models are a powerful and accessible way to surrogate lost field seasons to continue these projects or begin new ones. This innovative method is improving constantly and is an excellent tool for teaching and research in natural science.

Social–ecological system (SES) modeling involves developing and/or applying models to investigate complex problems arising from the interactions between humans and natural systems. Among the different types, agent-based models (ABM) and system dynamics (SD) are prominent approaches in SES modeling. However, few SES models influence decision-making support and policymaking. The objectives of this study were to explore the application of ABM and SD in SES studies through a systematic review of published real-world case studies and determine the extent to which existing SES models inform policymaking processes. We identified 35 case studies using ABM, SD, or a hybrid of the two and found that each modeling approach shared commonalities that collectively contributed to the policymaking process, offering a comprehensive understanding of the intricate dynamics within SES, facilitating scenario exploration and policy testing, and fostering effective communication and stakeholder engagement. This study also suggests several improvements to chart a more effective trajectory for research in this field, including fostering interdisciplinary collaboration, developing hybrid models, adopting transparent model reporting, and implementing machine-learning algorithms.

For most herbivorous animals, group‐living appears to incur a high cost by intensifying feeding competition. These costs raise the question of how gregariousness (i.e., the tendency to aggregate) could have evolved to such an extent in taxa such as anthropoid primates and ungulates. When attempting to test the potential benefits and costs, previous foraging models demonstrated that group‐living might be beneficial by lowering variance in intake, but that it reduces overall foraging success. However, these models did not fully account for the fact that gregariousness has multiple experiences and can vary in relation to ecological variables and foraging competition. Here, we present an agent‐based model for testing how ecological variables impact the costs and benefits of gregariousness. In our simulations, primate‐like agents forage on a variable resource landscape while maintaining spatial cohesion with conspecifics to varying degrees. The agents' energy intake rate, daily distance traveled, and variance in energy intake were recorded. Using Morris Elementary Effects sensitivity analysis, we tested the sensitivity of 10 model parameters, of which 2 controlled gregarious behavior and 8 controlled food resources, including multiple aspects of temporal and spatial heterogeneity. We found that, while gregariousness generally increased feeding competition, the costs of gregariousness were much lower when resources were less variable over time (i.e., when calorie extraction was slow and resource renewal was frequent). We also found that maintaining proximity to other agents resulted in lower variance in energy intake when resources were more variable over time. Thus, it appears that the costs and benefits of gregariousness are strongly influenced by the temporal characteristics of food resources, giving insight into the pressures that shaped the evolution of sociality and group living, including in our own lineage. We developed simulations in which primate‐like agents foraged on a variable resource landscape while maintaining spatial cohesion with conspecifics to varying degrees. It appears that the costs and benefits of foraging socially are strongly influenced by the temporal characteristics of food resources, giving insight into the pressures that shaped the evolution of sociality and group living, including in our own lineage.

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost‐ and time‐efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone‐based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent‐based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A  awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems. Drone flight pattern most influenced the count accuracy of a mobile animal. Large image overlaps frequently produced multiple counts of a moving animal. We recommend reducing overlap to increase the accuracy of drone surveys of mobile animals.

Abstract Collective motion provides a spectacular example of self-organization in Nature. Visual information plays a crucial role among various types of information in determining interactions. Recently, experiments have revealed that organisms such as fish and insects selectively utilize a portion, rather than the entirety, of visual information. Here, focusing on fish, we propose an agent-based model where the direction of attention is guided by visual stimuli received from the images of nearby fish. Our model reproduces a branching phenomenon where a fish selectively follows a specific individual as the distance between two or three nearby fish increases. Furthermore, our model replicates various patterns of collective motion in a group of agents, such as vortex, polarized school, swarm, and turning. We also discuss the topological nature of the visual interaction, as well as the positional distribution of nearby fish and the map of pairwise and three-body interactions induced by them. Through a comprehensive comparison with existing experimental results, we clarify the roles of visual interactions and issues to be resolved by other forms of interactions.

The role of consumers as price-sensitive participants in electricity markets is considered essential to ensure efficient and secure operations of electricity systems. Yet the uncertain or unknown consequences of active market participation remain a large barrier for active consumer-side market participation. Simulations are a powerful tool to reduce this uncertainty by giving consumers an insight on the potential benefits and costs of market participation. However, the simulation setup must be adapted to each market context and each consumer market participation strategy. To simplify the simulation development process and improve the comparability of simulation results, this paper proposes a modular yet systematic electricity market modelling framework. The framework applies object-oriented programming concepts for business ecosystem modelling presented in previous works to develop an agent-based model of a consumer-centric electricity market ecosystem. The market ecosystem is represented by a multitude of interacting submarkets with their own logic. Within submarkets, context-independent and context-dependent elements are distinguished to provide model abstraction which can be adapted to different contexts. This framework is illustrated by applying it to three different submarkets in the Western Danish electricity market context: the Nordpool day-ahead market, the Nordpool intraday market, and the Frequency Containment Reserve market. The submarket role abstractions allow to benefit from the commonalities between the analysed submarkets during model implementation, while the role parametrisations allow to quickly adapt the roles to each market context. The implementation of the modelling framework in the Nordic context highlights the benefits of a modular approach in a liberalised and unbundled market context.

Background In May 2024, a chickenpox outbreak was reported at Xiasha Primary School located in Nanshan District, Shenzhen City, China, with a total of 12 cases identified. Despite thorough on-site investigations, the source of infection remained undetected. The purpose of our study was to infer the timing and duration of the infectious period of the initial case using modeling techniques, thereby deducing the identity of the source. Methods We conducted an individual contact survey within the class affected by the epidemic and utilized an agent-based model (ABM) to estimate the key parameters related to the timing of the infectious source’s emergence and the duration of its infectiousness. The point estimates derived from the ABM served as prior information for a subsequent Bayesian analysis, which in turn provided the posterior distribution for these parameters. Results Our models suggested the infection source entered the classroom around April 24th (95% credible interval: April 22nd to April 26th), with an infectious period of approximately two days. Based on these findings, we should aim to detect students who may have been absent due to atypical chickenpox symptoms during this period and closely examine teachers who were present for two consecutive days for any indication of potential infection. Conclusion This study demonstrates the efficacy of combining contact surveys with mathematical modeling for outbreak source tracing, offering a novel approach to supplement field epidemiological surveys. Clinical trial number Not applicable.