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The COVID-19 pandemic exposed the vulnerabilities of global supply chains (SCs) and highlighted the need for more resilient and viable SCs. Panic-buying, in particular, has been a major challenge for SCs as it can create sudden surges in demand that are difficult to anticipate and manage. However, the literature lacks viable SC models and strategies to address panic-buying related challenges. As such, this research aims to identify and model viable recovery strategies to increase SC's agility, resilience, and survivability and reduce panic-buying's impact during a large-scale disruption in critical SCs. This study contributes by developing an integrated agent-based modeling (ABM) and optimisation method to simulate the behaviour of SCs under different scenarios and evaluating the effectiveness of four proposed strategies and three recovery plans. The findings reveal that increasing production at decentralised manufacturing facilities can be achieved by increasing order frequency to multiple suppliers and by partnering with third-party transporters, which can mitigate the effects of panic-buying. This results in higher output and availability of essential goods in the market, significantly managing panic-buying related challenges. Lastly, the study recommends practical solutions for businesses to enhance their SCs' responsiveness to sudden demand surges from panic-buying. [ABSTRACT FROM AUTHOR]

BACKGROUND AND AIM[|]Traffic-Related Air Pollution (TrAP) adversely impacts human health, disproportionately harming disadvantaged communities. New technologies and infrastructure offer opportunities to reduce TrAP, but the health outcomes of individuals are not fully understood due to a lack of high-resolution models that grasp the complexities of transportation systems and their health implications amid evolving policies and technologies.[¤]METHOD[|]We introduce BEAM CORE (beam.lbl.gov), a high-resolution, agent-based transportation framework that simulates detailed passenger and freight activities. It captures interactions between transportation, land use, demographic and vehicle ownership changes at various scales. Validating crucial factors of emission modeling, including link-level VMT, speed and regional fleet in the San Francisco Bay Area’s nine counties, demonstrates its potential to be extended for assessing health outcomes from changes in TrAP.[¤]RESULTS[|]All major outputs from the BEAM CORE 2018 baseline have been calibrated and validated. Mode split and demographics align closely with census and survey data. Passenger and freight activities were validated against public and private data, with CO2 emissions corresponding to 3.67Mt/yr for medium/heavy-duty (MHD) and 22.79Mt/yr for all vehicles, demonstrating the model’s alignment with empirical data. The NOx, PM2.5 and PM10 from MHD exhaust, PM brake and tire wear are 14.8kt/yr, 424t/yr and 606.9t/yr under the 2018 baseline with high fractions of conventional vehicles, while the wide adoption of clean truck technologies under 2050 resulted in 87\%, 75\% and 56\% reductions respectively. BEAM CORE generates detailed fleet and activity data at high spatiotemporal resolution, enabling the integration with air quality models, including InMAP/AERMOD, to explore the causal pathway of health impacts from transport policy changes.[¤]CONCLUSIONS[|]We developed a sophisticated multi-dimensional transportation model for integration with advanced air quality, and health assessment models. It enables a thorough analysis of health impacts of transportation policies and technologies across diverse communities. It supports similar analyses in any area using local data.[¤]

Enterprise social media (ESM) is changing how knowledge workers interact and share information; however, a debate persists as to whether ESM is an adequate knowledge management system. ESM provides a rich set of affordances for organizational knowledge work, such as improved organizational memory, but also constrains knowledge work performance because of digital interruptions. Extending and complementing existing scholarship, this study asks the following research question: How can organizations design ESM to realize its knowledge work benefits? Using a computational agent-based model that incorporates the design features of ESM, workers' attitudes, and resulting ESM-use affordances and constraints, this study shows how ESM-use outcomes are contingent both on the design of and users' attitudes toward ESM. Specifically, the negative effects of ESM interactivity are mitigated when employees have a low transparency preference and access ESM without posting as much. The study further unpacks asymmetric engagement as the mechanism that leads low transparency configurations to be more resilient to the negative effects of interruptions driven by ESM interactivity. Asymmetric engagement--learning from posted content without interacting often--enables the gradual creation of organizational memory while maintaining a broad user base by minimizing interruptions. These results ultimately contribute a multilevel model of ESM use and knowledge work outcomes, enhancing the theoretical understanding of previously studied mechanisms such as communication visibility and providing implications for organizations designing ESM. [ABSTRACT FROM AUTHOR]

Opinion dynamics are affected by cognitive biases and noise. While mathematical models have focused extensively on biases, we still know surprisingly little about how noise shapes opinion patterns. Here, we use an agent-based opinion dynamics model to investigate the interplay between confirmation bias-represented as bounded confidence-and different types of noise. After analysing where noise can enter social interaction, we propose a type of noise that has not been discussed so far, ambiguity noise. While previously considered types of noise acted on agents either before, after or independent of social interaction, ambiguity noise acts on communicated messages, assuming that socially transmitted opinions are inherently noisy. We find that noise can induce agreement when confirmation bias is moderate, but different types of noise require quite different conditions for this effect to occur. An application of our model to the climate change debate shows that at just the right mix of confirmation bias and ambiguity noise, opinions tend to converge to high levels of climate change concern. This result is not observed with the other types. Our findings highlight the importance of considering and distinguishing between the various types of noise and the unique role of ambiguity in opinion formation.

Sustainability outcomes are influenced by the laws and configurations of natural and engineered systems as well as activities in socio-economic systems. An important subset of human activity is the creation and implementation of institutions, formal and informal rules shaping a wide range of human behavior. Understanding these rules and codifying them in computational models can provide important missing insights into why systems function the way they do (static) as well as the pace and structure of transitions required to improve sustainability (dynamic). Here, we conduct a comparative synthesis of three modeling approaches- integrated assessment modeling, engineering-economic optimization, and agent-based modeling-with underexplored potential to represent institutions. We first perform modeling experiments on climate mitigation systems that represent specific aspects of heterogeneous institutions, including formal policies and institutional coordination, and informal attitudes and norms. We find measurable but uneven aggregate impacts, while more politically meaningful distributional impacts are large across various actors. Our results show that omitting institutions can influence the costs of climate mitigation and miss opportunities to leverage institutional forces to speed up emissions reduction. These experiments allow us to explore the capacity of each modeling approach to represent insitutions and to lay out a vision for the next frontier of endogenizing institutional change in sustainability science models. To bridge the gap between modeling, theories, and empirical evidence on social institutions, this research agenda calls for joint efforts between sustainability modelers who wish to explore and incorporate institutional detail, and social scientists studying the socio-political and economic foundations for sustainability transitions.

During the self-healing process of Bacillus subtilis biofilms on a solid MSgg substrate, large-scale ordered clusters emerge within the biofilm, providing an invasive advantages. To investigate the self-healing mechanism, an agent-based model is employed to simulate the self-healing processes of biofilms at two ages. The study reveals that a uniform cell distribution facilitates the healing of biofilm incisions. The nutrient diffusion rate within the biofilm and the elastic modulus (comprising cell and EPS) play a dominant role in the healing of circumferential incisions, while the diffusion rate outside the biofilm governs the healing of radial and penetrating incisions. These influencing factors can adjust cellular ordering, providing valuable insights for controlling the self-healing of Bacillus subtilis biofilms. [ABSTRACT FROM AUTHOR]

Understanding fish movement and response in relation to their environment near infrastructure and migratory barriers is crucial for developing sustainable fisheries management solutions. Intermediate-scale (time scales of minutes to days and spatial scales less than 2 km) movement models are a contemporary approach for understanding and predicting movement patterns of riverine fish in light of their changing environment, which is predominately water flow (i.e., flow direction, flow magnitude, and rates of change). These models can be complex and require interdisciplinary knowledge. For more than 60 years, different approaches have been developed for investigating, reproducing, and predicting the movement outcomes of fish decision making. Due to the breadth of model frameworks available, a systematic review is helpful to summarize the available knowledge including a description of general model properties, environment modeling, agent characteristics, and methods of data use, output, and validation. The analysis of 38 studies found a wide range of model frameworks and architectures. Despite the lack of consistency, each model imposed some combination of the following behaviors: response to flow direction (i.e., rheotaxis), response to flow velocity magnitude, response to turbulence, response to depth, and memory/experience of the individual. There is a clear need for more consistent modeling approaches, increased consideration of memory/experience, inclusion of a wider range of species, incorporation of more detailed environmental covariates, and use of time-dependent solutions in fish movement models. [ABSTRACT FROM AUTHOR]

Agent‐based modelling has gained recognition in the last years because it provides a natural way to explore the behaviour of social systems. However, agent‐based models usually have a considerable number of parameters that make it computationally prohibitive to explore the complete space of parameter combinations. A promising approach to overcome the computational constraints of agent‐based models is the use of machine learning‐based surrogates or metamodels, which can be used as efficient proxies of the original agent‐based model. As the use of metamodels of agent‐based simulations is still an incipient area of research, there are no guidelines on which algorithms are the most suitable candidates. In order to contribute to filling this gap, we conduct here a systematic comparative analysis to evaluate different machine learning‐based approaches to agent‐based model surrogation. A key innovation of our work is the focus on classification methods for categorical metamodeling, which is highly relevant because agent‐based simulations are very often validated in a qualitative way. To analyse the performance of the classifiers we use three types of indicators—measures of correctness, efficiency, and robustness—and compare their results for different datasets and sample sizes using an agent‐based artificial market as a case study. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, effective public policy interventions have been crucial in combating virus transmission, sparking extensive debate on crisis management strategies and emphasizing the necessity for reliable models to inform governmental decisions, particularly at the local level. Leveraging disaggregated socio-demographic microdata, including social determinants, age-specific strata, and mobility patterns, we design a comprehensive network model of Catalonia's population and, through numerical simulation, assess its response to the outbreak of COVID-19 over the two-year period 2020–21. Our findings underscore the critical importance of timely implementation of broad non-pharmaceutical measures and effective vaccination campaigns in curbing virus spread; in addition, the identification of high-risk groups and their corresponding maps of connections within the network paves the way for tailored and more impactful interventions. [ABSTRACT FROM AUTHOR]

Background: The models that historically have been used to model infectious disease outbreaks are equation-based and statistical models. However, these models do not capture the impact of individual and social factors that affect the spread of common blood-borne viruses (BBVs) such as human immunodeficiency virus (HIV), hepatitis C virus (HCV), and hepatitis B virus (HBV). Agent-based modelling (ABM) is an alternative modelling approach that is gaining popularity in public health and epidemiology. As the field expands, it is important to understand how ABMs have been applied. In this context, we completed a scoping review of research that has been done on the ABM of BBVs. Method: The inclusion/exclusion criteria were drafted using the idea of Population, Concept, and Context (PCC). The Preferred Reporting Item for Systematic Reviews and Meta-Analysis, an extension to scoping review (PRISMA-ScR), was employed in retrieving ABM literature that studied BBVs. Three databases (Scopus, Pubmed, and Embase) were systematically searched for article retrieval. 200 articles were retrieved from all the databases, with 10 duplicates. After removing the duplicates, 190 papers were screened for inclusion. After analysing the remaining articles, 70 were excluded during the abstract screening phase, and 32 were excluded during the full-text decision. Eighty-eight were retained for the scoping review analysis. To analyse this corpus of 88 papers, we developed a five-level taxonomy that categorised each paper based first on disease type, then transmission mechanism, then modelled population, then geographic location and finally, model outcome. Results: The result of this analysis show significant gaps in the ABM of BBV literature, particularly in the modeling of social and individual factors influencing BBV transmission. Conclusion: There is a need for more comprehensive models that address various outcomes across different populations, transmission and intervention mechanisms. Although ABMs are a valuable tool for studying BBVs, further research is needed to address existing gaps and improve our understanding of individual and social factors that influence the spread and control of BBVs. This research can inform researchers, modellers, epidemiologists, and public health practitioners of the ABM research areas that need to be explored to reduce the burden of BBVs globally. [ABSTRACT FROM AUTHOR]

The scenarios discussing the evolution of the spatiotemporal cognitive abilities of vagile plant eaters generally assume that their movement strategies are linked to their cognitive abilities, which are themselves shaped by the distribution patterns of plants considered invariant over long time periods. Yet, if plant distribution patterns are likely to remain unchanged over short time periods, they may change over long time periods as a result of animal exploitation. In particular, animals can shape the environment by dispersing plant seeds. Using an agent‐based model simulating the foraging behaviour of a seed disperser endowed with spatiotemporal knowledge of resource distribution, I investigated whether resource spatiotemporal patterns could be influenced by the level of spatiotemporal cognition involved in foraging. This level of spatiotemporal cognition represented how well the agent predicted resource locations and phenology. I showed that seed‐dispersing agents moving based on spatiotemporal memory could affect resource distribution on the long‐term through routine movements between known patches, with a larger effect the higher the level of spatiotemporal cognition. The level of engineering also resulted from the conjunction of two additional forces: the agents' movement strategy (e.g. including opportunistic visits to plants encountered en route or not) and the competition for space between plants. In turn, resource landscape modifications affected the benefits of spatiotemporal memory. This could create eco‐evolutionary feedback loops between animal spatiotemporal cognition and the distribution patterns of plant resources. Combined with previous works, the results emphasise that spatiotemporal cognition is a cause and a consequence of resource heterogeneity. [ABSTRACT FROM AUTHOR]

This qualitative case study investigated how computational models can help students engage in scientific practice and influence their emotional, epistemic, and conceptual aspects. Twenty-four sixth-graders were guided to conduct scientific practices as they predicted and modified the computational models on food web using StarLogo Nova. Three types of qualitative data were collected—video recordings, the researcher's field notes (teacher's diary), and post-interviews with students—and three themes about 'Act like scientists' were created inductively using the constant comparative method. The use of computational models was found to influence three primary aspects of students' scientific practices: emotional, epistemic, and conceptual. Regarding the emotional aspect, the students enthusiastically engaged in scientific practices while experiencing awe, an emotion that enhances learning and encourages focus on something larger than oneself. Regarding the epistemic aspect, the students actively evaluated and revised their food web model by redesigning codes. Lastly, they formulated theories for how to improve the sustainability of the food web, which improved their conceptual understanding. Based on the findings, we discussed how computational models can be meaningfully utilized in science classes. [ABSTRACT FROM AUTHOR]

Individual-based models (IBMs) enable modelers to avoid far-reaching abstractions and strong simplifications by allowing for a state-based representation of individuals. The fact that IBMs are not represented using a standardized mathematical framework such as differential equations makes it harder to reproduce IBMs and introduces difficulties in the analysis of IBMs. We propose a model architecture based on representing individuals via Markov models. Individuals are coupled to populations — for which individuals are not explicitly represented — that are modeled by differential equations. The resulting models consisting of continuous-time finite-state Markov models coupled to systems of differential equations are examples of piecewise-deterministic Markov processes (PDMPs). We will demonstrate that PDMPs, also known as hybrid stochastic systems, allow us to design detailed state-based representations of individuals which, at the same time, can be systematically analyzed by taking advantage of the theory of PDMPs. We will illustrate design and analysis of IBMs using PDMPs via the example of a predator that intermittently feeds on a logistically growing prey by stochastically switching between a resting and a feeding state. This simple model shows a surprisingly rich dynamics which, nevertheless, can be comprehensively analyzed using the theory of PDMPs. [ABSTRACT FROM AUTHOR]

Background: In 2019, a 5-year pre-exposure prophylaxis (PrEP) program started in the Netherlands, in which up to 8500 men who have sex with men (MSM) can obtain PrEP and 3-monthly consultations with HIV/STI testing. Setting: We assessed the impact of the PrEP program on transmission of HIV and Neisseria gonorrhea (NG) among MSM in the Netherlands and examined prospective variations of the program after 2024. Methods: We used an agent-based model to estimate the effect of the PrEP program. For hypothetical prospective PrEP programs starting in 2024, we varied the capacity (8,500; 12,000; 16,000 participants) and consultation frequency (3-monthly; 6-monthly; 70% 3-monthly and 30% 6-monthly). Results: At a capacity of 8,500 participants and 3-monthly consultations, the PrEP program could lead to 3,140 [95% credible interval (95% CrI): 1,780-4,780] and 27,930 (95% CrI: 14,560-46,280) averted HIV and NG infections, requiring 316,050 (95% CrI: 314,120-317,580) consultations. At a capacity of 16,000 participants, the programs with 3-monthly consultations and 6- monthly consultations could lead to comparable number of averted HIV [3,940 (95% CrI: 2,420-5,460), and 3,900 (2,320-5,630) respectively] and NG infections [29,970 (95% CrI: 15,490-50,350), and 29,960 (95% CrI: 13,610-50,620) respectively], while requiring substantially different number of consultations: 589,330 (95% CrI: 586,240-591,160) and 272,590 (95% CrI: 271,770-273,290), respectively. Conclusions: Continuation of a PrEP program could lead to a substantial reduction in HIV and NG transmission. More infections could be averted if the number of participants is increased. In turn, the consultation frequency could be reduced without reducing the number of averted infections if capacity is increased. [ABSTRACT FROM AUTHOR]

Integrated flood risk management based on urban policies remains challenging compared with infrastructure due to the unclear risk‐reduction effects over time. To consider heterogeneity in social responses to urban planning measures, a previous study developed an agent‐based household relocation model under flood risk (AHR‐FLOOD) by combining flood‐inundation and agent‐based relocation models. However, accurate modelling of relocation incentives remains challenging. This study aimed to improve AHR‐FLOOD by introducing spatial amenities based on transportation convenience and reflecting their impact on the decision‐making processes of agents to analyze the development of different flood risk areas. AHR‐FLOOD considering access to public transportation reproduced the spatial characteristics of the actual population and housing prices. The development of low‐risk areas reduced flood exposure and resulting flood damage. However, this effect was less clear for low‐income individuals, but the development of low‐amenity areas had the potential to induce low‐income population to move to safe areas. Chain migration was observed as a long‐term effect of the spatial amenity policy. This study presents insights into the effect of transportation policies on flood safety for long‐term spatial distance management in an agent‐based approach with the rigorous modelling and validation of local amenity impact on household relocation choices. [ABSTRACT FROM AUTHOR]

With improved technology and supported public policy, electric vehicles (EVs) are coming back in one-way carsharing from the 2010s. Although the addition of EVs offers a way to achieve carbon neutrality, the shorter vehicle range and longer charging time of EVs pose a greater challenge to the operation of one-way carsharing than fuel vehicles. Methods such as trial-and-error testing or mathematical models have difficulty in handling complex systems with mutual feedback between demand and supply. Therefore, this paper builds a one-way electric carsharing model and integrates it into an open-source multi-agent transport simulation platform (MATSim) to study its supply and demand relationship. A Shanghai baseline scenario was built to validate the model and test the impacts of vehicle range, charging rate, and power supply mode on carsharing demand. The results show that: (1) Vehicle range expansion and charging rate improvement have less impact on carsharing demand. The current vehicle range and charging rate can meet the daily use in Shanghai. (2) When the power supply mode changes from charging piles to battery swapping, the carsharing usage decreases slightly (-3%), while the carsharing trip characteristics remain almost the same. This means that operators could use battery swapping for power supply. This study provides suggestions for electric carsharing operators in Shanghai, as well as a simulation tool for more operators to test the supply and demand relationship of electric carsharing. [ABSTRACT FROM AUTHOR]

Collective migration underlies key developmental and disease processes in vertebrates. Mathematical models describing collective migration can shed light on emergent patterns arising from simple mechanisms. In this paper, a mathematical model for collective migration is given for arbitrary numbers and types of individuals using principles outlined as a set of assumptions, such as the assumed preference for individuals to be "close but not too close" to others. The model is then specified to the case of two species with arbitrary numbers of individuals in each species. A particular form of signal response is used that may be parameterized based on experiments involving two or three agents. In its simplest form, the model describes two species of individuals that emit distinct signals, distinguishes between them, and exhibits responses unique to the type by moving according to signal gradients in various planar regions, a situation described as "mixotaxis". Beyond this simple form, initial conditions and boundary conditions are altered to simulate specific, additional in vitro as well as in vivo dynamics. The behaviors that were specifically accounted for include motility, directed migration, and a functional response to a signal. Ultimately, the paper's results highlight the ability of a single framework for signal and response to account for patterns seen in multi-species systems, in particular the emergent self-organization seen in the embryonic development of placodal cells, which display chase-and-run behavior, flocking behavior, herding behavior, and the splitting of a herd, depending on initial conditions. Numerical experiments focus around the primary example of neural crest and placodal cell "chase-and-run" and "flocking" behaviors; the model reproduces the separation of placodal cells into distinct clumps, as described in the literature for neural crest and placodal cell development. This model was developed to describe a heterogeneous environment and can be expanded to capture other biological systems with one or more distinct species. [ABSTRACT FROM AUTHOR]

Intensive human activity in global coastal areas has led to increasing exposure to hazards. Cartagena Bay in Chile, an area with a long history of tsunami disasters, has undergone significant urbanization and experiences heavy tourist activity during the summer. While some studies have examined risk in Cartagena by focusing on hazard and vulnerability characteristics, challenges remain in delivering more spatially accurate studies and incorporating the population's coping capacities. We undertook a tsunami risk assessment of Cartagena Bay that disaggregates social vulnerability to the census block level and assesses the inhabitants' pedestrian evacuation potential through an agent-based model. Our findings indicate that urban coastal areas in Cartagena Bay might face substantial tsunami risk in a worst-case scenario, with 31.0% to 54.1% of its territory—depending on the scale of analysis—classified as high-risk areas. Of the examined urban blocks, 31.4% have average evacuation times exceeding 17 min (the critical time required by the tsunami to reach its run-up), and the most disadvantaged census block is 1,971.9 m away from its nearest shelter. We also demonstrated that a more spatially accurate vulnerability analysis is more conservative too. For instance, zones with high-risk levels decreased by 42.8% when the study scale moved from the block to the zone level of analysis. Similarly, areas with low risk increased by 80%. In comparison to previous studies, our findings show that tsunami risk in Cartagena Bay is significantly lower if coping capacities such as evacuation potential are included in the analysis. [ABSTRACT FROM AUTHOR]

Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns. [ABSTRACT FROM AUTHOR]

This paper develops two models to investigate the impact of a tariff on logistics strategies in an international e-commerce trade, where the methods of game theory and agent-based model are utilized. We consider a foreign supplier and a domestic e-tailer. There are two logistics strategies: the supplier undertakes the logistics and the e-tailer shoulders the logistics. First, two strategies are compared under the game theory model. Then, we capture the uncertainties of market demand and tariff. An agent-based model is adopted to further study how the tariff affects the supply chain's decisions and strategies. Some meaningful results are obtained. (i) The supplier should provide the logistics service when market demand and tariff are certain. (ii) Under demand uncertainty, the supplier and the e-tailer can reach an agreement on logistics strategies when the tariff is small. (iii) When tariff uncertainty is moderate, two players both abandon the logistics intention.. [ABSTRACT FROM AUTHOR]

This study shows the importance of considering two social network features in agent-based models of cultural and opinion dynamics – network segregation by groups (e.g. race, class, or ideology) and interaction frequency by structural embeddedness. I formalize the two features as modeling conditions and apply them to an existing model that shows cultural or opinion polarization can emerge in a small-world network by the bridging of long-range ties. I find that when a small-world network is segregated, the inactivity of long-range ties (i.e. infrequent interactions) – instead of the bridging – becomes the key feature that contributes conversely to consensus or more extreme polarization. This implies that a systemic understanding of dyadic-level tie characteristics and suitable approaches to considering social networks in agent-based models are necessary. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We created an agent-based model (ABM; a computational model) for human-whale interactions in the North Atlantic area. We specifically looked at boats, lobster lines, and rogue fishnet interactions with the North Atlantic right whales (Eubalaena glacialis), and we assessed the conditions under which such interactions are more likely to result in injuries and fatalities for this critically endangered whale, such as the density of ships in the water. ABMs are a methodology particularly useful in data poor problems (where machine learning cannot be used), and are based on rules of interactions between various agents (human and nonhuman) and/or a specific physical environment; they can showcase and assess potential future scenarios. We informed our model with current data from the North Atlantic Right Whale Consortium and the National Oceanic and Atmospheric Administration and present our preliminary findings here. [ABSTRACT FROM AUTHOR]

Agri-food systems (AFS) and their value chains are currently under pressure to minimize environmental degradation and secure a more sustainable future. Sustainability transitions are needed to shift from well-established, yet vulnerable systems to more sustainable systems. Agent-based modeling (ABM) as a method to study complex systems is now widely used in transition modeling. We performed a systematic review to analyze the potential of ABM to yield insights into sustainability transitions in AFS. Specifically, we investigated the understanding that agent-based models can support better understanding of sustainability transitions in AFS. We identified potential in participatory modeling methods as well as combining agent-based models with complementary methods. The assessment of the sustainability dimensions was quite balanced between economic and environmental dimensions, but the social dimension was underrepresented. Here, we identify the main features to further advance ABM of sustainability transitions in AFS. [ABSTRACT FROM AUTHOR]

Sprawling land development patterns have exacerbated ecological degradation, social fragmentation, and public health problems. Perverse incentives arise from the ability to privatize collectively created value in land rents and socialize ecological costs. Land value taxation (LVT) has been shown to encourage urban infill development by reducing or eliminating rent-seeking behavior in land markets. However, despite its purported benefits, this tax reform is value monistic in its definition of optimal land use and, therefore, does little to address the lack of non-market information to inform land use decisions. We propose an ecological-impact-weighted land value taxation policy (ELVT) which incorporates the ecological footprint of land use into one's land value tax burden. We test both proposed policies (LVT and ELVT) relative to a "status quo" (SQ) property tax scheme, utilizing a conceptual spatially explicit agent-based model of land use behaviors and housing development. Our findings suggest that both tax interventions can increase the capital intensity and decrease the land intensity of housing development. Furthermore, both tax interventions can lead to a net profit loss for speculators and a decrease in the average housing unit price. The ELVT scheme is shown to significantly increase urban nature provisions and dampen the loss of ecological value across a region. [ABSTRACT FROM AUTHOR]

In this paper, we consider the approach of applying state-of-the-art machine learning algorithms to simulate some financial markets. In this case, we choose the cryptocurrency market based on the assumption that such markets more active today. As a rule, they have more volatility, attracting riskier traders. Considering classic trading strategies, we also introduce an agent with a self-learning strategy. To model the behavior of such agent, we use deep reinforcement learning algorithms, namely Deep Deterministic policy gradient. Next, we develop an agent-based model with following strategies. With this model, we will be able to evaluate the main market statistics, named stylized-facts. Finally, we conduct a comparative analysis of results for constructed model with outcomes of previously proposed models, as well as with the characteristics of real market. As a result, we conclude that our model with a self-learning agent gives a better approximation to the real market than a model with classical agents. In particular, unlike the model with classical agents, the model with a self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for a complete understanding of market processes simulation models should take into account self-learning agents that have a significant presence at modern stock markets. [ABSTRACT FROM AUTHOR]

Top-down environmental policies aim to mitigate environmental risks but inevitably lead to economic losses due to the market entry or exit of enterprises. This study developed a universal dynamic agent-based supply chain model to achieve tradeoffs between environmental risk reduction and economic sustainability. The model was used to conduct high-resolution daily simulations of the dynamic shifts in enterprise operations and their cascading effects on supply chain networks. It includes production, consumption, and transportation agents, attributing economic features to supply chain components and capturing their interactions. It also accounts for adaptive responses to daily external shocks and replicates realistic firm behaviors. By coupling high spatial–temporal resolution firm-level data from 18 916 chemical enterprises, this study investigates the economic and environmental impacts of an environmental policy resulting in the closure of 1800 chemical enterprises over three years. The results revealed a significant economic loss of 25.8 billion USD, ranging from 23.8 billion to 31.8 billion USD. Notably, over 80% of this loss was attributed to supply chain propagation. Counterfactual analyses indicated that implementing a staggered shutdown strategy prevented 18.8% of supply chain losses, highlighting the importance of a gradual policy implementation to prevent abrupt supply chain disruptions. Furthermore, the study highlights the effectiveness of a multi-objective policy design in reducing economic losses (about 29%) and environmental risks (about 40%), substantially enhancing the efficiency of the environmental policy. The high-resolution simulations provide valuable insights for policy designers to formulate strategies with staggered implementation and multiple objectives to mitigate supply chain losses and environmental risks and ensure a sustainable future.

Abstract The role social media platforms play on the emergence of polarisation is an ongoing debate in the political communication literature. Social media’s filter bubbles and online echo chambers shape people’s opinions by curating the information they have available. However, the extent to which this is the case remains unclear. Social simulation scholars have provided valuable insights into the subject through opinion dynamics models and agent-based modelling approaches. This article proposes a social simulation approach to the topic of opinion dynamics from a political communication perspective to understand how social network configurations and the media environment contribute to the emergence of national identity polarisation. We built an agent-based simulation model of national identity dynamics with a multilayer multiplex network of interacting agents in a hybrid media environment of both, traditional media and social media platforms. We use the Catalan secessionist movement to ground, contextualise and empirically inform parts of our model. We found that the initial social network setup conditions had a large impact on the emergence of polarisation amongst agents. In particular, homophily-based social networks composed of a majority of like-minded individuals produced greater polarisation compared to random networks. This was aggravated in the presence of social media filtering algorithms, selectively exposing agents to supportive information. These results emphasise the importance of both the selective exposure by social media filtering algorithms and one’s social networks (echo chambers) for polarisation to emerge. This interaction reinforces the influence of social media platforms and social networks have on the emergence of polarisation.

Abstract 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.

Social influence aligns peoples opinions, but social identities and related in-group biases interfere with this alignment. For instance, the recent rise of young climate activists (e.g. Fridays for Future or Last Generation) has highlighted the importance of generational identities in the climate change debate. It is unclear how social identities affect the emergence of opinion patterns, such as consensus or disagreement, in a society. Here, we present an agent-based model to explore this question. Agents communicate in a network and form opinions through social influence. The agents have fixed social identities which involve homophily in their interaction preferences and in-group bias in their perception of others. We find that the in-group bias has opposing effects depending on the network topology. The bias impedes consensus in highly random networks by promoting the formation of echo chambers within social identity groups. By contrast, the bias facilitates consensus in highly clustered networks by aligning dispersed in-group agents across the network and, thereby, preventing the formation of isolated echo chambers. Our model uncovers the mechanisms underpinning these opposing effects of the in-group bias and highlights the importance of the communication network topology for shaping opinion dynamics.

Paleolithic archaeologists study regional variation among assemblages of stone tools in order to delineate cultural boundaries and reconstruct mechanisms of cultural transmission in the deep past. Structured population models are especially suited to aid in this endeavor, for they teach us how cultural evolutionary forces—copying error, intergroup transmission, drift, and selection imposed by functional constraints or biased cultural transmission—affect regional cultural variation. We use an agent-based model to address how copying error, intergroup transmission, and time-averaging affect the degree to which regional archaeological assemblages differ at a selectively neutral discrete trait passed from “experienced” to “naïve” individuals via one of four mechanisms of cultural transmission in a structured population of toolmakers. The results of our simulation experiment illustrate why researchers who use time-averaged archaeological data to identify past cultural boundaries or infer mechanisms of cultural transmission should be more mindful of the nature of the cultural trait(s) available for study. In light of our results, we discuss seven questions archaeologists ought to address before attempting to infer cultural boundaries or cultural transmission mechanisms from between-assemblage variation. [ABSTRACT FROM AUTHOR]

The extracellular matrix (ECM) is a complex structure involved in many biological processes with collagen being the most abundant protein. Density of collagen fibers in the matrix is a factor influencing cell motility and migration speed. In cancer, this affects the ability of cells to migrate and invade distant tissues which is relevant for designing new therapies. Furthermore, increased cancer cell migration and invasion have been observed in hypoxic conditions. Interestingly, it has been revealed that the Hypoxia Inducible Factor (HIF) can not only impact the levels of metabolic genes but several collagen remodeling genes as well. The goal of this paper is to explore the impact of the HIF protein on both the tumour metabolism and the cancer cell migration with a focus on the Warburg effect and collagen remodelling processes. Therefore, we present an agent-based model (ABM) of tumour growth combining genetic regulations with metabolic and collagen-related processes involved in HIF pathways. Cancer cell migration is influenced by the extra-cellular collagen through a biphasic response dependant on collagen density. Results of the model showed that extra-cellular collagen within the tumour was mainly influenced by the local cellular density while collagen also influenced the shape of the tumour. In our simulations, proliferation was reduced with higher extra-cellular collagen levels or with lower oxygen levels but reached a maximum in the absence of cell-cell adhesion. Interestingly, combining lower levels of oxygen with higher levels of collagen further reduced the proliferation of the tumour. Since HIF impacts the metabolism and may affect the appearance of the Warburg Effect, we investigated whether different collagen conditions could lead to the adoption of the Warburg phenotype. We found that this was not the case, results suggested that adoption of the Warburg phenotype seemed mainly controlled by inhibition of oxidative metabolism by HIF combined with oscillations of oxygen. [ABSTRACT FROM AUTHOR]