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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.

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]

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]

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]

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]

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]

The increasing adoption of electric vehicles (EVs) by the general population creates an opportunity to deploy the energy storage capability of EVs for performing peak energy shaving in their households and ultimately in their neighborhood grid during surging demand. However, the impact of the adoption rate in a neighborhood might be counterbalanced by the energy demand of EVs during off-peak hours. Therefore, achieving optimal peak energy shaving is a product of a sensitive balancing process that depends on the EV adoption rate. In this paper, we propose EOS, an agent-based simulation model, to represent independent household energy usage and estimate the real-time neighborhood energy consumption and peak shaving energy amount of a neighborhood. This study uses Residential Energy Consumption Survey (RECS) and the American Time Use Survey (ATUS) data to model realistic real-time household energy use. We evaluate the impact of the EV adoption rates of a neighborhood on performing energy peak shaving during sudden energy surges. Our findings reveal these trade-offs and, specifically, a reduction of up to 30% of the peak neighborhood energy usage for the optimal neighborhood EV adoption rate in a 1089 household neighborhood. [ABSTRACT FROM AUTHOR]

Recovering the fine-grained working process of couriers is becoming one of the essential problems for improving the express delivery systems because knowing the detailed process of how couriers accomplish their daily work facilitates the analyzing, understanding, and optimizing of the working procedure. Although coarse-grained courier trajectories and waybill delivery time data can be collected, this problem is still challenging due to noisy data with spatio-temporal biases, lacking ground truth of couriers' fine-grained behaviors, and complex correlations between behaviors. Existing works typically focus on a single dimension of the process such as inferring the delivery time and can only yield results of low spatio-temporal resolution, which cannot address the problem well. To bridge the gap, we propose a digital-twin-based iterative calibration system (DTRec) for fine-grained courier working process recovery. We first propose a spatio-temporal bias correction algorithm, which systematically improves existing methods in correcting waybill addresses and trajectory stay points. Second, to model the complex correlations among behaviors and inherent physical constraints, we propose an agent-based model to build the digital twin of couriers. Third, to further improve recovery performance, we design a digital-twin-based iterative calibration framework, which leverages the inconsistency between the deduction results of the digital twin and the recovery results from real-world data to improve both the agent-based model and the recovery results. Experiments show that DTRec outperforms state-of-the-art baselines by 10.8% in terms of fine-grained accuracy on real-world datasets. The system is deployed in the industrial practices in JD Logistics with promising applications. The code is available at https://github.com/tsinghua-fib-lab/Courier-DTRec. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate assessment of crowd evacuation inside the post-earthquake environment is critical from many perspectives, but this issue receives much less attention compared to the seismic losses of structural and non-structural components. This could be attributed to the fact that post-earthquake evacuation analysis is complex due to the interaction between human behavior and the actual built environment induced by different building contents. This study attempts to tackle this problem by investigating the impacts of different building contents on post-earthquake evacuation time by using an agent-based model that considers turning behavior. To this end, the agent-based model is first described, including: properties of the agent-based model with turning behavior, key aspects in its formulation considering different evacuation stages, and influence of different building contents (namely, debris from partition walls and ceiling systems, and various types of equipment) on the agent's behavior. Subsequently, a school building is used as a benchmark problem to validate the model without earthquake, and the findings indicate that the agent-based model can match the real safety drill results reasonably well. After the validation, the school building is subsequently designed in accordance with modern seismic design codes, and the influence of debris and equipment on post-earthquake evacuation time is quantitatively studied using a suite of pulse-type ground motions as input. Based on this case study, recommendations are made for structural and architectural designers in an effort to reduce the potential evacuation time. Specifically, debris induced by partition walls or ceiling systems should be controlled as it has the greatest impact on the total evacuation time. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to give an overview of the roles, objectives, and trade-offs in a two-pillar pension system consisting of tax-financed, public pensions and defined contribution, individual pensions. A pension system has many moving parts and our aim is to provide the reader with an understanding of how the parts interact and work together, a theme rarely addressed in the literature. In the first part of this paper, we give a qualitative overview of market failures, behavioural aspects, and distributional issues that form the background for a multi-pillar pension system design with mandatory components. In the second part of this paper, we present three thematic, quantitative analyses that illustrate fundamental relationship concerning wealth, inequality, insurance, and demographic changes. This paper also contains a detailed description of the agent-based model used for the analyses. The model is calibrated to Danish data, but the insights drawn from the model are of general validity. [ABSTRACT FROM AUTHOR]

Global climate change leads to an increase in the frequency and intensity of extreme rainstorms. At present, in China, which is experiencing rapid urbanization, urban flooding triggered by heavy rainstorms has emerged as a prominent issue, exerting far-reaching influences on socio-economic development, ecology, and people's livelihoods. In response to this, China has put forward the concept of sponge cities and has shifted from pilot projects to comprehensive and systematic citywide implementation, with the aim of strengthening urban resilience in rainstorm management. This article takes the Chaoyang River area in Nanning City, South China as an example and proposes a dynamic risk assessment framework integrating hydrodynamic models and ABM to reflect flooding and pedestrian response to rainstorms. The research results show that under the design rainstorm scenarios with return periods of 5 years and 30 years, the rainstorm-induced flooding process in the study area presents a dynamic evolution pattern. It develops rapidly to the extreme or severe hazard grade within 1–2 h, and then declines slowly and persists until 8 h. The exposure and sensitivity of pedestrian mobility to flooding disasters extend across most areas of the study area within 1–5 h during the rainstorm-induced flooding process, with medium, high, or extreme risk levels observed during the 2–3 h period.Among the affected sensitive pedestrians, the gender ratio is roughly equal, and the proportion of the elderly and child populations is as high as 46.5%. The overall disaster resilience capacity of the study area is significantly insufficient, leading to a predominance of medium, high, or extreme risk levels for pedestrian mobility during the 2–3 h period. In conclusion, the study area should accelerate the construction of various sponge facilities on the underlying surface and comprehensively enhance diverse emergency management measures for excessive flooding disasters. The dynamic disaster risk simulation and assessment techniques proposed in this article can serve as essential scientific support for the construction of a digital twin system in China's sponge cities, reflecting both virtual and real scenarios and facilitating comprehensive resilience capabilities such as forecasting, warning, simulation, and contingency planning for intelligent rainstorm management. [ABSTRACT FROM AUTHOR]

This study employs the MATSim agent-based simulation model to analyse bike-sharing station locations by examining dynamic trip flows and individual behavioural changes. It explores demand on a microscopic scale, capturing the behaviour of multi-segment trips. A protocol was established to evaluate different station configurations' impact on profitability and demand. Findings suggest that optimising the station count from 219 to 66 strategic locations can significantly enhance both revenue and operational efficiency. The simulation produces data on the number of users arriving and departing from each station in different configurations, indicating the size of each station. This data allows stations to be classified into three types: generator, attractor, and interchange; displaying their changes across different configurations. This quantification offers operators insights for predicting bike distribution and planning operational strategies. Considering spatial and built environment factors, the findings underscore the potential of bike-sharing stations to evolve into mobility hubs, offering valuable insights for policymakers. [ABSTRACT FROM AUTHOR]

This study aimed to connect the behavioral corporate finance perspective (micro level) with complexity theory via agent-based modeling to analyze the impact of selected psychological factors of chief executive officers (CEOs) on stock market volatility (macro level). Specifically, we wanted to explore whether Polish CEOs’ subjective well-being (SWB) influenced their managerial decisions during the COVID-19 pandemic and how it might be related to the volatility of stock prices during this critical period in Poland. Our study was based on a survey of Polish CEOs who managed companies listed on the Warsaw Stock Exchange. In particular, 255 CEOs completed the Satisfaction with Life Scale, the Positive and Negative Affect Scale, and a business survey on the impact of the COVID-19 pandemic on company management. Using the results of this survey, we built an agent-based model to investigate how CEOs’ decision-making, stemming from their SWB levels, influences the perception of prices by individual traders and, in turn, how it is translated into aggregate stock market volatility. The results indicate the pathways through which the microscopic-level SWB of CEOs influences market price formation at a macroscopic level. The findings obtained from our model may shed new light on the rational expectations theory applied to stock market volatility during the financial crisis. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) outbreaks in acute care settings can have severe consequences for patients due to their underlying vulnerabilities, and can be costly due to additional patient bed-days and the need to replace isolating staff. This study assessed the cost-effectiveness of clinical staff N95 respirators and admission screening testing of patients to reduce COVID-19 hospital-acquired infections. An agent-based model was calibrated to data on 178 outbreaks in acute care settings in Victoria, Australia between October 2021 and July 2023. Outbreaks were simulated under different combinations of staff masking (surgical, N95) and patient admission screening testing [none, rapid antigen test (RAT), polymerase chain reaction]. For each scenario, average diagnoses, COVID-19 deaths, quality-adjusted life years from discharged patients, and costs (masks, testing, patient COVID-19 bed-days, staff replacement costs while isolating) from acute COVID-19 were estimated over a 12-month period. Compared with no admission screening testing and staff surgical masks, all scenarios were cost saving with health gains. Staff N95 respirators + RAT admission screening of patients was the cheapest scenario, saving A$78.4M [95% uncertainty interval (UI) 44.4M–135.3M] and preventing 1543 (95% UI 1070–2146) deaths state-wide per annum. Both interventions were individually beneficial: staff N95 respirators saved A$54.7M and 854 deaths state-wide per annum, while RAT admission screening of patients saved A$57.6M and 1176 deaths state-wide per annum. In acute care settings, staff N95 respirators and admission screening testing of patients can reduce hospital-acquired COVID-19 and COVID-19 deaths, and are cost saving because of reduced patient bed-days and staff replacement needs. [ABSTRACT FROM AUTHOR]

We examine the dependence of the cyclical fluctuations of demand on specific behavioral attitudes of heterogeneous agents. Starting from a modified version of the model of Tassier (Complexity 9(5):51-61, 2004), we use simulations to explore consumption dynamics and market shares when agents are inclined both to conformism and distinction, two necessary conditions for fashion in Simmel (Am j Sociol 62(6): 541–558, 1957). Our results challenge the view stating that conspicuous consumption is typical only of a wealthy class and of some positional goods, since we make no assumptions about features of the goods or income distribution. [ABSTRACT FROM AUTHOR]

Recent trends in the market for corporate bonds -such as its increase in size, the significant presence of low-quality debt instruments and the trading activity of non-financial corporations- call for the need of an investigation of the possible implications on the real economy. Although the phenomenon is still minor, the present paper aims at giving some early insights concerning the introduction of a corporate bond market, where also nonfinancial firms can invest. To do so, we build on an existing macroeconomic agent-based model of the CATS tradition and we introduce the possibility for firms to raise money through an alternative credit channel, i.e., the corporate bond market- and the opportunity for households and firms to invest part of their financial wealth into a portfolio of Government bonds and corporate bonds. Experiments based on single runs or on 100 Monte Carlo simulations suggest that the introduction of a bond market may exacerbate existing crisis and recessions and that when firms buy bonds, inequality may escalate. To the best of our knowledge, this paper constitutes one of the first attempts to incorporate a corporate bond market into a macro ABM. [ABSTRACT FROM AUTHOR]

Aircraft carrier flight decks present high-risk mission-critical environments that need to be both efficient and safe. The concept of optimal manning, having just enough people to do the job safely and efficiently, is paramount in order to put the least amount of people at risk while not sacrificing mission effectiveness. To this end, an agent-based model, the optimal manning simulation (OMS) was developed, which specifically looks at the launch process of the flight deck in order to quantify the risk and efficiency of people working on the flight deck. OMS models different classes of crew members on the flight deck, aircraft, and resources like catapults. OMS measures safety through collisions or near-collisions of people and aircraft, as well as how long it takes to execute a launch cycle, the primary efficiency metric. Validation and sensitivity analyses provide confidence in OMS results. To demonstrate its utility, OMS is also used to predict how the future introduction of unmanned aerial vehicles could impact staffing and performance measures. [ABSTRACT FROM AUTHOR]

In this work, we study the sport of tennis, with the aim of understanding competitions and the associated quantities that determine their outcome. We construct an agent-based model that is able to produce data analogous to real data taken from Association of Tennis Professionals (ATP) tournaments. This model depends on three parameters: the talent weight, the talent distribution width, and the chance distribution width. Unlike other similar works, we do not fix the values of these parameters and we calibrate the model results with the help of a genetic algorithm, thus exploring all possible combinations of parameters in the parameter space that are able to reproduce real system data. We show that the model fits the real data well only for limited regions of the parameter space. Limiting the region of interest in the parameter space allows us to perform further calibrations of the model that give us more information about the competition under study. Finally, we are able to provide useful information about tennis competitions, obtaining quantitative information about all of the important parameters and quantities related to these competitions with very limited a priori constraints. Through our approach, differing from those of other works, we confirm the importance of chance in the studied competitions, which has a weight of around 80% in determining the outcome of tennis competitions. [ABSTRACT FROM AUTHOR]

Housing markets display several correlations to multiple economic sectors of an economy. Their enormous impact on economies' health, wealth, and stability is uncontroversial. Interestingly, the forms of financing residential property vary widely between the different countries in terms of both, the available product types and the institutions offering them. This research examines the implications of different financial intermediaries on housing market cycles with special emphasis on two institutional types, conventional banks and building and loan associations. Introducing a heterogeneous agent-based model, the interactions of buyers, sellers, and the two types of credit institutions are assessed. Heterogeneous economic principles and expectations of agents create endogenous market conditions which are strongly influenced by the lending practices of financial intermediaries. Focusing primarily on collateral values to decide about lending, conventional banks may contribute to volatile housing markets which are prone to recessions. Building and loan associations, on the other hand, rely to a greater extent on endogenously created borrower information. Thus, they are able to cushion the volatility of house prices caused by procyclical mortgage lending of conventional banks and increase the stability of the housing market. Simulations show that the most stable market conditions are attained if both types of financial intermediaries serve the mortgage lending market jointly. Furthermore, transaction and homeownership rates are the highest in this market setting. These findings advocate in favor of diversified financial markets. [ABSTRACT FROM AUTHOR]

In the event of radiological emergencies, such as nuclear accidents, the speedy but well-ordering evacuation of residents is imperative to ensure their health and safety. Staged evacuation has been studied as a method to reduce evacuation time. However, a method is needed to confirm the effectiveness of staged evacuation and to optimize the strategy. This study evaluates the performance of staged evacuation with more realistic factors which are implementable in a simulation platform. The evacuation simulation was conducted using PRISM (Platform for Radiological Emergency Integrated Simulation Model), developed by Kyung Hee University and utilizing an Agent-Based Model. A city was modeled using GIS data for road networks, buildings, and administrative districts. The population distribution was made non-uniform based on building layout data, and evacuation areas were divided based on administrative districts for demonstrating purpose. The simulation varied the order of evacuation groups and the evacuation start time interval, comparing evacuation times and average speeds to evaluate each strategy. The results reached a few insights: (1) Certain staged evacuation strategies are effective in reducing evacuation times within the PAZ. (2) A staged evacuation strategy that prioritizes areas closest to the nuclear plant resulted in longer evacuation times compared to other evacuation sequences. (3) There is no significant relationship between evacuation time and evacuation speed. In other words, even if the evacuation times of strategies are similar, the evacuation speeds may differ. Since evacuation speed indicates road congestion, a strategy with a slightly longer evacuation time but a faster evacuation speed may be chosen depending on the objective. [ABSTRACT FROM AUTHOR]

As society becomes more complex, the importance of quick and effective emergency response systems gets more emphasized. This paper introduces a way to reduce the time it takes for emergency staff assembly to be handled during radioactive accidents, which is one of significant factors to manage early stage situations. Using a type of computer modeling called agent-based modeling (ABM) on the NetLogo software, we examine how emergency responses are carried out, focusing on how emergency staff assembly workers are sent out and how they work together in a simulated setting. By integrating traffic and path-finding models, along with real-world GIS (Geographic Information System) data, our methodology allows for more realistic evaluation of response times under various scenarios, including harsh weather conditions (e.g., Heavy snow) and infrastructural disruptions (e.g., Earthquake). The findings reveal significant variability in emergency staff assembly times, underscored by the impact of environmental factors. This research not only contributes to the understanding of emergency response mechanisms but also provides a practical framework for improving the efficacy of disaster management systems, thereby enhancing public safety and resilience against future radiological events. [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]

Abstract Background Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. Methods In consultation with the Australian Government Department of Health and Aged Care, we developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. For a realistic assessment, our model included other generic interventions consistent with the Australian Government’s recommendations released during the COVID-19 pandemic: isolation of confirmed resident cases, furlough (mandatory paid leave) of staff members with confirmed infection, and deployment of personal protective equipment (PPE) after outbreak declaration. Results In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. Conclusions Under realistic sets of assumptions, our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our results also highlight the importance of effective case isolation, and indicate that asymptomatic screening of residents and staff may be warranted, especially if importation risk from the outside community is high. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.

Economic models based on multi-agents are increasingly attracting attention and can provide a new perspective for exploring the causes behind social phenomena at the individual level. Existing research usually adopts society-level learning methods, and more research on micro-level heterogeneity among individuals is needed. For this, we propose a high-fidelity multi-agent economy (HMAE) model based on evolutionary game theory, including three types of agents: workers, firms, and the government. In particular, we characterize worker heterogeneity regarding laziness factors, work endowments, and commuting distances. These agents continuously and iteratively update their strategies by randomly exploring and imitating their neighbors to maximize their utility value. We simulated the evolution process of agent behavioral decisions through experiments and found that individual heterogeneity can significantly affect the decisions of workers and firms. These phenomena are consistent with some economic evolution trends in real life, and our research can provide an analytical tool for analyzing the causes of emerging economic phenomena. [ABSTRACT FROM AUTHOR]

This study investigates the long-term effects of charging station construction on electric vehicle (EV) diffusion, considering different deployment approaches and scales. Using China as a case study, we first examined the impact of network effects on consumers’ willingness to purchase EVs through discrete choice experiments. Based on the survey, an agent-based model that integrates agents and geographical context was developed to simulate EV diffusion under various charging station deployment scenarios. The main results are: (i) both direct and indirect network effects jointly influence consumer adoption of EVs (ii) a U-shaped relationship between network effects intensity and charging station scale exists, and (iii) concentrated charging station deployment accelerates EV diffusion more effectively than a systematic approach. These findings provide new evidence and policy implications for the development of the EV industry. [ABSTRACT FROM AUTHOR]

The epidemic outbreaks of the last two decades have led governments to rely more on computational tools for establishing protection policies. Computational approaches to modeling epidemics traditionally rely on compartmental models, network models, or agent-based models (ABMs); however, each approach has its limitations, ranging from reduced realism to lack of tractability. Furthermore, the recent literature emphasizes the importance of points of interest (POIs) as sources of population mixing and potential outbreak hotspots. In response, this study proposes a novel urban spatial ABM validated using our augmented SICARQD epidemic model. To replicate daily activities more accurately, the urban area is divided into a matrix of points of interest (POIs) with agents that have unique paths that only permit infectious transmission within POIs. Our results provide a qualitative assessment of how urban characteristics and individual mobility patterns impact the infected population during an outbreak. That is, we study how population density, the total number of POIs (where the population concentrates), the average number of POIs visited by an agent, the maximum travel distance from the home location, and the quarantine ratio impact the dynamics of an outbreak. Our ABM simulation framework offers a valuable tool for investigating and controlling infectious disease outbreaks in urban environments with direct applicability to global policy makers. [ABSTRACT FROM AUTHOR]

Simple Summary: Immune checkpoint inhibitors (ICIs) are cancer immunotherapeutics that reinvigorate immune cells' ability to attack tumor cells. Despite remarkable results in some patients, ICIs do not demonstrate the same efficacy across all individuals. In this study, we present the first side-by-side comparison of an agent-based model (ABM) with an ordinary differential equation (ODE) model for ICIs targeting the PD-1/PD-L1 immune checkpoint. We consider tumor cells of high and low antigenicity and two distinct immune-cell kill mechanisms. Using key parameters calibrated from mouse bladder cancer studies, we simulate virtual tumors using both models. Our research identifies crucial tumor-immune characteristics that influence the efficacy of ICIs. By exploring the unique spatial insights provided by the ABM, we underscore the importance of considering the spatial complexity of the tumor microenvironment in mathematical models of ICIs, potentially paving the way for more effective cancer treatments. Since the introduction of the first immune checkpoint inhibitor (ICI), immunotherapy has changed the landscape of molecular therapeutics for cancers. However, ICIs do not work equally well on all cancers and for all patients. There has been a growing interest in using mathematical and computational models to optimize clinical responses. Ordinary differential equations (ODEs) have been widely used for mechanistic modeling in immuno-oncology and immunotherapy. They allow rapid simulations of temporal changes in the cellular and molecular populations involved. Nonetheless, ODEs cannot describe the spatial structure in the tumor microenvironment or quantify the influence of spatially-dependent characteristics of tumor-immune dynamics. For these reasons, agent-based models (ABMs) have gained popularity because they can model more detailed phenotypic and spatial heterogeneity that better reflect the complexity seen in vivo. In the context of anti-PD-1 ICIs, we compare treatment outcomes simulated from an ODE model and an ABM to show the importance of including spatial components in computational models of cancer immunotherapy. We consider tumor cells of high and low antigenicity and two distinct cytotoxic T lymphocyte (CTL) killing mechanisms. The preferred mechanism differs based on the antigenicity of tumor cells. Our ABM reveals varied phenotypic shifts within the tumor and spatial organization of tumor and CTLs despite similarities in key immune parameters, initial simulation conditions, and early temporal trajectories of the cell populations. [ABSTRACT FROM AUTHOR]

Models of human mobility can be broadly applied to find solutions addressing diverse topics such as public health policy, transportation management, emergency management, and urban development. However, many mobility models require individual-level data that is limited in availability and accessibility. Synthetic populations are commonly used as the foundation for mobility models because they provide detailed individual-level data representing the different types and characteristics of people in a study area. Thorough evaluation of synthetic populations is required to detect data biases before the prejudices are transferred to subsequent applications. Although synthetic populations are commonly used for modeling mobility, they are conventionally validated by their sociodemographic characteristics, rather than mobility attributes. Mobility microdata provides an opportunity to independently/externally validate the mobility attributes of synthetic populations. This study demonstrates a spatially-oriented data validation framework and independent data validation to assess the mobility attributes of two synthetic populations at different spatial granularities. Validation using independent data (SafeGraph) and the validation framework replicated the spatial distribution of errors detected using source data (LODES) and total absolute error. Spatial clusters of error exposed the locations of underrepresented and overrepresented communities. This information can guide bias mitigation efforts to generate a more representative synthetic population. [ABSTRACT FROM AUTHOR]

The production of public goods is a necessary condition for the survival of the species, but it comes at the expense of individual growth. In a prototype bacterial colony, we model the role of quorum sensing as a resource redistribution mechanism. Two types of bacterial colonies are analyzed, one made up of a single strain and one made up of two different strains. Based on a recent series of experimental data present in the literature, we analyze two types of strains with different extinction times: strains that consume available resources very quickly, therefore becoming extinct quickly, and strains that consume resources slowly and die due to aging. We show that the proposed quorum sensing model describes the main experimental result that coexistence may favor the survival of both strains. Furthermore, the production of public goods is maximized when both types of individuals have the maximum proliferation output. Finally, we highlight the role played by so-called dormant cells in the duration of survival time. These cells are of particular interest because their ability to counteract different types of stress (e.g., the use of antibiotics) still constitutes a challenge. [ABSTRACT FROM AUTHOR]