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For an e-hailing taxi operation, we analyse a driver's profit-maximising reactive strategy (to either accept or refuse a ride request) in response to the ride request broadcast by the platform. We analyse four operating modes, each of which is a combination of either of two reactive strategies: no refusal and refusal based on proximity, and either of two broadcasting methods. In an operating mode, our objective is to evaluate the expected total profit in a shift. We adopt a two-stage methodology to answer the research questions. In the first stage, we develop an agent-based simulation model to capture the effect of multiple taxis on driver's reactive strategy. Using real trip data, we find that a driver could follow a strategy of refusal based on proximity and earn approximately 25% more than the baseline no refusal strategy. In the second stage, we develop an approximate analytical model for a single taxi operation and compare the performance against the agent-based simulation model. We develop closed-form expressions of the expected total profit for each operating mode and topology of the service region. We find that our approximate analytical model provides an upper bound, and the profit deviation lies within 20% of the agent-based simulation model. [ABSTRACT FROM AUTHOR]

This paper studies a Deep Q-Learning (DQL) method for transaction sequencing problems in an automated warehousing system, Shuttle-based Storage and Retrieval System (SBSRS), in which shuttles can move between tiers flexibly. Here, the system is referred to as tier-to-tier SBSRS (t-SBSRS), developed as an alternative design to tier-captive SBSRS (c-SBSRS). By the flexible travel of shuttles between tiers in t-SBSRS, the number of shuttles in the system may be reduced compared to its simulant c-SBSRS design. The flexible travel of shuttles makes the operation decisions more complex in that system, motivating us to explore whether integration of a machine learning approach would help to improve the system performance. We apply the DQL method for the transaction selection of shuttles in the system to attain process time advantage. The outcomes of the DQN are confronted with the well-applied heuristic approaches: first-come-first-serve (FIFO) and shortest process time (SPT) rules under different racking and numbers of shuttles scenarios. The results show that DQL outperforms the FIFO and SPT rules promising for the future of smart industry applications. Especially, compared to the well-applied SPT rule in industries, DQL improves the average cycle time per transaction by roughly 43% on average. [ABSTRACT FROM AUTHOR]

An increasing number of exchanges, mainly in the U.S., have adopted a commission structure called maker–taker fees in which traders placing limit orders (makers) are paid a rebate (negative trading commission) and traders placing market orders (takers) are charged a trading fee. The reason is that by paying rebates to makers, exchanges can expect to receive a large number of maker's orders and gain market share. Makers include arbitrageurs who make large transactions. Maker–taker fees constitute one of the most important commission structures for exchanges, because they are expected to attract arbitrageurs who are looking for rebate profits, on top of their trading profits. There have been many studies about arbitrage trading, but none we could find focused on the impact of arbitrage trading between markets with maker–taker fees where arbitrage traders place limit orders and markets without maker–taker fees where they place market orders. In this study, we investigated volatility and market liquidity by changing the amount of rebate under our proposed artificial markets, where there are or are not maker–taker fees. Then we checked the performance of arbitrage trading when the rebate increased. The results were that volatility in the market with maker–taker fees decreased and that in the market without maker–taker fees increased, and that market liquidity and arbitrage performance both increased in the market with maker–taker fees when rebates increased. The above results indicate that exchanges that operate markets adopting maker–taker fees can provide investors with more attractive markets than those that do not adopt them. However, if more arbitrageurs participate in the market with maker–taker fees to take advantage of these rebates, the cost burden on exchanges may increase unnecessarily. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has significantly impacted healthcare systems, particularly altering the operations of emergency departments (EDs). This study evaluates ED operational efficiency during and after the pandemic using a hybrid approach that combines Agent-Based Simulation (ABS) and Discrete Event Simulation (DES), integrated with an anomaly detection system. The simulations provide a comprehensive analysis of patient flow, resource utilization, and treatment processes, while the anomaly detection system identifies unusual operational patterns and potential crises in real-time. Key performance metrics, including average dwelling time, wait times, treatment times, and staff utilization, were analyzed. Results show substantial differences between the pandemic and post-pandemic periods: during the pandemic, the ABS model recorded an average dwelling time of 44.3 minutes with 60% staff utilization, while the DES model showed 122.2 minutes with 110.1% utilization. Post-pandemic, the ABS model improved to a dwelling time of 24.5 minutes and 70% staff utilization, and the DES showed reductions to 69.2 minutes and 49.5% utilization. Moreover, integrating anomaly detection further enhanced the ED's ability to manage operational disruptions proactively, reducing response times and improving overall efficiency. This study underscores the importance of combining simulation and anomaly detection to enhance ED preparedness and resilience for future healthcare crises. [ABSTRACT FROM AUTHOR]

There are two contrasting hypotheses regarding the drivers of biomass‐distribution among trophic levels within ecosystems. Energetic or bottom–up models propose control by supply of energy, either from autotrophy or from the underlying trophic level. Dynamical or top–down models propose control by predators in the overlying trophic level. Although multiple approaches have been used to reconcile these hypotheses, they have rarely considered the evolutionary pressures created by different distributions of biomass. Here, we study the effects of these evolutionary processes using an agent‐based, spatially‐explicit, eco‐evolutionary model. We demonstrate that, when ecosystems are simple and predator–prey relationships between species are fixed and do not evolve, primary productivity limits the number of trophic levels. In this case, the abundance in the top trophic level is always limited by productivity. However, productivity‐limited trophic levels subject those below them to limitation by predation, and predation‐limited trophic levels allow trophic levels below them to grow until limited by productivity. These results match the expectations of the exploitation ecosystems hypothesis (EEH), which predicts the same pattern of alternating top–down and bottom–up control on trophic levels. When species are able to evolve to adaptively adjust their trophic levels, however, selection is liable to lead to collapse of the trophic pyramid through prey switching. Under these conditions, all trophic levels experience bottom–up control. When the evolution of prey switching is restricted, higher trophic levels are evolutionarily stable and the predictions of the EEH are once again met; the stability of long food chains is thus dependent on the difficulty associated with prey switching. These results suggest that, at least under the combinations of model parameters explored in this study, evolutionary limitation is key to maintaining trophic structure. [ABSTRACT FROM AUTHOR]

Autologous chimeric antigen receptor T-cell therapy presents promising treatment outcomes for various cancers. However, its potential is restrained by unique supply chain challenges, including dynamic patient health conditions and extended turnaround time. These challenges often lead to missed optimal treatment windows, impeding the effective delivery of life-saving treatments. This article presents SimPAC (simulation-based decision support for Patient-centric manufacturing of autologous cell therapies). SimPAC is designed to model and incorporate real-time patient health conditions into the supply chain decisions of autologous chimeric antigen receptor T-cell therapy. SimPAC integrates system dynamics and agent-based simulation techniques, facilitating the adaptation of manufacturing processes and production schedules based on real-time patient health conditions. SimPAC can model various patient disease progressions using parametric functions, nonparametric functions, or tabular data. Additionally, SimPAC offers easy configuration options to model various cell therapy supply chains. We provide two case studies to demonstrate the capabilities of SimPAC and highlight the benefits of patient-centric manufacturing, including improved survival rates and potential economic advantages. However, while the benefits are significant, our study also emphasizes the importance of balancing improved patient outcomes, economic viability and ethical considerations in the context of personalized medicine. SimPAC can be used to explore applications of this approach to diverse therapeutic contexts and supply chain configurations. [ABSTRACT FROM AUTHOR]

Most of today's inland freight transport in Switzerland is operated on the road system. In this study, an innovative agent-based simulation approach is developed to investigate the potential shift from road to rail. In a first step, future freight demand for inland road transport is calculated based on official governmental forecasting tools provided by ARE (Bundesamt für Raumentwicklung, Switzerland). In a second step, the agent-based simulation framework Multi-Agent Transport Simulation (MATSim) is used to investigate different supply concepts and estimate the mode shift effect from "road-only" to "intermodal road and rail transport". The simulated transport supply consists of the road network, the rail network, the cargo rail schedule, and the terminals where containers are loaded from Heavy Goods Vehicles to cargo trains and vice versa. For both, the road and rail system, dynamic queuing effects are explicitly taken into consideration. The illustrative case study for Switzerland reveals that intermodal road/rail transport provides a great potential to reduce road traffic. From the users' point of view, switching from road to intermodal transport yields an average cost reduction of 46%. Even without any optimization of the transit schedule and terminal capacities, a significant trip share of 23% is shifted from road to intermodal transport. Both train and terminal capacities as well as the number of train departures per origin destination relation are limiting factors and have a crucial impact on the demand for intermodal transport. [ABSTRACT FROM AUTHOR]

Rural areas near large cities do not satisfy the food needs of the city's population. In Medellín, Colombia, these areas satisfy only 2% of the city's food needs, highlighting an urgent need to review and improve policies supporting agriculture. This study was conducted over a ten-year period since the release of the Medellín policy related to land use. The model uses agent-based modelling, geographic analysis and dichotomous variables, combining these structures to create a decision-making element and thus identify changes to examine in relation to current land use and detect properties with a potential for conversion to agricultural use. By evaluating post-processed geographic layers, land use in agricultural rural environments is prioritized, setting up clusters of homogeneous zones and finding new areas of rural influence. The implications of this study extend beyond Medellín, offering a model that can be applied to other regions facing similar challenges in agricultural productivity and land use. This research supports informed and effective decision-making in agricultural policy, contributing to improved food security and sustainable development. The results show that some properties are susceptible to policy changes and provide a framework for the revision of local regulations, serving as a support tool for decision-making in rural public policies by giving the local administration key factors to update in the current policies. The findings are relevant to local stakeholders, including policymakers and rural landowners, suggesting that several properties are susceptible to policy changes promoting agriculture and supporting informed decision-making in agricultural policy, contributing to food security and sustainable development. Also, this approach promotes efficient and sustainable agriculture, highlighting the importance of geographic analysis and agent-based modelling in policy planning and evaluation. [ABSTRACT FROM AUTHOR]

Background: The concept of Hyperconnected Megacity Parcel Logistics (HMPL) was introduced in 2018 and aims to enhance the efficiency, responsiveness, resilience, and sustainability of parcel movements in megacities. However, evaluating such fundamental solutions presents challenges and requires a comprehensive understanding of all stakeholders and decisions involved. Methods: This study introduces a discrete-event agent-based simulation platform that encompasses critical stakeholders and addresses various levels of decision-making. This platform provides an opportunity to evaluate key decisions within an HMPL structure. Results: To demonstrate the capability of the simulator, we assess the impact of package routing and consolidation strategies facilitated by HMPL compared to traditional practices. Preliminary findings suggest that increased interconnection among nodes in HMPL reduces transit times, thereby enabling tighter customer delivery services. However, examining different consolidation heuristics reveals potential trade-offs between handling and shipping costs under fixed shipment schedules, prompting further investigation into dynamic shipment services. Conclusions: The findings of this study suggest that the benefits of innovative approaches in a complex environment, such as parcel logistics, cannot be evaluated in isolation from other decisions. Accurate assessment of the ultimate outcomes and underlying trade-offs requires multi-faceted models that incorporate all key variables. [ABSTRACT FROM AUTHOR]

Abstract The evolution of cooperation through indirect reciprocity is a pivotal mechanism for sustaining large-scale societies. Because third parties return cooperative behaviour in indirect reciprocity, reputations that assess and share these third parties’ behaviour play an essential role. Studies on indirect reciprocity have predominantly focused on the costs associated with cooperative behaviour, overlooking the costs tied to the mechanisms underpinning reputation sharing. Here, we explore the robustness of social norms necessary to secure the stability of indirect reciprocity, considering both the costs of reputation and the resilience against perfect defectors. Firstly, our results replicate that only eight social norms, known as the ‘leading eight,’ can establish a cooperative regime. Secondly, we reveal the robustness of these norms against reputation costs and perfect defectors. Our analysis identifies four norms that exhibit resilience in the presence of defectors due to their neutral stance on justified defection and another four that demonstrate robustness against reputation costs through their negative evaluation of unjustified cooperation. The study underscores the need to further research how reputational information is shared within societies to promote cooperation in diverse and complex environments.

Healthcare environment design evaluation (HEDE) can help create a safe environment, and interpersonal visibility among users is a key metric for assessing the safety of HEDE due to its role in caregiving observation. Traditional tools for visibility evaluation mainly focus on physical obstructions, neglecting the impact of the user’s mobility behavior, which is common in the healthcare environment. To address this challenge, this study developed a five-step workflow to evaluating interpersonal visibility among mobile users in healthcare environment: 1) collection of empirical behavior data from users; 2) construction of user journeys; 3) validation of agent-based simulation (ABS) reliability; 4) utilization of ABS to simulate user behavior in a virtual environment; and 5) computation of total visibility duration and time intervals between users using the parametric computation tool. This workflow was tested in 25 long-term care facilities for the elderly with dementia and subsequently evaluated by experts. The evaluation results confirm the effectiveness of this workflow. The core contribution and novelty of this workflow lie in its integration of ABS and parametric computation, which enables the evaluation of interpersonal visibility among mobile users, thereby contributing to the safety of the healthcare environment.

Recent dramatic increase in e-commerce has also increased the adoption of automation technologies in warehouses. Autonomous mobile robots (AMRs) are from those technologies widely utilized in warehouse operations. It is important to design the operation of those robotic systems in such a way that, they meet the current and future system requirements correctly. In this paper, we study flexible travel of AMRs in warehouses by developing smart deadlock and collision prevention algorithms on agent-based modelling. By that, AMR agents can interact with each other and environment, so that they can make smart decisions maximizing their goals. We compare the performance of the developed flexible travel system with non-flexible designs where there is a single AMR dedicated to a specific zone so that no deadlock or collision possibility takes place. The results show that AMRs may provide up to 39% improvement in the flexible system compared to its non-flexible design. [ABSTRACT FROM AUTHOR]

AbstractThis study addresses the problem of optimizing composition and distribution of an ambulance fleet within an existing emergency medical system. The primary objective is to formulate a strategy aimed at minimizing response times and enhancing the system's responsiveness, particularly to critically ill patients. The optimal spatial distribution of various types of vehicles is proposed using a hierarchical p-median model. The effects of these strategies are evaluated through a detailed computer simulation model. Experimental studies have shown that optimizing station locations contributes more significantly than altering the fleet composition and associated dispatching protocols. In the context of the Slovak Republic, the proportion of high-priority incidents responded to within 8 minutes could be increased by 8.7% in urban areas and 10.5% in rural areas, solely through optimal deployment of currently active ambulances. An additional improvement of nearly 2% could be achieved by introducing 13 patient transport ambulances, optimally distributed throughout the country. [ABSTRACT FROM AUTHOR]

This article presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location–allocation Mixed-Integer Programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ABSTRACT FROM AUTHOR]

We present a novel method for modeling adaptive movement behavior in force-based crowd simulations tailored to the requirements of interactive real-time applications. We aim for an efficient, controllable and situation-dependent synthesis of global path planning and local collision avoidance. To achieve this, we address two typical problems that can cause severe performance impacts and undesired motion: local neighbor search and the combination of goal directions with collision avoidance. We employ a direction-aware neighbor set to efficiently select agents for collision avoidance computation and crowd density estimation. We adaptively blend between goal and avoidance directions to utilize the entire spectrum of navigation strategies for goal attainment, ranging from completely goal-oriented to strongly avoidance-centric behaviors. Our method can be combined with established force-based simulation techniques and easily integrates into existing real-time simulation pipelines. Experiments show that our method can improve control over run-time performance as well as the movement behavior of agents compared to plain force-based methods. [ABSTRACT FROM AUTHOR]

During the Annual Operational Research Society Simulation Workshop in Worchester, UK, in 2010, a panel discussion entitled "discrete‐event simulation (DES) is dead, long live agent‐based simulation (ABS)!" was held. Subsequent debates on the topic have captured the interest of many simulation professionals and researchers. This study lends credence to the discourse with evidence. In January 2020, a global health crisis named coronavirus disease (COVID‐19) occurred, infecting and killing millions of people globally, leading to community lockdowns and travel restrictions, and bringing the world to a standstill. Scientists scrambled for quick, practical solutions to stem the tide. Operational research (OR) and management science (MS) techniques, including modeling and simulation, provide effective methods to make COVID‐19 infection forecasts, assign/optimize intensive care unit beds, initiate healthcare management/control strategies, and decision‐making. The global pandemic problems offer an opportunity to evaluate the discourse on DES's continuous relevance and potency as an OR/MS technique. Utilizing bibliometric performance evaluation and text analytics of publications addressing various COVID‐19 pandemic problems, the results show an extensive use, relevance, and impact of the DES technique across multidisciplinary domains, including medical/healthcare, social and behavioral sciences, business, molecular biosciences, and more. The intellectual structure highlights strong multidisciplinary collaboration among sources in decision sciences, healthcare, natural, behavioral, and social sciences. The social network analysis of the publications shows the contributions and collaborations among authors, institutions, and countries. The study demonstrates that DES is still alive and robust, notwithstanding the hype about ABS. [ABSTRACT FROM AUTHOR]

As the adoption of distributed energy resources (DERs) grows, the future of electricity distribution systems is confronted with significant challenges. These challenges arise from the transformation of consumers into prosumers and the resulting increased system complexity, leading to more pressure on the distribution grids. To address this complexity, a Digital Twin framework is designed to simulate DERs within distribution grids effectively. This framework is structured around four key modules: DERs, the electricity distribution grid, the energy management system, and the consumers. It incorporates a communication interface to facilitate interactions among these modules and includes considerations for grid topologies and demand-side configurations. The framework allows for the exploration of various DER adoption rates and capacities. The validation of this framework involves case studies on two Danish distribution grids with scenarios incorporating rooftop photovoltaic (PV) systems, batteries, and electric vehicles, considering different combinations of these technologies. The findings demonstrate the framework's ability to depict the states of the grid, PV systems, electric vehicles, and battery systems with a 10 min resolution over periods ranging from a day to over a decade. [ABSTRACT FROM AUTHOR]

Multi-agent approach is very popular for modelling and simulation of complex phenomena, design and programming of decentralised computing systems. Asynchronous beings, which do not share state but communicate using messages are a convenient abstraction for representing various phenomena observed in the real world. When the number of the considered agents grows, the designers and developers of such systems must address the problem of performance. Introducing distribution is often a weapon of choice, which, however, does not guarantee obtaining proper scalability and efficiency. The intensity of communication in a large-scale agent-based system can easily exceed the abilities of a distributed hardware architecture, leading to poor performance. After analysing various distributed agent-based systems, we identified several reasons for limited performance and several architectural solutions, which can help overcoming this problem. The main aim of the presented work is identification and systematization of these architectural solutions in the form of design patterns. As a result, we propose three new design patterns for building scalable distributed agent-based systems. A systematic description of their aims, structure, variants and features is provided, together with examples of applications. [ABSTRACT FROM AUTHOR]

Due to safety perceptions, Colombian banking clients prefer to visit bank branch offices instead of other channels. Thus, there are long waiting for lines at branch offices. Considering the need for more tools for strengthening and streamlining client service, the number of financial clients tends to diminish. In this context, this paper aims to measure the impact of technological innovation on the clients' waiting time and the attraction of new customers. We propose a simulation-based methodology to analyze customer behaviors and forecast the diffusion effect on mobile app adoption. Furthermore, our study provides managerial insights and future research lines. [ABSTRACT FROM AUTHOR]

Ecological disturbance has been proposed to have a variety of effects on biodiversity. These mechanisms are well studied over shorter timescales through experimental manipulation of ecosystems, but the effect of disturbance on longer timescales, where evolutionary processes operate, is less well understood. This is at least in part because evolutionary processes are too slow to observe in experimental manipulations of ecosystems. Here, we use the Rapid Evolutionary Simulator system (REvoSim) to solve this problem. REvoSim is a spatially explicit, agent-based simulation tool that models both ecological and evolutionary processes and is capable of simulating many thousands of generations of evolution per hour in a population of up to 1 million organisms. We use REvoSim to evaluate the biological cropping hypothesis, which predicts that the non-selective culling of organisms from populations ("cropping") can enhance diversity in those populations over long timescales by reducing the homogenising effects of competitive exclusion. Our experiments demonstrate that cropping alone can increase diversity within populations under certain circumstances: those where it has the net effect of reducing the selection pressure acting on those populations. However, intense cropping pressure may increase the selection pressure on organisms to reproduce rapidly, potentially offsetting the effects of reduced competitive exclusion on diversity. We also show that cropping alone is not sufficient to result in reproductive isolation within populations. This implies that, while cropping can maintain a high species diversity within an ecosystem, additional mechanisms must be invoked to generate that high diversity in the first instance. [ABSTRACT FROM AUTHOR]

Small cities and towns often struggle to provide high-quality public transport services to daily commuters. This is reflected in the modal split, where the share of car users dominates. Such a problem requires a modern solution, where transport planners can verify the impact of potential transport network improvements on the travel behavior of the residents before the changes are actually deployed. This study aims to demonstrate the usefulness of employing an agent-based simulation tool in the decision process for redesigning an express service regional bus route connecting a network of small cities and towns. The model was initially developed as a Mobility as a Service simulation solution for suburban areas of European metropolises. The model is adapted and applied to a case study for the region of Agder, Norway, simulating the impact of nine different scenarios on the patronage of a specific bus route. The simulation model proposes to upgrade the classic agent structure to a persona profile designed specifically for the case study. The main objective of this research is to identify the scenario that maximizes patronage while minimizing total route travel time and additional costs. The results suggest that the proposed model can be successfully adapted from suburban metropolitan areas to the realities of the considered case study, and potentially other similar regions. Specifically, out of the nine proposed scenarios, the model identified four promising ones. One of the four scenarios also fits the cost constraints imposed by the transport provider. The model provides a solid approach for analyzing complex transport systems that are practically impossible to consider in detail if the analysis is done without computer support. Thus, the results can be used as a decision support system for public transport planning and operations in networks of small cities and towns. [ABSTRACT FROM AUTHOR]

Abstract Shared Autonomous Vehicles (SAVs) are expected to have a transformative role in future transportation systems, by reducing vehicle ownership, helping in alleviating congestion, improving accessibility and traffic safety, and changing travel behavior and urban infrastructure. The potential introduction of SAVs in transportation systems has triggered the need of exploiting suitable tools for designing and planning SAV operations and services and assessing their impacts. An explicit category of such tools are agent-based models (ABMs), whose advantage in efficiently representing transportation systems with a fine level of detail, has allowed them to gain importance in modeling SAVs. This paper systematically reviews and organizes the current state-of-the-art on ABMs dealing with SAVs. The review is two-fold: first, the methodological aspects of exploiting ABMs in the context of SAV services and operations are analyzed and second, ABM-based findings on the anticipated impacts of SAVs to traffic, travel behavior, land uses, the environment and so on, are presented and discussed. The paper concludes with recommendations for future research on SAVs and other, potential ABM applications for that purpose.

Abstract Smallholder farms support the livelihoods of 2.5 billion people and their decisions on how to manage their land has profound consequences for the environment and the food security of billions of people. However, farmers' values, norms and resulting management practices are usually not formed in isolation. Triangulating multiple analytical, modelling and simulation methods, we investigated if and how social influence exerted through peer‐to‐peer information exchange affect soil nutrition management among 2734 Indonesian smallholder cocoa farmers across 30 different villages. The results show that the relational structures of these village‐based social networks strongly relate to farmers' use of fertiliser. In villages with highly centralised networks (i.e. hub‐and‐spoke networks where one or very few farmers holds disproportionately central position in the village network), a large majority of farmers report the same fertiliser use, and that practice is typically to avoid using fertilisers. By contrast, in less centralised networks, fertiliser use varies widely. The observed community‐level distributions of fertiliser use can be most closely reproduced through simulations by complex contagion mechanisms in which social influence is only exerted by opinion leaders that are much more socially connected than others. However, even such leaders' abilities to influence others to change fertiliser use may be limited in practice. The combination of our quantitative and qualitative findings provides significant policy implications for development programs targeting smallholder farming communities. An important practical lesson is that common interventions which primarily engage socially central farmers may not be effective in stimulating desired transitions in social‐ecological systems. Read the free Plain Language Summary for this article on the Journal blog.

People with disabilities are among the most vulnerable groups in building fires. According to the U.S. Fire Administration, an estimated 700 home fires involve people with physical disabilities each year. In parallel, the National Fire Protection Association estimates that 11% of civilian fire deaths were people with disabilities. Despite these statistics, the current body of literature shows few studies focused on the evacuation of disabled people. To bridge this knowledge gap, this paper presents findings on the evacuation processes of wheelchair users in a low-rise apartment (dormitory) building. More specifically, we simulate 1–3 wheelchair users in a dormitory building at our home institution via 327 simulations to examine evacuation time as well as identify structural aids and barriers. As a byproduct of this research, a new dynamic structural ranking system of egress components is proposed for wheelchair users, and a series of suggestions for structural modifications to improve the egressibility of the simulated building are provided.

Abstract Background Protecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration. Methods We propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis. Results Our results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor. Conclusions Our results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

Digital platforms have replaced traditional markets in most industries and orchestrate socioeconomic aspects of our lives. We address the problem of negative direct side network effects that arise with an increased number of agents on one side of the platform. Negative effects, if unaddressed, lead to undesired long-term consequences for the platform by developing a positive vicious cycle. Addressing negative effects require dynamic solution mechanisms that adapt to the changing landscape of platforms. The recommender systems literature has proposed multi-sided recommender systems (MSR) as a dynamic solution to many problems on platforms. However, current state-of-the-art MSRs do not consider uncertainty in predicting agents' choices, resulting in limited efficacy. We present a robust multi-sided recommender system that considers estimation errors in agents' choice to address this concern. Extensive experiments with agent-based models—ride-pooling and education platform—provide support for the efficacy and generalizability of the robust MSR to address negative effects. [ABSTRACT FROM AUTHOR]

We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant based on the data that would have been available at that time. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant.Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission rate advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial outbreak dynamics for Omicron. We found that the modelled dynamics hold up to retrospective scrutiny. The modelled imposition of extra non-pharmaceutical interventions planned by the Scottish Government at the time would likely have little effect in light of the transmission rate advantage held by the Omicron variant and the fact that the planned interventions would have occurred too late in the outbreak’s trajectory. Finally, we found that any assumptions made about the projected distribution of vaccines in the model population had little bearing on the outcome, in terms of outbreak size and timing. Instead, it was the landscape of prior immunity that was most important.

The adoption of agent-based models (ABMs) in transport research has highlighted their potential in digitally simulating travel behaviour from a bottom-up perspective. However, generating ABMs remains a complex multi-step process, often suffering from the lack of model representation or reliance on proprietary data, thereby limiting ABMs' transferability, especially in regions facing data scarcity. This research presents a systematic ABM generation algorithm for Brussels, Belgium, that generates high-quality ABMs more representative of real-life travel patterns, including incorporating external travel demand and allowing multiple destinations for the same activity types in activity chaining. Additionally, our algorithm's input data only contains social-spatial data and aggregated cellphone matrices. Due to the ubiquitous nature of such data worldwide, our algorithm demonstrates significant transferability to other regions for ABM generation while preserving individual privacy. We demonstrate the input data and the proposed algorithm step by step. The ABM generated using MATSim is validated against real-world data regarding mode share, distance share, travel time distribution and traffic counts. Overall, this research would serve as valuable guidance for ABM modellers in data collection and specific modelling setups, lowering the entry barrier of ABM research towards more efficient, representative and reproducible ABMs.

Shared Autonomous Vehicles (SAVs) are expected to have a transformative role in future transportation systems, by reducing vehicle ownership, helping in alleviating congestion, improving accessibility and traffic safety, and changing travel behavior and urban infrastructure. The potential introduction of SAVs in transportation systems has triggered the need of exploiting suitable tools for designing and planning SAV operations and services and assessing their impacts. An explicit category of such tools are agent-based models (ABMs), whose advantage in efficiently representing transportation systems with a fine level of detail, has allowed them to gain importance in modeling SAVs. This paper systematically reviews and organizes the current state-of-the-art on ABMs dealing with SAVs. The review is two-fold: first, the methodological aspects of exploiting ABMs in the context of SAV services and operations are analyzed and second, ABM-based findings on the anticipated impacts of SAVs to traffic, travel behavior, land uses, the environment and so on, are presented and discussed. The paper concludes with recommendations for future research on SAVs and other, potential ABM applications for that purpose. [ABSTRACT FROM AUTHOR]