Agent-based Research Articles

Comparative Study of SFPE and Steering Modes in Pathfinder to Optimise Evacuation Routes

This paper investigates the possibilities of using an agent-based evacuation model to analyse the formation of queues at the exits of enclosed spaces and the evacuation process of people. The aim is to investigate how the density of people in a crowd affects the safe movement of people and how the width, number, and location of exits affect evacuation time. The analysis was carried out using the Pathfinder evacuation model, which provides two modes to simulate the movement of people: steering and SFPE. An enclosed space of 20 m × 30 m was investigated. First, the differences between the modes of the evacuation model to simulate the movement of people were compared. Then, the steering mode with limited door flow was set and the effect of the width, number, and location of the exits with a total width of 4 m on the evacuation time was investigated. The results of this study highlight differences in the simulation of the movement of people according to the different modes and provide valuable information for the design of safe escape routes. Proper design of escape routes can prevent an adverse situation that could arise when evacuating a large number of people.

Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation

Modeling streamflow is essential for understanding flow inundation. Traditionally, this involves hydrologic and numerical models. This research introduces a framework using agent-based modeling (ABM) combined with data-driven modeling (DDM) and Artificial Intelligence (AI). An agent-driven model simulates streamflow and its interactions with river courses and surroundings, considering hydrologic phenomena related to precipitation, water level, and discharge as well as channel and basin characteristics causing increased water levels in the Medio River. A five-year dataset of hourly precipitation, water level, and discharge measurements was used to simulate streamflow. The model’s accuracy was evaluated using statistical metrics like correlation coefficient (r), coefficient of determination (R2), root mean squared error (RMSE), and percentage error in peak discharge (Qpk). The ABM’s simulated peak discharge (Qpk) was compared with the measured peak discharge across four experimental scenarios. The best simulations occurred in scenario 3, using only rainfall and streamflow data. Data management and visualization facilitated input, output, and analysis. This study’s ABM combined with DDM and AI offers a novel approach for simulating streamflow and predicting floods. Future studies could extend this framework to other river basins and incorporate advanced sensor data to enhance the accuracy and responsiveness of flood forecasting.

The role of digital social practices and technologies in the Swiss energy transition towards net-zero carbon dioxide emissions in 2050

Digitalization can be an enabler of new and less energy-intensive lifestyles. However, it can be associated with rebound effects in energy consumption. For example, home office can reduce the mobility demand but increase residential energy consumption. Here, we analyze the role of households' social practices induced by information and communication technologies (ICTs) in Switzerland's transition to net-zero carbon dioxide emissions in 2050. We couple the Swiss TIMES Energy Systems Model with a socio-technical-economic agent-based model (SEED) to perform long-term pathway analysis by accounting for the socio-economic and technical aspects of technology adoption. We find an overall net-positive contribution of digitalization to the Swiss energy transition. A society adopting more digital practices and ICTs requires 10–20% less energy in 2050 than a society with a digitalization rate frozen at 2020 levels. We show that any rebound effects in energy consumption that digital practices can induce are counterbalanced by more efficient technologies and optimized processes enabled by ICTs. While digitalization helps to meet the Swiss energy and climate targets for 2050 and reduces the energy transition costs for society, policy support is necessary to develop the population's digital skills, services' digitalization, and network infrastructures.

Residential consumer enrollment in demand response: An agent based approach

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ±10% variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector.

The importance of the incubation time distribution in compartmental epidemiological models

This study investigates the utilization of various mathematical models for comprehending and managing outbreaks of infectious diseases, with a specific focus on how different distributions of incubation times influence predictions regarding epidemics. Two methodologies are examined: a compartmental SEnIR ODE model, which represents an enhanced version of the mean-field SEIR model, and a stochastic agent-based complex network model. Our findings demonstrate that the selection of diverse incubation time distributions can result in noteworthy discrepancies in critical epidemic forecasts, highlighting the crucial role of precise modeling in shaping effective public health interventions. The research underscores the necessity of integrating authentic distribution patterns into epidemic modeling to increase its reliability and applicability.

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data

ABSTRACT Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.

Norms prioritizing positive assessments are likely to maintain cooperation in private indirect reciprocity

Exploring the evolutionary mechanisms of cooperation in societies where reputational consensus cannot be assumed, as in divided societies, is important for understanding the basic principles of human behavior in modern societies. Indirect reciprocity provides a major explanatory mechanism, but most studies have focused on how donors, i.e., one who decides whether to donate (cooperate or help) to a recipient or do nothing, are assessed. It is natural to think that there is no consensus among assessors in our society, and there is no reason to update only donor assessments when updating assessments. We constructed an agent-based model that enables updating of both donors’ and recipients’ images. Our exhaustive simulations showed that the well analyzed assessment rules updating donors’ images are only second best to an assessment rule updating bad images in most likely for maintaining cooperative regimes. Such a social norm that prioritizes a positive assessment is considered tolerant, which is also consistent with previous studies arguing that tolerant evaluation is important in private-assessment schemes.

A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences

This study presents a novel Coupled Human And Natural Systems (CHANS) modelling framework that integrates a hydrodynamic model with an agent-based model at the memory level within a multi-GPU computing environment. This two-way coupled model captures real-time interactions between human activities and flood dynamics, with a focus on the deployment of temporary flood defences during the 2015 Desmond flood in Carlisle, UK. The findings reveal that temporary defences can significantly reduce flood inundation by 30% with early warnings and 15% through real-time decision-making, leading to financial savings of £30 million and £15 million, respectively. The study further explores the decision-making process for effective emergency flood management, emphasising the importance of early warnings and resources optimisation. The new CHANS model provides a valuable tool for testing and optimising emergency flood management strategies, highlighting the necessity of directly incorporating human activities into flood risk management.

An agent-based model of cross-platform information diffusion and moderation

Social media platforms are highly interconnected because many users maintain a presence across multiple platforms. Consequently, efforts to limit the spread of misinformation taken by individual platforms can have complex consequences on misinformation diffusion across the social media ecosystem. This is further complicated by the diverse social structures, platform standards, and moderation mechanisms provided on each platform. We study this issue by extending our previous model of Reddit interactions and community-specific moderation measures. By adding a followership-based model of Twitter interactions and facilitating cross-platform user participation, we simulate information diffusion across heterogeneous social media platforms. While incorporating platform-specific moderation mechanisms, we simulate interactions at the user level and specify user-specific attributes. This allows practitioners to conduct experiments with various types of actors and different combinations of moderation. We show how the model can simulate the impacts of such features on discussions facilitated by Reddit and Twitter and the cross-platform spread of misinformation. To validate this model, we use a combination of empirical datasets from three U.S. political events and prior findings from user surveys and studies.

Modeling a game to shift peak water demands: WaterTime leaderboard

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study.

Interactions between biological systems and engineered nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patient's overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

Mutual Expected Rationality in Online Sharing: An Agent-Based Model Study

AbstractModels of content-sharing behavior on online social media platforms typically represent content spread as a diffusion process modeled on contagious diseases; users’ behavior is modeled with single-agent decision theory. However, social media platforms are interactive spaces where users care about reactions to, and further spread of, the content they post. Thus, social media interaction falls under the intended use cases for game theory. In contrast to existing models leaving strategic reasoning out, we capture agents’ social media decisions within a cognitive hierarchy framework, which can be interpreted as making formally precise how agents make strategic choices based on mutual expectations of rationality. Analytically, we identify limit cases in which a platform can be swamped with content that no agents personally like but all expect to elicit reactions (think obvious fake-news). We then use agent-based simulations to show that a range of more realistic cases give rise to similar outcomes.

Enhancing Radiological Emergency Response Through Agent-Based Model Case 1: Effectiveness of Staged Evacuation

Enhancing Radiological Emergency Response Through Agent-Based Model Case 1: Effectiveness of Staged Evacuation

Competitive dynamics in a multi-sided mobile payment platform market: an agent-based modeling perspective

PurposeThe purpose of this research is to develop a comprehensive model to better understand competitive dynamics between mobile payment providers in a multi-sided market featuring customers and merchants. This is undertaken by modeling customers performing financial transactions with merchants while two mobile payment systems (MPS) providers deploy different strategies to compete for market share.Design/methodology/approachThe authors developed an agent-based simulation model using the NetLogo environment. The simulation featured two competing platform providers, 1,000 customer agents and 50 merchant agents. Past research, interviews and surveys were conducted to accurately model the behavior of the agents. Each simulation run lasted for 50 time periods. A total of 1,024 experimental conditions were designed to model different competitive environments, and 50 replications were conducted for a total of 51,200 experiments.FindingsThe simulation model provides insight into MPS platform providers’ competitive strategies by simultaneously modelling socioeconomic interactions between customers, merchants and MPS.Research limitations/implicationsFrom a methodological perspective, the paper contributes a comprehensive model that can be used to study competitive dynamics between competing platforms in a multi-sided market. From the perspective of competitive strategies, the results show that pricing alone is not sufficient to influence MPS diffusion. Interactions between pricing, customers’ risk perception, perceived security and ease of use of the platform create unexpected same-side and cross-side network effects, which affect MPS diffusion.Practical implicationsWhile pricing remains a crucial lever for MPS to compete for market share, they should focus on enhancing customers’ and merchants’ trust and reduce their risk perception. This can be done through the improvement of the user experience of their platform, development of educational materials and marketing campaigns that address concerns around security, data breaches and perceived risk.Originality/valueThe paper is a direct response to a recent call for action on studying competition between MPS platforms by simultaneously modelling the socio-economic behavior of heterogeneous consumers and merchants. The proposed agent-based simulation model can be used to provide insights into competitive strategies and as a building block for subsequent research in this area.

Agent-Based Spatial Dynamic Modeling of Diatraea saccharalis and the Natural Parasites Cotesia flavipes and Trichogramma galloi in Sugarcane Crops

In recent years, sugarcane production areas in Brazil have experienced a slower evolution in productivity, and one of the reasons for this is related to the increase in phytosanitary problems, such as the presence of pests. Nevertheless, limited attention has been paid to the development of tools for simulating the spatial dynamics of pests, including their impact on production. This study aims to simulate the potential population growth and dispersal of Diatraea saccharalis (sugarcane borer) in sugarcane crop fields and its estimated impacts on this crop production and to simulate biological control strategies. We developed an agent-based model to simulate the pest population and its dispersal in a one-hectare sugarcane crop field in Pederneiras, São Paulo, Brazil, delimited with the aid of satellite imagery, considering two scenarios: the first without biological control and the second with biological control using the parasites Trichogramma galloi and Cotesia flavipes. The model was developed using the NetLogo 6.3.0 software. The results indicate that the model accurately reproduced the infestation rates reported in the literature. Additionally, it provided insights into expected pest dispersal, potential production losses, and how the use of T. galloi in association with C. flavipes could mitigate production losses. We believe that the model can be used to simulate different biological control strategies and the implementation of integrated pest management (IPM) to achieve adequate control levels and greater productivity in sugarcane production.

Potential impact of curative and preventive interventions toward hepatitis C elimination in people who inject drugs–A network modeling study

BackgroundInjection-equipment-sharing networks play an important role in hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Direct-acting antiviral (DAA) treatments for HCV infection and interventions to prevent HCV transmission are critical components of an overall hepatitis C elimination strategy, but how they contribute to the elimination outcomes in different PWID network settings are unclear. MethodsWe developed an agent-based network model of HCV transmission through the sharing of injection equipment among PWID and parameterized and calibrated the model with rural PWID data in the United States. We modeled curative and preventive interventions at annual coverage levels of 12.5 %, 25 %, or 37.5 % (cumulative percentage of eligible individuals engaged), and two allocation approaches: random vs targeting PWID with more injection partners (hereafter ‘degree-based’). We compared the impact of these intervention strategies on prevalence and incidence of HCV infections. We conducted sensitivity analysis on key parameters governing the effects of curative and preventive interventions and PWID network characteristics. ResultsCombining curative and preventive interventions at 37.5 % annual coverage with degree-based allocation decreased prevalence and incidence of HCV infection by 67 % and 70 % over two years, respectively. Curative interventions decreased prevalence by six to 12 times more than preventive interventions, while curative and preventive interventions had comparable effectiveness on reducing incidence. Intervention impact increased with coverage almost linearly across all intervention strategies, and degree-based allocation was always more effective than random allocation, especially for preventive interventions. Results were sensitive to parameter values defining intervention effects and network mean degree. ConclusionDAA treatments are effective in reducing both prevalence and incidence of HCV infection in PWID, but preventive interventions play a significant role in reducing incidence when intervention coverage is low. Increasing coverage, including efforts in reaching individuals with the most injection partners, preventing reinfection, and improving compliance and retention in preventive services can substantially improve the outcomes. PWID network characteristics should be considered when designing hepatitis C elimination programs.

An agent-based method to estimate 3D cell migration trajectories from 2D measurements: Quantifying and comparing T vs CAR-T 3D cell migration

Background and objective:Immune cell migration is one of the key features that enable immune cells to find invading pathogens, control tissue damage, and eliminate primary developing tumors. Chimeric antigen receptor (CAR) T-cell therapy is a novel strategy in the battle against various cancers. It has been successful in treating hematological tumors, yet it still faces many challenges in the case of solid tumors. In this work, we evaluate the three-dimensional (3D) migration capacity of T and CAR-T cells within dense collagen-based hydrogels. Quantifying three-dimensional (3D) cell migration requires microscopy techniques that may not be readily accessible. Thus, we introduce a straightforward mathematical model designed to infer 3D trajectories of cells from two-dimensional (2D) cell trajectories. Methods:We develop a 3D agent-based model (ABM) that simulates the temporal changes in the direction of migration with an inverse transform sampling method. Then, we propose an optimization procedure to accurately orient cell migration over time to reproduce cell migration from 2D experimental cell trajectories. With this model, we simulate cell migration assays of T and CAR-T cells in microfluidic devices conducted under hydrogels with different concentrations of type I collagen and validate our 3D cell migration predictions with light-sheet microscopy. Results:Our findings indicate that CAR-T cell migration is more sensitive to collagen concentration increases than T cells, resulting in a more pronounced reduction in their invasiveness. Moreover, our computational model reveals significant differences in 3D movement patterns between T and CAR-T cells. T cells exhibit migratory behavior in 3D whereas that CAR-T cells predominantly move within the XY plane, with limited movement in the Z direction. However, upon the introduction of a CXCL12 chemical gradient, CAR-T cells present migration patterns that closely resemble those of T cells. Conclusions:This framework demonstrates that 2D projections of 3D trajectories may not accurately represent real migration patterns. Moreover, it offers a tool to estimate 3D migration patterns from 2D experimental data, which can be easily obtained with automatic quantification algorithms. This approach helps reduce the need for sophisticated and expensive microscopy equipment required in laboratories, as well as the computational burden involved in producing and analyzing 3D experimental data.

Predicting Grapevine Physiological Parameters Using Hyperspectral Remote Sensing Integrated with Hybrid Convolutional Neural Network and Ensemble Stacked Regression

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R2 by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines.

La influencia de las autocitaciones en las decisiones editoriales a partir de la simulación de rankings de revistas

The manipulation of the Journal Impact Factor (JIF) through editorial decisions that increase self-citations has been empirically observed in recent studies, sparking discussions on how to avoid such practices. This study aims to verify whether a policy of removing self-citations from the JIF calculation is enough to eliminate incentives for manipulation. We introduce an agent-based model of the academic social network formed by journals to test this hypothesis. We model journals as rational agents seeking to gain positions in the JIF ranking. The model was implemented in NetLogo and calibrated using data from the SCIMago Journal Ranking. Simulation results demonstrate that the model is capable of reproducing patterns observed in empirical networks and that the removal of self-citations can significantly reduce the adoption of manipulation strategies (e.g., reduction from 90% to 30%). This study contributes to understanding the role of self-citations in the JIF and provides tools for developing policies that promote scientific integrity.

Modeling the United Oscillation and Wave of economic policy and urban planning employing spatial population dynamics.

Modern urban dynamics are increasingly shaped by the interplay between economic policy and urban planning, yet often lack an integrated approach. This study bridges this gap by examining the dynamic equilibrium between these two realms using the "Oscillation and Wave Framework." Specifically, we focus on the impact of variations in congestion parameter λ on urban sectoral spatial distribution and population dynamics. Our approach utilizes an advanced agent-based model to simulate interactions within an urban economic landscape, offering a detailed analysis of the relationship between agglomeration economies and congestion diseconomies. The results highlight the significant influence of congestion parameter adjustments on urban patterns, particularly in terms of cluster density and development. Therefore, this study not only provides a deeper understanding of the intricate balance between economic and urban planning factors but also emphasizes the necessity of incorporating these insights into urban planning and policy formulation for sustainable urban development. The findings also have important practical implications for addressing the dynamic complexities of urban environments, especially the interactions between different industries and their role in shaping urban structures.