Agent-based Approach Research Articles

An agent-based approach to simulating evacuation in elderly care facilities with disabled older adults

Emergencies in elderly care facilities are not uncommon. However, conducting evacuation drills for disabled older adults poses significant challenges due to their physical limitations. While simulation approaches offer a viable alternative, there is a dearth of research focused on evacuation simulation models considering the presence of disabled older adults. This study introduces an agent-based approach designed to simulate the evacuation of older adults within elderly care facilities. The approach incorporates the distinct characteristics and behaviors of nursing staff, self-sufficient, semi-disabled, and disabled older adults during evacuation. The simulation environment replicates the typical architectural layout of single-story elderly care facilities. Two evacuation strategies, specifying the order in which nursing staff assist disabled older adults during evacuation, are proposed. The performance of these strategies under scenarios with varying proportions of disabled older adults and caregiver ratios is compared through simulation experiments. The results indicate that the strategy requiring nursing staff to initially assist the disabled older adults under their responsibility and then seek out and aid the nearest disabled older adults is more effective in reducing total evacuation time in most cases. Furthermore, the impact of the spatial layout of disabled rooms on evacuation efficiency and safety is discussed. The results reveal that the opposite-side center concentration pattern of disabled rooms outperforms other spatial layout patterns of disabled rooms in terms of evacuation efficiency and safety. These findings contribute to the optimization of evacuation strategies and spatial layouts within elderly care facilities by providing quantitative evidence to support decision-makers.

In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics

The CD8 T cell immune response operates at multiple temporal and spatial scales, including all the early complex biochemical and biomechanical processes, up to long term cell population behavior.In order to model this response, we devised a multiscale agent-based approach using Simuscale software. Within each agent (cell) of our model, we introduced a gene regulatory network (GRN) based upon a piecewise deterministic Markov process formalism. Cell fate – differentiation, proliferation, death – was coupled to the state of the GRN through rule-based mechanisms. Cells interact in a 3D computational domain and signal to each other via cell–cell contacts, influencing the GRN behavior.Results show the ability of the model to correctly capture both population behavior and molecular time-dependent evolution. We examined the impact of several parameters on molecular and population dynamics, and demonstrated the add-on value of using a multiscale approach by showing the influence of molecular parameters, particularly protein degradation rates, on the outcome of the response, such as effector and memory cell counts.

Measuring the effectiveness of incorporating mobile charging services into urban electric vehicle charging network: An agent-based approach

Mobile charging service (MCS) has emerged as a cost-efficient solution to compensate for the limitations of fixed charging stations (FCSs) in service capacity and coverage. Nevertheless, the impacts of incorporating the MCS into the charging infrastructure network have not been well understood. Therefore, this study introduces an exhaustive framework for devising Agent-based Modeling (ABM) to delineate electric vehicle (EV) drivers' daily travels and charging behaviors within the integrated charging network. Specifically, the developed ABM framework encompasses both internal attributes of individuals and external variables of charging and transport networks that potentially shape EV drivers’ public charging decisions. It generates authentic daily charging demands for FCSs and MCSs and provides a streamlined process to further incorporate more charging modes into the EV charging ecosystem. Subsequently, we opted for the Salt Lake City metropolitan area to implement our framework and showcase the effectiveness of MCS integration on the charging network over various EV adoption levels and network settings. The findings of the designed case study unravel the choice patterns of EV drivers when facing diverse charging options and pinpoint the strengths and weaknesses of MCS incorporating into the urban charging network at different stages.

Optimizing Municipal Solid Waste (MSW) Collection and Transportation Network Using GIS and Agent-based Modeling Approach: A Case Study in Hung Yen City, Vietnam

Optimizing Municipal Solid Waste (MSW) Collection and Transportation Network Using GIS and Agent-based Modeling Approach: A Case Study in Hung Yen City, Vietnam

Emergent coordination in temporal partitioning congestion games.

In this article we study the social dynamic of temporal partitioning congestion games (TPGs), in which participants must coordinate an optimal time-partitioning for using a limited resource. The challenge in TPGs lies in determining whether users can optimally self-organize their usage patterns. Reaching an optimal solution may be undermined, however, by a collectively destructive meta-reasoning pattern, trapping users in a socially vicious oscillatory behavior. TPGs constitute a dilemma for both human and animal communities. We developed a model capturing the dynamics of these games and ran simulations to assess its behavior, based on a 2×2 framework that distinguishes between the players' knowledge of other players' choices and whether they use a learning mechanism. We found that the only way in which an oscillatory dynamic can be thwarted is by adding learning, which leads to weak convergence in the no-information condition and to strong convergence in the with-information condition. We corroborated the validity of our model using real data from a study of bats' behaviour in an environment of water scarcity. We conclude by examining the merits of a complexity-based, agent-based modelling approach over a game-theoretic one, contending that it offers superior insights into the temporal dynamics of TPGs. We also briefly discuss the policy implications of our findings.

Agent-based simulation model of micro-mobility trips in heterogeneous and perceived unsafe road environments

Safety concerns expressed by micro-mobility users seem to practically constrain the use and flexibility of these new modes. This paper introduces a new, parametric agent-based modeling approach for simulating micro-mobility, using MATSim as a platform. Perceived safety is introduced as a factor, which affects travel behavior of micro-mobility modes in a car-dominated, heterogeneous, and perceived unsafe road network. To meet the research objectives, different scenarios are simulated, using the city of Athens, Greece as a test bed, to examine the model performance and applicability. A universal scoring function based on travel time, cost, and safety, is proposed. This study also discusses the concept of unsafe discontinuities. Simulation results show that an unsafe road segment of 500 m long is sufficient to induce evident behavioral variations and hinder the practical use of e-bikes and e-scooters in the base scenario. The establishment of a cycle-friendly road network leads to significant changes in the chosen routes and decreases the average trip distance for micro-mobility users. In this case, the simulation process is guided by perceived safety, primarily focusing on formulating safer paths for micro-mobility mode users. Nevertheless, the routing behavior of car users is not modified due to the spatial uniformity of safety perceptions in a car-dominated road environment.

Analyzing the impact of concealed carry weapons and school resource officers on school shootings: An agent-based modeling approach.

This paper presents a simulation modeling study that examines the potential benefit of arming public school staff members with concealed carry weapons (CCWs) in combination with school resource officers (SROs) during active shooter events. By simulating real or hypothetical situations and altering various parameters, simulation modeling allows researchers to explore the potential factors that may influence the outcome of such situations. This study will analyze literature on active shootings to identify key characteristics that may impact the outcome of an event and will use an actual school active shooter event as a basis for developing a simulation model. The researchers will then introduce a CCW carrier and an SRO into the scenario to assess the potential impact and outcomes of such a change. The results of this study may inform the development of effective policies and procedures for addressing active shooter events in public settings.

A WEAP-assisted agent-based modeling approach for watershed planning in a coupled human and nature system

ABSTRACT Coupled human and nature systems exhibit dynamic water demands due to diverse consumer socio-economic characteristics, resulting in inherent complexities. Modeling such systems effectively entails adopting an agent-based methodology. To enhance comprehension of water resource management in the Zarrineh-Roud Basin, northwest of Iran, a comprehensive hydrological-behavioral model has been utilized. This model consists of two primary components: An agent-based model (ABM) for simulating farmers’ decision-making processes and a water evaluation and planning (WEAP) model for simulating catchment area, cultivated area, and agricultural water consumption. Validation outcomes of the simulation model demonstrate precise estimation, particularly in the runoff estimation of the Nezam Abad hydrometric station, with statistically favorable correlation coefficient (R2) and Nash–Sutcliffe index (NS) values of 0.78 and 0.75, respectively. Analysis of different management scenarios indicates that with ample government funds, simultaneous implementation of ‘Education and increasing environmental awareness’ and ‘Alternative employment’ policies can significantly improve water resources conditions and income. In scenarios with limited financial resources, a combined implementation of ‘Water pricing,’ ‘Alternative employment,’ and ‘Education and raising environmental awareness’ emerges as the most effective strategy for managing water resources, albeit with a slight reduction in farmers’ income.

COVID-19 Dynamics and Mutation: Linking Intra-Host and Inter-Hosts Dynamics Via Agent-Based Modeling Approach

COVID-19 Dynamics and Mutation: Linking Intra-Host and Inter-Hosts Dynamics Via Agent-Based Modeling Approach

Fostering urban resilience and accessibility in cities: A dynamic knowledge graph approach

This paper explores the utilisation of knowledge graphs and an agent-based implementation to enhance urban resilience and accessibility in city planning. We expand The World Avatar (TWA) dynamic knowledge graph to support decision-making in disaster response and urban planning. By employing a set of connected agents and integrating diverse data sources — including flood data, geospatial building information, land plots, and open-source data — through sets of ontologies, we demonstrate disaster response in a coastal town in the UK and various aspects relevant to city planning for a mid-sized town in Germany using TWA. In King’s Lynn, our agent-based approach facilitates holistic disaster response by calculating optimal routes, avoiding flooded segments dynamically, assessing infrastructure accessibility before and during a flood using isochrones, identifying inaccessible population areas, guiding infrastructure restoration, and conducting critical path analysis. In Pirmasens, for city planning purposes, the knowledge graph-driven isochrone generation provides evidence-based insights into current amenity coverage and enables scenario planning for future amenities while adhering to land regulations. The implementation of agents and knowledge graphs achieves interoperability and enhances urban resilience and accessibility by enabling cross-domain correlation analysis that extends various areas including geospatial buildings, population demographics, accessibility coverage, and land use regulations.

New Digital Divide Shaped by Algorithm? Evidence from Agent-Based Testing on Douyin’s Health-Related Video Recommendation

While recommendation algorithms have significantly empowered human communication process, there is an emerging scholarly and societal concern regarding the potential discrimination inherent in algorithmic decision-making. The present study employs a novel agent-based testing approach to conduct an automated audit of the Douyin algorithm’s recommendations for health-related videos, aiming to investigate its relationship with information inequalities between individuals who are socioeconomically advantaged and disadvantaged. Our findings imply the possible existence of a new digital divide, wherein the algorithm tends to recommend a smaller proportion of authenticated health-related videos on cheap phone models, representing users in low socioeconomic group. Furthermore, this divide was mitigated when both groups demonstrated the same watching strategy for authenticated information. This study implies that recommendation algorithms actively perpetuate and exacerbate discriminatory social structures in reality. We also advocate for increased attention and education among the low socioeconomic group to enhance their health information literacy and understanding of algorithmic mechanisms.

Predicting transit ridership using an agent-based modeling approach

Accurate ridership estimation is pivotal in the advancement of sustainable transit systems, be it for proposed or existing transit networks. A multitude of methods, including travel demand models, direct ridership models, and regression models, have been employed by practitioners and researchers to estimate ridership at both station and network levels. However, travel demand models, frequently utilized for new transit lines, exhibit intrinsic limitations due to their aggregate nature and complexity based on their types. Researchers have also identified deficiencies, such as the incapacity to capture small spatial resolutions and specific station characteristics, as these models are predominantly designed for large-scale analyses.This study aims to overcome these limitations by introducing a novel approach that utilizes three microscopic agent-based models to develop a travel demand modeling suite, providing a policy-sensitive forecasting tool. The suite comprises three agent-based models: SILO-MITO-MATSim. Validation of the model against previous year data is conducted, and projections are made for future years. The model is applied to estimate network-level ridership for the proposed ‘Purple Line,’ a light rail transit line planned by MDOT, MTA, Maryland, which will integrate with the Washington D.C. Metro, the fourth largest transit system in the USA, boasting an average daily ridership of half a million. The study’s findings indicate an anticipated ridership of approximately 31,230 passengers in the inaugural year of 2027. The proposed model offers a robust and policy-sensitive solution empowering decision-makers to make informed choices to support a sustainable transportation system.

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.

Integrating threshold models with Markov chains for information diffusion in social networks

In this paper, I introduce an agent-based approach that leverages Markov models to formalize and understand information propagation within social networks. This methodology aims to explain the internal information states of agents and employs probabilistic model checking to analyze these models. Initially, I provide a comprehensive overview of the current research on information diffusion in social networks. Following this, I delve into the fundamentals of model checking and illustrate how this technique can be used to assess model accuracy, particularly in managing large networks with high precision. To demonstrate the effectiveness of this approach, I conduct a series of experiments. The results show that this paper provides a robust and effective method for analyzing complex information diffusion and network behavior.

Parameter Tuning of Agent-Based Models: Metaheuristic Algorithms

When it comes to modelling complex systems using an agent-based approach, there is a problem of choosing the appropriate parameter optimisation technique. This problem is further aggravated by the fact that the parameter space in complex agent-based systems can have a large dimension, and the time required to perform numerical experiments can be large. An alternative approach to traditional optimisation methods are the so-called metaheuristic algorithms, which provide an approximate solution in an acceptable time. The purpose of this study is to compare various metaheuristic algorithms for parameter tuning and to analyse their effectiveness applied to two agent-based models with different complexities. In this study, we considered commonly used metaheuristic algorithms for agent-based model optimisation: the Markov chain Monte Carlo method, the surrogate modelling approach, the particle swarm optimisation algorithm, and the genetic algorithm, as well as the more novel chaos game optimisation algorithm. The proposed algorithms were tested on two agent-based models, one of which was a simple toy model of the spread of contagious disease, and the other was a more complex model of the circulation of respiratory viruses in a city with 10 million agents and 26 calibrated parameters.

Mitigating housing market shocks: an agent-based reinforcement learning approach with implications for real-time decision support

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

Diffusion mechanism of green building in industrial clusters: An agent-based modeling approach

Industrial clusters are conducive to accelerating the adoption of green technologies and green buildings, but the underlying mechanism of cluster-based adoption among different stakeholders is seldom examined. The paper aims to explore the diffusion mechanism of green building among stakeholders in industrial clusters based on an agent-based modeling (ABM) approach. Factors influencing stakeholders’ adoption of green buildings were identified in five dimensions: economic benefit, green responsibility, peer effect, market demand, and technological maturity. An ABM model was developed for simulating green building diffusion among stakeholders, and validated with a Shenzhen case. The results indicate an S-shape trend of the adoption rate within the cluster. Developers show a relatively laggard adoption compared to others. Mandatory policy and financial subsidies could effectively boost the adoption rate, while technological progress and market demand accelerate the diffusion progress. The study provides useful insights into the practice and policy-making of green buildings within industrial clusters.

An agent-based modelling approach to wave-like diversification of language families

Abstract In contrast to phylogenetic tree inference, wave model approaches are often regarded as difficult to computationally implement for inference of language relatedness. This paper proposes a basic framework for the computational modelling of wave-like diversification in language families and explains the model type of agent-based models for linguistic data. The approach is based on agent-based simulations which allow for the detailed simulation of speaker interactions within speech communities. The proposed framework operates by simulating a large number of possible diversification situations with different parameter settings and selecting those runs that yield a good fit to the linguistic data of the geographical spread of different languages. The model can be fed with the geographical extent of languages and their known innovations in order to computationally reconstruct the most likely diversification scenarios of these languages under the wave model.

Evaluating the Interaction of Emerging Diseases on White-Tailed Deer Populations Using an Agent-Based Modeling Approach.

Disease co-occurrence in wildlife populations is common yet understudied. In the case of disease-caused mortality, the mortality attributed to one disease has the potential to buffer populations against subsequent alternative disease outbreaks by reducing populations and thus contacts needed to sustain disease transmission. However, substantial disease-driven population declines may also prevent populations from recovering, leading to localized extinctions. Hemorrhagic disease (HD), a vector-transmitted, viral disease in white-tailed deer (WTD), similar to chronic wasting disease (CWD), a prion disease, has increased in frequency and distribution in the United States. However, unlike CWD, which progresses slowly, HD can cause mortality only days after infection. Hemorrhagic disease outbreaks can result in substantial localized mortality events in WTD near vector habitats such as wetlands and may reduce local deer densities and consequent CWD transmission. The objective of our study was to evaluate the potential for HD outbreaks to buffer CWD risk where the diseases co-occur. Using an agent-based modeling approach, we found that frequent, intense HD outbreaks have the potential to mitigate CWD risk, especially if those outbreaks occur shortly after CWD introduction. However, HD outbreaks that do not result in substantial WTD mortality are unlikely to impact CWD or WTD population dynamics. Severe HD outbreaks may reduce CWD cases and could present an opportunity for managers to boost CWD control initiatives in a post-HD outbreak year.

A Synthesis on Impact Assessment Models from the Perspective of Evolution of the EU Common Agricultural Policy

Purpose: This paper aims to provide a comprehensive analysis of the current quantitative impact assessment methodologies, examining their strengths and weaknesses in terms of data requirements, as well as their consideration of social, economic, and environmental factors. Furthermore, it aims to elucidate the necessity for new-generation agricultural impact assessment models to incorporate advancements in information technology, communication tools, and big data analytics. Design/Methodology/Approach: In this review, the term "agricultural modelling platforms" is used to denote different equilibrium models and these models are divided into two categories according on their methodological approaches. The first category adopts a "systems approach," which includes general and partial equilibrium type models, as well as sector models. The "agent-based approach" is used by the second category. This review, thus, is primarily concerned with contrasting farm-representative models with farm-based models. Findings: The study emphasizes the importance of methodology and criteria in modelling exercises, considering factors like analysis level, environmental impact, and relationship between agriculture and the environment. It suggests that selecting the right modelling scale and tool requires asking the right research questions in advance. Originality/Value: In this study, general and partial equilibrium models, which allow impact analysis of policies implemented in the agricultural sector, which is increasingly faced with ecological and social problems, and Agent Based Models (ABM), which allow the problems in question to be included in the models, are compared and provided guidance to policy makers. It is aimed to reveal their advantages/disadvantages against each other. The originality of this study is that this comparison is made by taking into account the evolution of the European Common Agricultural Policy (CAP) since its establishment.