Agent-based Simulation Model Research Articles

Coexistence of critical phenomena: the concept of manifold multi-spectral criticality

Prompted by the ubiquity of empirical observations of critical phenomena, often in non-equilibrium macrostates, we developed a modelling approach in which several critical phenomena coexist. Instead of a single critical point, many coexisting critical points in the system are identified, forming a one-dimensional critical manifold. Identified within our game-of-life-like heterogeneous agent-based simulation model, where agents can be created and annihilated in the presence of a catalyst, each critical point belonging to the critical manifold is associated with a multi-spectrum of critical exponents. We find this situation in non-equilibrium mixed percolation-like macrostates obeying continuous phase transitions. These macrostates are quasi-stationary, where some system characteristics are time-independent while others are not. This novel look at universality signals the existance of complexity of critical phenomena richer than described to date.

Data-driven modeling for designing a sustainable and efficient vaccine supply chain: A COVID-19 case study

We present an agent-based simulation-optimization modeling framework to determine the optimal location of warehouses for the distribution of vaccines. We first extend an agent-based epidemiological simulation model of COVID-19 to capture disease transmission and forecast the number of susceptible individuals and infections. We then develop a sustainable VSC considering the impact of greenhouse gases and integrate the simulation model into the VSC model to minimize total costs and environmental impacts. We validate our proposed model using a real-world COVID-19 VSC in the US. Our findings underscore the importance of strategically managing vaccine supplies to control COVID-19 and other infectious outbreaks.

Fault Tree Analysis of Trade-Offs between Environmental Flows and Agricultural Water Productivity in the Lake Urmia Sub-Basin Using Agent-Based Modeling

The recent problems of Lake Urmia (LU) are caused by extensive and complex socio-ecological factors that require a comprehensive approach to consider the relationships between users and identify failure factors at the basin level. For this purpose, an agent-based simulation model of farmers’ social interactions and economic interests (ABM) with various support scenarios and random supervision and training by the government agent is developed to evaluate its impact on independent farmers’ decision-making in the form of a complex adaptive system. Finally, a fault tree analysis (FTA) is created in the Cara-FaultTree 4.1. software to identify scenarios that lead to the non-development technology in irrigation management (non-DTIM) in the LU sub-basin. The assessment of the impact of government supervision and training revealed that the main causes of non-DTIM in the LU basin are a lack of demands from farmers and low awareness among residents of the basin, with failure probabilities of 0.90 and 0.86, respectively. Ultimately, the failure probability of the main event (non-DTIM) was 0.50. The paths of proper training and farmers’ requirements for sustainable agricultural water supply should become more stringent. The results confirm that appropriate measures to strengthen government supervision and training, as well as raise farmers’ awareness of the importance of long-term sustainability of water resources, can lead to greater resilience in the DTIM.

Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs.

Despite the availability of direct-acting antivirals that cure individuals infected with the hepatitis C virus (HCV), developing a vaccine is critically needed in achieving HCV elimination. HCV vaccine trials have been performed in populations with high incidence of new HCV infection such as people who inject drugs (PWID). Developing strategies of optimal recruitment of PWID for HCV vaccine trials could reduce sample size, follow-up costs and disparities in enrollment. We investigate trial recruitment informed by machine learning and evaluate a strategy for HCV vaccine trials termed PREDICTEE-Predictive Recruitment and Enrichment method balancing Demographics and Incidence for Clinical Trial Equity and Efficiency. PREDICTEE utilizes a survival analysis model applied to trial candidates, considering their demographic and injection characteristics to predict the candidate's probability of HCV infection during the trial. The decision to recruit considers both the candidate's predicted incidence and demographic characteristics such as age, sex, and race. We evaluated PREDICTEE using in silico methods, in which we first generated a synthetic candidate pool and their respective HCV infection events using HepCEP, a validated agent-based simulation model of HCV transmission among PWID in metropolitan Chicago. We then compared PREDICTEE to conventional recruitment of high-risk PWID who share drugs or injection equipment in terms of sample size and recruitment equity, with the latter measured by participation-to-prevalence ratio (PPR) across age, sex, and race. Comparing conventional recruitment to PREDICTEE found a reduction in sample size from 802 (95%: 642-1010) to 278 (95%: 264-294) with PREDICTEE, while also reducing screening requirements by 30%. Simultaneously, PPR increased from 0.475 (95%: 0.356-0.568) to 0.754 (95%: 0.685-0.834). Even when targeting a dissimilar maximally balanced population in which achieving recruitment equity would be more difficult, PREDICTEE is able to reduce sample size from 802 (95%: 642-1010) to 304 (95%: 288-322) while improving PPR to 0.807 (95%: 0.792-0.821). PREDICTEE presents a promising strategy for HCV clinical trial recruitment, achieving sample size reduction while improving recruitment equity.

Pyros: a raster–vector spatial simulation model for predicting wildland surface fire spread and growth

Background Euro–Mediterranean regions are expected to undergo a climate-induced exacerbation of fire activity in the upcoming decades. Reliable predictions of fire behaviour represent an essential instrument for planning and optimising fire management actions and strategies. Aims The aim of this study was to describe and analyse the performance of an agent-based spatial simulation model for predicting wildland surface fire spread and growth. Methods The model integrates Rothermel’s equations to obtain fire spread metrics and uses a hybrid raster–vector implementation to predict patterns of fire growth. The model performance is evaluated in quantitative terms of spatiotemporal agreement between predicted patterns of fire growth and reference patterns, under both ideal and real-world environmental conditions, using case studies in Sardinia, Italy. Key results Predicted patterns of fire growth demonstrate negligible distortions under ideal conditions when compared with circular or elliptical reference patterns. In real-world heterogeneous conditions, a substantial agreement between observed and predicted patterns is achieved, resulting in a similarity coefficient of up to 0.76. Conclusions Outcomes suggest that the model exhibits promising performance with low computational requirements. Implications Assuming that parametric uncertainty is effectively managed and a rigorous validation encompassing additional case studies from Euro–Mediterranean regions is conducted, the model has the potential to provide a valuable contribution to operational fire management applications.

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

Impact of Blockchain on Improving Taxpayers’ Compliance: Empirical Evidence from Panel Data Model and Agent-Based Simulation

ABSTRACT The purpose of this study is to examine the impact of blockchain technology on taxpayer compliance among U.S. taxpayers, using it as a case study. It aims to explore the critical factors affecting blockchain technology applications in tax compliance systems. We first utilized a panel data model to establish empirical parameters linking audit intensity and qualification rates of Internal Revenue Service (IRS) tax returns. We then applied these parameters to an agent-based simulation model powered by artificial intelligence. We showed that integrating blockchain technology can effectively address noncooperative behavior and reduce the tax gap. Moreover, we identified two key factors—the improvement of the IRS’s efficiency and increased punishment—that can accelerate the development of blockchain technology in the tax compliance system. Our research adds to the existing literature on applications of agent-based simulation models in tax compliance systems and provides policy implications for promoting the use of blockchain technology. Data Availability: Data are available from the public sources cited in the text.

Supply Chain 4.0: what the supply chains of the future might look like

Abstract The article mainly aims to try and create a new concept for developing logistics and supply chains in the era of Industry 4.0. Analyses of development trends in logistics and production management were used to create the new logistics and supply chain concept. Conclusions were used from the analyses of how the modern concepts of Industry 4.0, Logistics 4.0, Supply Chain 4.0, and 5.0 work. Analyses of the benefits of applying modern management concepts in these areas were carried out and criticised because of their inadequacies, which became apparent during the recent crises in the world. Although the sources of the crises were different, they could be eliminated by reconfiguring logistics systems and supply chains. The results aim to answer three questions: (1) Has the time come to change the current way of looking at logistics and supply chains? (2) What could Supply Chain 4.0 look like using Industry 4.0 tools? (3) How should Supply Chain 4.0 address the logistics and supply chain challenges? The presented answers do not exhaust the topic but rather open up a discussion on logistics and supply chains of the future. The presented concept allows for a completely new global and local view of logistics chains. The structure of the presented model can be subjected to scenario analysis using agent-based simulation modelling due to the structure’s emergent nature. The new approach can significantly benefit the development of local production centres and global supply networks. The benefits mainly come from reducing the environmental impact of manufacturing and logistics processes from the moment the new product idea is conceived.

Solid waste generation prediction model framework using socioeconomic and demographic factors with real-time MSW collection data.

This article proposes a framework for developing predictive models of end-of-life product flows, highlighting the importance of conducting thorough analyses before developing waste management and end-of-life product flow strategies. The framework emphasizes the importance of recognizing the nature and quality of the available data and finding a balance between model development time and detail requirements. It is designed to adapt to source material heterogeneity and address varying data availability scenarios, such as the presence or absence of radio frequency identification chips. A case study for the city of Gatineau is presented, showcasing the framework's application through agent-based simulation models in a geographic information systems environment. The study focuses on creating models of municipal solid waste generation based on socioeconomic and demographic factors and collection data to accurately predict the quantity and quality of waste streams, enabling municipalities to assess the environmental impact of their waste management strategies.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Agent-based simulation model to evaluate government policies for farmers’ adoption and synergy in improving irrigation systems: A case study of Lake Urmia basin

The complexities of water issues, in which human factors directly affect the qualitative and quantitative indicators of water resources in a variable, extensive, and undefined manner, make social interactions necessary for the water resources sustainable development. In this situation, local government admins (local divisions of the Water Institutions and Ministry of Agriculture) (LGA) can play a fundamental role in attracting the participation of independent farmers in sustainable agriculture and preserving water resources by modifying support policies. In this study, an agent-based simulation model of social interactions and economic interests of farmers is presented along with different scenarios of support policies as well as random supervision and training by LGA agents to evaluate its impact on the independent decision making of farmers in the form of a complex adaptive system. Questionnaires and expert interviews were used to determine the data and scenarios. Farmers’ adaptation for development technology in irrigation management (DTIM), simulated output of WP, field application efficiency (FAE), and their adaptation speed are influenced by input factors such as assumed cost of sustainable water supply (SWS), mean government expenditure (subsidy count), and the level of supervision and training by LGA. The results showed that the assumed cost of SWS and government supervision and training are the most influential indicators of the farmer agent’s propensity to DTIM with F-value=4375.59 and F-value=1055.10, respectively. The results confirm that appropriate policies in strengthening LGA supervision and training and increasing farmers’ awareness of the importance of long-term water resource sustainability can help achieve more adaptability.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code

BackgroundThe rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings.MethodsWe present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator’s descriptive accuracy.ResultsIn this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8–22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65–5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator.ConclusionsThe emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

An adaptive route choice model for integrated fixed and flexible transit systems

Over the past decade, there has been a surge of interest in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modelling decision in the development is how one chooses to represent the mode- and route-choices of travellers. The real-time adaptive behaviour of travellers is important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a near real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behaviour and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. We first explore model properties in a choice between fixed- and flexible-transit modes for a toy network. The adaptive route choice framework is then applied to a case study based on a real-life branched transit service in Stockholm, Sweden. This case study illustrates level-of-service trade-offs, in terms of waiting times and in-vehicle times, between passenger groups and analyzes traveller mode choices within a mixed fixed- and flexible transit system. Results show that the proposed framework is capable of capturing dynamic route choices in mixed flexible and fixed transit systems and that the day-to-day learning model leads to stable fixed-flexible mode choices.

Emergence of periodic circumferential actin cables from the anisotropic fusion of actin nanoclusters during tubulogenesis

The periodic circumferential cytoskeleton supports various tubular tissues. Radial expansion of the tube lumen causes anisotropic tensile stress, which can be exploited as a geometric cue. However, the molecular machinery linking anisotropy to robust circumferential patterning is poorly understood. Here, we aim to reveal the emergent process of circumferential actin cable formation in a Drosophila tracheal tube. During luminal expansion, sporadic actin nanoclusters emerge and exhibit circumferentially biased motion and fusion. RNAi screening reveals the formin family protein, DAAM, as an essential component responding to tissue anisotropy, and non-muscle myosin II as a component required for nanocluster fusion. An agent-based model simulation suggests that crosslinkers play a crucial role in nanocluster formation and cluster-to-cable transition occurs in response to mechanical anisotropy. Altogether, we propose that an actin nanocluster is an organizational unit that responds to stress in the cortical membrane and builds a higher-order cable structure.

Psychologically-Valid Generative Agents: A Novel Approach to Agent-Based Modeling in Social Sciences

Incorporating dynamic realistic human behaviors in population-scale computational models has been challenging. While some efforts have leveraged behavioral theories from social science, validated theories specifically applicable to Agent-based modeling remain limited. Existing approaches lack a comprehensive framework to model the situated, adaptive nature of human cognition and choice. To address these challenges, this paper proposes a novel framework, Psychologically-Valid Generative Agents. These agents consist of a Cognitive Architecture that provides data-driven and cognitively-constrained decision-making functionality, and a Large Language Model that generates human-like linguistic data. In addition, our framework benefits from Stance Detection, a Natural Language Processing technique, that allows highly personalize initialization of the agents, based on real-world data, within Agent-based modeling simulations. This combination provides a flexible yet structured approach to endogenously represent how people perceive, deliberate, and respond to social or other types of complex decision-making dynamics. Previous work has demonstrated promising results by using a subset of the components of our proposed architecture. Our approach has the potential to exhibit highly-realistic human behavior and can be used across a variety of domains (e.g., public health, group dynamics, social and psychological sciences, and financial markets).

Exploring parking choices under the coexistence of autonomous and conventional vehicles

The introduction of autonomous vehicles (AVs) is likely to alter conventional parking behaviors in urban areas. In particular, AVs would allow travelers to be dropped off at their destination and park at an economic location or keep self-driving, waiting for the travelers to complete their activities. The present study investigates the parking choices under the coexistence of AVs and conventional vehicles (CVs) and compares their travel impact. To this end, we use an agent-based simulation model to consider different parking options for both vehicles. Most notably, for AVs, these choices include returning to origins, parking at a lot, and continuing self-driving. Results show that if CVs and AVs each make up half of the vehicle fleet, the distance traveled by AVs accounts for over 60% of the total vehicle kilometers traveled (VKT) by all vehicles, and the occupant-free kilometers contributes to around 30% of the VKT traveled by AVs. At 100% penetration of AVs, the total VKT by all vehicles is more than 30% greater than that of completely CVs (i.e., 0% AV penetration). At lower vehicle demand, the percentage of different AV parking choices is relatively stable, and total VKT and travel time increase proportionally with the number of demands. In comparison, at a higher vehicle demand, total travel time increases exponentially while total VKT rises modestly over time. The two pricing policies—lowering parking prices and raising travel costs—have comparable effects in motivating more AVs to park in a parking spot instead of returning to the origin. The simulation and subsequent analyses help understand parking choices and their travel consequences when AVs and CVs coexist.

A framework for conceptualising hybrid system dynamics and agent-based simulation models

The growing complexity of systems and problems that stakeholders from the private and public sectors have sought advice on has led systems modellers to increasingly use multimethodology and to combine multiple OR/MS methods. This includes hybrid simulation that combines two or more of the following methods: system dynamics (SD), discrete-event simulation, and agent-based models (ABM). Although a significant number of studies describe the application of hybrid simulation across different domains, research on the theoretical and practical aspects of combining simulation modelling methods, particularly the combining of SD and ABM, is still limited. Existing frameworks for combining simulation methods are high-level and lack methodological clarity and practical guidance on modelling decisions and elements specific to hybrid simulation that modellers need to consider. This paper proposes a practical framework for developing a conceptual hybrid simulation model that is built on reviews and reflections of theoretical and application literature on combining methods. The framework is then used to inform and guide the process of conceptual model building for a case study in controlling the spread of COVID-19 in care homes. In addition, reflection on the use of the framework for the case study led to refining the framework itself. This case study is also used to demonstrate how the framework informs the structural design of a hybrid simulation model and relevant modelling decisions during the conceptualisation phase.

An agent-based simulation model for the growth of the Sydney Trains network

Agent-based models are computational methods for simulating the actions and reactions of autonomous entities with the ability to capture their effects on a system through interaction rules. This study develops an agent-based simulation model (RANGE) to replicate the growth of Sydney Trains network by given exogenous historical evolution in land use. A set of locational rules has been defined to find a sequence of optimal stations from an initial seed. The model framework is an iterative process that includes five consecutive components including environment loading, measuring access, locating stations, connecting stations, and evaluating connections. In each iteration, following the locating/connecting process in each line of railways network, the accessibility will be calculated, and land use will be updated. Based on the compilation of network topology and properties, each iteration will be a year-on-year time step analysis. The network evolves based on a set of locational rules in regards to changes in the historic land use. Also, two coverage indices are defined to evaluate the fitness of the simulated lines in comparison to the Sydney tram and train network.

Performance Evaluation of Unmanned Aerial Vehicles Usage in Wildlife Surveillance Operations Using Agent-Based Simulation Modeling

Many wildlife species, including Rhinoceros, Elephants, and mountain Gorillas, are at risk of extinction. To combat this, researchers and innovators have developed Unmanned Aerial Vehicles (UAVs), or Drones, for national park patrol to reduce poaching. This study evaluates UAV effectiveness in wildlife surveillance and provides a method for determining optimised system parameters for an effective wildlife protection strategy. Using agent-based simulation modelling with Anylogic, the study determines the best system setup. The key Performance Indicator considered was the number of poachers arrested. The optimal configuration involves 6 drones with a 700-meter detection range, resulting in 3 poacher arrests. Furthermore, the change in the drones’ detection capacity provided a greater impact on the system performance.

Complexity Induced Risks Posed by Upcoming Digital Ultra-Platforms: An Agent-Based Simulation Model

The global deployment of new digital technologies, such as fifth-generation broadband, Internet of Things, smart objects, and AI, is paving the way for new high-performance digital platforms. These platforms are capable of connecting both people and “things” at high speeds and near-zero latency, taking on the nature of digital “ultra-platforms”. However, the increased reach (i.e., more agents interacting across the platform) and responsiveness (i.e., more opportunities for agents to respond and adapt to each, as they interact) of digital “ultra-platforms” create an unprecedented level of complexity. In this article, we use an agent-based simulation model to study the different types of risks that arise from this increased level of complexity. Three different risks are identified that can hinder a platform's ability to create value. We refer to these risks as the digital black swan, the stacked deck, and push back. Each risk is considered and analyzed from the perspective of specific stakeholders who may be affected by platform externalities. These include the platform owner, platform users, and others who may have no direct interest in the platform. In this way, the article aims to contribute to the literature on digital platform management by focusing on challenges that are emerging from new digital technologies yet have thus far received little attention from either scholars or managers.