Agent-based Modeling Techniques Research Articles

A theory of innovation diffusion: a simulation study

ABSTRACT This study uses the agent-based modeling technique to discover propositions on innovation diffusion that are empirically testable. It finds the adoption rate is positively related to (1) the size of the population, (2) the degree of heterogeneity in consumer learning in the population, (3) the degree of heterogeneity in consumer innovativeness in the population, and (4) the fit-in- desire in the population for innovation. This research adds insights to the innovation diffusion literature and lays the groundwork for future empirical research.

Evaluating collective action theory-based model to simulate mobs

A mob is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people (who might have an agenda) get together online or offline to collectively conduct an act and then disperse (quickly or over a long period). In recent years, these events are increasingly happening worldwide due to the anonymity of the internet, affordability of social media, boredom, etc. Studying such a phenomenon is difficult due to a lack of data, theoretical underpinning, and resources. In this research, we use the Agent-Based Modeling (ABM) technique to model the mobbers and the Monte Carlo method to assign random values to the factors extracted from the theory of Collective Action and conduct many simulations. We also leverage our previous research on Deviant Cyber Flash Mobs to implement various scenarios the mobber could face when they decide to act in a mob or not. This resulted in a model that can simulate mobs, estimate the mob success rate, and the needed powerful actors (e.g., mob organizers) for a mob to succeed. We finally evaluate our model using real-world mob data collected from the Meetup social media platform. This research is one step toward fully understanding mob formation and the motivations of its participants and organizers.

Uncertainty-Based Validation Methodology and Experimental Analysis for External Control Room Human Performance Simulation: Application to Fire Probabilistic Risk Assessment of Nuclear Power Plants

In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation. To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model. Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions. The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.

A Data-Driven Pandemic Simulator with Reinforcement Learning

After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations’ impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents’ daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model’s effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies.

Exploring Education as a Complex System: Computational Educational Research with Multi-Level Agent-Based Modeling

Our study employs multi-level agent-based modeling and computational techniques to explore education as a complex system. With an underlying focus that education should be underpinned by a scientific understanding of student learning, we created computational models that simulated learning dynamics in classrooms, integrating both quantitative and qualitative insights. Through these models, we conducted experiments aligned with real classroom data to address key questions, such as “How can we effectively support the academic progress of underperforming students, who are disproportionately from low socio-economic status (SES) backgrounds, to close their multi-year achievement gap in mathematics?” Our study analyzes various instructional approaches for mathematical learning, and our findings highlight the potential effectiveness of Productive Failure as an instructional approach. Considerations of the broader applicability of computational methods in advancing educational research are also provided.

Digital cloning of online social networks for language-sensitive agent-based modeling of misinformation spread.

We develop a simulation framework for studying misinformation spread within online social networks that blends agent-based modeling and natural language processing techniques. While many other agent-based simulations exist in this space, questions over their fidelity and generalization to existing networks in part hinder their ability to drive policy-relevant decision making. To partially address these concerns, we create a 'digital clone' of a known misinformation sharing network by downloading social media histories for over ten thousand of its users. We parse these histories to both extract the structure of the network and model the nuanced ways in which information is shared and spread among its members. Unlike many other agent-based methods in this space, information sharing between users in our framework is sensitive to topic of discussion, user preferences, and online community dynamics. To evaluate the fidelity of our method, we seed our cloned network with a set of posts recorded in the base network and compare propagation dynamics between the two, observing reasonable agreement across the twin networks over a variety of metrics. Lastly, we explore how the cloned network may serve as a flexible, low-cost testbed for misinformation countermeasure evaluation and red teaming analysis. We hope the tools explored here augment existing efforts in the space and unlock new opportunities for misinformation countermeasure evaluation, a field that may become increasingly important to consider with the anticipated rise of misinformation campaigns fueled by generative artificial intelligence.

Modeling mate choice in a small-scale community: Applying couple simulation in the U.S. and Conambo, Ecuador

The near totality of human mate choice research occurs in large-scale, urban, industrial populations. It is unclear to what extent lessons learned from such populations reflect generalizable features of human mating psychology as opposed to localized responses to the demands of these historically unusual environments. Here, we use couple simulation, an agent-based modeling technique, to compare models of mate choice across both a U.S. sample (n = 1678) and a sample of k = 15 couples from Conambo, Ecuador—a relatively remote community of horticultural-foragers in the Ecuadorian Amazon. The Conambo sample provides a unique opportunity to evaluate models of mate choice in that (1) this sample represents approximately 50% of the households within this community and (2) all of the participants in this sample are acquainted with one another. Participants in Conambo completed a ranking task in which each participant ranked each opposite-sex adult in the community in terms of their quality as a spouse. We used these rankings to simulate the mating market in Conambo under alternative models of mate choice. We find that these models are able to reproduce Conambo marriages at a high degree of accuracy and perform comparably across both the Conambo sample and U.S. samples. Specifically, the resource allocation model performs best in reproducing mate choices in both the U.S. and Conambo samples. These results suggest that at least some aspects of human mating psychology generalize across both large-scale industrialized and small-scale populations.

An agent-based approach for energy-efficient sensor networks in logistics

As part of the fourth industrial revolution, logistics processes are augmented with connected information systems to improve their reliability and sustainability. Above all, customers can analyse process data obtained from the networked logistics operations to reduce costs and increase margins. The logistics of managing liquid goods is particularly challenging due to the strict transport temperature requirements involving monitoring via sensors attached to containers. However, these sensors transmit much redundant information that, at times, does not provide additional value to the customer, while consuming the limited energy stored in the sensor batteries. This paper aims to explore and study alternative approaches for location tracking and state monitoring in the context of liquid goods logistics. This problem is addressed by using a combination of data-driven sensing and agent-based modelling techniques. The simulation results show that the longest life span of batteries is achieved when most sensors are put into sleep mode yielding an increase of ×21.7 and ×3.7 for two typical routing scenarios. However, to allow for situations in which high quality sensor data is required to make decisions, agents need to be made aware of the life cycle phase of individual containers. Key contributions include (1) an agent-based approach for modelling the dynamics of liquid goods logistics to enable monitoring and detect inefficiencies (2) the development and analysis of three sensor usage strategies for reducing the energy consumption, and (3) an evaluation of the trade-offs between energy consumption and location tracking precision for timely decision making in resource constrained monitoring systems.

Simulation of urban non-motorized traffic: A agent-based modeling approach based on big data of bike sharing and social force model

Encouraging cycling, one of the urban non-motorized transport modes, has been well recognized as an environment-friendly way that alleviates urban traffic congestions and solves the first/last mile issue. However, concerns about the efficiency and safety of urban cycling have been widely made by urban planners due to the unclear right-of-way and discontinuous non-motorized corridor for cycling. This study uses the dynamic location data of the Meituan Bike (formerly Mobike) in the Hi-tech Park area of Shenzhen to analyze the spatial-temporal variations of bikeshare use, aiming at identifying the traffic corridors of cycling. Combined with agent-based modeling technique and social force model, this research proposes a new approach of simulating the urban non-motorized traffic, and hence provides valuable insights for building bicycle lanes for cycling corridors. The results show that (1) the usage of bike sharing during weekdays is 2.5 times that on weekdays, and the cycling corridors are usually the main and secondary roads that are in the vicinity of the metro stations; (2) adding bicycle lanes can reduce the traffic density of the non-motorized volume by 6% in an overall, and save the travel time of cyclists and pedestrians by 6.4% and 3.7%, respectively.

EVALUATION OF THE SPREAD OF RADICALISM, EXTREMISM, AND TERRORISM IN INDONESIA'S DEFENSE USING AGENT-BASED SIMULATIONS

The issue of national disintegration is one of the most crucial in the defense of Indonesia. One of the causes of national disintegration is the existence of Radicalism, Extremism, and Terrorism (RET). Radical entities can change using extreme means including violence through acts of terror in achieving their goals. Efforts continue to be made by the Government in anticipating the spread of this RET. Using agent-based simulation modeling techniques, some characteristics in the RET such as numbers are affected, driving factors, the number of agents in a region, and effective strategies. Therefore, this study is aimed at evaluating the spread of RET in Indonesia using agent-based simulations. This research is qualitative descriptive by trying to collect qualitative data as an initial input for the creation of flowchart designs and agent-based simulation models that will eventually be verified and validated. The results of this study illustrate the agent-based simulation in the spread of RET using Netlogo software by paying attention to several parameters such as incubation period, possible exposure to radicalism, extremist severity, availability of rehabilitation centers, and the rate of terrorism infection. From the results of the simulation, it can be said that the parameter greatly affects the percentage of radical agents, agents who are extremists until they become terrorists. This factor can be evidence for policymakers to reduce the spread of RET effectively by increasing religious understanding, strengthening the sense of nationalism and defending the country, and implementing community empowerment strategies. The evaluation of the spread of this RET can eventually strengthen Indonesia's non-military defense. Thus, good defense will have implications for the continuous development and achievement of Indonesia's goals.

Agent-based modelling and simulation for life-cycle airport flight planning and scheduling

ABSTRACT The airport flight planning and scheduling (AFPS) process involves many sub-systems in airport management, including flight arrival prediction, flight landing prioritisation, flight route scheduling, flight departure prioritisation and airport big-data planning. Existing literature has mainly focused on advancing the efficiency of specific sub-system(s) of AFPS. Therefore, there is a call to investigate the AFPS comprehensively and improve its performance systematically, instead of solely enhancing partial sub-system(s) in an airport. This paper proposes a life cycle analysis (LCA) framework to describe the “life cycle” that how an aircraft interacts with the AFPS system of an airport. The agent-based modelling and simulation technique is used to simulate the AFPS system under the LCA framework by treating each aircraft as a passive agent following the orders from the AFPS system. We demonstrate that the naive first-in-first-out principle can be modified to some simple prioritising rules to significantly reduce the overall delay (including importance-weighted delay) and processing time of flights, as well as to increase the number of departed aircraft. Hence, the produced agent-based model allows us to improve AFPS performance by optimising multiple sub-systems simultaneously.

Population Synthesis Accommodating Heterogeneity: A Bayesian Network and Generalized Raking Technique

Agent-based microsimulation modeling techniques are adopted for urban system modeling mainly because of their capacity to address the complex interactions among individuals, households, and other urban elements. The performance of urban simulation models is largely dependent on the quality of the input data, which is generated through a population synthesis procedure. This study proposes a Bayesian network and generalized raking techniques for population synthesis. The Bayesian network is used to generate the synthetic population pool from the microsample, and generalized raking is used to fit the synthetic population with the control total. Some of the key features of the proposed population synthesis are as follows: accommodating heterogeneity based on both household and individual attributes; tackling missing/incomplete observations in the microsample; and generating a true synthesis of the population from the microsamples. A data-driven structure learning technique is adopted to generate effective and optimal structures among the heterogenous households and individuals. This Bayesian network + generalized raking procedure is implemented to generate a 100% synthetic population at the smallest zonal level of dissemination area for the Central Okanagan region of British Columbia. The results suggest that capturing heterogeneity within the Bayesian network has tremendously benefitted the reconstruction process to efficiently and accurately generate a synthetic population from the available microsample. Finally, this population synthesis is developed as a component of the agent-based integrated urban model, currently under development at The University of British Columbia’s Okanagan campus.

The Effects of Information on the Formation of Migration Routes and the Dynamics of Migration.

Most models of migration simply assume that migrants somehow make their way from their point of origin to their chosen destination. We know, however, that-especially in the case of asylum migration-the migrant journey often is a hazardous, difficult process where migrants make decisions based on limited information and under severe material constraints. Here we investigate the dynamics of the migration journey itself using a spatially explicit, agent-based model. In particular we are interested in the effects of limited information and information exchange. We find that under limited information, migration routes generally become suboptimal, their stochasticity increases, and migrants arrive much less frequently at their preferred destination. Under specific circumstances, self-organised consensus routes emerge that are largely unpredictable. Limited information also strongly reduces the migrants' ability to react to changes in circumstances. We conclude, first, that information and information exchange is likely to have considerable effects on all aspects of migration and should thus be included in future modelling efforts and, second, that there are many questions in theoretical migration research that are likely to profit from the use of agent-based modelling techniques.

An agent-based and microsimulated model of the epidemiology impact of closure of schools during the COVID-19 pandemic in the region of Aysen in Chile

This paper analyzes the impact on the spread of the COVID-19 virus of making virtual the educational system in the Region of Aysen in Chile. The analyzes is made through a mix of agent-based modelling and microsimulation techniques, using multiple sources of administrative and census data. The behavior of 103,000 habitants of the region is modeled during each one of the seven days of the week, for 10 weeks. The diffusion of the virus is modeled as consequence of the social interaction of the individuals in five different environments: home, the transportation system, at work, the educational system and in places of entertainment. The results show that the closure of the educational system is important to flatten the curve of contagious. In a world where people don't do social distancing, it could lower R0 by one unit.

Emergent behaviours in multi-agent systems with Evolutionary Game Theory

The mechanisms of emergence and evolution of collective behaviours in dynamical Multi-Agent Systems (MAS) of multiple interacting agents, with diverse behavioral strategies in co-presence, have been undergoing mathematical study via Evolutionary Game Theory (EGT). Their systematic study also resorts to agent-based modelling and simulation (ABM) techniques, thus enabling the study of aforesaid mechanisms under a variety of conditions, parameters, and alternative virtual games. This paper summarises some main research directions and challenges tackled in our group, using methods from EGT and ABM. These range from the introduction of cognitive and emotional mechanisms into agents’ implementation in an evolving MAS, to the cost-efficient interference for promoting prosocial behaviours in complex networks, to the regulation and governance of AI safety development ecology, and to the equilibrium analysis of random evolutionary multi-player games. This brief aims to sensitize the reader to EGT based issues, results and prospects, which are accruing in importance for the modeling of minds with machines and the engineering of prosocial behaviours in dynamical MAS, with impact on our understanding of the emergence and stability of collective behaviours. In all cases, important open problems in MAS research as viewed or prioritised by the group are described.

Estimating social contacts in mass gatherings for disease outbreak prevention and management: case of Hajj pilgrimage

BackgroundMost mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals.MethodsWe sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram.ResultsThe simulation results showed that a plethora of risky contacts may occur during the rituals. Also, as the total number of pilgrims increases at each site, the number of risky contacts increases, and physical distancing measures may be challenging to maintain beyond a certain number of pilgrims in the site.ConclusionsThis study presented a simulation tool that can be relevant for the risk assessment of a variety of (respiratory) infectious diseases, in addition to COVID-19 in the Hajj season. This tool can be expanded to include other contributing elements of disease transmission to quantify the risk of the mass gathering events.

A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model

Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault Trees (DFT). The proposed model is called Generic Dynamic Agent-Based Model (GDABM) for risk analysis. GDABM is built on top of the well-known Agent-Based Modeling and Simulation (ABMS) technique. GDABM can model the dynamic system agents in both nominal (failure-free) and degraded (failure) modes. GDABM shows the propagation of failure between system elements and provides complete information about the system’s configurations. In this paper, a complete detailed case study is provided to show the GDABM capabilities to model and study the risk analysis for such dynamic systems. In the case study, the GDABM models the risk analysis for a chemical reactor/operator and performs a complete risk analysis for the entire system. The GDABM managed to simulate the dynamic behavior of the system’s components successfully using Repast Simphony 2.0. Detailed agent behavioral modes and failure modes are provided with various scenarios, including different time stamps. The proposed GDABM is compared to a reference model. The reference model is referred to as the ABM model. GDABM has given very promising results. A comparison study was performed on three performance measures. The performance measures used are (1) Accuracy, (2) response time, and (3) execution time. GDABM has outperformed the reference model by 15% in terms of accuracy and by 27% in terms of response time. GDABM incurs a slightly higher execution time (13%) when compared to the ABM reference model. It can be concluded that GDABM can deliver accepted performance in terms of accuracy and response time without incurring much processing overhead.

Modelling Maternal Depression: An Agent-Based Model to Examine the Complex Relationship between Relative Income and Depression.

Depression is a major public health concern among expectant mothers in Canada. Income inequality has been linked to depression, so interventions for reducing income inequality may reduce the prevalence of maternal depression. The current study aims to simulate the effects of government transfers and increases to minimum wage on depression in mothers. We used agent-based modelling techniques to identify the predicted effects of income inequality reducing programs on maternal depression. Model parameters were identified using the All Our Families cohort dataset and the existing literature. The mean age of our sample was 30 years. The sample was also predominantly white (78.6%) and had at least some post-secondary education (89.1%). When income was increased by just simulating an increase in minimum wage, the proportion of depressed mothers decreased by 2.9% (p < 0.005). Likewise, simulating the Canada Child Benefit resulted in a 5.0% decrease in the prevalence of depression (p < 0.001) and Ontario’s Universal Basic Income pilot project resulted in a simulated 5.6% decrease in the prevalence of depression (p < 0.001). We also assessed simulated changes to the mother’s social networks. Progressive income policies and increasing social networks are predicted to decrease the probability of depression.

Agent-Based Simulation and Modeling of COVID-19 Pandemic: A Bibliometric Analysis

The coronavirus disease has caused an ongoing pandemic worldwide since 2019. To slow the rapid spread of the virus, many countries have adopted lockdown measures. To scientifically determine the most appropriate measures and policies, agent-based simulation and modeling techniques have been employed. It can be challenging for researchers to select the appropriate tools and techniques as well as the input and output parameters. This study conducted a bibliometric analysis, especially a co-word network analysis, to classify relevant research articles into five clusters: conceptual, economic-based, organizational, policy-based, and statistical modeling. It then explained each approach and point of concern. Through this, researchers and modelers can identify the optimal approaches for their agent-based models.

Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana

The global urban population is expected to grow by 2.5 billion over the next three decades, and 90% of this growth will occur in African and Asian countries. Urban expansion in these regions is often characterised by ‘informal urbanization’ whereby households self-build without planning permission in contexts of ambiguous, insecure or disputed property rights. Despite the scale of informal urbanization, it has received little attention from scholars working in the domains of urban analytics and city science. Towards addressing this gap, we introduce TI-City, an urban growth model designed to predict the locations, legal status and socio-economic status of future residential developments in an African city. In a bottom-up approach, we use agent-based and cellular automata modelling techniques to predict the geospatial behaviour of key urban development actors, including households, real estate developers and government. We apply the model to the city-region of Accra, Ghana, drawing on local data collection, including a household survey, to parameterise the model. Using a multi-spatial-scale validation technique, we compare TI-City’s ability to simulate historically observed built-up patterns with SLEUTH, a highly popular urban growth model. Results show that TI-City outperforms SLEUTH at each scale, suggesting the model could offer a valuable decision support tool in similar city contexts.