Use Of Agent-based Models Research Articles

The diffusion of goods with multiple characteristics and price premiums: an agent-based model

According to innovation diffusion theories, the adoption of a new product is the result of a dynamic process whereby individuals become likelier to adopt as others do. Agent-based modelling has emerged as a useful technique to model and study processes of innovation diffusion within artificial societies, as it allows to easily programme and simulate the interaction of multiple agents among them and with their environment. Despite a large body of literature dealing with innovation of diffusions, including the use of agent-based modelling, there has been little to no consideration of two elements that are important features of consumption: the presence of multiple characteristics of goods, and that of price-premiums on the presence of added characteristics. We propose an agent-based model of the diffusion of such goods, and study its emerging properties when compared to standard ones. Our goal is to try and understand how social interaction affects the consumption of goods that are complex rather than uni-dimensional, and whose prices depend on the number of dimensions (characteristics) that are present. Testing the model for different parameters shows that as goods become more complex, social interaction becomes an increasingly important explanatory variable for purchases. This opens up interesting avenues of discussion for those seeking to bring together innovation diffusion theories and goods’ complexity, and can be linked with a number of issues in the social and sustainability sciences.

A method to create a synthetic population with social networks for geographically-explicit agent-based models

Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in Urban Science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of disease. These use cases not only demonstrate how our geographically-explicit synthetic population can be easily utilized for initializing agent populations which can explore a variety of urban problems, but also show how social networks can be integrated into such populations and large-scale simulations.

Comparative Agent-Based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-Case

We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique—the network regression, Multiple Regression Quadratic Assignment Procedure—in conducting a principled and interpretable comparison between the two models with strong results. This combined approach is novel and allows complex system modelers who utilize agent-based models to reduce their reliance on idealized network structures (small world, scale-free, erdos-renyi) when applying underlying network interactions to agent-based models that can often skew results and mislead from a full picture of system-level properties. This work serves as a proof-of-concept in the integration of classical social network analysis methods and contemporary agent-based modeling to compare software designs and to enhance the policy-generation process of online social networks.

An ideal solution for the deployment of photovoltaic generators using agent-based Nash Differential Evolution (NashDE) algorithm

Abstract This paper combines evolutionary game theory and Agent-based modelling for finding out the optimal location and size of multiple solar PVs in an unbalanced distribution system. The use of Agent-based modelling in solar PV placement is new and hence provides a valuable contribution. NashDE algorithm is implemented and simulated using an agent-based modelling framework MESA. In MA-NashDE, the optimization variables, i.e., the power system buses, are clustered into several zones using both the Loss Sensitivity Index and the Voltage Sensitivity Index. Each zone is under the control of a Nash/zonal player, and the strategy adopted by the zonal player will evolve through Multi-Agent Differential Evolution (MADE) algorithm. This is a novel study focusing on the PV placement problem by Agent-based Evolutionary Game Theory. The proposed methodology was tested using IEEE 34 and IEEE 123 bus radial distribution network using the OpenDSS-Python COM interface.

Real-time agent-based crowd simulation with the Reversible Jump Unscented Kalman Filter

Commonly-used data assimilation methods are being adapted for use with agent-based models with the aim of allowing optimisation in response to new data in real-time. However, existing methods face difficulties working with categorical parameters, which are common in agent-based models. This paper presents a new method, the RJUKF, that combines the Unscented Kalman Filter (UKF) data assimilation algorithm with elements of the Reversible Jump (RJ) Markov chain Monte Carlo method. The proposed method is able to conduct data assimilation on both continuous and categorical parameters simultaneously. Compared to similar techniques for mixed state estimation, the RJUKF has the advantage of being efficient enough for online (i.e. real-time) application. The new method is demonstrated on the simulation of a crowd of people traversing a train station and is able to estimate both their current position (a continuous, Gaussian variable) and their chosen destination (a categorical parameter). This method makes a valuable contribution towards the use of agent-based models as tools for the management of crowds in busy places such as public transport hubs, shopping centres, or high streets.

Construction work cost and duration analysis with the use of agent-based modelling and simulation

Abstract Assuming a systemic approach, a construction project can be treated as a complex system composed of many different interlinked elements such as construction works, human agents, equipment, materials and the knowledge needed to perform the said work. The system’s structure can be divided into many mutually connected precision levels. This multilevel decomposition of the system facilitates a bottom-up approach in assessing the performance of a planned project, while starting the analysis at its lowest aggregation levels. The basic level distinguishes three typical units and their attributes: persons, knowledge and construction resources. Unit attributes and their dynamic interactions under changing environmental conditions affect the properties and performance of a given construction work and, as a consequence, the properties and performance of the project. The objective of this article is to analyse the attributes and micro-behaviours of units through bottom-up project assessment, allowing the estimation of its parameters such as completion time and cost. We utilised multiagent modelling that allows for performing micro-simulations in complex systems with adaptive components. The analysis was backed by a case study of road renovation work performed under specific conditions on the grounds of a listed heritage site.

Sustainable Humanitarian Operations: Multi-Method Simulation for Large-Scale Evacuation

Integrating sustainability in humanitarian operations has been seen as a promising approach toward effective and long-term solutions. During disaster emergency management, the evacuation determines the risk of loss in a disaster. To better understand the effectiveness of the evacuation plan while considering the sustainability standpoint, this paper develops a multi-method simulation (MMS) approach to evaluate evacuation time, load balance of the shelters, and CO2 emission. The MMS integrating Agent-Based Modeling (ABM) and Discrete-Event Simulation (DES) incorporates evacuation decision-making and evacuation processes. Comparative analysis shows that the MMS outperforms the use of ABM solely. The simulation results indicate over-utilization and imbalanced load among the shelters, implying a need to expand shelters’ capacity and to revisit the evacuation plan concerning the location of the assembly points and the shelters and the resource allocation. Evacuation behavior heading to the nearest assembly point instead of the designated assembly point based on the evacuation plan worsens the imbalanced load among the shelters and results in higher CO2 emissions by 8%. The results demonstrate the necessity to include evacuation decision-making (social dimension) on top of the technical dimension and to adopt sustainable performance indicators in planning the evacuation sustainably. Avenues for future research are also discussed.

Agent-Based Modeling as a Legal Theory Tool

Agent-based modeling (ABM) is a versatile social scientific research tool that adapts insights from sociology and physics to study complex social systems. Currently, ABM is nearly absent from legal literature that evaluates and proposes laws and regulations to achieve various social goals. Rather, quantitative legal scholarship is currently most characterized by the Law and Economics (L&E) approach, which relies on a more limited modeling framework. The time is ripe for more use of ABM in this scholarship. Recent developments in legal theory have highlighted the complexity of society and law’s structural and systemic effects on it. ABM’s wide adoption as a method in the social sciences, including recently in economics, demonstrates its ability to address precisely these regulatory design issues.

Governance interactions of spatial data infrastructures: an agent-based modelling approach

ABSTRACT In order to facilitate and coordinate spatial data sharing and exchange, many organisations have developed spatial data infrastructures (SDIs). SDI governance plays a pivotal role in the development and evolution of an SDI, but as SDIs are complex adaptive systems, governing is a challenge. This research therefore proposes a complexity perspective to SDI governance by exploring the use of agent-based modelling to simulate and examine SDI governance interactions. In this agent-based simulation, we examine interactions between SDI stakeholders, data availability and the effects of different governance styles (hierarchical, network and laissez-faire governance) and budget policies. The simulation shows that it is possible to mimic SDI governance dynamics through agent-based modelling. By running different scenarios, it appears that a network approach is more successful compared to a hierarchical or laissez-faire approach. Expert validation shows that overall the results of the simulation are credible and insightful, although improvements can be made to make the model more realistic. With agent-based modelling, SDI governance becomes more tangible and visible, which facilitates discussion and understanding. Agent-based modelling therefore appears to be a helpful new approach in a better understanding of the complexities and dynamics of SDI governance.

Agent-Based Modeling of Systemic Inflammation: A Pathway Toward Controlling Sepsis.

Despite nearly 50years of research there currently remains no mediator-directed therapy approved for the treatment of sepsis. The failure to effectively translate the copious mechanistic knowledge regarding systemic inflammation to effective therapies is a dramatic example of the translational dilemma. Dynamic computational modeling has been proposed as a vital means of integrating community-wide knowledge into an investigatory framework that allows the application of engineering-like principles to the problem of sepsis. Agent-based modeling is a computational modeling method that has been used to address some of the fundamental issues facing the sepsis research community. This chapter will introduce the rationale to augment traditional research practices with agent-based modeling, describe the basic steps in the construction and use of agent-based models, and provide examples of how the use of agent-based modeling can provide an investigatory pathway to solving the challenge of sepsis.

Impact of Short-Term Exogenous Shocks on Taxpayer Behavior and Tax Evasion

The evolution of views on tax evasion following the introduction of limited rationality and social and psychological factors into the models of taxpayer behavior has increased the plausibility of the initial assumptions of the models, but it has made it difficult to use classical approaches based on the search for equilibrium states. The variety of behavioral responses of taxpayers due to the many factors that influence their choice has led to the fact that tax evasion has come to be considered as the result of nonlinear and dynamic interactions between the state and taxpayers. In such models, small short-term external influences can act as shocks, which leads to the emergence of a wide range of different long-term trends, the analysis of which within the framework of traditional approaches is difficult. In this regard, the purpose of this review study is to study the evolution of views on the behavior of taxpayers that has led to the emergence of new approaches to modeling tax evasion where the key role is assigned to the analysis of the impact of external shocks of various scales and nature. The research hypothesis is that modern approaches to the study of tax evasion problems make it necessary to consider the interaction of the state and taxpayers within the framework of the theory of non-equilibrium and nonlinear systems in which minor external influences can play the role of shocks, and the most promising direction of their study is the use of agent-based modeling tools. The results of the study confirm that the use of agent-based models is a promising approach for integrating existing approaches in the study of tax evasion processes. The proposed concept of building an agent-based model for analyzing the processes of tax evasion allows us to answer the question of how short-term exogenous shocks will affect the preferences of taxpayers, taking into account their individual characteristics and accepted behavioral patterns in society.

Toys for Conviviality. Situating Commoning, Computation and Modelling

AbstractThis article explores the use of agent-based modelling as a critical and playful form of engagement with cooperative housing organizations. Because of its inherent complexities vis-à-vis decision-making, commoning is a well-suited field of study to explore the potential of humanities-driven experimental design (media) research to provoke critical reflection, problem-finding and productive complication. By introducing two different agent-based models, the interdisciplinary research team discusses their experience with setting up parameters for modelling, their implications, and the possibilities and limits of employing modelling techniques as a basis for decision-making. While it shows that modelling can be helpful in detecting long-term results of decisions or testing out effects of unlikely yet challenging events, modelling might act as a discursive practice uncovering hidden assumptions inherent in the model setup and generating an increase of scientific uncertainty. The project “ThinkingToys for Commoning” thus argues for a critical modelling practice and culture, in which models act as toys for probing alternative modes of living together and exploring the constructedness of methods. In countering late forms of capitalism, the resulting situated and critical practice provides avenues for enabling more self-determined forms of governance.

ЗАСТОСУВАННЯ В МУЛЬТИАГЕНТНІЙ СИСТЕМІ УПРАВЛІННЯ МОДЕЛІ ФУНКЦІОНУВАННЯ ФРАГМЕНТУ ТЕЛЕКОМУНІКАЦІЙНОЇ МЕРЕЖІ CИСТЕМИ ЗВ’ЯЗКУ ЗБРОЙНИХ СИЛ УКРАЇНИ

Today, there are many standards that describe the architecture and mechanisms for monitoring and controlling elements of the Carrier Ethernet network, but there is no single methodology and requirements for the control system of such a communication network are not formed. This article proposes a solution to this problem by modeling the process of functioning of a telecommunication network based on this technology to manage its configuration. To create this model, the AnyLogic simulator was selected. The aim of the work is to identify the relationship between the reliability indicators of a telecommunication network based on Carrier Ethernet technology and the process of functioning of the telecommunication network management and recovery subsystems. As well as obtaining the dependence of the availability factor on the duration of the time between failures, the time of failure and on the quantitative characteristics of the configuration of the simulated network fragment. The multi-agent system, which is part of the control system, is in interaction with it. The use of a distributed telecommunications network control facility based on Carrier Ethernet technology at MASU will provide a demonstration of the dynamics of changes in the state of a fragment of a telecommunications network and obtain an estimate of network reliability. The solution to the problem of building the MASU model is based on the use of agent-based modeling, which belongs to the class of agent-based models. The paper uses the provisions of probability theory, control theory and system theory. The novelty of the presented model lies in the selection of a new Carrier Ethernet control object, the selection of an original set of control and management mechanisms for their inclusion in the model, and the use of the mathematical apparatus of agent modeling. Using the presented model to study the functioning of a telecommunication network allows you to track the behavior of each node and each route with its own failure and recovery rates for a network structure of any complexity, and allows you to solve problems associated with determining the length of time to lose connectivity in the route and the duration of the time between failures all routes simultaneously, allows you to form an assessment of network reliability and fault tolerance.

Application of agent-based modeling and life cycle sustainability assessment to evaluate biorefinery placement

Biofuels, such as those derived from woody biomass, are being developed as one part of a renewable energy portfolio; however, it remains unclear if they can be sustainably developed. This manuscript demonstrates that the use of agent-based modeling (ABM) and life cycle sustainability assessment (LCSA) as part of an integrated methodology allows for robust assessment of the possible impacts. A case study of a proposed biorefinery in Ontonagon, Michigan, USA is conducted. In the case study, ABM is used to project the harvest decision making of forest owners and loggers to determine if they were willing to harvest and supply woody biomass to the biorefinery under several different price points. The results from the ABM were used to inform inventory data for projections of environmental impacts, along with projected impacts for economic and social concerns. The results of the case study show that while Ontonagon is a promising site for a biorefinery, the quantity of woody biomass delivered is a concern along with potential impacts to wetlands in the region. Ultimately, this case study suggestions that the integration of ABM and LCSA may be effective the assessment of other biorefineries.

Research of migration behavior of the population and assessment of the level of attractiveness of territories based on the methods of agent-based modeling

Migration processes play a significant role in the socio-economic and demographic development of the Russian Federation. According to Rosstat, in more than 50 % of the constituent entities of the Russian Federation in 2019, there is a migration decline in the population. It can also be noted that the most attractive for the population are large cities with a high level of socio-economic development. In this regard, the aim of the study is to assess the factors of mutual influence of the migration behavior of the population and the socio-economic development of the territory on the basis of agent-based modeling methods. The Republic of Bashkortostan was chosen as the object of the study, as the region with the highest level of migration loss in the Volga Federal District. According to the strategy of the Republic of Bashkortostan for the period up to 2030, the Government of the Republic of Belarus faces the difficult task of making effective management decisions that will lead to an increase in the socio-economic development of the territories and, consequently, to an increase in the level of migration growth. A distinctive feature of this study is the use of agent-based modeling (to take into account the individual characteristics of agents), fuzzy logic (to form the rules of behavior for agents) and GIS maps (to take into account the geographic location). A modular scheme for the functioning of the economic-demographic model has been developed, which describes the events, the agents that participate in them, and the methods by which they were implemented. An algorithm for the implementation of the "Migration" block has been developed, which describes the logic of the migration behavior of the "Man" agent and his reaction to changes in socio-economic indicators taken into account within the framework of the "Municipality" agent. According to the results of the study, scenarios for the development of municipal districts of the Republic of Bashkortostan were substantiated on the basis of data from the Ministry of Economic Development and Investment Policy of the Republic of Bashkortostan, as well as the program "Comprehensive development of single-industry towns in the Republic of Bashkortostan".

Agent-Based Modeling Simulation of Nurse Medication Administration Errors.

It has been 20 years since the National Academy of Medicine released its report, "To Err Is Human," which shocked the healthcare community on the pervasiveness of medical error. While errors in medication administration are a significant contributor to medical error, research seeking to understand the complex systems nature and occurrence of medication administration error is limited. Computer modeling is increasingly being used in the healthcare industry to assess the impact of changes made to healthcare processes. The objective of the study is to evaluate the use of agent-based modeling, a type of computer modeling that allows the simulation of virtual individuals and their behavior, to simulate nurse performance in the medication administration process. The model explores the effect of Just-in-Time information, as an intervention, on the occurrence of medication error. The model demonstrated significant utility in understanding the interplay of the system elements of the nurse medication administration process. Therefore this approach, using systems-level computer simulation such as agent-based models, can help administrators understand the effects of changes to the medication administration process as they work to reduce errors and increase performance.

Agent-based modeling for migration and modern slavery research: a systematic review

This systematic review aims to synthesize how agent-based modeling (ABM) has been used in migration and modern slavery research and provide the basis to model development for social science researchers exploring the use of ABM. We searched five bibliographic databases using two terminology categories: (1) migration or modern slavery terminology; (2) complex system methods terminology. Two reviewers conducted independent article screening. Peer-reviewed articles presenting original migration or modern slavery ABMs were included. Data extraction included model development steps and model characteristics. The dataset was synthesized and compared across studies. We identified 28 articles for inclusion. Many of the ABMs tested theories and about half were based on empirical data. Model development varied considerably and reported methods were extremely opaque. Only five studies used a structured development framework. The most common model involved agents deciding whether and where to migrate and attempting migration. Climate change was a common exogenous scenario modeled. Most of the ABMs did not undergo any sensitivity analysis or validation.ABM has a greater capacity to account for heterogeneous and dynamic decision-making than more frequently applied methods in research on migration and modern slavery. However, there is still a paucity of studies adopting ABM methods. These reviewed ABMs highlight gaps in the reporting and implementing of model development. ABM is a promising technique to address many urgent and complex questions in research on migration and modern slavery to better support decision-makers, but addressing current methodological gaps is a critical first step.

Agent-Based Modeling, Scientific Reproducibility, and Taphonomy: A Successful Model Implementation Case Study

There is a growing use of agent-based model (ABM) simulations to reconstruct past human-environment interactions. ABMs are useful in that they offer scientists the opportunity to model processes and study phenomena and systems that may not be otherwise reproducible or testable. Replication or re-implementation studies of ABMs are, however, infrequently undertaken, and there are few examples within archaeology or other social sciences. This paper documents the process of a successful ABM replication study, as well as two additional modifications to the original model. Results corroborate the findings of the original geoarchaeological model and indicate that episodic geomorphic events significantly affect archaeological deposit formation and the inferences drawn from associated radiocarbon records. One revision of the model further demonstrates that episodic fluvial events can create highly varied radiocarbon distributions. The second modification illustrates that excavation data helps to fill in hiatuses in radiocarbon chronologies on depositional landforms, although there is no effect across landscapes subject to erosion. This successful replication exercise also illustrates the value of Open Access data and analyses in reproducing, testing, and expanding upon archaeological research and theory building.

USING AGENT-BASED MODELLING FOR EER EXPERIMENTAL DESIGN: PRELIMINARY VALIDATION BASED ON STUDENT CHEATING BEHAVIOURS

This paper is an extension of our CEEA-ACEG 2019 paper on the use of agent-based modelling for preliminary engineering education research design. The motivation for this work is to help researchers gain an understanding of the system being studied (e.g., classroom, program, etc.) before embarking on a full study. For this paper, we report on an extension of our agent-based model that allows cheating behaviour in the context of homework completion to be studied. Comparisons of our simulation results with observations from the literature on academic integrity support the use of this modelling approach.

NEAT approach for testing and validation of geospatial network agent-based model processes: case study of influenza spread

ABSTRACT Agent-based models (ABM) are used to represent a variety of complex systems by simulating the local interactions between system components from which observable spatial patterns at the system-level emerge. Thus, the degree to which these interactions are represented correctly must be evaluated. Networks can be used to discretely represent and quantify interactions between system components and the emergent system structure. Therefore, the main objective of this study is to develop and implement a novel validation approach called the NEtworks for ABM Testing (NEAT) that integrates geographic information science, ABM approaches, and spatial network representations to simulate complex systems as measurable and dynamic spatial networks. The simulated spatial network structures are measured using graph theory and compared with empirical regularities of observed real networks. The approach is implemented to validate a theoretical ABM representing the spread of influenza in the City of Vancouver, Canada. Results demonstrate that the NEAT approach can validate whether the internal model processes are represented realistically, thus better enabling the use of ABMs in decision-making processes.