Development Of Agent-based Model Research Articles

A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development

Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers’ preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity.

Linking transportation agent-based model (ABM) outputs with micro-urban social types (MUSTs) via typology transfer for improved community relevance

The human relationship with transportation is shaped by social, economic, demographic, and urban form variables, or socio-spatial factors. The spatial dynamics of these are key to generating and interpreting outputs of transportation models that are most relevant for a community and the diverse mobility needs of its members. Here we present a typology transfer framework, grounded in socio-spatial dynamics shaping people’s mobility, to take transportation-themed regional mobility model outcomes, in this case from two agent-based models (ABMs), and extrapolate them to other cities, with less time and resource intensity than new ABM development. The typology transfer process first identifies micro-urban social types (MUSTs) using socio-spatial factors, then defines city types based on spatial patterns of MUSTs to assess across which cities transfer results are likely to best hold. Lastly, a typology transfer multiplier matrix extrapolates a given variable, in our case the Mobility Energy Productivity (MEP) metric, to another city. The full process demonstration uses ABM results from Chicago (POLARIS model) and San Francisco (BEAM model), applying them to New York City. We discuss how MEP or other outputs can be appropriately estimated and used for integrated, human-centered mobility analysis. Key findings include that this MUST framework of user-defined dependent and independent variables allows tailoring ABM results and interpretations to specific community needs and data availability. Findings clarify that positive outcomes can be targeted towards user groups, based on sociospatial characteristics, using a typology approach, such as inclusive access to mobility choices, transportation affordability, and greater efficiency in resource use.

Proof of concept study using GPS-based tracking data to build agent-based models of visitors’ off-trail behavior in nature-based tourism settings

Spatial components of visitor behavior in nature-based tourism settings have the potential to influence both the biophysical environmental and recreational experience. Previous efforts to model visitor spatial behavior in these settings have largely been deterministic and probabilistic. Comparatively, agent-based models (ABM) are often considered a more accurate technique for representing complex human behaviors such as visitor use. A key challenge in the development of ABM in nature-based tourism settings has been the lack of detailed, individually-based, georeferenced data on visitors, especially in locations where visitors leave trails. This study is a proof of concept exercise using global position system (GPS) data, collected from visitors in Yosemite National Park, to develop an example ABM of visitor use. Results from this study demonstrate that it is feasible to use GPS tracking data to provide insight into the generation of agent rules, yet knowledge gaps remain for the further development of ABMs in nature-based tourism research.

Agent-based Modeling and Disaster Management

Social simulation is usually used to analyze issues in social science and to study behaviors of people inspecific events. Unlike scientific experiments in other hard sciences, which can be tested in a closed environmentor in a laboratory, social simulation applies computation methods to examine social phenomena.In urban planning, understanding stakeholders—which include residents, businesses, factories, and localgovernments—is one of the important factorsin a successful project. In many cases, these stakeholders areheterogeneous individuals who may have different behaviors. Thus, to effectively solve issues in urban planning,planners need to understand stakeholders. Agent-based modeling (ABM) is widely used to analyze behaviors ofstakeholders under implementation of urban policies, especially in the events of natural disasters, which areconsidered as complex systems. In these analyses, spatial structures of affected areason which stakeholdersare located and interact are a crucial ground of ABM. Together with the development of geographic informationsystems (GIS), the database systems and analyses of ABM become more accurate and reliable, especially onphenomena with the complexity of spatial structures.This review article explains the development and definition of ABM and introduces software and toolkitsfor building an agent-based model, as well as reviews articles and research that use ABM to analyze the issuesin theoretical testing and urban planning. Schelling’s Segregation and Hotelling’s Law models are discussed asexamples of theoretical testing while robbery and driving behavior models are selected as the implications ofABM in urban planning. This article also focuses on the use of ABM on natural disaster policies and managementusing case studies of Japan and the United Kingdom.

Simulating the role of knowledge brokers in policy making in state agencies: An agent-based model.

To model children's mental health policy making dynamics and simulate the impacts of knowledge broker interventions. Primary data from surveys (n=221) and interviews (n=64) conducted in 2019-2021 with mental health agency (MHA) officials in state agencies. A prototype agent-based model (ABM) was developed using the PARTE (Properties, Actions, Rules, Time, Environment) framework and informed through primary data collection. In each simulation, a policy is randomly generated (salience weights: cost, contextual alignment, and strength of evidence) and discussed among agents. Agents are MHA officials and heterogenous in their properties (policy making power and network influence) and policy preferences (based on salience weights). Knowledge broker interventions add agents to the MHA social network who primarily focus on the policy's research evidence. A sequential explanatory mixed method approach was used. Descriptive and regression analyses were used for the survey data and directed content analysis was used to code interview data. Triangulated results informed ABM development. In the ABM, policy makers with various degrees of decision influence interact in a scale-free network before and after knowledge broker interventions. Over time, each decides to support or oppose a policy proposal based on policy salience weights and their own properties and interactions. The main outcome is an agency-level decision based on policy maker support. Each intervention and baseline simulation runs 250 times across 50 timesteps. Surveys and interviews revealed that barriers to research use could be addressed by knowledge brokers. Simulations indicated that policy decision outcomes varied by policy making context within agencies. This is the first application of ABM to evidence-informed mental health policy making. Results suggest that the presence of knowledge brokers can: (1) influence consensus formation in MHAs, (2) accelerate policy decisions, and (3) increase the likelihood of evidence-informed policy adoption.

An Agent-Based Model-Driven Decision Support System for Assessment of Agricultural Vulnerability of Sugarcane Facing Climatic Change

In recent years, there have been significant changes in weather patterns, mainly caused by sharp increases in temperature, increases in carbon dioxide, and fluctuations in precipitation levels, negatively impacting agricultural production. Agricultural systems are characterized by being vulnerable to the variation of biophysical and socioeconomic factors involved in the development of agricultural activities. Agent-based models (ABMs) enable the study, analysis, and management of ecosystems through their ability to represent networks and their spatial nature. In this research, an ABM is developed to evaluate the behavior and determine the vulnerability in the sugarcane agricultural system; allowing the capitalization of knowledge through characteristics such as social ability and autonomy of the modeled agents through fuzzy logic and system dynamics. The methodology used includes information networks for a dynamic assessment of agricultural risk modeled by time series, system dynamics, uncertain parameters, and experience; which are developed in three stages: vulnerability indicators, crop vulnerability, and total system vulnerability. The development of ABM, a greater impact on the environmental contingency is noted due to the increase in greenhouse gas emissions and the exponential increase in extreme meteorological phenomena threatening the cultivation of sugarcane, making the agricultural sector more vulnerable and reducing the yield of the harvest.

Exploring Scientific Inquiry via Agent-Based Modelling

Abstract In this paper I examine the epistemic function of agent-based models (ABMs) of scientific inquiry, proposed in the recent philosophical literature. In view of Boero and Squazzoni’s (2005) classification of ABMs into case-based models, typifications and theoretical abstractions, I argue that proposed ABMs of scientific inquiry largely belong to the last category. While this means that their function is primarily exploratory, I suggest that they are epistemically valuable not only as a temporary stage in the development of ABMs of science, but by providing insights into theoretical aspects of scientific rationality. I illustrate my point with two examples of highly idealized ABMs of science, which perform two exploratory functions: Zollman’s (2010) ABM which provides a proof-of-possibility in the realm of theoretical discussions on scientific rationality, and an argumentation-based ABM (Borg et al. 2019, 2017b, 2018), which provides insights into potential mechanisms underlying the efficiency of scientific inquiry.

Agent-based model of land system: Theory, application and modelling framework

Land change science has become an interdisciplinary research direction for understanding human-natural coupling systems. As a process-oriented modelling approach, agent based model (ABM) plays an important role in revealing the driving forces of land change and understanding the process of land change. This paper starts from three aspects: The theory, application and modeling framework of ABM. First, we summarize the theoretical basis of ABM and introduce some related concepts. Then we expound the application and development of ABM in both urban land systems and agricultural land systems, and further introduce the case study of a model on Grain for Green Program in Hengduan Mountainous region, China. On the basis of combing the ABM modeling protocol, we propose the land system ABM modeling framework and process from the perspective of agents. In terms of urban land use, ABM research initially focused on the study of urban expansion based on landscape, then expanded to issues like urban residential separation, planning and zoning, ecological functions, etc. In terms of agricultural land use, ABM application presents more diverse and individualized features. Research topics include farmers’ behavior, farmers’ decision-making, planting systems, agricultural policy, etc. Compared to traditional models, ABM is more complex and difficult to generalize beyond specific context since it relies on local knowledge and data. However, due to its unique bottom-up model structure, ABM has an indispensable role in exploring the driving forces of land change and also the impact of human behavior on the environment.

Navigating Complexities: Agent-Based Modeling to Support Research, Governance, and Management in Small-Scale Fisheries

The sustainable governance and management of small-scale fisheries (SSF) is challenging, largely due to their dynamic and complex nature. Agent-based modeling (ABM) is a computational modeling approach that can account for the dynamism and complexity in SSF by modeling entities as individual agents with different characteristics and behavior, and simulate how their interactions can give rise to emergent phenomena, such as over-fishing and social inequalities. The structurally realistic design of agent-based models allow stakeholder, experts and scientists across disciplines and sectors to reconcile different knowledge bases, assumptions, and goals. ABMs can also be designed using any combination of theory, quantitative data, or qualitative data. In this publication we elaborate on the untapped potential of ABM to tackle governance and management challenges in SSF, discuss the limitations of ABM, and review its application in published SSF models. Our review shows that, although few models exist to date, ABM has been used for diverse purposes, including as a research tool for understanding cooperation and over-harvesting, and as a decision-support tool, or participatory tool, in case-specific fisheries. Even though the development of ABMs is often time- and resource intensive, it is the only dynamic modeling approach that can represent entities of different types, their heterogeneity, actions and interactions, thus doing justice to the complex and dynamic nature of SSF which, if ignored can lead to unintended policy outcomes and less sustainable SSF.

Methodological Issues of Spatial Agent-Based Models

Agent based modeling (ABM) is a standard tool that is useful across many disciplines. Despite widespread and mounting interest in ABM, even broader adoption has been hindered by a set of methodological challenges that run from issues around basic tools to the need for a more complete conceptual foundation for the approach. After several decades of progress, ABMs remain difficult to develop and use for many students, scholars, and policy makers. This difficulty holds especially true for models designed to represent spatial patterns and processes across a broad range of human, natural, and human-environment systems. In this paper, we describe the methodological challenges facing further development and use of spatial ABM (SABM) and suggest some potential solutions from multiple disciplines. We first define SABM to narrow our object of inquiry, and then explore how spatiality is a source of both advantages and challenges. We examine how time interacts with space in models and delve into issues of model development in general and modeling frameworks and tools specifically. We draw on lessons and insights from fields with a history of ABM contributions, including economics, ecology, geography, ecology, anthropology, and spatial science with the goal of identifying promising ways forward for this powerful means of modeling.

The impact of individual and environmental interventions on income inequalities in sports participation: explorations with an agent-based model

BackgroundIncome inequalities in sports participation are shaped by a system in which individuals and the environment interact. We developed an agent-based model (ABM) that could represent this system and used it to provide a proof-of-concept of its potential to explore the impact of individual and environmental interventions on reducing inequalities in sports participation.MethodsOur ABM simulates sports participation of individuals in the Dutch city of Eindhoven. In the model, sports participation is determined by an individual’s tendency to start sports (at a fitness center, sports club or self-organized), which is influenced by attributes of individuals (i.e. age, sex, income), sports facilities (i.e. price, accessibility) and the social environment (i.e. social cohesion, social influence). Sports facilities can adapt to changes in the demand by closures or startups, which in turn influence the tendency of individuals to participate in sport. We explored the impact of five interventions scenarios.ResultsExplorative results show that providing health education, increasing the availability of sports facilities, lowering prices of facilities and improving safety levels can increase sports participation and modestly reduce absolute income inequalities in sports participation. The largest gain can be attained through health education, if the effect and reach is sufficiently large. Environmental interventions alone have a modest impact. Marked effects are only achieved after five to 10 years.ConclusionsABMs have much potential to test the population-level effects of various interventions in the context of a system. Our study highlights the challenges of ABM development and reveals gaps in empirical data. With further refinements, our model could aid in understanding and finding optimal pathways to reduce income inequalities in sports participation.

Agent-based modeling of environment-migration linkages: a review

Environmental change can lead to human migration and vice versa. Agent-based models (ABMs) are valuable tools to study these linkages because they can represent individual migration decisions of human actors. Indeed, there is an increasing, yet small, number of ABMs that consider the natural environment in rural migration processes. Therefore, we reviewed 15 ABMs of environment-migration linkages in rural contexts to synthesize the current state of the art. The reviewed ABMs are mostly applied in tropical contexts, serve a wide range of purposes, and cover diverse scales and types of environmental factors, migration processes, and social-ecological feedbacks. We identified potential for future model development with respect to the (1) complexity of environmental influence factors, (2) representation of relevant migration flows, and (3) type of social-ecological couplings. We found that existing models tend to not include fully integrated feedbacks and provide recommendations for the further development of ABMs to contribute to an understanding of the environment-migration-nexus in the future.

Features of Agent-based Models

The design of agent-based models (ABMs) is often ad-hoc when it comes to defining their scope. In order for the inclusion of features such as network structure, location, or dynamic change to be justified, their role in a model should be systematically analysed. We propose a mechanism to compare and assess the impact of such features. In particular we are using techniques from software engineering and semantics to support the development and assessment of ABMs, such as graph transformations as semantic representations for agent-based models, feature diagrams to identify ingredients under consideration, and extension relations between graph transformation systems to represent model fragments expressing features.

USING NATIONAL SURVEY RESPONDENTS AS CONSUMERS IN AN AGENT-BASED MODEL OF PLUG-IN HYBRID VEHICLE ADOPTION

Plug-in hybrid electric vehicles (PHEVs) offer the potential to significantly reduce greenhouse gas emissions, if vehicle consumers are willing to adopt this new technology. Consequently, there is much interest in exploring PHEV market penetration models. In prior work, we developed an agent-based model (ABM) of potential PHEV consumer adoption that incorporated several spatial, social, and media influences to identify nonlinear interactions among potential leverage points that may impact PHEV market penetration. In developing that model, the need for additional data to properly inform both the decision-making rules and agent initialization became apparent. To address these issues, we recently conducted and analyzed an extensive consumer survey; in this paper, we modify the ABM to reflect the survey findings. A unique aspect is a one-to-one correspondence between agents in the model and survey respondents, and thus yielding distributions and cross correlations in agent attributes that accurately reflect the survey population. We also implement a used-PHEV market, and allow agents to purchase new or used compact PHEVs or vehicles of their current type. Based on our prior survey response analysis, our modified model includes a PHEV-technology threshold component, a multinomial logistic prediction of willingness to consider a compact PHEV based on dynamically changing attitudes, and agent-specific delay discounting functions that predict the amount agents are willing to pay up front for greater fuel savings. We thus independently account for agents’ discomfort with the new PHEV technology, their desire to drive a more environmentally friendly vehicle, and their willingness to pay a higher sticker price for a PHEV. Results of ten survey-based ABM scenarios are reported with implications for policy-makers and manufacturers. We believe close integration of the design of consumer surveys and the development of ABMs is a key step in developing useful decision-support models; this paper serves as an example of one way to achieve that.

Parallel agent-based simulation of individual-level spatial interactions within a multicore computing environment

The computational approach of agent-based models (ABMs) supports the representation of interactions among spatially situated individuals as a decentralized process giving rise to space–time complexity in geographic systems. To cope with the computational complexity of these models, this article proposes a parallel approach that leverages the power of multicore systems, as these architectures have quickly become ubiquitous in high-performance and desktop computing. An ABM of individual-level spatial interaction that simulates information exchange, spatial diffusion of opinion development, and consensus building among decision makers is proposed to demonstrate the advantages of the parallel approach against its sequential counterpart. This study focuses on two key spatial properties of the interaction system of interest, the extent and range of interaction, and examines their influence on the computing performance of the proposed parallel model and the performance scalability of the model as more computing resources are added. Significant influence from these two properties is found and can be attributed to three possible sources of effects, namely the model level, the parallelization level, and the platform level. It is suggested that these effects should be taken into consideration when leveraging multicore computing resources for the development of parallel ABMs.

A Framework to Structure Agent-Based Modeling Data for Social-Ecological Systems

Agent-based models (ABMs), perhaps used along with other methods, are increasingly being applied to address problems involving social-ecological systems (SES). However, systematic and standardized techniques for organizing data requirements and collecting information for such models are generally not applied. Defining the types of data that need to be collected is a critical step in instantiating relevant models and detailing behavioral patterns. By organizing data needs into pertinent categories, researchers can focus efforts to make data collection more efficient, provide a clear record of gathered data, conserve project resources, and create models more easily. In this paper, we present a formal method, which we call Delineate, Structure, and Gather (DSG), that has been applied to guide the determination of data requirements, structure data needs, and enable data collection for the development of ABMs addressing SES. The presented framework continues and supplements existing efforts that provide standardization and protocol techniques for constructing ABMs. An active project is presented in order to demonstrate how DSG can be implemented to address SES issues.

Multi-scale analysis of a household level agent-based model of landcover change

Scale issues have significant implications for the analysis of social and biophysical processes in complex systems. These same scale implications are likewise considerations for the design and application of models of landcover change. Scale issues have wide-ranging effects from the representativeness of data used to validate models to aggregation errors introduced in the model structure. This paper presents an analysis of how scale issues affect an agent-based model (ABM) of landcover change developed for a research area in the Midwest, USA. The research presented here explores how scale factors affect the design and application of agent-based landcover change models. The ABM is composed of a series of heterogeneous agents who make landuse decisions on a portfolio of cells in a raster-based programming environment. The model is calibrated using measures of fit derived from both spatial composition and spatial pattern metrics from multi-temporal landcover data interpreted from historical aerial photography. A model calibration process is used to find a best-fit set of parameter weights assigned to agents' preferences for different landuses (agriculture, pasture, timber production, and non-harvested forest). Previous research using this model has shown how a heterogeneous set of agents with differing preferences for a portfolio of landuses produces the best fit to landcover changes observed in the study area. The scale dependence of the model is explored by varying the resolution of the input data used to calibrate the model (observed landcover), ancillary datasets that affect land suitability (topography), and the resolution of the model landscape on which agents make decisions. To explore the impact of these scale relationships the model is run with input datasets constructed at the following spatial resolutions: 60, 90, 120, 150, 240, 300 and 480 m. The results show that the distribution of landuse-preference weights differs as a function of scale. In addition, with the gradient descent model fitting method used in this analysis the model was not able to converge to an acceptable fit at the 300 and 480 m spatial resolutions. This is a product of the ratio of the input cell resolution to the average parcel size in the landscape. This paper uses these findings to identify scale considerations in the design, development, validation and application of ABMs of landcover change.