Use Of Agent-based Models Research Articles

An overview of agent-based models in plant biology and ecology.

Agent-based modelling (ABM) has become an established methodology in many areas of biology, ranging from the cellular to the ecological population and community levels. In plant science, two different scales have predominated in their use of ABM. One is the scale of populations and communities, through the modelling of collections of agents representing individual plants, interacting with each other and with the environment. The other is the scale of the individual plant, through the modelling, by functional-structural plant models (FSPMs), of agents representing plant building blocks, or metamers, to describe the development of plant architecture and functions within individual plants. The purpose of this review is to show key results and parallels in ABM for growth, mortality, carbon allocation, competition and reproduction across the scales from the plant organ to populations and communities on a range of spatial scales to the whole landscape. Several areas of application of ABMs are reviewed, showing that some issues are addressed by both population-level ABMs and FSPMs. Continued increase in the relevance of ABM to environmental science and management will be helped by greater integration of ABMs across these two scales.

Fuzzy Monte Carlo Agent-Based Simulation of Construction Crew Performance

AbstractThe use of agent-based modeling (ABM) in the analysis of construction processes and practices has increased significantly over the last decade. However, the developed models are not able to...

Opportunities for agent‐based modelling in human dimensions of fisheries

AbstractModels of human dimensions of fisheries are important to understanding and predicting how fishing industries respond to changes in marine ecosystems and management institutions. Advances in computation have made it possible to construct agent‐based models (ABMs)—which explicitly describe the behaviour of individual people, firms or vessels in order to understand and predict their aggregate behaviours. ABMs are widely used for both academic and applied purposes in many settings including finance, urban planning and the military, but are not yet mainstream in fisheries science and management, despite a growing literature. ABMs are well suited to understanding emergent consequences of fisher interactions, heterogeneity and bounded rationality, especially in complex ecological, social and institutional contexts. For these reasons, we argue that ABMs of human behaviour can contribute significantly to human dimensions of fisheries in three areas: (a) understanding interactions between multiple management institutions; (b) incorporating cognitive and behavioural sciences into fisheries science and practice; and (c) understanding and projecting the social consequences of management institutions. We provide simple examples illustrating the potential for ABMs in each of these areas, using conceptual (“toy”) versions of the POSEIDON model. We argue that salient strategic advances in these areas could pave the way for increased tactical use of ABMs in fishery management settings. We review common ABM development and application challenges, with the aim of providing guidance to beginning ABM developers and users studying human dimensions of fisheries.

Building occupant transient agent-based model – Movement module

Simulation of occupant behaviour (OB) in buildings is a challenging task. Available software uses a broad spectrum of tools that try to reproduce the patterns of human activity. From building energy perspective, the main emphasis in research has been focused on discovering behaviour directly related to energy. In recent years, more attention has been given to simulating occupant actions that are indirectly influencing building energy use. In most of the cases, this is achieved with the use of agent-based modelling which allows describing occupant actions on a room level. According to the existing methodologies review, it is a proper step, but to include occupant behaviour in energy simulations, spatial and temporal resolution of the occupant behaviour model must be improved. Addressing this issue requires the development of a comprehensive model supported by numerous modules that would cover various significant occupant actions. This paper focuses on the development of the high-resolution, data-driven movement engine of occupants. It is one of the fundamental modules necessary to simulate occupant behaviour with high granularity. Once the model is developed within its essential functionalities, it will deliver a bottom-up model capable of testing various energy use strategies. It will allow for testing different heat, ventilation and air conditioning solutions and the responses provided by simulated occupants. The data used to develop this module was obtained thru in-situ measurements, with the use of depth registration. Information obtained from experiments is similar to previous research, but it also extends the investigation scope with an additional transition-based variable.

Применение агент-ориентированного подхода для анализа миграционных потоков с учетом эпидемиологической ситуации, вызванной пандемией COVID-19

Subject/topic. The paper is devoted to the analysis of trends related to migration rates in Russia during the spread of the new COVID-19 infection. Objectives. The purpose of this work is to study the experience of using the agent-based approach in modeling migration processes taking into account the epidemiological situation. Methodology. The research uses general scientific methods: economic analysis, ex-pert assessments, synthesis, deduction, induction, analogy, modeling, classification, historical and logical analysis. Results. Migration component is one of the main aspects in the demographic processes for each state. Various factors influence its estimated indicators but there are certain patterns and trends, such as seasonality, political situation in the world, living conditions in a particular country. The importance of this socio-demographic process remains unchanged. In Russia a new concept of state migration policy has been created for a period up to 2025. However, its implementation is complicated by the current epidemiological situation in the world. The spread of COVID-19 forced the country's borders to close which had a serious impact on migration flows. Due to the epidemiological situation migrants are forced to look for new jobs and change the usual routes that they used to move to other countries in order to earn money. In the Russian Federation the number of unemployed has increased and the process of ousting migrants from the labor market has intensified. The spread of the virus around the world has facilitated political decisions related to the return of Russians to their homeland. Thus, the social and political aspects of the consequences of the pandemic have seriously influenced the socio-demographic processes in the world. Conclusions. The specifics of assessing migration flows implies the use of software and analytical systems to study the general trend of migration including during special periods (economic crises, large-scale outbreaks of epidemics, etc.). The active use of agent-based models which recreates the system under study, as close to reality as possible, can help in solving problems related to the search, analysis, and forecasting of ways to solve acute problems as-sociated with changes in internal and external migration flows. Scope of the results. The results of the study can be applied for further research in the field of migration processes using agent-based modeling as well as in the process of improving migration policy in Russia with the development of global threats caused by the epidemiological situation in the world.

Incorporating an agent-based decision tool to better understand occupant pathways to GHG reductions in NYC buildings

A number of cities globally have developed ambitious goals to reduce greenhouse gas emissions (GHGs), and New York City has publicly committed to reducing emissions 80% by 2050 (80 × 50). While physical infrastructure is important, cities can gain important insights through information about human behavior, as people are the end users of buildings, transportation, and other physical assets. In this paper, we present a simplistic, pilot agent-based model (ABM) for New York City with projections about the city's potential for reaching the 80 × 50 goal in the building sector. Importantly, the ABM models occupant choices about technology adoption to predict the prevalence of green buildings in coming years. We find that even though traditional building types are slow to transition, CO2 production still decreases substantially over the forecast interval. Traditional buildings begin to slow their dominance in the model pathways by approximately 10 years into the forecast. Although the ABM presented here relies on simplistic assumptions about human agents and brings a high level of uncertainty, it presents a useful pilot tool to begin to understand system-level impacts from micro-level actions of households and individuals, and provides vast potential for future use of ABMs for this task.

USING AGENT-BASED MODELLING FOR PRELIMINARY EER EXPERIMENTAL DESIGN

In this paper, we explore the use of agentbased modelling for preliminary engineering education research design. The motivation for this work is to help researchers understand the behaviour of the system being studied (e.g., classroom, program, etc.) before embarking on the, often, long process of an engineering education research study. We provide an example model of the effect of homework difficulty on homework completion and study group formation to demonstrate this approach. The results indicate this approach holds considerable promise as a preliminary modelling tool that can help focus longer-term engineering education research studies.

Agent-Based Modeling: A Method for Investigating Challenging Research Problems.

For all our successes, many urgent health problems persist, and although some of these problems may be explored with established research methods, others remain uniquely challenging to investigate-maybe even impossible to study in the real world because of practical and pragmatic obstacles inherent to the nature of the research question. The purpose of this review article is to introduce agent-based modeling (ABM) and simulation and demonstrate its value and potential as a novel research method applied in nursing science. An introduction to ABM and simulation is described. Examples of current research literature on the subject are provided. A case study example of community nursing and opioid dependence is presented. The use of ABM and simulation in human health research has increased dramatically over the past decade, and meaningful research is now commonly found published widely in respected, peer-reviewed journals. Absent from this list is innovative ABM and simulation research published by nurse researchers in nursing-specific journals. ABM and simulation is a powerful method with tremendous potential in nursing research. It is vital that nursing embrace and adopt innovative and advanced research methods if we are to remain a progressive voice in health research, practice, and policy.

Agent-based models of inflammation in translational systems biology: A decade later.

Agent-based modeling is a rule-based, discrete-event, and spatially explicit computational modeling method that employs computational objects that instantiate the rules and interactions among the individual components ("agents") of system. Agent-based modeling is well suited to translating into a computational model the knowledge generated from basic science research, particularly with respect to translating across scales the mechanisms of cellular behavior into aggregated cell population dynamics manifesting at the tissue and organ level. This capacity has made agent-based modeling an integral method in translational systems biology (TSB), an approach that uses multiscale dynamic computational modeling to explicitly represent disease processes in a clinically relevant fashion. The initial work in the early 2000s using agent-based models (ABMs) in TSB focused on examining acute inflammation and its intersection with wound healing; the decade since has seen vast growth in both the application of agent-based modeling to a wide array of disease processes as well as methodological advancements in the use and analysis of ABM. This report presents an update on an earlier review of ABMs in TSB and presents examples of exciting progress in the modeling of various organs and diseases that involve inflammation. This review also describes developments that integrate the use of ABMs with cutting-edge technologies such as high-performance computing, machine learning, and artificial intelligence, with a view toward the future integration of these methodologies. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Models of Systems Properties and Processes > Organismal Models.

The complexity of knowledge sharing in multilingual corporations: evidence from agent-based simulations

This article discusses the possibility of adopting a complexity theory approach to the study of language policy and planning (LPP). Besides, it argues that agent-based modelling provides a significant support in this sense. Indeed, while agent-based modelling has become a major ally of researchers in the social sciences, it remains largely unexploited in the study of language-related issues in society. As a central tool of complexity theory, agent-based models (ABMs) lend themselves particularly well to the study of all sorts of complex systems. To provide justification for the use of ABMs in LPP, I show how language issues display the typical traits of complex systems and how ABMs can easily translate ideas and notions from the literature into computer-simulated processes. To support my argument, I discuss communication within multinational corporations as an example of a highly complex language matter. In particular, I focus on how language skills impact the process of knowledge creation and knowledge sharing among employees. By means of a model based on a number of straightforward rules, I show how poor language skills (or an utter lack thereof) risks creating an unbalanced distribution of knowledge (and, consequently, of power) across language groups and how this unbalanced distribution is very sensitive to initial conditions. On the contrary, average language skills seem to support communication well enough to avoid skews that favour even slightly more numerous language groups.

A narrative review of the use of agent-based modeling in health behavior and behavior intervention.

Studies of health behaviors and behavior intervention have begun to explore the potential of agent-based modeling (ABM). A review of how ABMs have been used in health behavior, behavior intervention, and corresponding insights is warranted. The goal of this study was to provide a narrative review of the applications of ABMs in health behavior change and intervention. I will focus on two perspectives: (a) the mechanism of behavior and behavior change and (b) ABMs' use for behavior intervention. I identified and reviewed 17 ABMs applied to behaviors including physical activity, diet, alcoholic drinking, smoking, and drug use. Among these ABMs, I grouped their mechanisms of behavior change into four categories and evaluated the advantages and disadvantages of each mechanism. For behavior intervention, I evaluated the use of ABMs on levels of individual, interpersonal, and neighborhood environment. Various behavior change mechanisms and simplifications existed because of our limited knowledge of behaviors at the individual level. Utility maximization was the most frequently used mechanism. ABMs offered insights for behavior intervention including the benefits of upstream interventions and multilevel intervention, as well as balances among various factors, outcomes, and populations. ABMs have been used to model a diversity of behaviors, populations, and interventions. The use of ABMs in health behavior is at an early stage, and a major challenge is our limited knowledge of behaviors at the individual level.

An agent-based evaluation of transit network design

This article presents the design and evaluation of transit networks using a route expansion heuristic and agent-based travel demand simulation. The route expansion mechanism is a type of constructive heuristic algorithm that derives new transit routes by inserting neighbouring nodes into existing routes with the aim of improving the demand coverage. The resulting networks are evaluated with an agent-based travel demand simulation model. The use of agent-based modelling is a departure from the existing route expansion literature and indeed the broader transit network design discipline in which the four step model has been extensively used. The procedure is tested on a bus rapid transit network in the City of Cape Town in South Africa.

Extreme-scale Dynamic Exploration of a Distributed Agent-based Model with the EMEWS Framework.

Agent-based models (ABMs) integrate multiple scales of behavior and data to produce higher-order dynamic phenomena and are increasingly used in the study of important social complex systems in biomedicine, socio-economics and ecology/resource management. However, the development, validation and use of ABMs is hampered by the need to execute very large numbers of simulations in order to identify their behavioral properties, a challenge accentuated by the computational cost of running realistic, large-scale, potentially distributed ABM simulations. In this paper we describe the Extreme-scale Model Exploration with Swift (EMEWS) framework, which is capable of efficiently composing and executing large ensembles of simulations and other "black box" scientific applications while integrating model exploration (ME) algorithms developed with the use of widely available 3rd-party libraries written in popular languages such as R and Python. EMEWS combines novel stateful tasks with traditional run-to-completion many task computing (MTC) and solves many problems relevant to high-performance workflows, including scaling to very large numbers (millions) of tasks, maintaining state and locality information, and enabling effective multiple-language problem solving. We present the high-level programming model of the EMEWS framework and demonstrate how it is used to integrate an active learning ME algorithm to dynamically and efficiently characterize the parameter space of a large and complex, distributed Message Passing Interface (MPI) agent-based infectious disease model.

Kesuksesan Knowledge Transfer Di Laboratorium Perguruan Tinggi Melalui Pengelompokkan Rekan Kerja Menggunakan Agent-Based Modeling And Simulation

One of aspect that contributes to improving organizations performance is knowledge transfer effectiveness. On the other hand, knowledge transfer is a dynamic interaction between people, process, and environment so that activity related to knowledge transfer categorized as a complex and adaptive process. The use of agent-based modeling and simulation approach is very appropriate in modeling knowledge transfer due to its ability to deal with the dynamic, complex, and adaptive process. Moreover, the agent-based approach has the ability in describing the interaction between the social agent that possess particular behavior. The purposes of this study are to identify the factors that influence the success of knowledge transfer in the university laboratory. In addition, the results show that agent-based modeling and simulation approach can determine the best scenario that can increase the success of knowledge transfer based on grouping of colleagues.

Kesuksesan Knowledge Transfer Di Laboratorium Perguruan Tinggi Melalui Pengelompokkan Rekan Kerja Menggunakan Agent-Based Modeling And Simulation

One of aspect that contributes to improving organizations performance is knowledge transfer effectiveness. On the other hand, knowledge transfer is a dynamic interaction between people, process, and environment so that activity related to knowledge transfer categorized as a complex and adaptive process. The use of agent-based modeling and simulation approach is very appropriate in modeling knowledge transfer due to its ability to deal with the dynamic, complex, and adaptive process. Moreover, the agent-based approach has the ability in describing the interaction between the social agent that possess particular behavior. The purposes of this study are to identify the factors that influence the success of knowledge transfer in the university laboratory. In addition, the results show that agent-based modeling and simulation approach can determine the best scenario that can increase the success of knowledge transfer based on grouping of colleagues.

Agent-Based Model for End-of-Life Product Flow Analysis

This paper presents an agent-based simulation model for end-of-life product flow analysis in recuperation and recycling supply networks that focuses on individual consumer behaviors. The simulation model is applied to a deposit-return program on wine bottles that could be developed in the province of Quebec. Canadian data was used to calibrate and validate the model. A series of experiments was then conducted with three artificial populations to analyse how they would react to several implementation scenarios of this end-of-life product flow strategy. The results suggest that the distance to the nearest depot is an important decision factor, but less predominant than the ownership of a private vehicle and the deposit value. The results also indicate that the use of agent-based modeling combined with the theory of planned behavior (TPB) can produce modular behavior models, that are intuitive and simple, to better understand consumer-behavior-driven supply chains. Such models can be used to give insights to decision-makers and policy-makers about the potential performance of end-of-life product flows strategies and further facilitate efficient resource management.

Agent-based modelling to predict policy outcomes: A food waste recycling example

Optimising policy choices to steer social/economic systems efficiently towards desirable outcomes is challenging. The inter-dependent nature of many elements of society and the economy means that policies designed to promote one particular aspect often have secondary, unintended, effects. In order to make rational decisions, methodologies and tools to assist the development of intuition in this complex world are needed. One approach is the use of agent-based models. These have the ability to capture essential features and interactions and predict outcomes in a way that is not readily achievable through either equations or words alone.In this paper we illustrate how agent-based models can be used in a policy setting by using an example drawn from the biowaste industry. This example describes the growth of in-vessel composting and anaerobic digestion to reduce food waste going to landfill in response to policies in the form of taxes and financial incentives. The fundamentally dynamic nature of an agent-based modelling approach is used to demonstrate that policy outcomes depend not just on current policy levels but also on the historical path taken.

A review of agent-based modeling approach in the supply chain collaboration context

Collaboration is considered as the key aspect of supply chain management (SCM) success. This issue has been addressed by many studies in recent years, but there are still few research employs agent-based modeling (ABM) approach to study business partnerships in SCM. This paper reviews the use of ABM in modeling collaboration in supply chains and inform the scope of ABM application in the existing literature. The review reveals that ABM can be an effective tool to address various aspects in supply chain relationships, but its applications in SCM studies are still limited. Moreover, where ABM is applied in the SCM context, most of the studies focus on software architecture rather than analyzing the supply chain issues. This paper also provides insights to SCM researchers about the opportunity uses of ABM in studying complexity in supply chain collaboration.

Towards agent-based integrated assessment models: examples, challenges, and future developments

Understanding the complex, dynamic, and non-linear relationships between human activities, the environment and the evolution of the climate is pivotal for policy design and requires appropriate tools. Despite the existence of different attempts to link the economy (or parts of it) to the evolution of the climate, results have often been disappointing and criticized. In this paper, we discuss the use of agent-based modeling for climate policy integrated assessment. First, we identify the main limitations of current mainstream models and stress how framing the problem from a complex system perspective might help, in particular when extreme climate conditions are at stake and general equilibrium effects are questionable. Second, we present two agent-based models that serve as prototypes for the analysis of coupled climate, energy, and macroeconomic dynamics. We argue that such models constitute examples of a promising approach for the integrated assessment of climate change and economic dynamics. They allow a bottom-up representation of climate damages and their cross-sectoral percolation, naturally embed distributional issues, and traditionally account for the role of finance in sustaining economic development and shaping the dynamics of energy transitions. All these issues are at the fore-front of the research in integrated assessment. Finally, we provide a careful discussion of testable policy exercises, modeling limitations, and open challenges for this stream of research. Notwithstanding great potential, there is a long way-to-go for agent-based models to catch-up with the richness of many existing integrated assessment models and overcome their major problems. This should encourage research in the area.

In search of missing time: A review of the study of time in leadership research

Many studies describe leadership as a dynamic process. However, few examine the passage of time as a critical dimension of that dynamism. This article illuminates this knowledge gap by conducting a systematic review of empirical studies on temporal effects of leadership to identify if and how time has been considered as a factor. After synthesizing key findings from the review, the article discusses methodological implications. We propose that a computational science approach, particularly agent-based modeling, is a fruitful path for future leadership research. This article contributes to leadership scholarship by shedding light on a missing variable (time) and offering a novel way to investigate the temporal, dynamic, emergent, and recursive aspects of leadership. We demonstrate the usefulness of agent-based modeling with an example of leader-member exchange relationship development.