Applications of agent-based modeling in trauma research.

This article presents an overview of the agent-based modeling and simulation approach and its recent developments in transport fields, with the purpose of discovering the advantages and gaps and encouraging more valuable investigations and applications of agent-based models. We clarify the agent-based model from agents, the background of development, and the basic structure applied in transport systems. Then, the agent-based transport modeling toolkits are discussed. The applications of agent-based models in transport systems are reviewed in three time scale models followed by an additional discussion of hybrid modeling approaches. The extensive modeling of the beliefs, desires, learning, and adaptability of individuals and the optimization problems using agent-based models are explored. Besides, we point out some limitations in terms of calibration and validation procedure, agents’ behavior modeling, and computing efficiency. In conclusion, some recommendations are given and suggest potential and insightful directions such as Big Data and Digital Twin for future research.

Agent-based models (ABMS) simulate individual units within a system, such as the bacteria in a biological wastewater treatment system. This paper outlines past, current and potential future applications of ABMs to wastewater treatment. ABMs track heterogeneities within microbial populations, and this has been demonstrated to yield different predictions of bulk behaviors than the conventional, "lumped" approaches for enhanced biological phosphorus removal (EBPR) completely mixed reactors systems. Current work included the application of the ABM approach to bacterial adaptation/evolution, using the model system of individual EBPR bacteria that are allowed to evolve a kinetic parameter (maximum glycogen storage) in a competitive environment. The ABM approach was successfully implemented to a simple anaerobic-aerobic system and it was found the differing initial states converged to the same optimal solution under uncertain hydraulic residence times associated with completely mixed hydraulics. In another study, an ABM was developed and applied to simulate the heterogeneity in intracellular polymer storage compounds, including polyphosphate (PP), in functional microbial populations in enhanced biological phosphorus removal (EBPR) process. The simulation results were compared to the experimental measurements of single-cell abundance of PP in polyphosphate accumulating organisms (PAOs), performed using Raman microscopy. The model-predicted heterogeneity was generally consistent with observations, and it was used to investigate the relative contribution of external (different life histories) and internal (biological) mechanisms leading to heterogeneity. In the future, ABMs could be combined with computational fluid dynamics (CFD) models to understand incomplete mixing, more intracellular states and mechanisms can be incorporated, and additional experimental verification is needed.

The growing body of knowledge on modeling land use systems points to epistemic uncertainty as one of the challenging obstacles in development and application of agent-based models (ABMs). To decrease outcome uncertainty, sensitivity analysis (SA) is performed as part of model verification and validation. Oftentimes, however, it is inadequately addressed, partly because of the lack of tools and techniques that focus on an explicit evaluation of ABM dynamics. The nonlinear processes, inherent in such models, necessitate longitudinal SA with time path investigation of input–output relationships of endogenous variables. In response to the outlined deficiencies, this study investigates the potential of time-dependent global sensitivity analysis (time-GSA) in examining the dynamics of outcome uncertainty of a simple ABM of land use change. Specifically, we apply first and total order sensitivity indices to decompose variance of output landscape fragmentation, apportioned to model inputs for multiple time steps and multiple realizations of the ABM. We focus the analysis on selected complex systems characteristics including preference uncertainty, path dependence, access to information, and magnitude of interactions and feedbacks. We conclude that the factor sensitivity measures vary significantly during model execution. Consequently, a static snapshot of ABM sensitivity, taken at the end of the simulation, is inadequate when deciding on factor prioritization and reduction. Assuming that ABM dynamics is a result of factor interaction, we observe a distinct time lag of nonlinearity, which unfolds after the formation of the seeds of development. Therefore, we argue for further application of time-GSA in ABM as one of the visual quantitative techniques contributing to evaluation of ABM nonlinearity.

Chapter 28 focuses on agent-based modelling. Agent-based models are computer programs composed of autonomous agents that are diverse and interact with one another and their environment. The program is simulated over time, exploring how the microlevel actions and interactions of many agents give rise to macrolevel patterns and dynamics. The chapter discusses the application of agent-based models to exploring emergent social-ecological systems (SES) outcomes and dynamics, policy assessment, the assessment of response of SES to environmental or social change, explaining emergent SES phenomena, and as boundary objects in participatory processes. It goes on to discuss the types of SES problems and research questions commonly addressed by this set of methods, as well as their limitations, resource implications and new emerging research directions. The chapter also includes an in-depth case study showcasing the application of agent-based modelling, and suggested further readings.

This is an introduction to the special section "Causal Inference and Agent-Based Modeling" in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The primary objective of this research paper is to conduct a bibliometric analysis of the prevailing research landscape pertaining to agent‐based modeling (ABM). This analysis encompasses an examination of key contributors, affiliated academic institutions, influential publications, and prominent journals within the domain. To achieve this, a dataset consisting of 11,477 scholarly papers retrieved from the ISI Web of Science database has been curated, using keywords specifically related to ABM, spanning the period from 1996 to 2024. Employing n ‐gram analysis techniques on titles, keywords, abstracts, and keyword‐plus fields has unearthed a multitude of domains wherein ABM has been applied with notable success. Our findings, as delineated in this paper, underscore a sustained and robust growth in scholarly interest in the realm of ABM during the specified temporal span, characterized by an impressive annual growth rate of 25.29%. Furthermore, our study contributes to the identification and analysis of salient keywords and emerging trends, thereby elucidating key research trajectories within this domain. The identification of collaborative networks among authors, their respective academic affiliations, and the geographical distribution across various countries and territories offers valuable insights into the global proliferation of ABM as a research methodology. The findings offer valuable insights into the widespread applications of ABM across various domains, including climate change, social networks, supply chain dynamics, public health studies, financial market analysis, and population dynamics. The results of the study can help in guiding future research and practical applications of ABM in these and other multifaceted areas.

For many years, computer modeling and simulations in business have been used for statistical analysis or for visual representations of complex data. Recently, a new modeling approach has been developed—agent-based modeling—in which the agents in a complex simulated world interact with each other and the environment based on a set of often simple rules. Agent-based models (ABM) were initially developed for advanced scientific, social science, and military research (Bar-Yam, 1997; Epstein & Axtell, 1996; Holland, 1995; Langton, 1995E; Pagels, 1988), but to date, there have been relatively few applications of ABMs in business and industry (Farrell, 1998). This paper describes our preliminary work on an ABM for business that deals with adaptability and co-evolution involving alternate distribution channels and electronic commerce.

Urban land-use modeling methods have experienced substantial improvements in the last several decades. With the advancement of urban land-use change theories and modeling techniques, a considerable number of models have been developed. The relatively young approach, agent-based modeling, provides urban land-use models with some new features and can help address the challenges faced by traditional models. Applications of agent-based models to study urban dynamics have increased steadily over the last twenty years. To offer a retrospective on the developments in agent-based models (ABMs) of urban residential choices, we review fifty-one relevant models that fall into three general categories: (i) urban land-use models based on classical theories; (ii) different stages of the urbanization process; and (iii) integrated agent-based and microsimulation models. We summarize and compare the main features of these fifty-one models within each category. This review focuses on three fundamental new features introduced byABMs. The first is agent heterogeneity with particular attention to the method of introducing heterogeneity in agents' attributes and behaviors. The second is the representation of land-market processes, namely preferences, resources constraints, competitive bidding, and endogenous relocation. The third is the method of measuring the extensive model outputs. In addition, we outline accompanying challenges to, and open questions for, incorporating these new features. We conclude that, by modeling agent heterogeneity and land markets, and by exploiting a much broader dimension of output, we will enhance our understanding of urban land-use change and are hopefully able to improve model fitness and robustness.

Spread of two linked social norms on complex interaction networks

Agent-based modeling of China’s rural–urban migration and social network structure

Agent-based models (ABMs) are gaining traction as one of the most powerful modelling tools within the social sciences. They are particularly suited to simulating complex systems. Despite many methodological advances within ABM, one of the major drawbacks is their inability to incorporate real-time data to make accurate short-term predictions. This paper presents an approach that allows ABMs to be dynamically optimized. Through a combination of parameter calibration and data assimilation (DA), the accuracy of model-based predictions using ABM in real time is increased. We use the exemplar of a bus route system to explore these methods. The bus route ABMs developed in this research are examples of ABMs that can be dynamically optimized by a combination of parameter calibration and DA. The proposed model and framework is a novel and transferable approach that can be used in any passenger information system, or in an intelligent transport systems to provide forecasts of bus locations and arrival times.

The human dimension is one major source of uncertainty in the management of social-ecological systems such as fisheries. Agent-based models (ABMs) can help to reduce these uncertainties by making it possible to model and simulate human behavior. To understand how ABMs can be applied in fisheries science, a classification scheme was developed based on reviews in other social-ecological domains, theoretical frameworks, and documentation standards. This classification scheme was subsequently used to review agent-based simulation studies that modeled human decision-making in a fisheries context to identify trends and knowledge gaps. Applying the classification scheme revealed that the existing fisheries-related ABMs employ a variety of decision theories, policies, social interactions, agent memories, and data sources, and revealed a wide potential for applications of ABMs to a broad range of research questions and management recommendations. Nevertheless, it turned out that it is, so far, virtually unexplored how environmental factors influence fishing decisions or how social norms and learning influence fishing behavior. It also became clear that the documentation and provenance information of ABMs need to be improved – e.g., by applying standardized documentation procedures, such as ODD + D and TRACE – to enhance the credibility, transparency, and reusability of ABMs in fisheries science.

Tacit knowledge transfer is essential since it is considered as a source of sustainable competitive advantage in an organization. According to the previous study, source, recipient, knowledge characteristic and transfer mechanism play important role in determining the effectiveness of tacit knowledge transfer. Moreover, this study also considered the relationship between tacit knowledge transfer effectiveness and individual performance. An agent-based modeling approach was employed in this study since the approach is considered perfectly qualified in dealing with a complex system like knowledge transfer. This study aimed to investigate the change of individual work performance with the consideration of the feedback loop mechanism in the model using the agent-based approach. Data used in this study were collected from 15 university laboratories in a university in Indonesia. Three scenarios with various conditions are generated in this study. The result of all scenarios indicates that several strategies can be implemented in real condition to enhance organization member work performance based on the simulation output. In addition, there is a finding that recipient characteristic likeabsorptive capacity is a key driver to enhance knowledge transfer effectiveness and individual performance.

System dynamics modelling (SDM) applications in health research began to appear in 1972 while agent‐based modelling (ABM) applications first appeared in 1995, but their applications have increased dramatically over the last decade. This study examines the diffusion patterns of these two approaches and the main driving factors. We conducted a systematic analysis of 653 journal articles published from 1972 to 2014 that employed two approaches in health research and found that over the last few years, there has been a rapid increase of studies in using ABM compared with SDM, partly due to the launching of new journals and the application of ABM to a broad range of topics. The concentration of SDM studies in just a few journals may be adversely affecting its diffusion. The last decade saw acceleration in using ABM to investigate infectious diseases and cancer while SDM applications have predominantly focused on non‐communicable diseases such as obesity. Copyright © 2017 John Wiley & Sons, Ltd.

A major challenge facing archaeologists is communicating our research to the public. Thankfully, new computational tools have enabled the testing and visualization of complex ideas in an easily packageable format. In this article we illustrate not only how agent-based modeling provides a platform for communicating complex ideas, but also how these game-like computer models can be explored and manipulated by members of the public therefore increasing their engagement in archaeological explanations. We suggest that these new digital tools serve as an excellent aid for education on the importance of archaeological sites and artifacts. To illustrate the above we walk the reader through a step-by-step pipeline of how to run an ABM model as an experiment and how to export it into a form ready to be sent to SHPO and THPO offices in tandem with reports. Ultimately, we hope that this work will help demystify the computational archaeology process and lead to more fluency in using agent-based modeling in research and outreach.