Application Of Agent-based Model Research Articles

Applications of agent-based models for green development: a systematic review

Applications of agent-based models for green development: a systematic review

СОВРЕМЕННЫЕ ИНСТРУМЕНТЫ МОДЕЛИРОВАНИЯ СОЦИАЛЬНО-ЭКОНОМИЧЕСКИХ ПРОЦЕССОВ

The article discusses a brief history of the emergence and main areas of application of agent-based models. Examples of their practical use are given, including in the field of economics and for modeling business processes. The software and analytical complex «MOBIUS» and the new ambitious project «Digital Earth», supported and implemented by all countries of the BRICS interstate association, are briefly described.

Artificial Societies: Design and Applications

The researchers generally monitor the dynamics in scientific areas following the relatively less popular micropublications, but also attending the scientific events – ​conferences, round tables, seminars. Modeling of socio-economic systems is one of the rapidly developing areas, and the latest results, the promising ideas, and the new hypotheses were discussed at the events on artificial societies and supercomputer technologies held in September 2023. These events are the conference «Artificial Societies and Information Technologies» and the round table «Supercomputer Technologies and Artificial Societies». The relevance of the topics of the events justify an overview of the research in this subject area. The common issues among the reports presented underpin the consistent summary of the messages ensuring the clarity of the narration. The main issues of the events involve the application of agent-based models for public administration and methodological aspects of computer models.

From Data to Insights: A Bibliometric Assessment of Agent-Based Modeling Applications in Transportation

This paper presents a bibliometric analysis within the research domain dedicated to the utilization of agent-based modeling (ABM) in the field of transportation. By employing specific keywords related to both agent-based modeling and transportation, we have identified and extracted 1016 scholarly papers from the ISI Web of Science database, spanning the period from 2002 to 2023. Through the application of bibliometric methods, we have systematically examined key contributors, affiliations of academic institutions, influential publications, and renowned journals within this domain. Our analysis reveals a consistent and robust growth in scholarly interest pertaining to agent-based modeling in the field of transportation throughout the considered period. Notably, within approximately four decades of ABM’s application in transportation, a distinct upward trajectory began in 2008, culminating in the year 2021. The entire considered period witnessed a remarkable surge in paper production, characterized by an annual growth rate of 21.67%. Furthermore, employing an n-gram analysis, we have delineated and discussed the principal areas within transportation that have progressively benefited from the advancements in agent-based modeling. Prominently, the domains of air transport and road transport have exhibited substantial development over time, while the implications of climate change have emerged as a persistent concern throughout the entire study period.

Systematic Review of Agent-Based and System Dynamics Models for Social-Ecological System Case Studies

Social–ecological system (SES) modeling involves developing and/or applying models to investigate complex problems arising from the interactions between humans and natural systems. Among the different types, agent-based models (ABM) and system dynamics (SD) are prominent approaches in SES modeling. However, few SES models influence decision-making support and policymaking. The objectives of this study were to explore the application of ABM and SD in SES studies through a systematic review of published real-world case studies and determine the extent to which existing SES models inform policymaking processes. We identified 35 case studies using ABM, SD, or a hybrid of the two and found that each modeling approach shared commonalities that collectively contributed to the policymaking process, offering a comprehensive understanding of the intricate dynamics within SES, facilitating scenario exploration and policy testing, and fostering effective communication and stakeholder engagement. This study also suggests several improvements to chart a more effective trajectory for research in this field, including fostering interdisciplinary collaboration, developing hybrid models, adopting transparent model reporting, and implementing machine-learning algorithms.

Computer Simulations of Ethics: The Applicability of Agent-Based Modeling for Ethical Theories

Abstract I consider the applicability of Agent-Based Modeling (ABM) and computer simulations for ethical theories. Though agent-based modeling is already well established in the social sciences, it has not yet found acceptance in the field of philosophical ethics. Currently, there are only a few works explicitly connecting ethics with agent-based modeling. In this paper, I show that it is possible to build computer simulations of ethical theories and that there are also potential benefits in doing so: (1) the opportunity for virtual ethical experiments that are impossible to do in real life, and (2) an increased understanding and appreciation of an ethical theory either through the programming implementation or through the visual simulation. In the first part of the paper, I mention some social science simulations with ethical import that could encourage ethicists to work with ABM. Second, I list the few pioneering works that attempt to combine computer simulation with philosophical ethics, the most prominent being Evolving Ethics: The New Science of Good and Evil (2010) by Mascaro et al. Third, I give pointers for the computer simulation of the most prominent ethical theories: deontological ethics, utilitarianism, feminist care ethics, and virtue ethics. In the final part, I consider the potential of using an existing reference model for the simulation of human behavior, the PECS model, as the foundation for a computer simulation of virtue ethics.

Pollution Abatement and Lobbying in a Cournot Game: An Agent-Based Modelling Approach

AbstractThe application of Agent-Based Modelling to Game Theory allows us to benefit from the strengths of both approaches, and to enrich the study of games when solutions are difficult to elicit analytically. Using an agent-based approach to sequential games, however, poses some issues that result in a few applications of this type. We contribute to this aspect by applying the agent-based approach to a lobbying game involving environmental regulation and firms’ choice of abatement. We simulate this game and test the robustness of its game-theoretical prediction against the results obtained. We find that while theoretical predictions are generally consistent with the simulated results, this novel approach highlights a few differences. First, the market converges to a green state for a larger number of cases with respect to theoretical predictions. Second, simulations show that it is possible for this market to converge to a polluting state in the very long run. This result is not envisaged by theoretical predictions. Sensitivity experiments on the main model parameters confirm the robustness of our findings.

Applications of agent-based modeling in trauma research.

Trauma, violence, and their consequences for population health are shaped by complex, intersecting forces across the life span. We aimed to illustrate the strengths of agent-based modeling (ABM), a computational approach in which population-level patterns emerge from the behaviors and interactions of simulated individuals, for advancing trauma research; Method: We provide an overview of agent-based modeling for trauma research, including a discussion of the model development process, ABM as a complement to other causal inference and complex systems approaches in trauma research, and past ABM applications in the trauma literature; Results: We use existing ABM applications to illustrate the strengths of ABM for trauma research, including incorporating interactions between individuals, simulating processes across multiple scales, examining life-course effects, testing alternate theories, comparing intervention strategies in a virtual laboratory, and guiding decision making. We also discuss the challenges of applying ABM to trauma research and offer specific suggestions for incorporating ABM into future studies of trauma and violence; Conclusion: Agent-based modeling is a useful complement to other methodological advances in trauma research. We recommend a more widespread adoption of ABM, particularly for research into patterns and consequences of individual traumatic experiences across the life course and understanding the effects of interventions that may be influenced by social norms and social network structures. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Applications of agent-based modeling and simulation in organization management: a quarter-century review through bibliometric mapping (1998–2022)

Applications of agent-based modeling and simulation in organization management: a quarter-century review through bibliometric mapping (1998–2022)

Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process.

The application of agent-based modelling for the diffusion of innovation research: a case study

The application of agent-based modelling for the diffusion of innovation research: a case study

Application of Agent-Based Modelling for Evaluating Level of Service in Airports

Airports worldwide evaluate Level of service (LOS) to assess performance of the terminal for the current scenario and to compare it to a projected scenario. The International Air Transport Association (IATA) global standard is the widely accepted framework that evaluate airport LOS based on space and time characteristics. The application of simulation tools can be used in this regard to depict the airport LOS scenarios more interactively and in detail. Moreover, this approach also enables modelling future scenarios of technological or infrastructure modifications to cater terminal capacity constraints. The use of Agent-Based Modelling (ABM) in evaluating airport LOS is discussed in this paper. The study utilized field study at the check-in hall of a large-sized airport, the Chennai International airport, India to capture the passenger flow characteristics such as passenger entry rate, walking speed, group size, passenger flow sequence and flight movement frequency. These are used as inputs to a microscopic pedestrian modelling tool, AnyLogic. Three scenarios of airport LOS are modelled to evaluate the passenger density and the dwell times. The results show that the variation in number of service points and flow rate significantly affect the passenger dwell time, queuing length, queuing time and passenger density at the check-in zone. Visual and quantitative representations of the scenarios are also presented, analyzed and compared with the existing standard.

Understanding Misimplementation in U.S. State Health Departments: An Agent-Based Model

The research goal of this study is to explore why misimplementation occurs in public health agencies and how it can be reduced. Misimplementation is ending effective activities prematurely or continuing ineffective ones, which contributes to wasted resources and suboptimal health outcomes. The study team created an agent-based model that represents how information flow, filtered through organizational structure, capacity, culture, and leadership priorities, shapes continuation decisions. This agent-based model used survey data and interviews with state health department personnel across the U.S. between 2014 and 2020; model design and analyses were conducted with substantial input from stakeholders between 2019 and 2021. The model was used experimentally to identify potential approaches for reducing misimplementation. Simulations showed that increasing either organizational evidence-based decision-making capacity or information sharing could reduce misimplementation. Shifting leadership priorities to emphasize effectiveness resulted in the largest reduction, whereas organizational restructuring did not reduce misimplementation. The model identifies for the first time a specific set of factors and dynamic pathways most likely driving misimplementation and suggests a number of actionable strategies for reducing it. Priorities for training the public health workforce include evidence-based decision making and effective communication. Organizations will also benefit from an intentional shift in leadership decision-making processes. On the basis of this initial, successful application of agent-based model to misimplementation, this work provides a framework for further analyses.

Innovations in integrating machine learning and agent-based modeling of biomedical systems

Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.

Agent-Based Modeling of Microbial Communities.

Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.

71 Agent-Based Modeling: A Historical Perspective and Comparison to Other Modeling Techniques

Abstract The earliest known history of agent-based modeling (ABM) is traced back to Von Neumann’s self-reproducing cellular automata, designed in the 1940s. The usage of ABM in different scientific fields accelerated in the 1990s, supported by the phenomenal increase in computing capabilities. ABM is gaining acceptance in animal systems, as daily decision-making is becoming complex due to multiple competing outcomes of interest to food systems stakeholders. ABM typically replicates complex real-world systems, for example, a herd of animals that dynamically interact based on simple rules. ABM simulate the heterogenous, stochastic characteristics of agents observed in the real-world. The dynamic interaction of agents replicates the observable real-world complex system patterns. Application of ABM in animal systems ranges from modeling cell behavior, precision nutrition and herd management to predicting the spread of epidemics, and food animal supply chain optimization. Animal science's most widely used alternative modeling techniques include system dynamic models, differential equations-based models, and statistical modeling. The list of unrealistic assumptions that limit the utility of these modeling techniques includes the assumptions of linearity, homogeneity, normality, and stationarity. The advantageous characteristics of an agent in ABM that set it apart from other techniques include being identifiable, capable of existing in an environment where it interacts with other agents while being autonomous and self-directed, goal-oriented behavior, flexible learning, and capability of adaptations in its behavior over time, based on experience. Identifying the purpose of the model, the questions the model will answer, and the potential users are the key decision variables modelers should ponder upon before embarking on building ABM. The commonly used ABM software includes Repast, Swarm, Netlogo, and MASON. Developing an ABM has several highly interleaved stages: concept development, requirements definition, design, implementation, and operationalization, each of which will be illustrated during the hands-on training session on ABM.

Agent-based modelling for Urban Analytics: State of the art and challenges

Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual ‘agents’, and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.

Mapping the knowledge frontiers and evolution of decision making based on agent-based modeling

With increasing attention paid to the application of agent-based modeling in decision-making issues, a large number of related studies have been published in management science and operational research areas. This study adopts multiple methods, including bibliometric mapping, text mining, and qualitative analysis, to comprehensively review relevant research to explore knowledge frontiers and evolution in decision-making research based on agent-based modeling (DM-ABM). This review is based on 1190 relevant journal articles from the Web of Science Core Collection dataset and 37,167 collectively cited references of the articles. The top ten most-cited studies that constitute the intellectual milestones of DM-ABM were identified. Keywords and research topics develop rapidly; recent research have paid most attention to the keywords “model,” “system,” and “simulation” and topics “learning,” “contracts,” “protocols,” and “self-learning.” The top 24 references with the strongest citation bursts were displayed to show that the area was increasingly active from 2001 to 2010. Transition points were mapped to reveal the top five studies with the highest betweenness centrality, which considerably influences knowledge evolution. Then, the top three clusters are identified as the frontier areas and analyzed by text mining, including intelligent agents, model validation, and collaborative decision making. Finally, the most recent research in this field is investigated, and four future research directions are proposed: the advanced intelligence of agents, approach to reality, group decision making, innovative modeling methodologies and diversified applications.

Why do we need agent-based macroeconomics?

We are entering the third decade of the twenty-first century with profound uncertainties and crucial challenges for the world economy. Phenomena like climate change, digital transformation, migration, demographic changes, and the ongoing COVID pandemic need to be understood and promptly addressed. We argue that the agent-based approach in economics is well suited to tackle these topics, because of its capacity to integrate the “micro” and “macro” dimensions by modelling the network of interactions among heterogeneous economic agents and their aggregate outcomes. This paper explains why the agent-based methodology is needed to overcome the limitations of the neoclassical approach in economics, which has not been able to properly address those challenges. To do so, the paper retraces the main stages of the scientific evolution in a general historical and epistemological perspective, showing how the paradigm of reductionism, which led to extraordinary advances after the scientific revolution of the seventeenth century, is less effective when addressing the main challenges ahead. On the other hand, the sciences of chaos theory and complex systems can provide the economic discipline with more suitable instruments to face those challenges. Finally, the paper briefly presents the contributions of the special issue, which use applications of agent-based models to study the main problems of our times.

Application of Agent-Based Modeling in Agricultural Productivity in Rural Area of Bahir Dar, Ethiopia

Effective weather forecast information helps smallholder farmers improve their adaptation to climate uncertainties and crop productivity. The main objective of this study was to assess the impact of weather forecast adoption on crop productivity. We coupled agent-based and crop productivity models to study the impact of farmers’ management decisions on maize productivity under different rainfall scenarios in Ethiopia. A household survey was conducted with 100 households from 5 villages and was used to validate the crop model. The agent-based model (ABM) analyzed the farmers’ behaviors in crop management under different dry, wet, and normal rainfall conditions. ABM results and crop data from the survey were then used as input data sources for the crop model. Our results show that farming decisions based on weather forecast information improved yield productivity from 17% to 30% under dry and wet seasons, respectively. The impact of adoption rates due to farmers’ intervillage interactions, connections, radio, agriculture extension services, and forecast accuracy brought additional crop yields into the Kebele compared to non-forecast users. Our findings help local policy makers to understand the impact of the forecast information. Results of this study can be used to develop agricultural programs where rainfed agriculture is common.