Using Machine Learning for Agent Specifications in Agent-Based Models and Simulations: A Critical Review and Guidelines

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field’s impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka’s law is reflected in the author’s contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford’s Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.

Exploring the Use of Artificial Intelligence in Agent-Based Modeling Applications: A Bibliometric Study

Agent-based models (ABMs) offer promise for realistically simulating human behaviours and interactions during emergency evacuations. This review aims to systematically assess the state of the art in ABM-based evacuation modelling with respect to methodologies, validation practices, and the associated challenges over the past decade. The review critically examines 134 studies from 2013 to 2023 that have applied ABMs for pedestrian evacuation simulation to synthesise current capabilities, limitations, and advancement pathways. Findings identify persistent challenges related to modeller bias, computational complexity, data scarcity for calibration and validation, and the predominance of simplistic rule-based decision-making models, while promise exists with the adoption of flexible behavioural frameworks, high-performance computing architectures, machine learning techniques for adaptive agent behaviours and surrogate modelling, and evolutionary computation methods for transparent rule generation. The findings underscore the importance of interdisciplinary collaboration among behavioural scientists, modellers, and emergency planners to enhance the realism and reliability of ABMs. By providing a critical synthesis of the state-of-the-art and proposing future research directions, this review aims to accelerate the development and application of ABMs that can meaningfully enhance the safety and resilience of communities facing emergencies. 

Agent-based simulation for pedestrian evacuation: A systematic literature review

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. 

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

Scenario planning is a powerful tool for cities to navigate uncertainties and mitigate the impacts of adverse scenarios by projecting future outcomes based on present-day decisions. This approach is becoming increasingly important given the growing call for building resilient cities to face adverse future scenarios posed by emerging disruptive technologies and climate change. However, conventional scenario planning practices predominantly rely on expert knowledge and judgment, which may be limited in accounting for the complexity of future scenarios. Therefore, we explored the potential integration of artificial intelligence (AI) techniques to assist scenario planning practices. We synthesized related studies from various disciplines (e.g., engineering, computer science, and urban planning) to identify the potential applications of AI in the three key components of scenario planning: plan generation, scenario generation, and plan evaluation. We then discuss the challenges and possible solutions for integrating AI into the scenario planning process and highlight the critical role of planning experts in this process. We conclude by outlining future research opportunities in this context. Ultimately, this study contributes to the advancement of scenario planning practices and aids the creation of more resilient cities that can thrive in an uncertain future. 

Empowering Scenario Planning with Artificial Intelligence: A Perspective on Building Smart and Resilient Cities

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Using Machine Learning for Agent Specifications in Agent-Based Models and Simulations: A Critical Review and Guidelines