ABSTRACT Participatory planning is a democratic spatial decision-making process involving multiple stakeholders. The integration of artificial intelligence (AI) methods in participatory planning has the potential to improve the decision-making process. However, there are challenges and limitations that need to be addressed. In this paper, we systematically review the progress of AI-enabled participatory planning, identifying strengths and weaknesses. We used a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework for our analysis, highlighting the opportunities for advancing AI in participatory planning and the potential threats that may arise. Our study provides valuable insights into the current state of AI-enabled participatory planning, paving the way for future developments and improvements.

Artificial intelligence enabled participatory planning: a review

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

Examining the ethical aspects of artificial intelligence (AI) and data science (DS) recognizes their impressive progress in innovation while emphasizing the pressing necessity to tackle intricate ethical dilemmas. The chapter provides a detailed framework for navigating the changing environment, beginning with an examination of the increasing ethical challenges. The study highlights transparency, fairness, and responsibility as crucial for cultivating confidence in AI systems. The chapter emphasizes the urgent requirement to address problems such as algorithmic bias and privacy breaches with strong mitigation techniques. Furthermore, it promotes flexible policies that strike a balance between innovation and ethical safeguards. The examination of societal effects, particularly on various socioeconomic groups, economies, and cultures, is conducted thoroughly, with a focus on equity and the protection of individual rights. Finally, to proactively tackle future ethical challenges in technology, it is advisable to employ proactive solutions such as implementing AI ethics by design. 

Toward a More Ethical Future of Artificial Intelligence and Data Science

The Symposium on Human Dynamics Research, first organized at the 2015 AAG Meeting in Chicago, celebrated its 10th anniversary at the 2024 AAG Meeting in Honolulu, marking a decade of transformative advancements in the field. Over the past decade, the focus of human dynamics research has shifted from traditional spatial-temporal analyses to sophisticated modeling of human behavior in a hybrid physical-virtual world. This evolving field now examines the intricate interdependencies between physical and digital environments, addressing critical issues such as urban resilience, public health, social equity, and community sustainability. The symposium emphasized the growing importance of interdisciplinary collaboration, advanced data-driven analytical platforms, and innovative theoretical frameworks to better understand human interactions across these spaces. As human dynamics continue to shape global urban systems, these advancements are pivotal for future research and real-world problem-solving, offering novel insights into the interconnectedness of mobility, technology, and societal well-being in a rapidly changing world. 

Human Dynamics Research in GIScience: challenges and opportunities

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.

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

Agent-based modeling (ABM) has been widely used in numerous disciplines and practice domains, subject to many eulogies and criticisms. This article presents key advances and challenges in agent-based modeling over the last two decades and shows that understanding agents’ behaviors is a major priority for various research fields. We demonstrate that artificial intelligence and data science will likely generate revolutionary impacts for science and technology towards understanding agent decisions and behaviors in complex systems. We propose an innovative approach that leverages reinforcement learning and convolutional neural networks to equip agents with the intelligence of self-learning their behavior rules directly from data. We call for further developments of ABM, especially modeling agent behaviors, in the light of data science and artificial intelligence. 

Modeling agent decision and behavior in the light of data science and artificial intelligence

Corrigendum to “Challenges, tasks, and opportunities in modeling agent-based complex systems”, Ecological Modeling, 2021, 457: 109,685, page 1–15

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Modeling agent decision and behavior in the light of data science and artificial intelligence

Corrigendum to “Challenges, tasks, and opportunities in modeling agent-based complex systems”, Ecological Modeling, 2021, 457: 109,685, page 1–15