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Key research themes
1. How does Individual-Based Modeling capture emergent phenomena and complex system dynamics?
This research area investigates the ability of Individual-Based Modeling (IBM) or Agent-Based Modeling (ABM) to simulate emergent behaviors arising from interactions among autonomous agents. It matters because many real-world systems exhibit properties at the macro-level that cannot be deduced from individual parts alone due to nonlinear interactions and adaptation, which traditional equation-based models struggle to capture. Capturing emergent phenomena enables deeper understanding and prediction of complex systems in social sciences, ecology, business, and more.
Key finding: This foundational paper clarifies that ABM captures emergent phenomena by modeling the autonomous decision-making of agents following simple rules whose interactions generate complex and sometimes counterintuitive group-level... Read more
Key finding: This paper demonstrates the practical integration of microsimulation data with ABM to generate heterogeneous artificial societies that produce complex population dynamics. It highlights how ABM enables modeling of nonlinear,... Read more
Key finding: The authors argue that ABM’s key strength lies in representing diverse heterogeneous agents and relational constraints to simulate the micro-macro feedback loops, enabling exploration of social phenomena with complex... Read more
Key finding: This survey emphasizes that ABM and related kinetic theory approaches facilitate modeling of complex human behaviors characterized by bounded rationality, heterogeneity, and nonlinear interactions. This leads to emergent... Read more
Key finding: Focusing on ecological applications, this work demonstrates that IBMs are vital for accurate, robust environmental decision support because they predict organismal behaviors and population consequences from individual... Read more
2. How can Individual-Based Modeling frameworks be designed to handle complex, heterogeneous, and dynamic agent behaviors in diverse domains?
This theme focuses on methodological frameworks and implementations that enable IBMs/ABMs to capture complex, adaptive, and heterogeneous behaviors across domains such as healthcare, social sciences, and engineering. It is important because realistic agent behavior modeling requires modular, extensible, and computationally efficient structures, supporting large-scale, data-driven simulations that can incorporate cognitive, biological, and environmental factors.
Key finding: Introduces the Human Data Model (HDM), a flexible programming model implemented in JavaScript that aggregates and abstracts heterogeneous personal data from sensors and applications to support modeling of human–computer... Read more
Key finding: Presents 'villager,' a modular, extensible, and R-native ABM framework tailored for social science researchers. It supports subclassing of agents and resources to incorporate complex agent attributes, and flexible data... Read more
Key finding: This chapter surveys agent conceptualizations focused on autonomy, adaptivity, and social ability, emphasizing architectures and methods to realize such agents. It bridges agent design and simulation, outlining taxonomies and... Read more
Key finding: Describes MIDAS, a comprehensive human performance modeling tool integrating perceptual, cognitive, and motor sub-models incorporating workload, stress, fatigue, and situational awareness factors. MIDAS enables agent-based... Read more
Key finding: Develops a mathematical agent-based framework grounded in PSI theory to model personality differences via interactions among cognitive systems regulated by affect. The model shows how stochastic perturbations in positive and... Read more
3. How can Individual-Based Modeling be applied to predict and optimize real-world outcomes in health, environmental, and engineering contexts?
This theme addresses applied research leveraging IBMs to model individualized heterogeneity and interactions to inform predictions, optimize interventions, and guide decision-making in complex systems such as family planning, disease progression, healthcare delivery, environmental management, and materials design. The approach is critical for accommodating personal-level dynamics and variability, which aggregate to population-level patterns and outcomes, thus enhancing the relevance and precision of policy and design strategies.
Key finding: Introduces FPsim, an open-source, data-driven agent-based model that explicitly simulates individual heterogeneity in biological and behavioral processes over the reproductive life course to inform family planning dynamics.... Read more
Key finding: Presents a statistical modeling framework blending patient-level and population data to predict personalized degenerative disease trajectories, handling sparse longitudinal and heterogeneous data. This approach captures... Read more
Key finding: Demonstrates the utility of ABMs in healthcare by simulating dynamic human behaviors and social network effects to forecast infectious disease spread, risk factors, and intervention impacts. Examples include modeling HIV... Read more
Key finding: Case studies exemplify how environmentally-focused IBMs co-developed by practitioners accurately capture individual organismal behavior and fitness maximization strategies, producing population-level predictions robust to... Read more
Key finding: Combines individual-based biofilm modeling with Bayesian optimization to automate the design of nanosurface topographies that optimize biofilm removal under varying physical conditions. The framework identifies surface... Read more