Paul M. Torrens - US grants

Self-organizing animal and human groups have increasingly become the focus of research by scientists interested in social dynamics. While a substantial amount of literature exists on the behavioral interaction patterns found in animal groups, there is not a comparable body of work in the social sciences. From hunter-gatherers to city-dwellers, structured gatherings of humans appear in all cultures. These groups range from married couples and co-workers to large crowds and neighborhoods, with each type having a distinct structure and history. What is not clear, however, is how individuals, each with unique attributes and preferences, contribute to the formation of these groups. Even less is known about how the socio-developmental processes observed in groups modify its constituents. Because of this complexity, this study brings together a multi-disciplinary team that integrates human development, computer simulation, biology, mathematics, and geography to study how fundamental social processes are critical to human development and life-course trajectory. To investigate the fundamental properties of sociality, this study will, for 3 years, observe and catalog how and where 3-5 year old children form groups and dyadic friendships. The study of young children is fortuitous for answering process-driven questions about group formation and group stability for several reasons: (1) This is the first time that many of the children are consistently exposed to a large number of peers -- the sizeable pool of eligibles can provide information about the selection process in the formation and evolution of groups; (2) Given the relative social inexperience of this age group, there should be basic and simple process components common to all social entities (e.g., exchange of communicative signals, role differentiation); and (3) There is long-term societal utility for studying children's abilities to form and maintain relationships with their peers -- this phenomena has been closely associated with academic and social competencies. At the end of three years of data collection, computer models from the behavioral and geo-spatial data will be constructed to inform scientists and policy makers about how these critical social processes emerge and evolve. Although the focus is on young children, the core of this endeavor is an attempt to understand and model the reciprocal evolutionary dynamics basic to understanding all social processes. As such, the research is informed by multiple scientific disciplines ranging from human development, anthropology, and sociology to computer science, physics, biology, and applied mathematics.

Crowds are vital to the lifeblood of cities. Crowd behavior has largely been veiled from traditional academic inquiry, however. For example, it is impractical to establish live experiments with hundreds or thousands of people along busy streetscapes, to reproduce mob behavior during riots for the purposes of academic experimentation, or to expect to replicate the life-and-death behavior under emergency situations in a fabricated fashion. Modeling and simulation occupy a pivotal role in the research of crowd behavior as synthetic laboratories for exploring ideas and hypotheses that are simply not amenable to investigation by other means. Major advances have been made in modeling crowd dynamics, but challenges remain. The goal of this Faculty Early-Career Development (CAREER) award is to support research, education, and related activities that will develop a reusable and behaviorally founded computer model of pedestrian movement and crowd behavior amid dense urban environments. The investigator intends for this work to serve as a test-bed for experimentation with ideas, hypotheses, and plans that would otherwise lie beyond the reach of academic inquiry. The research will seek to advance the state-of-the-art in crowd modeling by representing individuals, crowds, and the ambient city with rich detail. Models will be built with theory-informed algorithms that capture the intricacies of human behavior. The model will be realized as a fully immersive three-dimensional environment that engages both the public and students, and it will convey intuitively complicated ideas about human movement and crowd behavior. A robust calibration and validation scheme will be employed to facilitate evaluation of policies and plans in simulation and mapping of models to real-world scenarios in public health, downtown revitalization, public safety, defense, large-scale event-planning, escape, evacuation, and emergencies.

The project will be innovative in areas of methodological and substantive interest in many ways. It will push the current state-of-the-art in spatial modeling in the geographical sciences. The work will broaden the behavioral base for computational modeling of human movement. The project will contribute to the development of dynamic geographic information science. The work also will produce a novel validation scheme that combines GIS analytics based on time geography with spatial analysis, landscape metrics, and spatial statistics. Substantively, the model will be used to build theory in areas of human and urban geography that are traditionally ill-equipped for investigation and examination at the micro-scale and in massively dynamic contexts. Moreover, the model will serve as an experimental but wholly realistic environment for exploring "what-if" and unforeseen scenarios of relevance to cities and their citizens.

Increasingly, the engineering of complex systems requires consideration of an intricate web of components and their interaction in diverse social and technical environments. Simulation can assist in designing and testing socio-technical systems by allowing the potential space of outcomes to be explored under given designs. Agent-based models have been developed as a method for building models of complex systems, with great success. Agents may be designed to represent system components and to specify the interactions between them in an incredible level of detail. While popular, the full potential of the methodology to support engineering of complex systems has not been reached, however, because of a set of key challenges. First, there exists a relative lack of robust methods for calibrating agent-based models to theory. Second, there is a paucity of reliable approaches for extracting coarse-grained, system level information as it emerges in agent-based simulations. Third, there is a dearth of schemes for handling uncertainty in the application of agent-based rules to system behavior. Fourth, computation of agent-based models is inefficient when agents are numerous in volume and richly-specified in behavior. Together, these impediments constrain the ability of agent-based modeling to enable prediction, to support decisions, and to facilitate the design, control, and optimization of complex systems. The main objective of this project is to broaden the extensibility of agent-based modeling beyond these constraints. This will be achieved by developing novel computational methods to fuse agent-based modeling, uncertainty measurement and quantification, and mathematics for pattern-extraction.

This project will expand the capabilities of agent-based modeling in supporting the design, engineering, and testing of complex systems. Our initial focus is to develop a prototype scheme that can be applied to complex socio-behavioral systems, but the project is of potential relevance across a diverse array of substantive areas. Indeed, one of our central aims is to provide the glue that can bridge diverse schemes for agent-based simulation across application areas. This could be incredibly useful in reconciling agent-based modeling into a larger "ecology" of mathematical modeling and computation, fundamentally expanding the range of questions that can be posed and systems that can be explored in simulation, while better linking simulation to real-world dynamics.