Humans are being continuously involved as cognitive and social agents in engineered systems, and, in a similar vein, engineered systems are becoming increasingly integral to systems of interacting humans. A particularly elusive issue entails understanding and predicting the evolution of the collaboration between humans and engineered systems, across behavioral and technological domains. This award supports experimental and theoretical research to elucidate the dynamics of collaboration in human-engineered systems, through the integration of dynamical systems theory, robotics, and human-computer interactions. Humans will socially interact with engineered systems in an engineering context, which will be, in turn, enabled by an engineering infrastructure. This research will contribute to lay the foundation for the next generation of autonomous environmental monitoring systems, which capitalize on human intelligence and low-cost distributed robots for rapidly and accurately monitoring the environment. Complementing the research are interdisciplinary formal and informal education activities that will benefit the training of underprivileged students and reach out to economically-disadvantaged local communities.
This research program seeks to establish a transformative experimental and theoretical framework for understanding, predicting, and, ultimately, controlling the evolution of the collaboration between humans and engineered systems. In a novel crowdsourcing infrastructure, online groups comprised of real humans and artificial experts will collaboratively perform aquatic environmental monitoring, by virtually patrolling mobile aquatic robots in a polluted canal in Brooklyn, NY, to explore the water basin, collect and classify wildlife images, and identify sources of pollution. Similar to a mechanics experiment in which one applies a sequence of mechanical forces to a solid and measures its mechanical deformation, this project will study the response of humans to the controlled actions of artificial experts and investigate their intertwined dynamics, in terms of social interactions and task performance. A series of hypothesis-driven studies will explore the roles of human cognitive abilities, dispositional factors, and behavioral plasticity on technology-mediated social interactions and performance. A new data-driven mathematical framework based on signal processing, network science, and information theory will be formulated to uncover the interplay between personal attributes and plasticity of humans, their interactions with engineered systems, and how to better design a human-engineered system for collective expertise.
