ASU Scholarship Showcase

Human team members show a remarkable ability to infer the state of their partners and anticipate their needs and actions. Prior research demonstrates that an artificial system can make some predictions accurately concerning artificial agents. This study investigated whether an artificial system could generate a robust Theory of Mind of human teammates. An urban search and rescue (USAR) task environment was developed to elicit human teamwork and evaluate inference and prediction about team members by software agents and humans. The task varied team members’ roles and skills, types of task synchronization and interdependence, task risk and reward, completeness of mission planning, and information asymmetry. The task was implemented in MinecraftTM and applied in a study of 64 teams, each with three remotely distributed members. An evaluation of six Artificial Social Intelligences (ASI) and several human observers addressed the accuracy with which each predicted team performance, inferred experimentally manipulated knowledge of team members, and predicted member actions. All agents performed above chance; humans slightly outperformed ASI agents on some tasks and significantly outperformed ASI agents on others; no one ASI agent reliably outperformed the others; and the accuracy of ASI agents and human observers improved rapidly though modestly during the brief trials.

Green infrastructure serves as a critical no-regret strategy to address climate change mitigation and adaptation in climate action plans. Climate justice refers to the distribution of climate change-induced environmental hazards (e.g., increased frequency and intensity of floods) among socially vulnerable groups. Yet no index has addressed both climate justice and green infrastructure planning jointly in the USA. This paper proposes a spatial climate justice and green infrastructure assessment framework to understand social-ecological vulnerability under the impacts of climate change. The Climate Justice Index ranks places based on their exposure to climate change-induced flooding, and water contamination aggravated by floods, through hydrological modelling, GIS spatial analysis and statistical methodologies. The Green Infrastructure Index ranks access to biophysical adaptive capacity for climate change. A case study for the Huron River watershed in Michigan, USA, illustrates that climate justice hotspots are concentrated in large cities; yet these communities have the least access to green infrastructure. This study demonstrates the value of using GIS to assess the spatial distribution of climate justice in green infrastructure planning and thereby to prioritize infrastructure investment while addressing equity in climate change adaptation.