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.

Accessibility is increasingly used as a metric when evaluating changes to public transport systems. Transit travel times contain variation depending on when one departs relative to when a transit vehicle arrives, and how well transfers are coordinated given a particular timetable. In addition, there is necessarily uncertainty in the value of the accessibility metric during sketch planning processes, due to scenarios which are underspecified because detailed schedule information is not yet available. This article presents a method to extend the concept of "reliable" accessibility to transit to address the first issue, and create confidence intervals and hypothesis tests to address the second.

Mexicans and Mexican Americans have resided in Arizona since the early 16th century. Their history, however, is severely under-documented in the state’s archival repositories. As of 2012, this community is represented in a mere 1-2% of the state’s known archival holdings, and 98% of such documentation is held at Arizona State University’s Chicano/a Research Collection (CRC). This article provides a historical review of the CRC’s establishment in 1970 and how its founding Curator, Dr. Christine Marín, transformed a small circulating book collection into Arizona’s largest repository for Mexican American history. It goes on to examine how the CRC’s sitting Archivist is using social media in tandem with a community-based workshop, bilingual promotional materials and finding aids, and description of unprocessed collections as community outreach and collection development tools in order to remedy the under-documentation of Mexican American history in Arizona. We argue that augmenting traditional archival field collecting methods with these strategies enables the CRC to build a more robust relationship with Arizona’s Mexican American community, allows us to continue expanding our archival holdings, and serves as an example for other repositories seeking to enhance their documentation of marginalized communities.