Automation is becoming more autonomous, and the application of automation as a collaborator continues to be explored. A major restriction to automation’s application as a collaborator is that people often hold inaccurate expectations of their automated collaborator. Goal alignment has been shown to be beneficial in collaborations and delegation decisions among human-human and human-automation collaborations. Few studies have investigated the difference that goal alignment has on human collaborators compared to automated collaborators. In this 2 (goal aligned or misaligned) x 2 (human or automated) between-subjects study, participants complete a simplified triage patient task and then are given the opportunity to stay with their manual task solution or to delegate their decision and go with their collaborator’s recommendation. Participants never delegated to collaborators with goals that were not aligned to theirs. Participants working with human collaborators that have similar goals to them were more often delegated to and more often associated with a better triage performance. These results can inform the design of similar systems that foster collaboration and achieve better team performance. Although goal alignment was crucial for delegation decisions, it was difficult to achieve complete agreement of goals. Future research should investigate effective methods to better communicate goals among collaborators.

The prevalence of autonomous technology is advancing at a rapid rate and is becoming more sophisticated. As this technology becomes more advanced, humans and autonomy may work together as teammates in various settings. A crucial component of teaming is trust, but to date, researchers are limited in assessing trust calibration dynamically in human-autonomy teams. Traditional methods of measuring trust (e.g., Likert scale questionnaires) capture trust after the fact or at a specific time. However, trust fluctuates, and determining what causes this might give machine designers insight into how machines can be improved upon so that operator’s trust towards the machines is more properly calibrated. This thesis aimed to assess the validity of an interaction-based metric of trust: anticipatory pushing of information. Anticipatory pushing of information refers to teammate A anticipating the needs of teammate B and pushing that information to teammate B. It was hypothesized there would be a positive relationship between the frequency of anticipatory pushing and self-reported trust scores. To test this hypothesis, text chat data and self-reported trust scores were analyzed in a previously conducted study in two different sessions (routine and degraded). Findings indicate that the anticipatory pushing of information and the self-reported trust scores between the human-human pairs in the degraded sessions were higher than the routine sessions. In degraded sessions, the anticipatory pushing of information between the human-human pairs was associated with human-human trust.

Computational social choice theory is an emerging research area that studies the computational aspects of decision-making. It continues to be relevant in modern society because many people often work as a group and make decisions in a group setting. Among multiple research topics, rank aggregation is a central problem in computational social choice theory. Oftentimes, rankings may involve a large number of alternatives, contain ties, and/or be incomplete, all of which complicate the use of robust aggregation methods. To address these challenges, firstly, this work introduces a correlation coefficient that is designed to deal with a variety of ranking formats including those containing non-strict (i.e., with-ties) and incomplete (i.e., unknown) preferences. The new measure, which can be regarded as a generalization of the seminal Kendall tau correlation coefficient, is proven to satisfy a set of metric-like axioms and to be equivalent to a recently developed ranking distance function associated with Kemeny aggregation. Secondly, this work derives an exact binary programming formulation for the generalized Kemeny rank aggregation problem---whose ranking inputs may be complete and incomplete, with and without ties. It leverages the equivalence of minimizing the Kemeny-Snell distance and maximizing the Kendall-tau correlation, to compare the newly introduced binary programming formulation to a modified version of an existing integer programming formulation associated with the Kendall-tau distance. Thirdly, this work introduces a new social choice property for decomposing large-size problems into smaller subproblems, which allows solving the problem in a distributed fashion. The new property is adequate for handling complete rankings with ties. The property is leveraged to develop a structural decomposition algorithm, through which certain large instances of the NP-hard Kemeny rank aggregation problem can be solved exactly in a practical amount of time. Lastly, this work applies these rank aggregation mechanisms to novel contexts for extracting collective wisdom in crowdsourcing tasks. Through this crowdsourcing experiment, we assess the capability of aggregation frameworks to recover underlying ground truth and the usefulness of multimodal information in overcoming anchoring effects, which shows its ability to enhance the wisdom of crowds and its practicability to the real-world problem.