Performance Decision makers frequently encounter environments without perfect information, in which factors such as the distribution of missing information and estimates of missing information significantly impact decision accuracy and speed. This work presents an experiment which modifies an environment with missing information (total information, option imbalance, cue balance) and examines user estimates of the missing information to understand how accuracy and decision speed respond under time pressure. Results indicate that regardless of the way missing information is estimated, certain distributions of missing information reduce decision accuracy. Results from this work also indicate that beyond information distribution and estimation strategy, differences in decision strategy adopted may explain significant differences in decision performance. High performers tend to ignore a greater percentage of information instead of attempting to estimate it, thereby adopting a strategy more heuristic in nature.
Author: William Sealy
Impact of Shared Mental Models on Human-AI Interaction and Mission Effectiveness
Human teams are most effective when the members of the team utilize a shared mental model (SMM), meaning a shared perception of goals and actions through effective communication and an understanding of their fellow team members’ goals and likely methods. Currently humans and AI teams share no such model. At best, humans working closely with AI begin to anticipate what the AI can do and when it can be trusted, as is the case in medical decision making. But more commonly, as in the case of the Tesla Autopilot mistaking a truck for a cloud, the human often does not have sufficient insight or experience to understand when to distrust the AI.
These real-life examples bring to light the fact that while AI is becoming more accurate, users often do not understand when it can be trusted and more importantly when it cannot be relied upon. By utilizing the concept of a shared mental model, I assert that, human-AI teams can become more effective, and reduce the dissonance between us and AI systems.
The objective of this research is to develop a shared mental model that is accessible and updatable by both humans and AI and to demonstrate that joint human-AI systems which include a shared mental model (SMM) perform better at dynamic decision making tasks. Central to this research is the belief that the human must be supported in an intelligible way (meaning the human must have some understanding of the AI-system) and that the AI must have understanding of its human teammate. This concept of mutual understanding of the problems, goals, information cues, strategies, and roles of each teammate is referred to as the SMM.
We use a combination of the theories from robotics, computer science, and psychology to develop a proactive AI-agent that can advise a human decision-maker, acting as a teammate rather than a tool. This AI-agent will improve human decision making by utilizing not only the problem parameters but developing cognizance of both the heuristic and analytic strategies that decision makers rely on during high pressure decision tasks.