Human factors in computer decision-making

This thesis investigates whether incorporating ideas from human decision-making in computer algorithms may help improve agents’ decision-making performance, as either independent actors or in collaboration with humans. For independent actors, psychological cognitive appraisal theories of emotion are used to develop a lightweight algorithm that dynamically re-prioritizes their goals to direct their attention. In experiments in quickly changing and highly uncertain domains these agents are shown to perform as well as agents that compute expensive optimal solutions, and exhibit robustness with respect to the parameters of the environment. For agents interacting with humans, it is investigated whether expressing emotions has the ability to convey traits like trustworthiness and skill, and whether the appropriate emotional expression can help forge mutually beneficial relationships with the human. Finally, the theory of reasoning patterns [7] is leveraged to analyze games and make it possible to answer questions about a system’s strategic behavior without having to compute an expensive, precise solution. This theory is also employed to the generate advice for human decision-makers in complex games. This advice has been experimentally shown to improve their decision-making performance.