Human AI Decision Making

Teams of the future will likely consist of humans and AI agents. To this purpose, we conducted experiments to explore how such teams integrate their individual decisions into a group response. We propose a set of models to explain team decision making using appraisal dynamics, Prospect Theory, Bayes rule, and eigenvector centrality. Decision-making in the experiments proceeds consists of two sequential tasks: a first task in which the teams decide to report one of the presented options to a multiple-choice question or to choose one of the agents, and if the teams decide to use an agent, the second decision-making task consists of integrating the agent's response with their previous responses and reporting an answer. 

Affiliated People

Photo of Zexi Huang.
Research interests: 

Graph Data Mining, Representation Learning

Zexi received his B.Eng. in Computer Science and Technology at University of Electronic Science and Technology of China, Chengdu in 2018. He joined Dynamo lab in 2018. His research interests span the analysis of social, informational, and biological networks with machine learning and data mining techniques.