Chase McDonald

I’m a student at Carnegie Mellon University pursuing a Ph.D. in Cognitive Decision Science. In December 2022, I received an M.S. in Machine Learning and before that a B.A. in Computational Social Science (through the individual major program) from UCLA. I am interested in cooperative artificial intelligence and the design of human-compatible autonomous systems. In my work, I ask how we can design AI to complement human decision-making and improve human experiences.

In tandem with my graduate studies, I am a Research Scientist on Riot Games’ Game AI team, where I conduct applied research on multi-agent reinforcement learning.

news

Dec 21, 2022	Completed the Master’s in Machine Learning at Carnegie Mellon!
Sep 21, 2022	Received the Tata Consultancy Services Presidential Fellowship at Carnegie Mellon!
Sep 12, 2022	Joined Riot Games as a Data Scientist to work on applied research in their Game AI team.
May 24, 2022	Joined Riot Games as a summer intern, where I will work on reinforcement learning in the AI Accelerator, Riot’s AI and machine learning applied research group.
Nov 6, 2021	I presented work on multi-agent coordination and specialization (with Thuy Ngoc Nguyen and Coty Gonzalez) at the AAAI Fall Symposium on Computational Theory of Mind for Human-Machine Teams.

selected papers

ICCM

Diverse Experience Leads to Improved Adaptation: An experiment with a cognitive model of learning

McDonald, C., Bugbee, E., McCormick, E., Fiechter, J., Blaha, L, Lebiere, C., and Gonzalez, C.

International Conference on Cognitive Modeling Jul 2021

Abs

In dynamic decision tasks, the situations we confront are never the same: the world is constantly changing. Generally, our ability to generalize learned skills depends on the similarity between the learned skills and the situations in which we will apply those skills. However, in dynamic tasks, the situations we are trained in will most likely be different from the situations in which we need to apply skills. For example, in the face of emergencies, one could be trained to handle hypothetical disaster scenarios, but remain unprepared for the emergency that is actually experienced. This raises an important question: how can we best prepare for the unexpected? Cognitive science research suggests that heterogeneity during training helps people adapt to unexpected situations. However, evidence for a general diversity hypothesis is limited. In this research, we investigate this Diversity Hypothesis using a cognitive model of learning and decisions from experience based on Instance-Based Learning (IBL) Theory. We focus on the concept of decision complexity to investigate whether confronting decisions of diverse complexities results in improved adaptation to unexpected decision complexities, compared to situations of constant decision complexity. We conduct a simulation experiment using an IBL model in a Gridworld task, and expose agents to various degrees of diversity as they learn; we then observe how these agents transfer their acquired knowledge to a situation of novel decision complexity. Our results support the Diversity Hypothesis and the benefits of diversity on adaptation.