matt hardy

mdhardy at princeton dot edu
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I'm a PhD student and NDSEG fellow at Tom Griffiths' Computational Cognitive Science Lab at Princeton University. I use machine learning, computational modeling, and behavioral experiments to study how people make decisions. Often, this involves integrating individual-level models of cognition with distributed models of social dynamics. I am especially interested in using this framework to improve network design and choice architectures.

Before my PhD, I majored in both statistics and economics and minored in math at the University of Toronto. During this time, I spent two summers at MIT working with Whitney Newey and Jerry Hausman on applied and theoretical econometrics, and one summer at Via as a data science intern. More recently, I interned at Microsoft Research and worked with Dan Goldstein and Jake Hofman to develop tools for out-of-sample forecasting.

I also enjoy data and model visualization and have recently started building electric scatter.

selected papers

Bias amplification in experimental social networks is reduced by resampling
Matt Hardy, Bill Thompson, P.M. Krafft, and Tom Griffiths (under review).

Optimal nudging for cognitively bounded agents: A framework for modeling, predicting, and controlling the effects of choice architectures
Fred Callaway, Matt Hardy, and Tom Griffiths (under review).

How do humans overcome individual computational limitations by working together?
Natalia VĂ©lez, Brian Christian, Matt Hardy, Bill Thompson, and Tom Griffiths (2023). Cognitive Science.

Overcoming individual limitations through distributed computation: Rational information accumulation in multi-generational populations
Matt Hardy, P.M. Krafft, Bill Thompson, and Tom Griffiths (2022). Topics in Cognitive Science.