  Newly-developed large language models (LLM) -- because of how they are
trained and designed -- are implicit computational models of humans -- a homo
silicus. These models can be used the same way economists use homo economicus:
they can be given endowments, information, preferences, and so on and then
their behavior can be explored in scenarios via simulation. I demonstrate this
approach using OpenAI's GPT3 with experiments derived from Charness and Rabin
(2002), Kahneman, Knetsch and Thaler (1986) and Samuelson and Zeckhauser
(1988). The findings are qualitatively similar to the original results, but it
is also trivially easy to try variations that offer fresh insights. Departing
from the traditional laboratory paradigm, I also create a hiring scenario where
an employer faces applicants that differ in experience and wage ask and then
analyze how a minimum wage affects realized wages and the extent of labor-labor
substitution.
