  There is increasing interest in using LLMs as decision-making "agents." Doing
so includes many degrees of freedom: which model should be used; how should it
be prompted; should it be asked to introspect, conduct chain-of-thought
reasoning, etc? Settling these questions -- and more broadly, determining
whether an LLM agent is reliable enough to be trusted -- requires a methodology
for assessing such an agent's economic rationality. In this paper, we provide
one. We begin by surveying the economic literature on rational decision making,
taxonomizing a large set of fine-grained "elements" that an agent should
exhibit, along with dependencies between them. We then propose a benchmark
distribution that quantitatively scores an LLMs performance on these elements
and, combined with a user-provided rubric, produces a "STEER report card."
Finally, we describe the results of a large-scale empirical experiment with 14
different LLMs, characterizing the both current state of the art and the impact
of different model sizes on models' ability to exhibit rational behavior.
