Leveraging Large Language Models to Investigate the Decoy Effect in Route Choice Behavior

Route choice behavior is a cornerstone of transport
research, traditionally modeled under the assumption that trav
elers act as rational agents who maximize utility by trading off
time, cost, and reliability. However, behavioral economics shows
that real-world decision making often departs from rationality
due to systematic biases. One such bias is the decoy effect, which
occurs when the introduction of a dominated alternative increases
the attractiveness of another option. While widely studied in
consumer contexts, its role in transport decision making remains
underexplored. This paper presents a novel methodology that
leverages large language models (LLMs) to investigate the decoy
effect in route choice. Using ChatGPT-4o mini, we generated
textual framings of a dominated route alternative and employed
the model as a synthetic respondent to simulate route selections.
Four experimental conditions were tested: baseline (two routes),
decoy with neutral framing, decoy with positive framing, and
decoy with negative framing. Results demonstrate that the decoy
increased the relative attractiveness of the premium route, partic
ularly under positive framing, while negative framing attenuated
the effect. Synthetic responses closely aligned with predictions
from a multinomial logit model, confirming consistency with
behavioral theory. The findings illustrate that LLMs can serve
as both framing generators and behavioral simulators, offering
a rapid testbed for prototyping hypotheses in transport research.
This work establishes proof-of-concept evidence that LLMs can
complement traditional behavioral models, opening pathways
for future integration of artificial intelligence into the study of
systematic biases in mobility decisions.