  Discrete choice models (DCMs) have been widely utilized in various scientific
fields, especially economics, for many years. These models consider a
stochastic environment influencing each decision maker's choices. Extensive
research has shown that the agents' socioeconomic characteristics, the chosen
options' properties, and the conditions characterizing the decision-making
environment all impact these models. However, the complex interactions between
these factors, confidentiality concerns, time constraints, and costs, have made
real experimentation impractical and undesirable. To address this, simulations
have gained significant popularity among academics, allowing the study of these
models in a controlled setting using simulated data. This paper presents
multidisciplinary research to bridge the gap between DCMs, experimental design,
and simulation. By reviewing related literature, the authors explore these
interconnected areas. We then introduce a simulation method integrated with
experimental design to generate synthetic data based on behavioral models of
agents. A utility function is used to describe the developed simulation tool.
The paper investigates the discrepancy between simulated data and real-world
data.
