  This paper provides a framework to quantify the sensitivity associated with
behavioral models based on Cumulative Prospect Theory (CPT). These are used to
design dynamic pricing strategies aimed at maximizing performance metrics of
the Shared Mobility On-Demand Service (SMoDS), as solutions to a constrained
nonlinear optimization problem. We analyze the sensitivity of both the optimal
tariff as well as the optimal objective function with respect to CPT model
parameters. In addition to deriving analytical solutions under certain
assumptions, more general numerical results are obtained via computational
experiments and simulations to analyze the sensitivity. We find that the model
is relatively robust for small to moderate parameter perturbations. Although
some of the trends in sensitivity are fairly general, the exact nature of
variations in many cases depends heavily on the specific travel scenarios and
modes being considered. This is primarily due to the complex nonlinearities in
the problem, as well as the significant heterogeneity in passenger preferences
across different types of trips.
