On the robustness of efficient experimental designs towards the underlying decision rule

Abstract

We present a methodology to derive efficient designs for Stated Choice (SC) experiments based on Random Regret Minimisation (RRM) behavioural assumptions. This complements earlier work on the design of efficient SC experiments based on Random Utility Maximisation (RUM) models. Capitalizing on this methodology, and using both analytical derivations and empirical data, we investigate the importance of the analyst's assumption regarding the underlying decision rule used to generate the efficient experimental design. We find that conventional RUM-efficient designs can be statistically highly inefficient in cases where RRM is the better representation of the actual choice behaviour, and vice versa. Furthermore, we present a methodology to construct efficient designs that are robust towards the uncertainty on the side of the analyst regarding the underlying decision rule.

Context-aware stated preferences with smartphone-based travel surveys

“…Rose et al (2008) also introduced an efficient method for pivoting attribute levels around a reference alternative. More recently, van Cranenburgh et al (2018) proposed efficient designs that are robust for decision rule uncertainty (such as random regret minimization).…”

Fitting mixed random regret minimization models using maximum simulated likelihood

“…To show how we can fit mixed RRM models using the command, we use data from a stated choice experiment that was utilized in van Cranenburgh, Rose, and Chorus (2018). The participants answered 10 choice situations where they chose from 3 unlabeled route alternatives with 2 attributes: travel cost and travel time.…”