Simulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs

Abstract

Discrete choice models are widely used in economics, transportation, and marketing to explain and predict how decision makers choose among a finite set of alternatives. The resulting choices depend on the socioeconomic characteristics of the decision makers, the attributes of the alternatives, and the conditions under which the decision is made. Because these factors interact in complex ways, and because real experiments raise confidentiality concerns, take time, and are costly, controlled experimentation with real respondents is often impractical. Simulation offers an alternative in which the data-generating process, and hence the true parameters, are known. This paper connects discrete choice modeling, experimental design, and simulation. We review the three areas in a common notation and state the results on which the simulation approach relies. We then formalize a simulation tool that combines an experimental design with a behavioral model of artificial agents to generate synthetic stated choice data, and we prove that the generated choices follow a panel mixed multinomial logit model with known parameters. On this basis we define measures of estimator performance and of the discrepancy between simulated and real data, illustrate the tool with a stated choice experiment on the willingness to pay for environmental attributes of roses, and discuss the behavioral factors that separate simulated choices from real ones.