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

TL;DR

This paper introduces SDCM, a simulation tool designed to evaluate discrete choice models by generating synthetic data through an integrated simulation and experimental design approach, addressing challenges of real-world experimentation.

Contribution

The paper presents a novel simulation method combined with experimental design for generating synthetic data to evaluate DCMs, bridging gaps between modeling, simulation, and experimental research.

Findings

SDCM effectively generates synthetic data for DCM evaluation

The simulation approach captures complex interactions in decision-making

Discrepancies between simulated and real data are analyzed

Abstract

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.