Discrete Choice Modelling serves as a robust framework for modelling human choice behaviour across various disciplines. Building a choice model is a semi structured research process that involves a combination of a priori assumptions, behavioural theories, and statistical methods. This complex set of decisions, coupled with diverse workflows, can lead to substantial variability in model outcomes. To better understand these dynamics, we developed the Serious Choice Modelling Game, which simulates the real world modelling process and tracks modellers' decisions in real time using a stated preference dataset. Participants were asked to develop choice models to estimate Willingness to Pay values to inform policymakers about strategies for reducing noise pollution. The game recorded actions across multiple phases, including descriptive analysis, model specification, and outcome interpretation, allowing us to analyse both individual decisions and differences in modelling approaches. While our findings reveal a strong preference for using data visualisation tools in descriptive analysis, it also identifies gaps in missing values handling before model specification. We also found significant variation in the modelling approach, even when modellers were working with the same choice dataset. Despite the availability of more complex models, simpler models such as Multinomial Logit were often preferred, suggesting that modellers tend to avoid complexity when time and resources are limited. Participants who engaged in more comprehensive data exploration and iterative model comparison tended to achieve better model fit and parsimony, which demonstrate that the methodological choices made throughout the workflow have significant implications, particularly when modelling outcomes are used for policy formulation.

翻译：离散选择建模作为一个稳健的框架，广泛应用于多个学科中的人类选择行为建模。构建选择模型是一个半结构化的研究过程，涉及先验假设、行为理论和统计方法的结合。这一系列复杂的决策，加上多样化的研究流程，可能导致模型结果出现显著差异。为了更好地理解这些动态，我们开发了“严肃选择建模游戏”，该游戏模拟了现实世界的建模过程，并使用陈述偏好数据集实时追踪建模者的决策。参与者被要求开发选择模型来估算支付意愿值，以便为政策制定者提供减少噪音污染的策略信息。游戏记录了包括描述性分析、模型设定和结果解释在内的多个阶段的行为，使我们能够分析个体决策和建模方法的差异。虽然我们的研究发现，在描述性分析中建模者强烈偏好使用数据可视化工具，但也指出了在模型设定前处理缺失值方面存在的不足。我们还发现，即使建模者使用相同的选择数据集，其建模方法也存在显著差异。尽管存在更复杂的模型，但诸如多项Logit模型之类的简单模型往往更受青睐，这表明当时间和资源有限时，建模者倾向于避免复杂性。那些进行了更全面数据探索和迭代模型比较的参与者，往往能获得更好的模型拟合度和简约性，这证明了整个工作流程中所采用的方法选择具有重要影响，尤其是在建模结果用于政策制定时。