At the time of creating an experimental design for a stated choice experiment, the analyst often does not precisely know which model, or decision rule, he or she will estimate once the data are collected. This paper presents two new software tools for creating stated choice experimental designs that are simultaneously efficient for regret minimisation and utility maximisation decision rules. The first software tool is a lean, easy-to-use and free-of-charge experimental design tool, which is dedicated to creating designs that incorporate regret minimisation and utility maximisation decision rules. The second tool constitutes a newly developed extension of Ngene – a widely used and richly featured software tool for the generation of experimental designs. To facilitate the use of the new software tools, this paper presents clear worked examples. It focusses on practical issues encountered when generating such decision rule robust designs, such as how to obtain priors and how to deal with alternative specific parameters. Furthermore, we analyse the robustness of the designs that we created using the new software tools. Our results provide evidence that designs optimised for one decision rule can be inefficient for another – highlighting the added value of decision rule robust designs.

中文翻译：

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新的软件工具可以有效地创建陈述选择的实验设计，从而有效地减少后悔和效用最大化的决策规则

在为指定的选择实验创建实验设计时，分析人员通常并不精确地知道收集数据后他或她会估计哪种模型或决策规则。本文介绍了两个用于创建指定选择实验设计的新软件工具，这些设计工具对于后悔最小化和效用最大化决策规则同时有效。第一个软件工具是一种精简，易用且免费的实验设计工具，专用于创建包含后悔最小化和效用最大化决策规则的设计。第二种工具构成了Ngene的最新开发扩展-Ngene是一种广泛使用的功能强大的软件工具，用于生成实验设计。为了促进新软件工具的使用，本文提供了清晰的示例。它着重于在生成此类决策规则健壮设计时遇到的实际问题，例如如何获得先验信息以及如何处理替代的特定参数。此外，我们分析了使用新软件工具创建的设计的健壮性。我们的结果提供了证据，表明针对一种决策规则进行优化的设计可能对另一种决策效率低下，从而突出了决策规则稳健设计的附加值。