Reducing sample size requirements by extending discrete choice experiments to indifference elicitation

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2023-09-01 DOI:10.1016/j.jocm.2023.100426
Ambuj Sriwastava, Peter Reichert
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引用次数: 1

Abstract

Discrete choice (DC) methods provide a convenient approach for preference elicitation and they lead to unbiased estimates of preference model parameters if the parameterization of the value function allows for a good description of the preferences. On the other hand, indifference elicitation (IE) has been suggested as a direct trade-off estimator for preference elicitation in decision analysis decades ago, but has not found widespread application in statistical analysis frameworks as for discrete choice methods. We develop a hierarchical, probabilistic model for IE that allows us to do Bayesian inference similar to DC methods. A case study with synthetically generated data allows us to investigate potential bias and to estimate parameter uncertainty over a wide range of numbers of replies and elicitation uncertainties for both DC and IE. Through an empirical case study with laboratory-scale choice and indifference experiments, we investigate the feasibility of the approach and the excess time needed for indifference replies. Our results demonstrate (i) the absence of bias of the suggested methodology, (ii) a reduction in the uncertainty of estimated parameters by about a factor of three or a reduction of the required number of replies to achieve a similar accuracy as with DC by about a factor of ten, (iii) the feasibility of the approach, and (iv) a median increase in time needed for indifference reply of about a factor of three. If the set of respondents is small, the higher elicitation effort may be worth to achieve a reasonable accuracy in estimated value function parameters.

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通过将离散选择实验扩展到无差别启发来减少样本量需求
离散选择(DC)方法为偏好启发提供了一种方便的方法,并且如果值函数的参数化允许对偏好进行良好的描述,则它们会导致偏好模型参数的无偏估计。另一方面,几十年前,无差别启发(IE)就被认为是决策分析中偏好启发的直接权衡估计器,但在离散选择方法的统计分析框架中尚未得到广泛应用。我们为IE开发了一个分层的概率模型,使我们能够进行类似于DC方法的贝叶斯推理。综合生成数据的案例研究使我们能够调查潜在的偏差,并估计DC和IE在大量回复和启发不确定性中的参数不确定性。通过实验室规模选择和无差异实验的实证案例研究,我们研究了该方法的可行性以及冷漠回复所需的多余时间。我们的结果表明:(i)所建议的方法没有偏差,(ii)估计参数的不确定性减少了大约三倍,或者所需的回复数量减少了大约十倍,以实现与DC类似的准确性,(iii)该方法的可行性,以及(iv)冷漠回复所需时间的中位数增加了约三倍。如果受访者的数量很小,那么更高的启发努力可能是值得的,以实现估计值函数参数的合理准确性。
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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
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