Common set of weights in data envelopment analysis under prospect theory
Common set of weights in data envelopment analysis under prospect theory
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DOI：
10.1111/exsy.12602
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发表时间：
2020-07
期刊：
影响因子：
3.3
通讯作者：
Yu Yu-Yu;Weiwei Zhu;Qinfen Shi;S. Zhuang
中科院分区：
文献类型：
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作者：
Yu Yu-Yu;Weiwei Zhu;Qinfen Shi;S. Zhuang
数据包络分析(DEA)是一种数据驱动的绩效评估工具，用于衡量决策单元(DMU)并为其指定特定的权重。标准的DEA模型通常认为，决策者(DM)完全理性地选择最有利的权重以获得最高的绩效得分，但在评估过程中不考虑他们对风险的态度。前景理论大体上符合人类的心理行为。因此，我们的研究捕捉了决策者在风险情景下的非理性行为，以构建一个新的共同权重DEA模型，该模型最大化总的前景价值，损失的变化可能比收益的变化更大，从而获得更现实的共同权重方案。我们提出的模型不仅生成了具有更高总前景价值的决策单元，而且还产生了更高的满意度。本研究表明，前景理论可以很好地推广到DEA研究领域，为今后的DEA研究提供适当的指导。
Data envelopment analysis (DEA) is a data‐driven tool for performance evaluation, measuring decision‐making units (DMUs) and designating them with specific weightings. The standard DEA model typically sets up that decision‐makers (DMs) are wholly rational to select the most favourable weights to obtain the maximum performance score, but does not take into account their attitude toward risk during the assessment. The prospect theory generally matches humans' psychological behaviours. Thus, our study captures the non‐rational behaviours of DMs, performing under risk scenarios, in order to construct a novel common‐weights DEA model that maximizes the total prospect value, which can vary more steeply for losses than for gains, hence obtaining a more realistic common weight scheme. Our proposed model not only generates DMUs, with higher total prospect values, but also greater degrees of satisfaction. The current study shows that the prospect theory can be aptly extended to the DEA research area, supplying a proper guideline for future DEA research.