Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand
通过实验室实验，研究了非平稳需求下人类在收益管理中的决策策略与偏差，发现参与者表现出乐观和损失厌恶偏差，并锚定于客户支付意愿，为设计人机协同分析系统提供启示。
Abstract State‐of‐the‐art revenue management systems combine forecasting and optimization algorithms with human decision‐making. However, only a few existing contributions consider the behavioral aspects of revenue management. To extend the related research, we examine the impact of nonstationary demand and two dynamic decision tasks. We examine human decision‐making strategies and biases by implementing a related experimental design in a laboratory study and comparing participant decisions to systematic heuristics. Our results highlight that participants struggle to accommodate a nonstationary willingness to pay. In that, they exhibit a combination of optimism and loss aversion biases. We further find that participants anchor their decisions on customers' willingness to pay. We draw implications and further research opportunities to behaviorally inform the design of symbiotic analytics systems from these results.