计算机科学
理性
服务(商务)
样品(材料)
布线(电子设计自动化)
结果(博弈论)
合理规划模型
采样(信号处理)
样本量测定
服务提供商
人口
趋同(经济学)
订单(交换)
运筹学
营销
微观经济学
业务
经济
数学
统计
电信
计算机网络
法学
经济增长
化学
探测器
社会学
管理
色谱法
政治学
人口学
财务
作者
Andrew Frazelle,Tingliang Huang,Yehua Wei
标识
DOI:10.1177/10591478231224957
摘要
Self-interested customers’ form of reasoning and its consequences for system performance affect the planning decisions of service providers. We study procedurally rational customers—customers who make decisions based on a sample containing anecdotes of the system times experienced by other customers. Specifically, we consider procedurally rational customers in two-station service networks with open routing, that is, customers can choose the order in which to visit the stations. Because some actions may be less represented in the population, a given customer may not succeed in obtaining anecdotes about all possible actions. We introduce a novel sampling framework that extends the procedurally rational framework to incorporate the possibility that a customer may not receive any anecdotes for one of the actions; in this case, the customer uses a prior point estimate in lieu of the missing anecdotes. Under this framework, we study the procedurally rational equilibrium in open routing. We show first that as the sample size grows large, customers’ estimates become more accurate, and the procedurally rational equilibrium converges to the fully rational equilibrium (which is also socially optimal). We then uncover two main findings. First, we obtain bounds on the distance between the procedurally rational and fully rational equilibrium, aiding operational planning and showing the rate of convergence to the fully rational outcome as the sample size of anecdotes of each individual customer grows. Second, if customers obtain anecdotes of both actions with high probability, then the equilibrium will approximate the fully rational outcome, despite the sampling error inherent to procedural rationality.
科研通智能强力驱动
Strongly Powered by AbleSci AI