Dynamic pricing with stochastic reference effects based on a finite memory window
提出消费者记忆有限且回忆服从一阶马尔可夫过程的参考价格更新模型，研究动态定价策略，发现最优价格路径单调收敛到稳态，且损失厌恶会扩大稳态范围。
Inspired by the latest empirical studies, we propose a new updating model for reference prices by assuming that consumers’ memories are limited and their recall of previous prices obeys a first-order Markov stochastic process. We investigate a dynamic pricing model with stochastic reference effects and finite memory. Consistent with the exponential smoothing model, we indicate that reference effects lead to monotonic convergence of the optimal price path to an expected steady-state price. The steady-state range tends to widen as consumers become loss-averse. The results of our numerical experiments differ from findings of certain models under the assumption of stochastic recall memory of consumers. The optimal price path fluctuates consistently around the steady state instead of remaining constant. The effect of the first price on the memory window and long-term profits decreases as the length of memory window increases.