Dynamic Nonlinear Pricing Model Based on Adaptive and Sophisticated Learning

Wenjie Bi, Yinghui Sun, Haiying Liu and Xiaohong Chen

Mathematical Problems in Engineering, 2014, vol. 2014, 1-11

Abstract:

Existing dynamic pricing models which take consumers’ learning behavior into account generally assume that consumers learn on the basis of reinforcement learning and belief-based learning. Nevertheless, abundant empirical evidence of behavior game indicates that consumers’ learning is normally described as a process of mixed learning. Particularly, for experience goods, a consumer’s purchase decision is not only based on his previous purchase behavior (adaptive learning), but also affected by that of other consumers (sophisticated learning). With the assumption that consumers are both adaptive and sophisticated learners, we study a dynamic pricing model dealing with repeated decision problems in a duopoly market. Specifically, we build a dynamic game model based on sophisticated experience-weighted attraction learning model (SEWA) and analyze the existence of the equilibrium. Finally, we show the characteristics and differences of the steady-state solutions between models considering adaptive consumers and models considering sophistical consumers by numerical results.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:791656

DOI: 10.1155/2014/791656

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