Dynamic nonlinear pricing model based on adaptive and sophisticated learning
type: Item
instance of: scholarly article
title: Dynamic nonlinear pricing model based on adaptive and sophisticated learning
zbMATH Open document ID: 1407.91111
DOI: 10.1155/2014/791656
author: Wenjie Bi
author: Yinghui Sun
author: Hai Ying Liu
author: Xiaohong Chen
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 91B24
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 7017270
Wikidata QID: Q59072223
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/791656
OpenAlex ID: W1970329549
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description: scientific article; zbMATH DE number 7017270