Stability and chaos in demand-based pricing under social interactions

Yuan, Xuchuan; Hwarng, H. Brian

doi:10.1016/j.ejor.2016.02.047

Cited by 17 publications

(6 citation statements)

References 37 publications

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“…Figure 2 gives the bifurcation diagram of prices with respect to 1 when other parameters are fixed, where the blue orbit represents the evolution of 1 , the red orbit represents the evolution of 2 , and the black orbit shows the largest Lyapunov exponent (LLE) of this system. LLE is widely used to mark chaos; it is positive when chaos occurs [51]. From Figures 1 and 2, we can see that the Nash equilibrium point is locally stable when 1 and 2 are small; this is consistent with Proposition 1.…”

Section: Proposition 5 For System (9) Without Delay Decision ( = 1)supporting

confidence: 75%

Implications for Firms with Limited Information to Take Advantage of Reference Price Effect in Competitive Settings

Guo

2017

Complexity

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This paper studies internal reference price effects when competitive firms face reference price effects and make decisions based on partial information, where their decision-making mechanism is modeled by a dynamic adjustment process. It is shown that the evolution of this dynamic adjustment goes to stabilization if both adjustment speeds are small and the complexity of this evolution increases in adjustment speeds. It is proved that the necessary condition for flip bifurcation or Neimark-Sacker bifurcation will occur with the increase of adjustment speed in two special cases. What is more, numerical simulations show that these bifurcations do occur. Then, the impacts of parameters on stability and profits are investigated and some management insights for firms with limited information to take advantage of reference price effects are provided.

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Section: Proposition 5 For System (9) Without Delay Decision ( = 1)supporting

confidence: 75%

Implications for Firms with Limited Information to Take Advantage of Reference Price Effect in Competitive Settings

Guo

2017

Complexity

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“…They find that customers’ memory affects the supplier’s profit by developing an approximate dynamic programming policy which dynamically rationalizes the fill rates to consumers. Yuan and Hwarng (2016) investigate the demand dynamics under a demand-based pricing policy for a frequently purchased service under the influence of social interactions. In their model, customers are assumed to be heterogeneous and forward-looking.…”

Section: Related Studiesmentioning

confidence: 99%

Managing service capacity with boundedly rational consumers under social interactions

Jiang

Yuan

2019

Self Cite

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Purpose Consumers are inclined to join longer queues due to social interactions in service consumptions. This purchase behavior brings in operational challenges in terms of capacity planning, which affects consumers’ demand, leading to an unstable and fluctuated arrival process. This paper aims to investigate the dynamic characteristics of the arrival process of a service system with boundedly rational consumers whose purchase decisions are influenced by the queue length under social interactions. Design/methodology/approach Consumers’ bounded rationality is modeled based on the random utility theory. Due to social interactions, the equilibrium queue length and its interaction with the expected waiting time affect consumers’ value perception. The authors first analyze the optimal service capacity decision with or without considering the influence of social interactions in a static setting. They then focus on the dynamic characteristics of the arrival process by a one-dimensional dynamical model in terms of the arrival rate. Findings This paper finds that the service system can behave chaotic in terms of arrival rate dynamics under social interactions. The results highlight the dynamical complexity of a simple service system due to consumers’ behavioral factors and the influence of social interactions, which may be the critical drivers leading to fluctuated and uneven demand. Originality/value The findings demonstrate that due to consumers’ limited cognitive ability and the influence of social interactions, the demand to a service system can be stable, periodic or even chaotic in terms of the arrival process. This study provides an alternative explanation to the observed demand fluctuations in various service processes under the influence of social interactions, which is important for service providers to effectively manage service capacity to achieve a stable service process and improve operational efficiency.

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“…Much of the recent work in this field focuses on enhancement techniques and methods of increasing the signal-to-noise ratio [14][15][16][17]. Methods like linear regression, logic regression model, Poisson model and negative binominal model are subject to strong assumption and limitations in applications [18].…”

Section: Literature Overviewmentioning

confidence: 99%

Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

Mrgole

Sever

2017

PROMET

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Stability and chaos in demand-based pricing under social interactions

Cited by 17 publications

References 37 publications

Implications for Firms with Limited Information to Take Advantage of Reference Price Effect in Competitive Settings

Implications for Firms with Limited Information to Take Advantage of Reference Price Effect in Competitive Settings

Managing service capacity with boundedly rational consumers under social interactions

Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction

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