A Revenue Maximizing Strategy Based on Bayesian Analysis of Demand Dynamics
A Revenue Maximizing Strategy Based on Bayesian Analysis of Demand Dynamics
复制标题
基于需求动态贝叶斯分析的收入最大化策略
DOI：
--
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Haibing Gao
中科院分区：
文献类型：
--
作者：
Jiahan Li;Tao Yao;Haibing Gao
对于寡头垄断服务网络中的企业来说，基于需求学习的动态定价是最大化其收入的有效途径。本文从博弈论动力学的角度介绍了一种学习需求行为的贝叶斯方法，该方法结合了非线性时间序列的非参数技术，去掉了严格的参数假设。我们通过马尔可夫链蒙特卡罗(MCMC)算法从历史市场数据中确定需求学习模型中的未知量。基于校准后的模型，可以预测未来需求对价格的反应，并通过启发式优化算法获得下一个计划周期的最优定价策略。仿真算例表明，该方法能有效地将需求学习和动态定价结合起来，能够发现更广泛的需求动态。
For firms in an oligopoly service network, demand learning based dynamics pricing is an efficient way to maximize their revenues. This paper introduces a Bayesian method to learn demand behavior from the perspective of game-theoretic dynamics, where non-parametric techniques for nonlinear time series are incorporated, such that stringent parametric assumptions are removed. We determine the unknown quantities in our demand learning model from historical market data through a Markov chain Monte Carlo (MCMC) algorithm. Based on the calibrated model, how future demands respond to prices can be predicted, and the optimal pricing policy for the next planning period is obtained by a heuristic optimization algorithm. Simulated examples show that our new method is efficient for integrating demand learning and dynamic pricing, and capable of discovering a wide range of demand dynamics.