Dynamic pricing

About: Dynamic pricing is a research topic. Over the lifetime, 4144 publications have been published within this topic receiving 91390 citations. The topic is also known as: surge pricing & demand pricing.

Simplification of network dynamics in large systems

Abstract: We show that when networks are large significant simplicity can be achieved for pricing-based control. We first consider a general loss network with Poisson arrivals and arbitrary holding time distributions. In dynamic pricing schemes, the network provider can charge different prices to the user according to the current utilization level of the network and also other factors. We show that when the system becomes large the performance (in terms of expected revenue) of an appropriately chosen static pricing scheme, whose price is independent of the current network utilization, will approach that of the optimal dynamic pricing scheme. Further, we show that under certain conditions, this static price is independent of the route that the flows take. We then extend the result to the case of dynamic routing, and show that the performance of an appropriately chosen static pricing scheme with bifurcation probability determined by average parameters can also approach that of the optimal dynamic routing scheme when the system is large. These results deepen our understanding of pricing-based network control. In particular, they provide us with the insight that, when the system is large, an appropriate pricing strategy based on the average network conditions (hence, slowly changing) can approach optimality.

Robust Multi-Product Pricing

Abstract: This paper concerns dynamic pricing of multiple perishable products when there is model uncertainty, which we formulate as a worst-case stochastic intensity control problem where ambiguity is modeled using the notion of relative entropy. One feature of our formulation is that the demand models for different products can have different levels of ambiguity, a situation that arises (for instance) if a new product is being sold along side an established one. We show that this multiple-ambiguity multi-product robust pricing problem is equivalent to another (non-standard) risk-sensitive pricing problem, and show that it can be decentralized under additional assumptions on the demand rate model. The risk-sensitive problem has several unusual features: (i) the net income from sales of each product is valued by its certainty equivalent under an exponential utility function where the aversion parameter is determined by the level of ambiguity of its demand model, (ii) the overall goal is to maximize the sum of the certainty equivalents over all products, and (iii) products making sales are required to compensate other products for the use of commonresources according to a revenue sharing rule. We characterize the revenue sharing rule which leads to an equivalence between the risk-sensitive problem we have just described and the original robust pricing problem. This generalizes risk-sensitive/robust control duality to the case where different components of the model have different levels of model uncertainty. Finally, we show that the robust multi-product problem can be decentralized and solved in terms of modified robust/risk-sensitive single-product problems, if the demand rate functions satisfy certain independence assumptions. The modification of the single product problems involves the introduction of a cost to account for the value of inventory that is used at each sale. This cost is closely related to the revenue sharing rule associated with the robust/risk-sensitive control equivalence.

Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach

Abstract: Most online markets are characterized by competitive settings and limited demand information. Due to the complexity of such markets, efficient pricing strategies are hard to derive. We analyze stochastic dynamic pricing models in competitive markets with multiple offer dimensions, such as price, quality, and rating. In a first step, we use a simulated test market to study how sales probabilities are affected by specific customer behaviors and the strategic interaction of price reaction strategies. Further, we show how different state-of-the-art learning techniques can be used to estimate sales probabilities from partially observable market data. In a second step, we use a dynamic programming model to compute an effective pricing strategy which circumvents the curse of dimensionality. We demonstrate that the strategy is applicable even if the number of competitors is large and their strategies are unknown. We show that our heuristic can be tuned to smoothly balance profitability and speed of sales. Further, our approach is currently applied by a large seller on Amazon for the sale of used books. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%.

Dynamic pricing based on asymmetric multiagent reinforcement learning

Abstract: A dynamic pricing problem is solved by using asymmetric multiagent reinforcement learning in this article. In the problem, there are two competing brokers that sell identical products to customers and compete on the basis of price. We model this dynamic pricing problem as a Markov game and solve it by using two different learning methods. The first method utilizes modified gradient descent in the parameter space of the value function approximator and the second method uses a direct gradient of the parameterized policy function. We present a brief literature survey of pricing models based on multiagent reinforcement learning, introduce the basic concepts of Markov games, and solve the problem by using proposed methods. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 73–98, 2006.

Dynamic pricing with reference price effect and price-matching policy in the presence of strategic consumers

Abstract: In this paper, we consider a retailer that sells a product with high storage cost over a two-period horizon. The goal is to investigate the single and combined effects of reference price and price-...