Selling luxury fashion to conspicuous consumers in the presence of discount sensitivity behavior

In the luxury fashion industry, with the possibility of stockout, conspicuous consumers may purchase products at the full price rather than wait for any potential discount. However, in the presence of a possible future discount, conspicuous consumers may also feel regret to purchase at the full price if they find that the product is still available at the markdown price. Considering the possible stockout and future discount, in this paper, we evaluate the firm's pricing and production decisions on conspicuous consumers in the presence of discount sensitivity behavior. We build up an analytical model and consider that the customer purchasing utility decreases with discount sensitivity if she/he purchases at the full price. The firm can create scarcity in the market by reducing inventory to induce customers to purchase early. If the fraction of the customers with discount sensitivity is lower than a certain level, the firm should intentionally produce a large amount of products. Moreover, we find that product design enhancement can effectively weaken the negative effects of customer discount sensitivity on the quantity decisions. More managerial insights are discussed.     

Unified discount pricing models of a two-echelon channel with a monopolistic manufacturer and heterogeneous retailers

This paper considers a two‐echelon channel in which a monopolistic manufacturer supplies a single product to multiple heterogeneous retailers who are in separate markets. The present paper studies the problem of how the manufacturer in a manufacturer‐Stackelberg game designs a unified quantity‐discount pricing scheme to improve the whole channel's profit as well as each partner's profit. Considered in the paper are two types of unified quantity‐discount pricing schemes: the regular quantity‐discount pricing scheme and the incremental volume discount pricing scheme. Each of the two types of schemes includes a single price‐break discount policy and a non‐linear quantity discount policy. Optimal solutions are derived and numerical examples presented to illustrate the efficiency of each discount policy.     

移动P2P文件共享系统中基于用户偏好的激励机制

提出一种基于用户偏好的激励机制,鼓励用户通过提供共享文件和转发操作为系统做出贡献。IMBPC结合了虚拟价格机制和预期文件传输延时,基于用户对价格和延时的偏好度来选择最合适的节点进行文件下载。通过设置模拟实同仅通过虚拟价格机制来激励用户做出贡献的策略进行比较,显示应用IMBPC策略的系统,节点的贡献积极性明显增加。     

基于多变量LS-SVM和模糊循环推理系统的负荷预测

智能电网环境下,电力需求响应的发展给传统用电模式带来重大变化,用户可以根据电能需求结合实时电价调整用电模式,这使得负荷预测变得更加复杂。通过相似日负荷序列局部形相似计算,选取样本数据,采用多输入双输出的最小二乘支持向量机(LS-SVM),对负荷和价格进行同时预测,得到初步预测结果。考虑需求响应条件下实时电价与负荷之间的相互影响,采用基于数据挖掘技术的模糊循环推理系统模拟人的思维过程,通过挖掘电价变化量、负荷变化量等变量之间的关联规则,模拟电价与负荷预测之间存在的博弈过程,对多变量最小二乘支持向量机预测算法的初步预测结果进行循环修改,直至负荷和电价预测结果趋于稳定。多变量最小二乘支持向量机不存在容易陷入局部最优等问题,并且有良好的泛化能力,基于改进的模糊关联规则挖掘算法和循环预测控制算法具有良好的完备性和鲁棒性,能够逼近现实环境的各种可能情况,修正负荷预测结果。针对某电网的实际预测结果表明,该方法具有较好的预测效果。     

Optimal pricing of online products based on customer anchoring-adjustment psychology

Online product pricing is an important factor that affects the purchase of customers and the earnings of e-commerce platform enterprises. It has an increasingly prominent influence on consumers’ purchasing decisions. To study the influence of customers’ anchoring psychology and product pricing on customers’ purchasing behaviors, this paper proposes a pricing model that considers online consumers to be anchored by the product price and sales. The cognitive bias of online consumers is described by a utility function based on an anchoring-adjustment heuristic, and consumer choice behavior and an online product pricing strategy are studied with a multinomial logit model. Then, it studies the influence of the anchoring point and anchoring degree on optimal pricing, sales and profit. In particular, when consumers are anchored only by sales of goods, the optimal solution is monotonic with respect to the anchoring degree. However, when consumers are anchored by both price and sales, both factors affect consumer behaviors and produce more complex results. This paper provides a flexible pricing mechanism for platform enterprises, and it can provide a theoretical basis and decision support for dynamic pricing of platform enterprises based on historical consumption data.     

市场环境下输配电价测算模型的研究与分析

电改实施以来,输配电价改革主要集中在对现有改革成效的分析以及提出相应的对策和建议等理论层面研究上,缺乏对成本加收益管制模型具体实施的相关实践性研究,无法满足当前输配电价测算的实践需求;本文基于以上情况就冀北公司电价管理模拟测算模型进行了深入研究,分析以输配电价改革方案下的电价组成情况,模型框架,结合冀北电力公司测算输配电价的实际情况,从含义、范围及水平方面分析了影响准许成本和准许收益的各个具体参数,并阐述了冀北电力公司现有电价测算工具(电价综合分析系统),为争取有利电价政策、提升盈利能力提供依据,深入细致研究电价政策,通过购售电测算模型、成本收益测算、智能报告、全国对标等项目快速找准差距,针对性改善考核指标,提高工作效率。     

Fuzzy analytical hierarchy process and multi-segment goal programming applied to new product segmented under price strategy

New product development (NPD) is becoming an important competitive advantage in the marketing strategies of current businesses. Developing a new product will incur fixed and variable costs, which then determine the product prices. Although this is a fundamental issue of marketing theory and practice, only a few papers on marketing models deal with price levels. The objective of this paper is proposing a model based on fuzzy analytical hierarchy process (AHP) and multi-segment goal programming (MSGP) to help decision makers to select the best pricing strategy for NPD. A case study of NPD under market selection strategy in multiple segment pricing levels for a Taiwan-based Watch Company is presented to illustrate the proposed methodology. The proposed method will guide the product development team to select the best market strategy by taking into account the price level and product/market segmentation.     

基于增强学习的代理谈判模型

利用增强学习的Q-leanling理论，构造了一个基于时间信念、价格信念和状态期望Q值的代理谈判模型。将代理交互报价的过程看成代理选择报价行动，实现状态的迁移，可以计算出代理在不同状态采取行动的Q值。代理可以通过修改信念函数及时问贴现率来调整报价。该文实现了谈判模型的报价算法，并从理论和实验数据两方面进行了分析比较。     

Smart bank card for PlayStation addicts

Dutch bank ING is to release a smart card-based loyalty card that features discount pricing on Sony PlayStation 2 consoles and games. The MasterCard-branded smart card, provided by Axalto, features a rainbow design and is being marketed at students and professionally active people in the 20-30 year-old age group.     

A mathematical analysis of the cumulus pricing scheme

One important task in current and future communication networks is to define a suitable pricing scheme. It is then preferable to formulate a mathematical model, so that parameters will be optimized and important properties such as fairness or truthful anticipated load revelation (or incentive compatibility) will be verified. In this paper we study a simple and promising scheme called the cumulus pricing scheme, which can address service differentiation and scalability among other issues. Based on a mathematical model, we determine values for optimizing the provider’s revenue, which happens under the constraint that each user has an incentive to reveal its anticipated load. This has led to a small variation of the initial model from the literature as in the modelling, cumulus points are translated into financial terms, and measurements induce a cost as well.     

基于交叉干扰的多优先级网络线性激励价控

讨论了多优先级网络系统交叉干扰的线性激励价控问题.针对多用户多优先级系统的通信量价控管理的数学模型,利用对策论中的激励Stackelberg策略的概念,建立了基于交叉干扰的线性激励价格策略.通过对用户行为的激励,调整网络系统通信量的分配,使系统达到一个理想且稳定的状态,且用户和网络管理者都达到最佳盈余.同时讨论了激励参数的确定方法,给出了一般的激励参数矩阵.通过数值例子验证了该激励价控策略的有效性.     

A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid

This paper proposes a Stackelberg game approach to maximize the profit of the electricity retailer (utility company) and minimize the payment bills of its customers. The electricity retailer determines the retail price through the proposed smart energy pricing scheme to optimally adjust the real-time pricing with the aim to maximize its profit. The price information is sent to the customers through a smart meter. According to the announced price, the customers can automatically manage the energy use of appliances in the households by the proposed optimal electricity consumption scheduling system with the aim to minimize their electricity bills. We model the interactions between the retailer and its electricity customers as a 1-leader, N-follower Stackelberg game. At the leader’s side, i.e., for the retailer, we adopt genetic algorithms to maximize its profit while at the followers’ side, i.e., for customers, we develop an analytical solution to the linear programming problem to minimize their bills. Simulation results show that the proposed approach is beneficial for both the customers and the retailer.     

Scanbacks and direct rebates: manufacturer's tools against forward buying

This paper evaluates the role of trade incentives specifically designed to fight forward‐buying practices on the part of the retailers, by examining the use of scanbacks and direct rebates as manufacturers' tools for the prevention of these practices. Scanner data allows the manufacturer to keep track of the retailer's pricing policies at the point of sale and hence tie its discount policy to the magnitude of the retailer's pass‐through to the final customers. Trade incentives of this type are called scanbacks, whereby the determination of the retailer's compensation is based on actual performance normally measured by scanner data. Another incentive is the direct rebate, whereby the manufacturer passes on directly to the final customer some discount, normally in the form of a coupon, upon proof of purchase. Rebates are one of the oldest trade incentives and certainly predate the advent of electronic commerce. Their relevance is enhanced by the fact that they can be easily adapted to the modern B2B marketplace. The economic effects of these incentives are evaluated in terms of their effect on the three basic links of the supply chain in question, namely (i) the manufacturer that offers the incentive; (ii) the retailer that develops the optimal pricing and ordering policy for each manufacturer's incentive; and (iii) the final customer who is the ultimate purchaser of the merchandise.     

遗传算法的阶梯电价用电需求管理方案

用电需求管理是智能电网中的重要部分,能让消费者根据自己的用电量做出合适的决策,帮助供电者减少峰值负载,让负载的时空分布更为均衡,从而增加智能电网的可持续性,并减少运营成本和碳排放量。一种基于负载转移技术的用电需求管理方案可以满足对大量、多种设备的调节需求。用电需求管理的解决方案使用改进的遗传算法,并引入了一个新的算子,模拟测试的结果显示通过改进的算法获得的方案节省了可观的成本,并且在使用阶梯式电价的情况下,减少了智能电网的峰值负载。     

基于以太坊和动态定价的数据交易模式研究

为了解决现有数据交易模式中交易流程耗时较大且效率较低,信息泄露和公平支付问题,提出一种改进的数据交易模式,通过智能合约预置额外的约束条件,集成了数据交易和仲裁纠纷解决的功能,用于实现交易的公平自治性和交易时间控制,以规避数据交易过程中恶意交易行为。在此基础上,为实现所提出的数据交易模式中价格的动态平衡,基于经济建模方法和动态定价的公平合理性,设计一个自动平衡总供给和总需求的动态定价机制,依据购买需求和数据资源的市场供给进行价格动态调整。从模型的动态性对模型进行了论证,证明了交易价格和需求可以收敛。基于以太坊实验环境部署并执行该合约,并对该智能合约的各功能成本和安全性进行测试和分析。仿真实验结果表明,该改进交易模式在动态定价下能够以较低的执行成本进行数据交易,并且该智能合约存在较少代码漏洞,满足可行性和安全性要求。     

基于多摇臂赌博机的产品定价算法

针对在线零售商在不完全需求信息下的单产品定价问题,提出了一种基于多摇臂赌博机的产品定价算法。为了提升多摇臂赌博机算法在定价问题中的效果,该算法利用了需求曲线的单调性,并加入了消费者偏好识别。对消费者的保留价格进行分析得到消费者购买概率,将在线零售商的定价问题建模为多摇臂赌博机模型,给出了相应的定价算法并进行了理论分析,最后通过仿真实验比较了相关算法的定价效果。仿真结果表明该算法提高了在线零售商的收益。     

Incentive mechanisms,fairness and participation in online group-buying auctions

The main idea of an online group-buying auction is to recruit enough people to generate a sufficient volume of orders to create the basis for a lower transaction price. Typically, the larger the number of orders, the more consumers will wish to participate. This is because the final auction price is likely to be lower than it would be otherwise. This positive feedback is a demand externalities phenomenon. Nevertheless, there also is a phenomenon of inertia that often occurs at the beginning of online group-buying auctions. Due to the small number of orders that occur in the beginning of an online group-buying auction, many potential participants are inclined to wait until the auction price for the sale item falls to an acceptable level. This only comes through the participation of more people in the auction. As a result, consumers will tend to wait for one another to join the auction. Thus, the startup inertia must be overcome before the number of orders will increase. We explore three incentive mechanisms to address this problem: sequence-based, time-based and quantity-based incentives. We conducted a series of experiments using an online group-buying auction experimental test bed deployed on the Internet to develop deeper insights into how these incentives work in the context of bakery cookie sales. Using theory to represent a variety of decision-making perspectives that can be applied in the online group-buying auction context, we explore whether the different incentive mechanisms cause consumers to perceive a lack of price fairness and procedural fairness in an auction’s operations. Our results suggest that consumers view participation discounts as creating the basis for perceptions of greater price fairness in online group-buying auctions. Compared with other incentive mechanisms, a sequence-based incentive mechanism gives consumers a sense of less procedural fairness. Finally, perceptions of fairness tend to have a positive association with price satisfaction and purchase intention.     

Dynamic pricing incentive for participatory sensing

User participation is one of the most important elements in participatory sensing application for providing adequate level of service quality. However, incentive mechanism and its economic model for user participation have been less addressed so far in this research domain. This paper studies the economic model of user participation incentive in participatory sensing applications. To stimulate user participation, we design and evaluate a novel reverse auction based dynamic pricing incentive mechanism where users can sell their sensing data to a service provider with users’ claimed bid prices. The proposed incentive mechanism focuses on minimizing and stabilizing the incentive cost while maintaining adequate level of participants by preventing users from dropping out of participatory sensing applications. Compared with random selection based fixed pricing incentive mechanism, the proposed mechanism not only reduces the incentive cost for retaining the same number of participants but also improves the fairness of incentive distribution and social welfare. It also helps us to achieve the geographically balanced sensing measurements and, more importantly, can remove the burden of accurate price decision for user data that is the most difficult step in designing incentive mechanism.     

一种基于灵活市场的区分服务网络定价策略

基于区分服务的网络模型,提出了一种新的灵活市场的定价策略。为了使定价策略能够更好地应用在区分服务网络中,在边界路由器中使用了探测和反馈机制,并且对灵活市场定价策略中的竞标值进行了归一化处理。通过仿真实验验证了基于区分服务模型的灵活市场定价策略的自适应性。     

Efficient bandwidth utilization in LambdaGrids using pricing incentives

The ability to reserve network bandwidth is a critical factor for the success of high-performance grid applications. Reservation of lightpaths in dynamically switched optical networks facilitates guaranteed bandwidth. However, reservation of bandwidth can often lead to bandwidth fragmentation which significantly reduces system utilization and increases the blocking probability of the network. An interesting approach to mitigating this problem is to induce quasi-flexibility in the user requests. A smart scheduling strategy can then exploit this quasi-flexibility and optimize bandwidth utilization. However, there has to be an incentive for flexibility from the user’s perspective as well. In this paper, we explore how the network service provider (NSP) can influence user flexibility by dynamically engineering pricing incentives. Ultimately, user flexibility will lead to efficient network utilization, reduce the price for the users, and increase the revenue for the NSP.