Dynamic Consumer Profiling and Tiered Pricing Using Software Agents

Shopbots or software agents that enable comparison shopping of items from different online sellers have become popular for quick and easy shopping among online buyers. Rapid searches and price comparison by shopbots have motivated sellers to use software agents called pricebots to adjust their prices dynamically so that they can maintain a competitive edge in the market. Existing pricebots charge the same price for an item from all of their customers. Online consumers differ in their purchasing preferences and, therefore, a seller's profit can be increased by charging two different prices for the same good from price-insensitive and price-sensitive consumers. In this paper, we present an algorithm that partitions the buyer population into different segments depending on the buyers' purchase criteria and then charges a different price for each segment. Simulation results of our tiered pricing algorithm indicate that sellers' profits are improved by charging different prices to buyers with different purchase criteria. Price wars between sellers that cause regular price fluctuations in the market, are also prevented when all the sellers in the market use a tiered pricing strategy.     

Computational pricing in Internet era

Pricing plays a central rule to a company’s profitability, and therefore has been extensively studied in the literature of economics. When designing a pricing mechanism/ model, an important principle to consider is “price discrimination”, which refers to selling the same resources with different prices according to different values of buyers. To meet the “price discrimination” principle, especially when the number of buyers is large, computational methods, which act in a more accurate and principled way, are usually needed to determine the optimal allocation of sellers’ resources (^{whom to sell to}) and the optimal payment of buyers (^{what to charge}). Nowadays, in the Internet era in which quite a lot of buy and sell processes are conducted through Internet, the design of computational pricing models faces both new challenges and opportunities, considering that (i) nearly realtime interactions between people enable the buyers to reveal their needs and enable the sellers to expose their information in a more expressive manner, (ii) the large-scale interaction data require powerful methods for more efficient processing and enable the sellers to model different buyers in a more precise manner. In this paper, we review recent advances on the analysis and design of computational pricing models for representative Internet industries, e.g., online advertising and cloud computing. In particular, we introduce how computational approaches can be used to analyze buyer’s behaviors (i.e., equilibrium analysis), improve resource utilization (i.e., social welfare analysis), and boost seller’s profit (i.e., revenue analysis). We also discuss how machine learning techniques can be used to better understand buyer’s behaviors and design more effective pricing mechanisms, given the availability of large scale data. Moreover, we make discussions on future research directions on computational pricing, which hopefully can inspire more researchers to contribute to this important domain.     

Implications of a Reserve Price in an Agent-Based Common-Value Auction

Auction sellers can use a reserve price to require a minimum bid before items are sold. Theoretical and experimental research has tested the influence of a reserve price in an independent private values auction, but little focus has been given to the influence of a reserve price in a first-price common-value auction. We establish an agent-based first-price common-value auction to determine the impact of the reserve price with two buyers and with three buyers. An agent-based approach to this problem is both a unique contribution to the literature and appropriate since finding analytical solutions with common-value auctions is difficult. The agent-based model approach also allows us to consider buyers that have non-symmetric bid functions. Furthermore, we introduce a combination of numerical integration techniques with a new particle swarm learning algorithm. The buyers in the model choose their expected-net-revenue-maximizing bid price, and sellers choose their expected-revenue-maximizing reserve price. In the two-buyer and three-buyer auction, a reserve price increases the equilibrium winning bid price, decreases the probability that the item is sold, and increases the seller’s expected revenue. A reserve price in a two-buyer auction increases the winning bid price more than including an additional buyer in the auction with no reserve price. However, due to only receiving a salvage value when the item does not sell in the auction, the seller’s expected revenue is higher in the three-buyer first-price common-value auction without a reserve price than in the two-buyer auction with a reserve price.     

COMBINING TRUST MODELING AND MECHANISM DESIGN FOR PROMOTING HONESTY IN E‐MARKETPLACES

In this paper, we propose a novel incentive mechanism for promoting honesty in electronic marketplaces that is based on trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbors to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide truthful ratings for sellers, and are likely to be neighbors of many other buyers. Sellers will provide more attractive products to reputable buyer to build their own reputation. We theoretically prove that a marketplace operating with our mechanism leads to greater profit both for honest buyers and honest sellers. We emphasize the value of our approach through a series of illustrative examples and in direct contrast to other frameworks for addressing agent trustworthiness. In all, we offer an effective approach for the design of e‐marketplaces that is attractive to users, through its promotion of honesty.     

An incentive mechanism designed for e-marketplaces with limited inventory

In electronic marketplaces, reputation systems and incentive mechanisms are prevalently employed to promote the honesty of sellers and buyers. In this article, we focus on the scenario in which the inventory is in short supply, i.e. an e-marketplace with limited inventory (EMLI). The challenges are in twofold: (a) for sellers who aim to maximize their profit, they may intentionally conduct dishonest transactions since the limited products are likely to be sold out regardless of their reputation; (b) for buyers who intend to gain the limited products, they may provide untruthful ratings to mislead other buyers. To address these issues, we propose an incentive mechanism to promote buyer and seller honesty for this type of e-marketplaces. Specifically, the mechanism models the honesty of buyers and sellers as scores and reputation, respectively. It then offers a higher price to the products of more honest sellers (with higher reputation) and allocates the products to more honest buyers (with higher scores). In this way, both sellers and buyers are well encouraged to be honest. Furthermore, we impose proper membership fee on new sellers to cope with the whitewashing attack. We finally theoretically analyze and empirically demonstrate the efficacy of the proposed mechanism and its nice properties.     

基于群组的C2C电子商务信任模型研究

国内的电子商务平台大多采用eBay信任模型,对买家反馈评分进行简单累加得到卖家信誉值,未区分买家反馈评分的合理性及其参考价值的重要程度。为此,提出一种基于群组的C2C电子商务信任模型,通过计算买家与卖家的熟悉度,确定买家的可信度,综合考虑反馈评分、交易次数、交易价格、交易时间、以往买家的可信度对信誉的影响,构建电子商务信任模型。模拟实验结果证明,相比Sporas模型,该模型能为买家提供更全面、更准确的卖家信誉信息。     

优化网上数码商店设计与实现

随着网络的不断普及,网上购物依靠其方便、实惠等优点成为一种流行的购物方式.支付宝等安全支付手段,让众多的消费者对网上购物更加信任.本网站除了具有普通网站的基本功能外,同时增加了商品参数的对比,并利用第三方控件,方便卖家对于商品信息的管理;合理的设计从而方便买家和卖家更符合B2C的人性化.     

Shopbot Economics

Shopbots are Internet agents that automatically search for information pertaining to the price and quality of goods and services. As the prevalence and usage of shopbots continues to increase, one might expect the resultant reduction in search costs to alter market behavior significantly. We explore the potential impact of shopbots upon market dynamics by proposing, analyzing, and simulating a model that is similar in form to some that have been studied by economists investigating the phenomenon of price dispersion. However, the underlying assumptions and methodology of our approach are different, since our ultimate goal is not to explain human economic behavior, but rather to design economic software agents and study their behavior. We study markets consisting of shopbots and other agents representing buyers and sellers in which (i) search costs are nonlinear, (ii) some portion of the buyer population makes no use of search mechanisms, and (iii) shopbots are economically motivated, strategically pricing their information services so as to maximize their own profits. Under these conditions, we find that the market can exhibit a variety of hitherto unobserved dynamical behaviors, including complex limit cycles and the co-existence of several buyer search strategies. We also demonstrate that a shopbot that charges buyers for price information can manipulate markets to its own advantage, sometimes inadvertently benefitting buyers and sellers.     

Revisiting feedback systems: Trust building in digital markets

Data from ebay.com online auctions were used to examine the impact of buyer feedback and other trust-evoking variables on the final auction prices realized by sellers. Direct counts of positive and negative feedback were found to influence ending auction price significantly. However, when difference measures between positive and negative feedback were used as buyer feedback measures, no significant price effects were found. Since much more positive, than negative, feedback is obtained by sellers, the marginal impact of negative feedback is much greater than that of positive feedback. Therefore, subtracting negative feedback counts from positive feedback counts may not be appropriate. Support was also found for other factors, such as product warranties, impacting final auction prices.     

Reputation mechanism for e-commerce in virtual reality environments

The interest in 3D technology and virtual reality (VR) is growing both from academia and industry, promoting the quick development of virtual marketplaces (VMs) (i.e. e-commerce systems in VR environments). VMs have inherited trust problems, e.g. sellers may advertise a perfect deal but doesn’t deliver the promised service or product at the end. In view of this, we propose a five-sense feedback oriented reputation mechanism (supported by 3D technology and VR) particularly for VMs. The user study confirms that users prefer VMs with our reputation mechanism over those with traditional ones. In our reputation mechanism, five-sense feedback is objective and buyers can use it directly in their reputation evaluation of target sellers. However, for the scenarios where buyers only provide subjective ratings, we apply the approach of subjectivity alignment for reputation computation (SARC), where ratings provided by one buyer can then be aligned (converted) for another buyer according to the two buyers’ subjectivity. Evaluation results indicate that SARC can more accurately model sellers’ reputation than the state-of-the-art approaches.     

A Behavioural Learning Approach to the Dynamics of Prices

The paper studies the dynamics of market prices under theassumption of behavioural learning by sellers and buyers. Thebuyers are assumed to be price takers who learn about choosingsellers and acceptable prices, whilst the sellers have thepossibility to offer a fixed price or to make successivereductions and learn about adequate selling behaviour andadequate price levels. The learning process was simulated andcompared to the game theoretic prediction under the assumptionof rational agents. The main areas of interest of the analysisconcern the questions of whether bargaining on prices becomescommon and whether the dynamics of prices converges.     

多智能体协同选择的智能导购模型

基于多智能体协同选择提出了一种导购选择模型,该模型可识别其他可信买方智能体("值得信赖的朋友"),并将它们关于卖方的信息结合自身关于卖方的信息综合起来协同选择质高价低的卖方,从而实现高质量的导购性能。构建了一个存在多种类型的买方和卖方的购物模拟环境,并进行了多组实验。实验结果表明,该模型可以准确地识别可信买方智能体,并可在复杂的购物环境中高效地选择出优质卖方。此外,实验结果还表明,有了该模型,单个买方智能体选择优质卖方的能力要明显高于无多智能体协同选择情况下单个买方智能体的选择能力。     

电子市场中组合客户联盟模型及遗传算法设计

在电子市场中,客户联盟可以为买卖双方赢得更多利润。该文提出了一个组合顾客联盟模型,可同时描述顾客偏好和商品数量。并针对此复杂的组合优化问题,给出了用遗传算法求解的框架。     

Pricing commodities

How should a seller price her goods in a market where each buyer prefers a single good among his desired goods, and will buy the cheapest such good, as long as it is within his budget? We provide efficient algorithms that compute near-optimal prices for this problem, focusing on a commodity market, where the range of buyer budgets is small. We also show that our LP rounding based technique easily extends to a different scenario, in which the buyers want to buy all the desired goods, as long as they are within budget.     

An options-based solution to the sequential auction problem

The sequential auction problem is commonplace in open, electronic marketplaces such as eBay. This is the problem where a buyer has no dominant strategy in bidding across multiple auctions when the buyer would have a simple, truth-revealing strategy if there was but a single auction event. Our model allows for multiple, distinct goods and market dynamics with buyers and sellers that arrive over time. Sellers each bring a single unit of a good to the market while buyers can have values on bundles of goods. We model each individual auction as a second-price (Vickrey) auction and propose an options-based, proxied solution to provide price and winner-determination coordination across auctions. While still allowing for temporally uncoordinated market participation, this options-based approach solves the sequential auction problem and provides truthful bidding as a weakly dominant strategy for buyers. An empirical study suggests that this coordination can enable a significant efficiency and revenue improvement over the current eBay market design, and highlights the effect on performance of complex buyer valuations (buyers with substitutes and complements valuations) and varying the market liquidity.     

The Value of Temptation

There is an implicit assumption in electronic commerce that induces the buyers to believe that their deals will be handled appropriately. However, after a seller has already committed to a buyer, he may be tempted by several requests though he will not be able to supply them all. We analyze markets in which a finite set of automated buyers interacts repeatedly with a finite set of automated sellers. These sellers can satisfy one buyer at a time, and they can be tempted to break a commitment they already have. We have found the perfect equilibria that exist in markets with a finite horizon, and with an unrestricted horizon. A significant result stemming from our study reveals that sellers are almost always tempted to breach their commitments. However, we also show that if markets' designers implement an external mechanism that restricts the automated buyers actions, then sellers will keep their commitments.     

Next-generation agent-enabled comparison shopping

Agents are the catalysts for commerce on the Web today. For example, comparison-shopping agents mediate the interactions between buyers and sellers in order to yield more efficient markets. However, today's shopping agents are price-dominated, unreflective of the nature of seller/buyer differentiation or the changing course of differentiation over time. This paper aims to tackle this dilemma and advances shopping agents into a stage where both kinds of differentiation are taken into account for enhanced understanding of the realities. We call them next-generation shopping agents. These agents can leverage the interactive power of the Web for more accurate understanding of buyer's preferences. This paper then presents an architecture of the next-generation shopping agents. This architecture is composed of a Product/Merchant Information Collector, a Buyer Behavior Extractor, a User Profile Manager and an Online Learning Personalized-Ranking Module. We have implemented a system following the core of the architecture and collected preliminary evaluation results. The results show this system is quite promising in overcoming the reality challenges of comparison shopping.     

电子结算系统的设计方案

网上购物是指买卖双方利用简单、快捷和低成本的电子通讯方式进行各种商贸活动的一种交易模式。电子结算作为其中的组成部分,决定了交易进行的最终步骤能否成功,其重要性不言而喻。系统主要以ASP.NET作为开发基础,使用了Microsoft公司的Visual Studio 2008作为开发环境。基本实现了网上购物中的电子结算应具备的主要功能,包括:在线支付,支付网关管理,买家购物管理和卖家销售及库存管理等功能。该系统界面简洁,操作容易,维护方便。     

SPRITE: a novel strategy-proof multi-unit double auction scheme for spectrum allocation in ubiquitous communications

With the increasing number of handheld and mobile devices, the demand for available spectrum resource is experiencing a rapid growth. Auction has been widely used for spectrum resource allocation in ubiquitous communications due to its fairness. However, many existing spectrum auction schemes inherently limit users’ ability to fully utilize the spectrum due to the assumption that the spectrum resource is single unit and indivisible. Besides, most of them fail to take into account of buyer/seller’s distinctive demands in auction and consider spectrum allocation as single-sided auction. In this paper, we consider the multi-unit double auction problem under the context that multiple buyers/sellers have different demands to buy/sell. Particularly, we present a novel strategy-proof multi-unit double auction scheme (SPRITE). SPRITE establishes a series of bid-related buyer group construction and winner determination strategies. It improves the spectrum reusability and achieves sound spectrum utilization, fairness, and essential economic properties. In the theoretical analysis, we have shown the correctness, effectiveness, and economic properties of SPRITE and prove that SPRITE is strategy-proof. In the evaluation study, we further show that SPRITE can achieve multi-unit spectrum auction with better auction efficiency compared with existing double auction mechanisms. To the best of our knowledge, SPRITE is the first multi-unit double auction approach that guarantees the competitive fairness among buyers while remaining strategy-proof for spectrum allocation. Furthermore, the multi-unit double auction mechanism proposed in this work helps increasing flexibility of the ubiquitous devices for spectrum usage.     

Nash equilibrium solution in a vendor–buyer supply chain model with permissible delay in payments

In practice, vendors (or sellers) often offer their buyers a fixed credit period to settle the account. The benefits of trade credit are not only to attract new buyers but also to avoid lasting price competition. On the other hand, the policy of granting a permissible delay adds not only an additional cost but also an additional dimension of default risk to vendors. In this paper, we will incorporate the fact that granting a permissible delay has a positive impact on demand but negative impacts on both costs and default risks to establish vendor–buyer supply chain models. Then we will derive the necessary and sufficient conditions to obtain the optimal solution for both the vendor and the buyer under non-cooperative Nash equilibrium. Finally, we will use two numerical examples to show that (1) granting a permissible delay may significantly improve profits for both the vendor and the buyer, and (2) the sensitivity analysis on the optimal solution with respect to each parameter.