MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.     

eMediator: A Next Generation Electronic Commerce Server

This paper presents eMediator , an electronic commerce server prototype that demonstrates ways in which algorithmic support and game–theoretic incentive engineering can jointly improve the efficiency of e–commerce. eAuctionHouse , the configurable auction server, includes a variety of generalized combinatorial auctions and exchanges, pricing schemes, bidding languages, mobile agents, and user support for choosing an auction type. We introduce two new logical bidding languages for combinatorial markets: the XOR bidding language and the OR–of–XORs bidding language. Unlike the traditional OR bidding language, these are fully expressive. They therefore enable the use of the Clarke–Groves pricing mechanism for motivating the bidders to bid truthfully. eAuctionHouse also supports supply/demand curve bidding. eCommitter , the leveled commitment contract optimizer, determines the optimal contract price and decommitting penalties for a variety of leveled commitment contracting mechanisms, taking into account that rational agents will decommit strategically in Nash equilibrium. It also determines the optimal decommitting strategies for any given leveled commitment contract. eExchangeHouse , the safe exchange planner, enables unenforced anonymous exchanges by dividing the exchange into chunks and sequencing those chunks to be delivered safely in alternation between the buyer and the seller.     

Antecedents of trust in online auctions

Customer loyalty or repeat purchasing is critical to the online auction sellers’ survival and success. Previous research has established that online repeat purchase intentions are the product of buyer assessments of trust in the online seller. Previous research has also affirmed the importance of justice perceptions in engendering trust. These perspectives, however, have been examined independently by IS and management researchers. By integrating these two perspectives, a richer understanding of buyers’ underlying beliefs and subsequent repeat purchase intentions can be gained. In the research model, bidding justice is proposed as a formative second-order construct driven by distributive justice, procedural justice, interpersonal justice, and informational justice. Bidding justice is hypothesized to positively affect trust in the community of sellers, which in turn is hypothesized to positively affect repeat purchase intentions. Data collected from 412 buyers in Yahoo-Kimo’s online auction marketplace provide support for the proposed model. The study shows that trust is a significant positive predictor of buyers’ intentions to repeat purchase. The study also shows that the four dimensions of justice are important components of bidding justice, which in turn has a strong positive effect on trust in the community of sellers. Implications for theory and practice and suggestions for future research are discussed.     

MoCAAS: auction agent system using a collaborative mobile agent in electronic commerce

To get the items that a buyer wants in an Internet auction, he must search for the items through several auction sites. When the bidding starts, the buyer needs to connect to these auction sites frequently so that he can monitor the bid states and re-bid. A reserve-price auction reduces the number of connections, but this limits the user's bidding strategy. Another problem is equity between the buyer and the seller. Both the buyer and the seller should profit within proper limits. In this paper, we propose an auction agent system using a collaborative mobile agent and a brokering mechanism called MoCAAS (Mobile collaborative auction agent system), which mediates between the buyer and the seller and executes bidding asynchronously and autonomously. This reduces the network load more than with other auction-agents, offers more intelligent bidding, and increases the clear ratio.     

How to Avoid Selecting Bids Based on Overoptimistic Cost Estimates

This article documents important connections between cost overruns and bidding. It also recommends how clients should design their bidding process to avoid selecting bids based on overoptimistic cost estimates. The recommendations provided reduce the risk of initiating projects that have unrealistic plans, time overruns, low software quality, high maintenance costs, and inflexible providers. Although this article aims to improve the clients' bidding processes, the authos believes that it also has important implications for software providers. They could use the reported results to identify bidding rounds that they're likely to win only if they provide a bid based on strongly overoptimistic cost estimates. Software providers should consider carefully whether it's worthwhile to participate in bidding rounds in which this is the case.     

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。     

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.     

Correcting vindictive bidding behaviors in sponsored search auctions

In this study, we aim to develop a pricing mechanism that reduces the effects resulted by vindictive advertisers who bid on sponsored search auctions run by search engine providers. In particular, we aim to ensure payment fairness and price stability in these auctions. With the generalized second price principle, advertisers pay the next-ranked bid value rather than the price that they bid. Vindictive bidders take advantage of this principle to manipulate the payment of a previously-ranked advertiser. Vindictive bidding results in unfair outcomes and eliminates equilibria. However, it is difficult to compute rational payments for all advertisers as advertisers’ valuations are private. Our proposed mechanism decreases the payment to make up for the utility loss that is induced by vindictive bidding. The vindictive advertiser is simultaneously punished with an additional payment. According to our theoretical analyses and simulations, the proposed mechanism efficiently decreases the effects that result from vindictive bidding, and guarantees equilibrium outcomes.     

Intelligent agents in electronic markets for information goods: customization, preference revelation and pricing

Electronic commerce has enabled the use of intelligent agent technologies that can evaluate buyers, customize products, and price in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of agent-based technologies that price dynamically based on product preference information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off has a higher impact on buyers on the higher end of the market and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, seller revenues decrease since the gains from better buyer information are dominated by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.     

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.     

A strategy of knowledge elicitation for developing an integrated bidding/production management expert system for the precast industry

Bidding and production decisions, including the estimation of optimal mark-up on price, represent major decision problems for companies formulating a successful business strategy. The objective of this research was to develop a strategy for developing an integrated bidding/production management expert system to assess the suitability of incoming enquiries for a particular company and suggests a ‘bid/no bid’ decision. If a decision to bid is taken then the expert system should provide advice on the optimal mark-up to maximise the chance of winning potential contracts. The system developed in this paper is composed of an information system, that integrates design, estimation and production planning, and a knowledge-base to provide abstracted information and advice to managers in charge of bidding. The information system analyses the records of previous contracts and presents managers with vital information that minimises the risk of poor decisions associated with bidding. The knowledge-base is composed of intelligent rules which were elicited from previous contract records and experienced managers in charge of bidding. They are designed to advise managers on two major issues: bid/no bid and estimation of optimal tender price. A number of factors that affect bidding strategies were identified from reviewing previous bidding methodologies and surveying eight major companies in the UK by means of semi-structured interviews.     

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.     

公平视角下全渠道服务商定价和服务能力分配策略

随着移动互联网的发展,基于本地服务的全渠道运营模式被广大服务运营商和消费者所接受.对于有限服务能力的服务商在全渠道运营的过程中,需要考虑其服务能力在不同渠道中的公平分配.针对同时运营实体和O2O渠道的服务商进行研究,探索考虑公平的服务商不同分配机制下的渠道定价及分配策略的选择.通过消费者效用模型的构建,刻画消费者的渠道...     

无收据的密封式电子拍卖协议

提出一个高效的无收据密封式电子拍卖协议，无收据的意义是在协议进行过程中没有可以作为“收据”的公开数据，从而能够更好地保证竞拍者的安全。与现有的无收据密封式电子拍卖协议相比，该文的协议在竞拍价格变化较大的情况下，效率有很大的提高。     

A collaborative platform for buyer coalition: Introducing the Awareness-based Buyer Coalition (ABC) system

A variety of buyer coalition schemes already exist in the current e-Commerce literature by which buyers form some sort of coalition in order to enjoy added discounts as a result of purchasing in larger bundles. One major problem in all existing schemes is that none of those schemes explicitly treat the coalition process as a collaborative business process; and as a result, the awareness and knowledge-sharing requirements are not explicitly recognized in the design process of the existing systems. This study proposes a conceptual framework for a buyer coalition system called the Awareness-based Buyer Coalition (ABC) system that allows a buyer to bid on the basis of various levels of awareness that s/he may have about other roles’ actions/intentions. The study is an early attempt for explicitly considering awareness and knowledge-sharing requirements of various roles within the buyer coalition process. The theoretical foundations of the study are rooted in the fields of Game Theory, e-Commerce, and Knowledge Management. The research methodology adopted for the study is design science. Various existing buyer coalition algorithms were reviewed and their strengths and weaknesses identified in terms of addressing the information-sharing needs of collaborating buyers. Furthermore, the existing literature on Knowledge Management was reviewed in order to identify an appropriate process model for the proposed buyer coalition framework with specific emphasis on awareness and knowledge-sharing requirements of its collaborating actors. For validation of the proposed conceptual model simulation software was developed to demonstrate results of a variety of simulations for the proof of the concept.     

Combining bundle search with buyer coalition formation in electronic markets: A distributed approach through explicit negotiation

In electronic markets, both bundle search and buyer coalition formation are profitable purchasing strategies for buyers who need to buy small amount of goods and have no or limited bargaining power. In this paper, we present a distributed mechanism that allows buyers to use both purchasing strategies. The mechanism includes a heuristic bundle search algorithm and a distributed coalition formation scheme, which is based on an explicit negotiation protocol with low communication cost. The resulting coalitions are stable in the core in terms of coalition rationality. The simulation results show that this mechanism is very efficient. The resulting cost to buyers is close to the optimal cost.     

Modeling software bidding risks

We discuss a method of developing a software bidding model that allows users to visualize the uncertainty involved in pricing decisions and make appropriate bid/no bid decisions. We present a generic bidding model developed using the modeling method. The model elements were identified after a review of bidding research in software and other industries. We describe the method we developed to validate our model and report the main results of our model validation, including the results of applying the model to four bidding scenarios.     

Single-vendor multi-buyer discount pricing model under stochastic demand environment

In this paper, a single-vendor multi-buyer discount pricing model has been developed under stochastic demand information. The vendor offers multiple pricing schedules to encourage the buyers to adopt the global optimal policy instead of their individual optimal ordering policy. The global optimal solution ensures that each buyer is assigned to the best schedule with maximum benefit. The results show that coordination benefit increases with increase in the number of pricing schedules. However, the system performance deteriorates with increasing demand variability and service level. Hence, if the system contains high degree of uncertainty or each buyer sets her service level too high, coordination through discount policy may not be an efficient mechanism to enhance channel profitability.     

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.