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。     

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.     

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.     

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.     

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.     

On the Natural Selection of Market Choice

An evolutionary approach to the problem of economic mechanism choice is presented. It demonstrates the power that a single participant has on the choice of a preferred market mechanism. A population of sellers, each with one of two possible economic mechanisms, facing potential buyers, is presented as a test model. It is shown that if two auctions, such as first- and second-price auctions, are to attract an identical number of buyers, although under the model assumptions their expected revenues are identical, sellers using a first-price auction mechanism will be selected for. However, if a second-price auction attracts one additional buyer, then it will be selected for by the evolutionary process. These results are extended to the choice between an arbitrary k- and l-price auctions.     

Why the first provider takes it all: the consequences of a low trust culture on pricing and ratings in online sourcing markets

Electronic markets are ruled by price and reputation, and, at least in the case of Online Sourcing Markets (OSM), also by preference for providers the buyer already contracted with. OSM are online markets for software development. Adding Fukuyama's notion of a low trust culture, an argument is advanced why buyers in OSM may give absolute preference to providers with whom they had previous contracts, presenting a special case of neoclassical contracting. Examining all the transactions in one calendar year at a leading OSM supports this proposition. All it took to be given the tender was to be the only bidding provider with at least one successful previous contract with the buyer, rendering pricing and ratings immaterial to bid choice. Only when none of the bidding providers had previous successful projects with the buyer did pricing and rating affect bid choice. The proposition is also consistent with the buyers’ comments about their providers. Implications are discussed about how a low trust culture affects OSM behavior.     

The role of intermediaries in electronic marketplaces: developing a contingency model

Abstract. Early research in electronic markets seemed to suggest that e‐ commerce transactions would result in decreased costs for buyers and sellers alike, and would therefore ultimately lead to the elimination of intermediaries from electronic value chains. However, a careful analysis of the structure and functions of electronic marketplaces reveals a different picture. Intermediaries provide many value‐adding functions that cannot be easily substituted or ‘internalized’ through direct supplier–buyer dealings, and hence mediating parties may continue to play a significant role in the e‐commerce world. In this paper we provide an analysis of the potential roles of intermediaries in electronic markets and we articulate a number of hypotheses for the future of intermediation in such markets. Three main scenarios are discussed: the disintermediation scenario, in which market dynamics will favour direct buyer–seller transactions; the reintermediation scenario, in which traditional intermediaries will be forced to differentiate themselves and re‐emerge in the electronic marketplace; and the cybermediation scenario, in which wholly new markets for intermediaries will be created. The analysis suggests that the likelihood of each scenario dominating a given market is primarily dependent on the exact functions that intermediaries play in each case. A detailed discussion of such functions is presented in the paper, together with an analysis of likely outcomes in the form of a contingency model for intermediation in electronic markets.     

Online double auction mechanism for perishable goods

One-sided auctions are used for market clearing in the spot markets for perishable goods because production cost in spot markets is already “sunk.” Moreover, the promptness and simplicity of one-sided auctions are beneficial for trading in perishable goods. However, sellers cannot participate in the price-making process in these auctions. A standard double auction market collects bids from traders and matches the higher bids of buyers and lower bids of sellers to find the most efficient allocation, assuming that the value of unsold items remains unchanged. Nevertheless, in the market for perishable goods, sellers suffer a loss when they fail to sell their goods, because their salvage values are lost when the goods perish. To solve this problem, we investigate the suitable design of an online double auction for perishable goods, where bids arrive dynamically with their time limits. Our market mechanism aims at improving the profitability of traders by reducing trade failures in the face of uncertainty of incoming/departing bids. We develop a heuristic market mechanism with an allocation policy that prioritizes bids of traders based on their time-criticality, and evaluate its performance experimentally using multi-agent simulation. We find out that our market mechanism realizes efficient and fair allocations among traders with approximately truthful behavior in different market situations.     

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.     

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.     

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.     

Trust-oriented buyer strategies for seller reporting and selection in competitive electronic marketplaces

In competitive electronic marketplaces where some selling agents may be dishonest and quality products offered by good sellers are limited, selecting the most profitable sellers as transaction partners is challenging, especially when buying agents lack personal experience with sellers. Reputation systems help buyers to select sellers by aggregating seller information reported by other buyers (called advisers). However, in such competitive marketplaces, buyers may also be concerned about the possibility of losing business opportunities with good sellers if they report truthful seller information. In this paper, we propose a trust-oriented mechanism built on a game theoretic basis for buyers to: (1) determine an optimal seller reporting strategy, by modeling the trustworthiness (competency and willingness) of advisers in reporting seller information; (2) discover sellers who maximize their profit by modeling the trustworthiness of sellers and considering the buyers’ preferences on product quality. Experimental results confirm that competitive marketplaces operating with our mechanism lead to better profit for buyers and create incentives for seller honesty.     

Enhancing comparison shopping agents through ordering and gradual information disclosure

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.     

Evaluating the trustworthiness of advice about seller agents in e-marketplaces: A personalized approach

In this paper, we present a model for evaluating the trustworthiness of advice about seller agents in electronic marketplaces. In particular, we propose a novel personalized approach for effectively handling unfair ratings of sellers provided to buyer agents from other buyers (called advisors). Our approach offers flexibility for buyers to weight their value for private and public knowledge about advisors. A personalized approach is proposed as well for buyers to model the trustworthiness of sellers, based on the advice provided. Experimental results demonstrate that our approach can effectively model trustworthiness for both advisors and sellers, even when there are large numbers of unfair ratings.     

Analyzing strategies of mobile agents on malicious cloud platform with Agent-Based Computational Economic Approach

Cloud computing is a service model that enables resource-limited mobile devices to remotely execute tasks from a server. The mobile agent is a software program installed in the mobile device to negotiate a diversity of commerce transactions with other mobile agents in the cloud. However, the negotiation plans carried by mobile agents are easily be eavesdropped on by the malicious cloud platforms, since the codes of mobile agents are read and executed by the cloud platform. Thus, sellers can tailor the negotiation plans to cheat buyers for seizing buyers’ profits in negotiations after eavesdropping on buyers’ negotiation plans. In this paper, we consider the buyers can take actions to resist the sellers’ cheatings, which is that the buyers can tailor their plans with extremely low demand to decrease sellers’ profits before migrate to the hosts. In this paper, we consider actions that buyers can take to resist sellers’ cheatings, that is the buyers can tailor their plans with extremely low demands before migrate to the cloud platform. Above situations between buyers and sellers are modeled as a mathematical model called Eavesdropping and Resistance of Negotiation (ERN) Game. The strategies of the buyers and sellers playing the ERN Game are analyzed by the Agent-Based Computational Economic approach. The simulation results show the cooperative strategies will be emerged between buyers and sellers in the ERN Game.     

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.     

An economics-based negotiation scheme among mobile devices in mobile grid

In this paper, we propose an economics-based distributed negotiation scheme among mobile devices in mobile grid. In our model, there are energy negotiation and transactions between buyer devices and seller devices. Dynamic allocation of energy resources in mobile grid is performed through online transactions within markets. Mobile devices can be sellers and buyers that use optimization algorithms to maximize predefined utility functions during their transactions. Seller device agents sell the underlying energy resources of the mobile device. Buyer device agent makes buying decisions within the budget constraints to acquire energy resources. An economics-based negotiation algorithm among mobile devices is proposed. The proposed algorithm decomposes mobile grid system optimization problem into a sequence of two sub-problems. In the simulation, the performance evaluation of economics-based negotiation algorithm is evaluated.