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.     

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.     

Analyzing the impact of intermediaries in electronic markets: an empirical investigation of online consumer-to-consumer (C2C) auctions

It has been widely believed that the Internet and electronic markets will eliminate traditional intermediaries. However, a close examination of the market mechanism indicates that the impact of intermediaries is indispensable. Specifically, we consider intermediaries’ impacts in price discovery and trust building. Intermediaries provide a buffer for temporary misalignment between supply and demand by buying low and selling high, and also help build trust by engaging in transactions with risk-averse buyers and sellers who otherwise will not participate in the market. Using a dataset from eBay’s online auctions, we examine empirically these two impacts of human intermediaries. We find that the presence of brokers has a significant impact on market liquidity, resulting in more successful trades and higher auction prices. In addition, we find that brokers are more likely to engage in transactions with less established sellers. Their presence reduces reputation penalty faced by these players and further facilitates the successful sale of items in auctions.     

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.     

一种基于双向拍卖机制的计算网格资源分配方法

针对计算网格资源的特点以及运用经济机制进行网格资源管理所具有的灵活性及有效性,提出一种改进的基于双向拍卖机制的网格资源分配方法.首先,描述了基于双向拍卖机制的资源分配框架,整个系统由买方、卖方和计算资源经纪人组成.然后,针对网格中的CPU资源,提出一种改进的双向拍卖机制,采用统一拍卖方式,可以灵活调节交易双方的付费.进而,分析了该双向拍卖机制满足优势策略激励相容、预算平衡以及个人理性的特点,并定义了拍卖机制的效率.最后,通过实验分析了双向拍卖分配机制的效率.     

Normalized particle swarm optimization for complex chooser option pricing on graphics processing unit

An option is a financial instrument that derives its value from an underlying asset, for example, a stock. There are a wide range of options traded today from simple and plain (European options) to exotic (chooser options) that are very difficult to evaluate. Both buyers and sellers continue to look for efficient algorithms and faster technology to price options for better profit and to beat the competition. There are mathematical models like the Black–Scholes–Merton model used to price options approximately for simple and plain options in the form of closed form solution. However, the market is flooded with various styles of options, which are difficult to price, and hence there are many numerical techniques proposed for pricing. The computational cost for pricing complex options using these numerical techniques is exorbitant for reasonable accuracy in pricing results. Heuristic approaches such as particle swarm optimization (PSO) have been proposed for option pricing, which provide same or better results for simple options than that of numerical techniques at much less computational cost. In this study, we first map the PSO parameters to option pricing parameters. Analyzing the characteristics of PSO and option pricing, we propose a strategy to normalize some of the parameters, which helps in better understanding of the sensitivity of these and other parameters on option pricing results. We then avail of the inherent concurrency of the PSO algorithm while searching for an optimum solution, and design an algorithm for implementation on a modern state-of-the-art graphics processor unit (GPU). Our implementation makes use of the architectural features of GPU in accelerating the computing performance while maintaining accuracy on the pricing results.     

Signaling theory and information asymmetry in online commerce

An e-business environment results in information asymmetry because buyers cannot physically evaluate the quality of products and easily assess the trustworthiness of sellers. Product and seller quality are communicated through website signals. Using signaling theory, we developed a three-dimensional framework to classify website signals. We empirically tested the framework with a comparative content analysis of websites from a sample of online pharmacies. We found that low-quality sellers were likely to avoid costly and easy-to-verify signals and used fewer signals than did high-quality sellers, who used costly and difficult-to-verify signals and displayed more signals. These results provide information to online buyers and regulatory institutions in charge of online retailer evaluation.     

Development of a DRAM spots negotiation system using core hierarchy negotiation mechanism

The major factors of the DRAM negotiable transactions system in this research are the concession strategy and the negotiation mechanism of the sellers and buyers. In terms of negotiation mechanism, the core hierarchy model negotiation mechanism makes use of multistage sub-negotiates procedure to do multi-attribute negotiation for the sellers and buyers. The negotiable mechanism can further increase the successful rate of negotiation and the acceptable degree. In terms of concession strategy, research in the Liang and Doong, the concession strategy is constituted by times of negotiation, method of concession and times of persistence. There are less interactive concession for the sellers and buyers in the concession method of the negotiation in the related research. This research is to study the negotiable results that using various combinations of interactive concession strategy and the core hierarchy model negotiation mechanism. Be used as the reference of negotiation for the DRAM to trade on network.     

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.     

Efficient allocation and composition of distributed storage

In this paper, we investigate the composition of cheap network storage resources to meet specific availability and capacity requirements. We show that the problem of finding the optimal composition for availability and price requirements can be reduced to the knapsack problem, and propose three techniques for efficiently finding approximate solutions. The first algorithm uses a dynamic programming approach to find mirrored storage resources for high availability requirements, and runs in the pseudo-polynomial O(n ² c) time where n is the number of sellers’ resources to choose from and c is a capacity function of the requested and minimum availability. The second technique is a heuristic which finds resources to be agglomerated into a larger coherent resource, with complexity of O(nlog?n). The third technique finds a compromise between capacity and availability (which in our phrasing is a complex integer programming problem) using a genetic algorithm. The algorithms can be implemented on a broker that intermediates between buyers and sellers of storage resources. Finally, we show that a broker in an open storage market, using the combination of the three algorithms can more frequently meet user requests and lower the cost of requests that are met compared to a broker that simply matches single resources to requests.     

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.     

Mechanism design for coalition formation and cost sharing in group-buying markets

We study the mechanism design problem of coalition formation and cost sharing in a group-buying electronic marketplace, where buyers can form coalitions to take advantage of volume based discounts. The desirable mechanism properties include stability (in the core), and incentive compatibility with good efficiency. We show the impossibility to simultaneously satisfy efficiency, budget balance and individual rationality at a Bayesian–Nash equilibrium, and propose a mechanism in the core of the game. We then present and evaluate a group of reasonable mechanisms. Empirical results show positive correlation between stability and incentive compatibility (which is in turn related to efficiency).     

Performance of online reputation mechanisms under the influence of different types of biases

Rating mechanisms are becoming an essential tool for online shoppers. There have been many reputation mechanisms proposed in the literature that try to mitigate the sometimes adverse effects of strategic behavior of the traders to alter their true reputation scores. At the same time, several studies have shown the presence of bias (often unintentional) in product ratings and their harmful effect on buyers and product sales. To date there has not been any work done that studies the performance of reputation mechanisms in the presence of various types of biases. This study is a first attempt to fill that gap. We present three reputation mechanism models that have been discussed in the literature, discuss three types of biases that have been studied in the past, and study the performance of the three reputation mechanisms in the presence of these biases by using a multi-agent system that simulates the interactions among buyers and sellers in an electronic marketplace. Using replicator dynamics we also show the effect of these models on the behavior of buyers and sellers as the buyers choose the type of reputation models they prefer to use and the sellers change their strategic behavior in response.     

Trust ontologies for e‐service environments

In this article, we introduce trust ontologies. An ontology represents a set of concepts that are commonly shared and agreed to by all parties in a particular domain. Here, we introduce generic and specific trust ontologies. These ontologies include the following: an agent trust ontology and trustworthiness; agents include sellers, service providers, Web sites, brokers, shops, suppliers, buyers, or reviewers. A services trust ontology and trustworthiness assists in measuring the quality of service that agents provide in the service‐oriented environment such as sales, orders, track and trace, warehousing, logistics, education, governance, advertising, entertainment, trading, online databases, virtual community services, security, information services, opinions, and e‐reviews. A goods or products trust ontology and trustworthiness is useful for measuring the quality of products such as commercial products, information products, entertainment products, or second‐hand products. We present a trust ontology that is suitable for all types of agents that exist in the service‐oriented environment. As agent trust is measured through the quality of goods and services, we introduce two additional distinct concepts of service trust ontology and product trust ontology. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 519–545, 2007.     

Market power and efficiency in a computational electricity marketwith discriminatory double-auction pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth-Erev individual reinforcement learning algorithm (1995) to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained and that market microstructure is strongly predictive for the relative market power of buyers and sellers, independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms     

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.     

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.     

Optimal lot-sizing policy for a manufacturer with defective items in a supply chain with up-stream and down-stream trade credits

In this paper, we establish an economic production quantity model for a manufacturer (or wholesaler) with defective items when its supplier offers an up-stream trade credit M while it in turn provides its buyers (or retailers) a down-stream trade credit N. The proposed model is in a general framework that includes numerous previous models as special cases. In contrast to the traditional differential calculus approach, we use a simple-to-understand and easy-to-apply arithmetic–geometric inequality method to find the optimal solution. Furthermore, we provide some theoretical results to characterize the optimal solution. Finally, several numerical examples are presented to illustrate the proposed model and the optimal solution.