A cooperative exchanging mechanism among seller agents for group-based sales

Agent-mediated electronic markets have been a growing area in intelligent agent research and development in recent years. Agents can act autonomously and cooperatively in an electronic market on behalf of their users. In such an electronic market, if a seller agent does not have enough of a particular item, it misses the opportunity to sell the item. Buyers also miss the opportunity to purchase the item. Namely, the overall negotiation utility is decreased. Thus, we propose a new cooperation mechanism among seller agents based on exchanging their goods in our agent-mediated electronic market system, G-Commerce. In G-Commerce, seller agents and buyer agents negotiate with each other. In our model, seller agents cooperatively negotiate in order to sell goods in stock. Buyer agents cooperatively form coalitions in order to buy goods based on discount prices. Seller agents’ negotiations are completed by using an exchanging mechanism for selling goods. Our experiments show that this exchanging mechanism enables seller agents to sell goods in stock effectively. We also demonstrate how our exchanging mechanism satisfies Pareto optimality.     

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 interactive approach for multi-attribute auctions

Auction processes are commonly employed in many environments. With rapid advances in Internet and computing technologies, electronic auctions have become very popular. People sell and buy a wide range of goods and services online. There is a growing need for the proper management of online auctions and for providing support to parties involved. In this paper, we develop an interactive approach supporting both the buyer and the bidders in a multi-attribute, single-item, multi-round, reverse auction environment. We demonstrate the algorithm on a number of problems.     

Trend following deep Q-Learning strategy for stock trading

Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q-Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q-Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting-based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.     

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.     

Automatic price negotiation on the web: An agent-based web application using fuzzy expert system

A traditional internet auction is restricted by the limitation of time. It is necessary to conduct an internet auction in a certain time period. The final trading price is determined until this certain period ends. This study improves this situation by removing the time limitation. Based on the fuzzy inference theory, this paper proposes an agent-based price negotiation system for on-line auctions. Mainly, three agents are used in the study: a seller agent, a buyer agent, and a mediator agent. The proposed system provides an easy-to-use environment and good customizability for users (buyers or sellers) to customize their price negotiation strategies using user-defined fuzzy rules. The final negotiated price is immediately determined after the buyer sends his bids to the proposed system. This study develops a Java-based computer package to implement the price negotiation system where Model-View-Controller (MVC) design pattern is employed in design of the package. Unified Modeling Language (UML) is also utilized to describe the structures and behaviors of the package. To validate the proposed system, this study built an on-line auction website with the proposed price negotiation mechanism for internet users to buy or sell their merchandises. An evaluation was finally conducted to investigate the users’ satisfaction with the proposed system.Briefly, the proposed system is featured by: (1) instantly getting negotiated price without waiting; (2) conducting price negotiation at any time; (3) determining strategy rules easily, and (4) using customizable negotiation strategies defined by users.     

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.     

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.     

The Tech Buzz Game [stock market prediction]

Prediction markets, also known as information or decision markets, are designed to forecast future events or trends. Internet-based prediction markets can easily aggregate the insights of an unlimited number of potentially knowledgeable people asynchronously. The Tech Buzz Game - a joint venture between Yahoo! Research Labs and O'Reilly Media - is a fantasy prediction market launched in March 2005 at the O'Reilly Emerging Technology (ETech) Conference. The game consists of multiple sub-markets that pit a handful of rival technologies, each represented by a stock, against one another. The game's object is to anticipate future search buzz and buy and sell stocks accordingly. Thus, a player who believes BitTorrent stock is undervalued might buy shares, while a player who thinks BitTorrent is overpriced might sell the stock or instead purchase shares in a competing peer-to-peer technology. The Tech Buzz Game serves two key research-oriented goals. One is to evaluate the power of prediction markets to forecast high-tech trends. The other goal of the Tech Buzz Game is to field test the dynamic pari-mutuel market, a Yahoo! Research Labs trading mechanism designed to price and allocate shares.     

Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals

This paper investigates whether trading volume and price distortion can be explained by the investor’s bounded rationality. Assuming that agents are bounded by their information access and processing, what are the consequences on market dynamics? We expose the result of simulations in an ABM that considers the liquidity as an endogenous characteristic of the market and allows to design investors as bounded rational. In a call auction market, where two risky assets are exchanged, traders are defined as a mix between fundamentalist and trend-follower outlook. Each one differs as to behaviour, order-placement strategy, mood, knowledge, risk-aversion and investment horizon. We place agents in a context of evolving fundamental values and order placement strategy; they perceive the fundamental but they also have some heterogeneous belief perseverance; and they adapt their orders to the market depth so as to maximise their execution probability and their profit. By adding bounded rationality in their information processing, we show that (1) usual features as trend-follower outlook and heterogeneous investment horizon are important features to generate excess volatility of asset prices and market inefficiency; (2) the learning fundamental value stabilises the market price and the trading volume; (3) the order-placement strategy increases trading volume, but reduces market efficiency and stability; (4) the agent’s mood prevents illiquid market and weakly increases the market volatility as classical noise trader agents; (5) the impatience to sell of traders is always present in the market: the market sell orders are always more numerous than the market buy orders.     

An efficient Nash-implementation mechanism for network resource allocation

We propose a mechanism for auctioning bundles of multiple divisible goods in a network where buyers want the same amount of bandwidth on each link in their route. Buyers can specify multiple routes (corresponding to a source-destination pair). The total flow can then be split among these multiple routes. We first propose a one-sided VCG-type mechanism. Players do not report a full valuation function but only a two-dimensional bid signal: the maximum quantity that they want and the per-unit price they are willing to pay. The proposed mechanism is a weak Nash implementation, i.e., it has a non-unique Nash equilibrium that implements the social-welfare maximizing allocation. We show the existence of an efficient Nash equilibrium in the corresponding auction game, though there may exist other Nash equilibria that are not efficient. We then generalize this to arbitrary bundles of various goods. Each buyer submits a bid separately for each good but their utility function is a general function of allocations of bundles of various divisible goods. We then present a double-sided auction mechanism for multiple divisible goods. We show that there exists a Nash equilibrium of this auction game which yields the efficient allocation with strong budget balance.     

FUZZY TRUST AGGREGATION AND PERSONALIZED TRUST INFERENCE IN VIRTUAL SOCIAL NETWORKS

Virtual marketplaces on the Web provide people with great facilities to buy and sell goods similar to conventional markets. In traditional business, reputation is subjectively built for known persons and companies as the deals are made in the course of time. As it is important to do business with trustful individuals and companies, there is a need to survive the reputation concept in virtual markets. Auction sites generally employ reputation systems based on feedbacks that provide a global view to a cyber dealer. In contrast to global trust, people usually infer their personal trust about someone whose reputation is completely or partially unknown by asking their trusted friends. Personal reputation is what makes a person trusted for some people and untrusted for others. There should be a facility for users in a virtual market to specify how much they trust a friend and also a mechanism that infers the trust of a user to another user who is not directly a friend of her. There are two main issues that should be addressed in trust inference. First, the trust modeling and aggregation problem needs to be challenged. Second, algorithms should be introduced to find and select the best paths among the existing trust paths from a source to a sink. First, as trust to a person can be stated more naturally using linguistic expressions, this work suggests employing linguistic terms for trust specification. To this end, corresponding fuzzy sets are defined for trust linguistic terms and a fuzzy trust aggregation method is also proposed. Comparing the fuzzy aggregation method to the existing aggregation methods shows superiority of fuzzy approach especially at aggregating contradictory information. Second, this paper proposes an incremental trust inference algorithm. The results show improvement in preciseness of inference for the proposed inference algorithm over the existing and recently proposed algorithm named TidalTrust.     

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.     

A graphical formalism for mixed multi-unit combinatorial auctions

Mixed multi-unit combinatorial auctions are auctions that allow participants to bid for bundles of goods to buy, for bundles of goods to sell, and for transformations of goods. The intuitive meaning of a bid for a transformation is that the bidder is offering to produce a set of output goods after having received a set of input goods. To solve such an auction the auctioneer has to choose a set of bids to accept and decide on a sequence in which to implement the associated transformations. Mixed auctions can potentially be employed for the automated assembly of supply chains of agents. However, mixed auctions can be effectively applied only if we can also ensure their computational feasibility without jeopardising optimality. To this end, we propose a graphical formalism, based on Petri nets, that facilitates the compact represention of both the search space and the solutions associated with the winner determination problem for mixed auctions. This approach allows us to dramatically reduce the number of decision variables required for solving a broad class of mixed auction winner determination problems. An additional major benefit of our graphical formalism is that it provides new ways to formally analyse the structural and behavioural properties of mixed auctions.     

To be or not to B2B: Evaluating managerial choices for e-procurement channel adoption

E-procurement systems are computer systems and communication networks through which firms buy and sell products. We identify two types of e-procurement systems: extranets and e-markets. Extranets connect the buyer and its suppliers with a closed network, while e-markets create open networks for buyer and supplier interactions. The differences between them lie in system implementation costs, marketplace benefits, and the extent of supplier competitive advantage that develops due to information sharing. In this article, we develop a new theoretical model to analyze the adoption of e-procurement systems from the buyer’s perspective, to explore the set of conditions under which the buyer will prefer to procure via an electronic market instead of using proprietary extranet connections. The primary finding is that a buyer will adopt an e-market approach when the supplier’s competitive advantage derived from access to strategic information is modest compared with the marketplace benefits less the channel costs. In addition, we find that the buyer is likely to have a bigger trading network with an e-market than with an extranet in order to capture the greatest available benefits. Overall, this study offers guidelines for managers to design and select e-procurement channels to fit different procurement needs.     

一口价的信息与信誉机制——基于网上拍卖数据的实证研究

经典拍卖理论无法理解网络拍卖中的一口价拍卖,已有文献从风险态度和交易成本的角度给出了解释,但却难以解释"一口价"设定比例的横截面差异。利用网站大样本数据,本文对已有理论进行了检验,证实了风险态度和交易成本的作用。然而控制这些因素之后,卖家信用水平仍然对一口价的设定和执行具有显著的影响,一口价对于买家估价和出价的参考作用同样显著,而且效果与卖家的信用水平正相关,从而验证了一口价的信息与信誉机制。     

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.     

Dynamic Pricing for Time-Limited Goods in a Supplier-Driven Electronic Marketplace

Existing e-commerce systems employ a pull model of marketing where buyers, possibly through agents, search the e-market for suppliers offering the product of their choice. In contrast, the push model where suppliers agents approach buyers with their products, has been relatively less investigated. Push strategies are particularly appropriate for commodities that have a short shelf-life and, therefore, an elastic demand curve, allowing suppliers to exploit unexpected supply. The speed and low cost of e-commerce makes it particularly suited to the push paradigm. In this paper, we consider time-limited goods in a supplier driven marketplace that employs the push model of marketing. When constrained by a strict deadline to sell the good, the supplier uses a mobile sales agent that visits every buyer and estimates the short run demand curve of the good. At every buyer, the sales agent also employs a heuristic technique called the Maximum Returns Algorithm to recalculate the price of the good, so that the supplier can obtain the best possible gross returns from trading with the buyers. On the other hand, when the deadline to sell is not stringent, the sales agent negotiates the exchange at a point that improves both the buyers utility and the suppliers profit, as compared to the exchange point without negotiation.This research has been supported by QAD Inc. through the California Micro Program, Grants 97-122 and 98-107 and by the DARPA/ONR Grant N66001-00-1-8931.     

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.     

Inventory control model for a supply chain system with multiple types of items and minimum order size requirements

This paper considers a replenishment problem for a single buyer who orders multiple types of items from two or more heterogeneous suppliers in order to sell to end customers. The buyer periodically orders each type of item from the suppliers according to a select inventory control policy. Processing the order, each supplier enforces the policy that an order from the buyer must meet a predetermined minimum order quantity (MOQ). Therefore, the buyer must decide how much to order from each supplier considering the current inventory level, demand forecast, and MOQ requirement. The buyer's problem is formulated as an integer programming model and an efficient implementation strategy is suggested to apply the model to real problems. Numerical experiments are performed to test the validity of the proposed model as well as the efficiency of the implementation strategy. The experimental results show that this model combined with the implementation method yields a considerable cost reduction compared to the most efficient policy currently available.