Seller Agents Research Articles

Heterogeneous Real Estate Agents and the Housing Cycle

The real estate market is highly intermediated, with 90% of buyers and sellers hiring an agent to help them transact a house. However, low barriers to entry and fixed commission rates result in a market where inexperienced intermediaries have a large market share, especially following house price booms. Using rich micro-level data on 10.4 million listings, we first show that seller agents’ experience is an important determinant of client outcomes, particularly during real estate busts. Houses listed for sale by inexperienced agents spend more time on the market and have a lower probability of selling. We then study the aggregate implications of the agents’ experience distribution on real estate market liquidity by building a theoretical entry and exit model of real estate agents with aggregate shocks. Several policies that raise the barriers to entry for agents are considered: 1) increased entry costs; 2) lower commission rates; and 3) more informed clients. Across each counterfactual, increasing barriers to entry shift the distribution of agents across experience to the right, improves liquidity, and reduces the amplitude of liquidity cycles in the housing market.

Dynamic selling strategy for a firm under asymmetric information: Direct selling vs. agent selling

In this paper, we study the dynamic selling strategy for a firm under asymmetric market and product information over two periods. In each period, the firm can sell the product directly to customers, i.e., direct selling, or through an intermediary retailer such as Amazon and eBay, i.e., agent selling. Before the selling season, the firm is uncertain about the market size and the customers are unaware of whether the product matches their tastes. However, by directly interacting with the customers, the firm under direct selling or the retailer under agent selling will obtain additional market information at the end of the first period. Meanwhile, customers in the second period can resolve their product fit uncertainty by accessing the review comments posted by previous customers, if agent selling is adopted in the first period.We show that if the firm and retailer do not share the additional market information with each other, agent selling is never optimal in the second period. However, both direct selling and agent selling may be optimal in the first period, due to the relative effects of the firm's market uncertainty and customers' product fit uncertainty. Interestingly, converse to traditional wisdom, we show that with information sharing, the retailer will always share the additional market information with the firm. Furthermore, in equilibrium, both agent selling and direct selling may be optimal in both periods, depending on the benefit from the additional market information.

The Impact of Consumer Fairness Seeking on Distribution Channel Selection: Direct Selling vs. Agent Selling

Consumers seek for not only base functionalities of products they buy but also fairness in transactions. In this work, we investigate how such fairness‐seeking behavior affects a manufacturer's distribution channel structure selection. Specifically, the manufacturer can sell the product directly to consumers (named direct selling) or via a middleman retailer (named agent selling). The manufacturer then decides which distribution channel to adopt with an aim to maximize his profit. Under a newsvendor framework, the distribution channel structure endogenizes the procurement cost and thus impacts consumers’ fairness perception and willingness to pay. Interestingly, we show that it may be in the manufacturer's best interest to downward decentralize his distribution channel by adopting agent selling when consumers are extremely fairness‐minded. However, when the consumer's fairness concern is weak, direct selling is preferred by the manufacturer. We further show that the above results qualitatively hold when we take into consideration the downstream competition, the dominance of the manufacturer in retail pricing and the heterogeneity of the consumers in their fairness seeking.

An incentive mechanism to promote honesty among seller agents in electronic marketplaces

With the rapid development of online shopping, usually, sellers and buyers have virtual identities, which are not verified beforehand. As a result, establishing trust between sellers and buyers is much harder than before where sellers and buyers would meet face to face before making any transaction. In this work, we propose a method for the marketplace management under anonymous buyers and sellers which makes the honest behavior the most profiting action for rational sellers. In this method, the market operator adopts an honesty promoting mechanism based on direct reward and punishment in which being honest is the most profitable action. In most online marketplaces, a fee or commission is deducted from every payment before settling the account up with the seller. As such, with the dynamic adjustment of the fee, it is possible to reward or punish the seller. The correctness of this method is proven using mathematical models. The apparatus, processes, and algorithms for the marketplace are elaborated in detail so that market operators can easily apply the method in their existing marketplaces.

Machine learning techniques for categorization of buyer and seller agents

Machine learning techniques for categorization of buyer and seller agents

Multi-Agent Bargaining Learning for Distributed Energy Hub Economic Dispatch

This paper proposes a novel multi-agent bargaining learning (MABL) for the distributed energy hub economic dispatch (EHED) of multiple energy carrier systems (MECS). Distributed EHED is developed by extending the conventional economic dispatch (ED) into MECS in a distributed manner, in which each energy hub is regarded as a learning agent for self-scheduling. The classical Q-learning with associative memory is employed for knowledge learning of each agent, while the non-uniform mutation operator is adopted for handling the continuous control variables. To maximize the total payoff of all the energy hubs, the bargaining game is presented for achieving an effective coordination between the buyer agents and a seller agent, where the slack energy hub is designed as the seller agent and the others are the buyer agents. MABL has been thoroughly evaluated for the distributed EHED on a high-complex 39-hub MECS with 29 energy hub structures and 76 energy production units. Case studies verify the superior performance of MABL for the distributed EHED compared with six centralized heuristic optimization algorithms.

Compensation plan, pricing and production decisions with inventory-dependent salvage value, and asymmetric risk-averse sales agent

In this paper, we investigate the joint decision on production and pricing, and the compensation strategy of a supply chain, where the manufacturer relies on a risk-averse sales agent to sell the products. The sales outcome is determined by the sales agent's selling effort and the product price. Most of the previous research about salesforce assumes that the risk attitude to an agent is known to each other and the salvage value is a constant. In this study, we have considered that the salvage value is a function of inventory, and both of the sales agent's selling effort and risk attitude are their private information on the general framework of dual information asymmetric. With the help of revelation principle and principal-agent theory, we have been able to derive the optimal compensation contracts, and joint decision on production and pricing for the manufacturer. Analyzing them and comparing to the symmetric scenario, we found that only the optimal production strategy and the manufacturer's profit depended on the variation rate of salvage value. When the manufacturer comes across asymmetric risk-averse sales agents its profit decreases, whereas the sales agent with private information obtains higher income but exerts less effort, which implies the value of information. The results also mean that the manufacturer should not only focus on offering a lower commission rate to the more risk-averse sales agent, but also on screening his risk information.

A Design of LPG Enterprise Agent Selling Mechanism with Asymmetric Information

A Design of LPG Enterprise Agent Selling Mechanism with Asymmetric Information

Measured anxiety affects choices in experimental “clock” games

We measure anxiety by skin conductance response (SCR) in an economic setting. In “clock” games, six agents receive private signals when an asset's price exceeds its fundamental value. They can sell for immediate value or wait to sell at a higher value. Waiting is risky because the price crashes to a lower value when three agents sell. Anxiety could lead people to sell too quickly when the game is played dynamically over time, compared to a static version with precommitted selling. Empirically, delays are shorter in dynamic games than in payoff-equivalent static games, and are associated with anxiety as measured.

Salesforce contract design, joint pricing and production planning with asymmetric overconfidence sales agent

We study a supply chain in which a rational manufacturer relies on an overconfident sales agent to sell the products. The actual sales outcome is determined by the sales agent's selling effort, the product price and a random market condition, and the sales agent is overconfident in their estimation of sales outcome. Apart from them, both of the sales agent's degree of overconfidence and selling effort are his private information. We consider the salesforce incentive to motivate the sales agent and screen his real degree of overconfidence using a principle-agent method under dual information asymmetry, then the manufacturer uses the information to realize her joint decision on pricing and production. Furthermore, we derive the optimal compensation contract as well as the optimal pricing and production, and compare it to the symmetric overconfidence scenario. Finally, some interesting insights are found: when the manufacturer is uncertain about the sales agent's the degree of overconfidence, her expected profit decreases while the sales agent with private information exerts less effort but obtains higher income, which implies the value of information; the manufacturer should hire a more overconfident sales agent, while a higher commission rate is not guaranteed. These results suggest that the manufacturer should not only focus on hiring the overconfident sales agent but also on disclosing the degree of overconfidence.

Safeguarding Buyers with Attack-Resilient Reputation Parameters

Agent-mediated reputation systems provide the necessary decision making support to facilitate the automation of processes, and to improve the quality of services in an information asymmetric e-market. Reputation systems encourage honest behavior and discourage malicious behavior of buyer and seller agents by safeguarding honest participants and by laying a foundation for security through social control mechanisms. In this paper, we propose a reputation system that aims to safeguard honest buyers from rogue behavior by counterparties and deceptive buying peers. It employs reputation functions with attack-resilient parameters based on the e-market parameters like value of the transaction and varying experience of agents. These parameters help to reduce the incentive for dishonest behavior, and to minimize harm in case of attacks by rogue sellers in order to improve the trustworthiness among participants in the e-market.

A Smart and Automated Negotiation System based on Linear Programming in a Multilateral Environment

Automated negotiation is a branch of e-commerce. Successful management of purchase and sale activities depends not only on selecting the right products or services but also on choosing the best method for buying and selling them. We propose a linear programming and pattern matching–based multilateral automated negotiation system. We compared some methods of automated negotiation. A multilateral negotiation system gives better results for participants than bilateral automated negotiation. The technique of pattern matching–based automated negotiation gives fast results and reduces the overhead from calculations. Automated negotiations are not an easy task without the interactions of human beings. Automated negotiation can be done in bilateral or multilateral environments. In this paper, we consider many-to-many negotiations where agents concurrently negotiate with many other agents. In this paper, we also propose a matrix method to manage the concurrent negotiations conducted by multiple buyer agents with multiple seller agents.

Selecting advisor agents using reliability, reputation and risks

This paper presents an enhanced trust model for evaluating and selecting trustworthy advisor agents in multi-agent systems. In particular, the study proposes a computational trust model based on three main components, reliability, reputation and risk of interactions (3-R) in order to select trustworthy advisor agents. These advisors are buyer agents which suggest a trustworthy seller agent for transaction. The 3-R model represents a method for calculating reliability, with each advisor that has similar preferences with the buyer. Experimental results are presented in defense of the proposed approach and to demonstrate the accuracy of the presented model in evaluating the trustworthiness of advisors, even when a social network of advisors contains few numbers of advisors. The proposed 3-R model will be useful for researchers to improve the safety of electronic marketplaces in the case where the buyer agent has no previous experience with sellers, and it has to trust the other buyers as an advisor to make a decision about sellers.

Fuzzy rule-based system for evaluation of uncertainty transaction in cassava chain

A fuzzy ruled-based system was developed in this study and resulted in an index indicating the level of uncertainty related to commercial transactions between cassava growers and their dealers. The fuzzy system was developed based on Transaction Cost Economics approach. The fuzzy system was developed from input variables regarding information sharing between grower and dealer on “Demand/purchase Forecasting”, “Production Forecasting” and “Production Innovation”. The output variable is the level of uncertainty regarding the transaction between seller and buyer agent, which may serve as a system for detecting inefficiencies. Evidences from 27 cassava growers registered in the Regional Development Offices of Tupa and Assis, São Paulo, Brazil, and 48 of their dealers supported the development of the system. The mathematical model indicated that 55% of the growers present a Very High level of uncertainty, 33% present Medium or High. The others present Low or Very Low level of uncertainty. From the model, simulations of external interferences can be implemented in order to improve the degree of uncertainty and, thus, lower transaction costs.

An Approach to One-to-Many Concurrent Negotiation

Automated negotiation is a market mechanism that can improve the electronic transactions in terms of both the time needed to complete the transactions and the quality of the transactions. The quality of a transaction can be measured—for example—by its utility. This paper addresses the problem of the bidding strategy for a buyer agent negotiating concurrently with multiple seller agents over multiple distinct objects, e.g., resources. Each distinct object has multiple providers and a single negotiation issue. In our scenario, we assume that all objects have the same negotiation issue. For the stated scenario, we propose novel coordination mechanisms that manage the bidding strategy for the buyer agent benefiting from the different objectives of the seller agents over all negotiation objects and from the different objectives of the seller agents of a given object. We propose three dynamic bidding strategies: global, local and hybrid. The strategies are tested against two other benchmark negotiation strategies used in the literature. The experimental results show that the proposed strategies perform well when compared with the other benchmark strategies under tough negotiation conditions.

A dynamic personalised product pricing strategy using multiple attributes in agent mediated e-market - a neural approach

To attract buyers in the uncertain and distrusted environment of e-market, seller agents must use flexible and adaptive strategies. Being able to compute the right price of a good is vital for a seller agent to succeed in e-market that allows for prices to fluctuate due to uncertainty, different conditions, context and buyers’ requirements. This paper addresses the problem of dynamically computing the appropriate selling price of a good for a prospective buyer, in response to the buyers’ specifications for the goods’ attributes in linguistic terms using artificial neural network in a competitive e-market. The proposed model helps in improving buyer-seller satisfaction by offering customised products to buyers where at the same time realising the expected revenue of sellers by enticing buyers to return in future transactions. It encourages trustworthy sharing of information among sellers by associating the concept of reputation among selling peers.

Strategies for avoiding preference profiling in agent-based e-commerce environments

Agent-based electronic commerce is known to offer many advantages to users. However, very few studies have been devoted to deal with privacy issues in this domain. Privacy is of great concern and preserving users' privacy plays a crucial role to promote their trust in agent-based technologies. In this paper, we focus on preference profiling, which is a well-known threat to users' privacy. Specifically, we review strategies for customers' agents to prevent seller agents from obtaining accurate preference profiles of the former group by using data mining techniques. We experimentally show the efficacy of each of these strategies and discuss their suitability in different situations. Our experimental results show that customers can improve their privacy notably with these strategies.

An Agent-based Framework for Partner Selection with Sustainability Considerations

This paper presents a multi-agent system (MAS) to support partner selection in a sustainable supply chain network. With the growing awareness of environmental and social issues globally, governments and industry have recognized that they have vital roles in supporting and assuring sustainable development. In consideration of these concerns, companies worldwide have begun to adopt sustainability practices in the supply chain. An important issue of sustainability practices is to involve the sustainability considerations into organizational purchasing decision and long term relationships with suppliers. That is, in the selection of suppliers and outsourcing partners, enterprises should not only consider the general criteria, but also should consider the environmental and social impacts of their partners. Evaluation and selection of partners is a typical multi-criteria decision making (MCDM) problem involving both qualitative and quantitative issues. For the implementation of supply chain sustainability, issues regarding environmental and social responsibility are to be included in partner evaluation, in addition to the conventional criteria such as price, quality, delivery, and service. In this paper, the MAS is established to integrate sustainability concerns into the supply chain partner evaluation and selection process. Autonomous agents are established to represent various parties and functions of the supply chain, for instance, a buyer agent represents the buyer and the potential suppliers are represented by seller agents. The buyer agent has to exchange bids with the seller agents on the multiple criteria repeatedly. The MAS is equipped with suitable negotiation strategies and negotiation protocol to all agents. It supports multilateral negotiation, that is, the buyer agent is able to interact with many seller agents concurrently in the bidding process.

AN INTEGRATED CLUSTERING-BASED APPROACH TO FILTERING UNFAIR MULTI-NOMINAL TESTIMONIES

Reputation systems have contributed much to the success of electronic marketplaces. However, the problem of unfair testimonies has to be addressed effectively to improve the robustness of reputation systems. Until now, most of the existing approaches focus only on reputation systems using binary testimonies, and thus have limited applicability and effectiveness. In this paper, We propose an integrated CLUstering-Based approach called iCLUB to filter unfair testimonies for reputation systems using multinominal testimonies, in an example application of multiagent-based e-commerce. It adopts clustering techniques and considers buyer agents’ local as well as global knowledge about seller agents. Experimental evaluation demonstrates the promising results of our approach in filtering various types of unfair testimonies, its robustness against collusion attacks, and better performance compared to competing models.

A dynamic reputation system with built-in attack resilience to safeguard buyers in e-market

Reputation systems aim to reduce the risk of loss due to untrustworthy participants by providing a mechanism for establishing trustworthiness between mutually unknown online entities in an information asymmetric e-market. These systems encourage honest behavior and discourage malicious behavior of buyer and seller agents by laying a foundation for security and stability in the e-market. However, the success of a reputation system depends on its built-in resilience capabilities to foil various attacks. This paper focuses on how to safeguard buyers from dishonest sellers and advisors by incorporating an attack resilient reputation computation methodology. The objectives of the proposed dynamic reputation system in the distributed environment are to reduce the incentive for behaving dishonestly, and to minimize harm in case of attacks by dishonest participants with the inherent purpose of improving the quality of services in the e-market.