Customer Coalitions in Electronic Markets

Tsvetovat, Maksim; Sycara, Katia; Chen, Yi‐An; Ying, James

doi:10.1007/3-540-44723-7_9

Cited by 52 publications

(47 citation statements)

References 4 publications

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“…Yet, the majority of cooperation and coalition formation MAS-related research tends to focus on the way coalitions are formed and consequently concerns issues such as the optimal division of agents into disjoint exhaustive coalitions, division of coalition payoffs and enforcement methods for interaction protocols. While coalition formation and coordination models can be widely found in the electronic market domain, most work in this domain emphasizes mechanisms for forming cooperation for the purpose of aggregating demands in order to obtain volume discounts (Tsvetovat, Sycara, Chen, & Ying, 2000;Yamamoto & Sycara, 2001;Sarne & Kraus, 2003). Several authors have considered the problem of determining the strategy of a group once formed (Ito, Ochi, & Shintani, 2002;Sarne, Manisterski, & Kraus, 2010;Rochlin, Sarne, & Zussman, 2011;Mash, Rochlin, & Sarne, 2012), however their focus was mostly on fully-cooperative agents.…”

Section: Related Workmentioning

confidence: 99%

Constraining Information Sharing to Improve Cooperative Information Gathering

Rochlin

Sarne

2015

jair

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This paper considers the problem of cooperation between self-interested agents in acquiring better information regarding the nature of the different options and opportunities available to them. By sharing individual findings with others, the agents can potentially achieve a substantial improvement in overall and individual expected benefits. Unfortunately, it is well known that with self-interested agents equilibrium considerations often dictate solutions that are far from the fully cooperative ones, hence the agents do not manage to fully exploit the potential benefits encapsulated in such cooperation. In this paper we introduce, analyze and demonstrate the benefit of five methods aiming to improve cooperative information gathering. Common to all five that they constrain and limit the information sharing process. Nevertheless, the decrease in benefit due to the limited sharing is outweighed by the resulting substantial improvement in the equilibrium individual information gathering strategies. The equilibrium analysis given in the paper, which, in itself is an important contribution to the study of cooperation between self-interested agents, enables demonstrating that for a wide range of settings an improved individual expected benefit is achieved for all agents when applying each of the five methods.

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Section: Related Workmentioning

confidence: 99%

Constraining Information Sharing to Improve Cooperative Information Gathering

Rochlin

Sarne

2015

jair

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“…Yamamoto and Sycara [6] propose a buyer coalition for-mation scheme GroupBuyAuction, where buyers form a group based on a category of items. Tsvetovat et al [7] use economic incentives to create buying group of buyers who procure goods at a volume discount. Li and Sycara [1] combine coalition formation with combinatorial auction, where each buyer places a bid on items with reservation prices.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Combinational Coalition Formation Based On Genetic Algorithm

Li¹,

Jiang²

2010

Proceedings of the 2010 International Conference on E-Business Intelligence

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In electronic commerce, the purchasing power of an individual buyer is limited. Sellers' discount policies are often based on the cost of a single transaction. A profitable purchasing strategy for buyers is to form buyer coalition in order to lower their cost. The key problem is how to form combinational coalitions among buyers in order to minimize the total cost of all buyers. In this paper, we formally analyze the problem and solve it by using the genetic algorithm. Our method can optimally allocate buyers in an efficient time. The experimental results show that the algorithm is feasible and effective.

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“…Cooperation is mainly useful when an agent is incapable of completing a task by itself or when operating as a group can improve the overall performance [17]. Consequently, groupbased cooperative behavior has been suggested in various domains [28], [30]. The recognition of the advantages encapsulated in teamwork and cooperative behaviors is the main driving force of many coalition formation models in the area of cooperative game theory and MAS [26].…”

Section: Extension To Multi-agentmentioning

confidence: 99%

Sequential Multilateral Search for a Common Goal

Rochlin

Sarne

Zussman

2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-Motivated by applications in Dynamic Spectrum Access Networks, we focus on a system in which a few agents are engaged in a costly individual search where each agent's benefit is determined according to the minimum obtained value. Such a search pattern is applicable to many systems, including shipment and travel planning. This paper formally introduces and analyzes a sequential variant of the general model. According to that variant, only a single agent searches at any given time, and when an agent initiates its search, it has complete information about the minimum value obtained by the other agents so far. We prove that the search strategy of each agent, according to the equilibrium of the resulting Stackelberg game, is reservation-value based, and show how the reservation values can be calculated. We also analyze the agents' optimal search strategies when they are fully cooperative (i.e., when they aim to maximize the expected joint benefit). The equilibrium strategies and the expected benefit of each agent are illustrated using a synthetic homogeneous environment, thereby demonstrating the properties of this new search scheme and the benefits of cooperation.

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Customer Coalitions in Electronic Markets

Cited by 52 publications

References 4 publications

Constraining Information Sharing to Improve Cooperative Information Gathering

Constraining Information Sharing to Improve Cooperative Information Gathering

An Optimal Combinational Coalition Formation Based On Genetic Algorithm

Sequential Multilateral Search for a Common Goal

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