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Long Waves and Short Waves: Growth through Intensive and Extensive
 Search,” Econometrica
, 1990
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Cited by 51 (3 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Managing Parallel Inquiries in Agents’ TwoSided Search
, 2006
"... In this paper we address the problem of agents engaged in a distributed costly twosided search for pairwise partnerships in MultiAgent Systems (MAS). While traditional twosided search mechanisms are based on a purely sequential search of all searchers, our mechanism integrates an ability of some ..."
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In this paper we address the problem of agents engaged in a distributed costly twosided search for pairwise partnerships in MultiAgent Systems (MAS). While traditional twosided search mechanisms are based on a purely sequential search of all searchers, our mechanism integrates an ability of some of the agents to maintain several search efforts in parallel at each search stage. We show that in many environments the transition to the new mechanism is inevitable since the adoption of the parallelinteractions based search suggests a greater utility for the searching agents. By exploring the appropriate model equations, we present the new dynamics that drive the equilibrium when using such a mechanism in MAS environments. Complementary algorithms are offered, based on the unique equilibria characteristics found, for facilitating the extraction of the agents ’ strategies. The analysis methodology used supplies a comprehensive solution to a self contained model, and also offers a great value for future work concerning other model variants in which parallel search is used in MAS twosided mechanisms. Towards the end of the paper we review two such important models that can benefit from the proposed analysis.
Enhancing Cooperative Search with Concurrent Interactions
"... In this paper we show how taking advantage of autonomous agents ’ capability to maintain parallel interactions with others, and incorporating it into the cooperative economic search model results in a new search strategy which outperforms current strategies in use. As a framework for our analysis we ..."
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In this paper we show how taking advantage of autonomous agents ’ capability to maintain parallel interactions with others, and incorporating it into the cooperative economic search model results in a new search strategy which outperforms current strategies in use. As a framework for our analysis we use the electronic marketplace, where buyer agents have the incentive to search cooperatively. The new search technique is quite intuitive, however its analysis and the process of extracting the optimal search strategy are associated with several significant complexities. These difficulties are derived mainly from the unbounded search space and simultaneous dual affects of decisions taken along the search. We provide a comprehensive analysis of the model, highlighting, demonstrating and proving important characteristics of the optimal search strategy. Consequently, we manage to come up with an efficient modular algorithm for extracting the optimal cooperative search strategy for any given environment. A computational based comparative illustration of the system performance using the new search technique versus the traditional methods is given, emphasizing the main differences in the optimal strategy’s structure and the advantage of using the proposed model. 1.
Modeling the Search for the Least Costly Opportunity
, 2005
"... Modeling the Search for the Least Costly Opportunity With the continuing growth in the number of opportunities available at virtual stores over the Internet there is also a growing demand for the services of computer programs capable of scanning a large number of stores in a very short time. We assu ..."
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Modeling the Search for the Least Costly Opportunity With the continuing growth in the number of opportunities available at virtual stores over the Internet there is also a growing demand for the services of computer programs capable of scanning a large number of stores in a very short time. We assume that the cost associated with each scan is linear in the number of stores scanned, and that the resulting list of price quotes is not always satisfactory to the customer, in which case an additional scan is performed, and so on. In such a reality the customer, wishing to minimize her expected cost, must specify the requested sample size and a rule (control limit) to stop the search. In the context of search theory, the above model can be categorized as “fixedsamplesize, sequential, with infinite horizon”. According to this model the expected search cost is a function of two decision variables: the sample size and the control limit. We prove that for arbitrary sample size the expected search cost is either quasiconvex or strictly decreasing in the control limit, and that the optimal expected search cost is quasiconvex in the sample size. These properties allow an efficient calculation of the optimal policy. We also develop analytic formulas to calculate the cost’s variance, allowing customers to choose a slightly higher expected cost if there is a considerable decrease in the variance. Finally, we present detailed examples for price quotes that are either uniform or exponential. 21.
Enhancing mas cooperative search through coalition partitioning
 In Proc. Int’l Joint Conference on Artificial Intelligence
, 2007
"... This paper presents new search strategies for agents with diverse preferences searching cooperatively (i.e., as a coalition) in complex environments where searching is a costly activity. The uniqueness of our proposed mechanism is in the ability of the coalition to partition itself into subcoaliti ..."
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Cited by 4 (1 self)
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This paper presents new search strategies for agents with diverse preferences searching cooperatively (i.e., as a coalition) in complex environments where searching is a costly activity. The uniqueness of our proposed mechanism is in the ability of the coalition to partition itself into subcoalitions that continue the search autonomously (a capability neglected in earlier cooperative search models). As we show throughout the paper, this strategy is always favorable in comparison to cooperative and autonomous search techniques currently in use. The paper presents a comprehensive analysis of the new cooperative search method and highlights the unique characteristics of the optimal search strategy in such a search model. Furthermore, for many common environments we manage to eliminate the consequential added computational complexity associated with the partitioning option by introducing innovative efficient algorithms for extracting the coalition’s optimal search strategy.
MultiGoal Economic Search using Dynamic Search Structures
, 2009
"... This paper investigates cooperative search strategies for agents engaged in costly search in a complex environment. Searching cooperatively, several search goals can be satisfied within a single search effort. Given the searchers ’ preferences, the goal is to conduct a search in a way that the expec ..."
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Cited by 4 (2 self)
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This paper investigates cooperative search strategies for agents engaged in costly search in a complex environment. Searching cooperatively, several search goals can be satisfied within a single search effort. Given the searchers ’ preferences, the goal is to conduct a search in a way that the expected overall utility out of the set of opportunities found (e.g., products when operating in a market) minus the costs associated with finding that set is maximized. This search scheme, given in the context of a group search, applies also to scenarios where a single agent has to search for a set of items for satisfying several different goals. The uniqueness of the proposed mechanism is in the ability to partition the group of agents/goals into subgroups where the search continues for each group autonomously. As we show throughout the paper, this strategy is favorable as it weakly dominates (i.e., can improve but never worsen) cooperative and autonomous search techniques. The paper presents a comprehensive analysis of the new search method and highlights the specific characteristics of the optimal search strategy. Furthermore, we introduce innovative algorithms for extracting the optimal search strategy in a range of common environments, that eliminates the computational overhead associated with the use of the partitioning technique.
Agents strategies for the dual parallel search in partnership formation applications
 In Proc. of AMEC2004, LNCS 3435
, 2004
"... Abstract. In many twosided search applications, autonomous agents can enjoy the advantage of parallel search, powered by their ability to handle an enormous amount of information, in a short time, and the capability to maintain interaction with several other agents in parallel. The adoption of the ..."
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Abstract. In many twosided search applications, autonomous agents can enjoy the advantage of parallel search, powered by their ability to handle an enormous amount of information, in a short time, and the capability to maintain interaction with several other agents in parallel. The adoption of the new technique by an agent suggests a reduction in the average cost per interaction with other agents, resulting in an improved overall utility. Nevertheless, when all agents use parallel search in MultiAgent Systems (MAS) applications, the analysis must take into consideration mainly equilibrium dynamics which shape their strategies. In this paper we introduce a dual parallel twosided search model and supply the appropriate analysis for finding the agents ’ equilibrium strategies. As a framework application for our analysis we suggest and utilize the classic voice communication partnerships application in an electronic marketplace. By identifying the specific characteristics of the equilibria, we manage to supply efficient means for the agents to calculate their distributed equilibrium strategies. We show that in some cases equilibrium dynamics might eventually drive the agents into strategies by which all of them end up with a smaller expected utility. Nonetheless, in most environments the technique has many advantages in improving the agents expected utility. 1
Integrating parallel interactions into cooperative search
 In AAMAS06
, 2006
"... In this paper we incorporate autonomous agents ’ capability to perform parallel interactions into the cooperative search model, resulting in a new method which outperforms the currently used ones. As a framework for our analysis we use the electronic marketplace, where buyer agents have the incentiv ..."
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Cited by 2 (2 self)
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In this paper we incorporate autonomous agents ’ capability to perform parallel interactions into the cooperative search model, resulting in a new method which outperforms the currently used ones. As a framework for our analysis we use the electronic marketplace, where buyer agents have the incentive to search cooperatively. The new search technique is quite intuitive, however its analysis and the process of extracting the optimal search strategy are associated with several significant complexities. These difficulties are derived mainly from the unbounded search space and simultaneous dual affects of decisions taken in different world states. We provide a comprehensive analysis of the model, highlighting, demonstrating and proving important characteristics of the optimal search strategy. Consequently, we manage to come up with an efficient modular algorithm for extracting the optimal cooperative search strategy for any given environment. A computational based comparative illustration of the system performance using the new search technique versus the traditional methods is given, emphasizing the main differences in the optimal strategy’s structure and the advantage of using the proposed model.
Sequential Decision Making in Parallel TwoSided Economic Search
, 2007
"... This paper presents a twosided economic search model in which agents are searching for beneficial pairwise partnerships. In each search stage, each of the agents is randomly matched with several other agents in parallel, and makes a decision whether to accept a potential partnership with one of the ..."
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This paper presents a twosided economic search model in which agents are searching for beneficial pairwise partnerships. In each search stage, each of the agents is randomly matched with several other agents in parallel, and makes a decision whether to accept a potential partnership with one of them. The distinguishing feature of the proposed model is that the agents are not restricted to maintaining a synchronized (instantaneous) decision protocol and can sequentially accept and reject partnerships within the same search stage. We analyze the dynamics which drive the agents ’ strategies towards a stable equilibrium in the new model and show that the proposed search strategy weakly dominates the one currently in use for the twosided parallel economic search model. By identifying several unique characteristics of the equilibrium we manage to efficiently bound the strategy space that needs to be explored by the agents and propose an efficient means for extracting the distributed equilibrium strategies in common environments.
Constraining information sharing to improve cooperative information gathering
 In Proceedings of the 13th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014
"... This paper considers the problem of cooperation between selfinterested 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 ..."
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Cited by 1 (0 self)
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This paper considers the problem of cooperation between selfinterested 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 selfinterested 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 selfinterested 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.