FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3149031972> ?p ?o ?g. }

• W3149031972 abstract "Present development of Internet leads us to imagine a near future in which electronic transactions between companies and consumers will be performed by electronic agents [1]. As these new markets will develop, interaction between buyers and sellers on the Internet will become more complex and more flexible evolving from mere search of information to communication and negotiation. For an agent to be able to adequately represent her user in a negotiation process, a fundamental point to be considered is her personalization by some set of parameters (as, for example, reservation value, utility function or time-constraints). On electronic markets, the user' agents will be self-interested in the sense that they will seek to maximize only their interests. In terms of economic theory, such an agent should have, not only some insights about what the user's interests are, but she should also be able to identify, by herself, an optimal negotiation strategy within the protocol which governs her interactions with other agents. Usually, computer scientists move away from such optimal design methods while programming negotiation strategies for agents based on rules of thumb [2] [3]. Although such approach can be partially justified by technical and theoretical difficulties related to the design of an agent capable to optimize on the behalf of her user, a great number of problems arise. Indeed, in a complex and uncertain environment like an agent-based market on Internet, the choice of a successful heuristic is rather difficult. Moreover, as agents will act in an open architecture, an agent using an unfitted heuristic could be taken advantage of by an opponent conceived to exploit this weakness. In this article, we propose a different prescriptive approach inspired by negotiation analysis [5]. We study the optimal decision-making process of an agent which enters on a market where agents negotiate, in a sequential way, the price of a product with an opponent who uses a rule of thumb. In this study, we are mainly concerned by the following two questions : how to compute the optimal strategy of the entrant agent ? and What price announced at the beginning of the negotiation can lead the entrant to obtain the best agreement ? In our analysis, we chose a buyer agent as entrant on the market. We defined seller agents by three characteristics: an utility function, a reservation price and a negotiation behavior. We limited the possible negotiation behaviors at three (conceder behavior, boulware behavior and imitative behavior) and we expressed the corresponding strategies of negotiation in term of probabilities. For example, if the seller agent adopts a conceder behavior, the probability for him to concede is relatively high during the first periods of negotiation and decreases while the time passes. Then, we studied the optimal strategy of the entrant buyer which has a good description of its opponent (i.e. he knows the reservation price and the probability associated with his concession decisions). His decision process was treated as a stochastic control problem. At a given stage, we assumed that the buyer agent can represent the negotiation as a state vector containing the observed decision of his opponent at previous period and the actual positions of the two agents on the price scale [4]. The use of dynamic programming techniques allowed us to compute an optimal strategy which maximizes the entrant's expected utility relative to the random decisions of her opponent in the next periods. In order to prescribe the best price to announce at the beginning of the negotiation, repeated rounds of negotiation were performed with different seller populations and several buyer's parameters were varied from one simulation to another (as reservation price or impatience)." @default.
• W3149031972 created "2021-04-13" @default.
• W3149031972 creator A5024326285 @default.
• W3149031972 creator A5029362404 @default.
• W3149031972 creator A5034796548 @default.
• W3149031972 date "2000-01-01" @default.
• W3149031972 modified "2023-09-23" @default.
• W3149031972 title "Optimal Buyer Negotiation Strategy On Agent Based Internet Markets" @default.
• W3149031972 hasPublicationYear "2000" @default.
• W3149031972 type Work @default.
• W3149031972 sameAs 3149031972 @default.
• W3149031972 citedByCount "0" @default.
• W3149031972 crossrefType "posted-content" @default.
• W3149031972 hasAuthorship W3149031972A5024326285 @default.
• W3149031972 hasAuthorship W3149031972A5029362404 @default.
• W3149031972 hasAuthorship W3149031972A5034796548 @default.
• W3149031972 hasConcept C110875604 @default.
• W3149031972 hasConcept C111919701 @default.
• W3149031972 hasConcept C11413529 @default.
• W3149031972 hasConcept C136764020 @default.
• W3149031972 hasConcept C14036430 @default.
• W3149031972 hasConcept C154945302 @default.
• W3149031972 hasConcept C173801870 @default.
• W3149031972 hasConcept C177264268 @default.
• W3149031972 hasConcept C17744445 @default.
• W3149031972 hasConcept C183003079 @default.
• W3149031972 hasConcept C199360897 @default.
• W3149031972 hasConcept C199539241 @default.
• W3149031972 hasConcept C199776023 @default.
• W3149031972 hasConcept C2777632111 @default.
• W3149031972 hasConcept C31258907 @default.
• W3149031972 hasConcept C41008148 @default.
• W3149031972 hasConcept C74072328 @default.
• W3149031972 hasConcept C78458016 @default.
• W3149031972 hasConcept C78597825 @default.
• W3149031972 hasConcept C86803240 @default.
• W3149031972 hasConcept C89246107 @default.
• W3149031972 hasConcept C98045186 @default.
• W3149031972 hasConceptScore W3149031972C110875604 @default.
• W3149031972 hasConceptScore W3149031972C111919701 @default.
• W3149031972 hasConceptScore W3149031972C11413529 @default.
• W3149031972 hasConceptScore W3149031972C136764020 @default.
• W3149031972 hasConceptScore W3149031972C14036430 @default.
• W3149031972 hasConceptScore W3149031972C154945302 @default.
• W3149031972 hasConceptScore W3149031972C173801870 @default.
• W3149031972 hasConceptScore W3149031972C177264268 @default.
• W3149031972 hasConceptScore W3149031972C17744445 @default.
• W3149031972 hasConceptScore W3149031972C183003079 @default.
• W3149031972 hasConceptScore W3149031972C199360897 @default.
• W3149031972 hasConceptScore W3149031972C199539241 @default.
• W3149031972 hasConceptScore W3149031972C199776023 @default.
• W3149031972 hasConceptScore W3149031972C2777632111 @default.
• W3149031972 hasConceptScore W3149031972C31258907 @default.
• W3149031972 hasConceptScore W3149031972C41008148 @default.
• W3149031972 hasConceptScore W3149031972C74072328 @default.
• W3149031972 hasConceptScore W3149031972C78458016 @default.
• W3149031972 hasConceptScore W3149031972C78597825 @default.
• W3149031972 hasConceptScore W3149031972C86803240 @default.
• W3149031972 hasConceptScore W3149031972C89246107 @default.
• W3149031972 hasConceptScore W3149031972C98045186 @default.
• W3149031972 hasLocation W31490319721 @default.
• W3149031972 hasOpenAccess W3149031972 @default.
• W3149031972 hasPrimaryLocation W31490319721 @default.
• W3149031972 hasRelatedWork W1508773862 @default.
• W3149031972 hasRelatedWork W1523459335 @default.
• W3149031972 hasRelatedWork W1569595378 @default.
• W3149031972 hasRelatedWork W1573385359 @default.
• W3149031972 hasRelatedWork W16554708 @default.
• W3149031972 hasRelatedWork W2027491671 @default.
• W3149031972 hasRelatedWork W2068311341 @default.
• W3149031972 hasRelatedWork W2117902618 @default.
• W3149031972 hasRelatedWork W2248541821 @default.
• W3149031972 hasRelatedWork W2395825647 @default.
• W3149031972 hasRelatedWork W2500135237 @default.
• W3149031972 hasRelatedWork W2504600832 @default.
• W3149031972 hasRelatedWork W2798813080 @default.
• W3149031972 hasRelatedWork W3016819384 @default.
• W3149031972 hasRelatedWork W3123580166 @default.
• W3149031972 hasRelatedWork W3124164442 @default.
• W3149031972 hasRelatedWork W647925024 @default.
• W3149031972 hasRelatedWork W86575217 @default.
• W3149031972 hasRelatedWork W966778545 @default.
• W3149031972 hasRelatedWork W1480982438 @default.
• W3149031972 isParatext "false" @default.
• W3149031972 isRetracted "false" @default.
• W3149031972 magId "3149031972" @default.
• W3149031972 workType "article" @default.