Indirect Dynamic Negotiation in the Nash Demand Game
The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologically the paper casts heuristically-motivated bar...
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| Veröffentlicht in: | IEEE access 2022, Vol.10, p.105008-105021 |
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| container_title | IEEE access |
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| creator | Guy, Tatiana V. Homolova, Jitka Gaj, Aleksej |
| description | The paper addresses a problem of sequential bilateral bargaining with incomplete information. We proposed a decision model that helps agents to successfully bargain by performing indirect negotiation and learning the opponent's model. Methodologically the paper casts heuristically-motivated bargaining of a self-interested independent player into a framework of Bayesian learning and Markov decision processes. The special form of the reward implicitly motivates the players to negotiate indirectly, via closed-loop interaction. We illustrate the approach by applying our model to the Nash demand game, which is an abstract model of bargaining. The results indicate that the established negotiation: i) leads to coordinating players' actions; ii) results in maximising success rate of the game and iii) brings more individual profit to the players. |
| doi_str_mv | 10.1109/ACCESS.2022.3210506 |
| format | Article |
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The results indicate that the established negotiation: i) leads to coordinating players' actions; ii) results in maximising success rate of the game and iii) brings more individual profit to the players.</description><subject>Bayes methods</subject><subject>Biological system modeling</subject><subject>Games</subject><subject>Learning</subject><subject>Learning systems</subject><subject>Machine learning</subject><subject>Markov decision process</subject><subject>Markov processes</subject><subject>Nash demand game</subject><subject>Nash equilibrium</subject><subject>negotiation</subject><subject>Negotiations</subject><subject>Players</subject><subject>Resource management</subject><subject>Uncertainty</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUMFuwjAMraZNGmJ8AZdKO8OcuEmbIyqMISF2YDtHaZpCEG1YWg78_cKK0Hyx9fTes_2iaExgSgiIt1meL7bbKQVKp0gJMOAP0YASLibIkD_-m5-jUdseIFQWIJYOomTVlNYb3cXzS6Nqq-ON2bnOqs66JrZN3O1NvFHtPp6bWjVlvFS1eYmeKnVszejWh9H3--Ir_5isP5erfLaeaGRZN8GUJ1WVZpwKICJFxlLOICmRUiZSlVENXGRlwAoCWBTAC5pRLEpCCxVwHEar3rd06iBP3tbKX6RTVv4Bzu-k8p3VRyNJhlplhaYMMeGai4TxMtGVQm64YRC8Xnuvk3c_Z9N28uDOvgnnS5pSFEABSGBhz9Leta031X0rAXlNW_Zpy2va8pZ2UI17lTXG3BVCQPg4xV8Xpnak</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Guy, Tatiana V.</creator><creator>Homolova, Jitka</creator><creator>Gaj, Aleksej</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; IEEE Xplore Open Access Journals |
| subjects | Bayes methods Biological system modeling Games Learning Learning systems Machine learning Markov decision process Markov processes Nash demand game Nash equilibrium negotiation Negotiations Players Resource management Uncertainty |
| title | Indirect Dynamic Negotiation in the Nash Demand Game |
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