Learning bidding strategies with autonomous agents in environments with unstable equilibrium
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| Title: | Learning bidding strategies with autonomous agents in environments with unstable equilibrium |
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| Authors: | Sikora, Riyaz T. rsikora@uta.edu, Sachdev, Vishal1 vsachdev@uta.edu |
| Source: | Decision Support Systems. Dec2008, Vol. 46 Issue 1, p101-114. 14p. |
| Subjects: | Automation, Decision support systems, Electronic commerce, Artificial intelligence, Economists, Strategic planning, Bidding strategies |
| Abstract: | The role of automated agents for decision support in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. In this paper, we study the efficacy of using automated agents for learning bidding strategies in contexts of strategic interaction involving multiple sellers in reverse auctions. Standard game-theoretic analysis of the problem assumes completely rational and omniscient agents to derive Nash equilibrium seller policy. Most of the literature on use of learning agents uses convergence to Nash equilibrium as the validating criterion. In this paper, we consider a problem where the Nash equilibrium is unstable and hence not useful as an evaluation criterion. Instead, we propose that agents should be able to learn the optimal or best response strategies when they exist (rational behavior) and should demonstrate low variance in profits (convergence). We present rationally bounded, evolutionary and reinforcement learning agents that learn these desirable properties of rational behavior and convergence. [Copyright &y& Elsevier] |
| Copyright of Decision Support Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 35324911 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning bidding strategies with autonomous agents in environments with unstable equilibrium – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sikora%2C+Riyaz+T%2E%22">Sikora, Riyaz T.</searchLink><i> rsikora@uta.edu</i><br /><searchLink fieldCode="AR" term="%22Sachdev%2C+Vishal%22">Sachdev, Vishal</searchLink><relatesTo>1</relatesTo><i> vsachdev@uta.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Decision+Support+Systems%22">Decision Support Systems</searchLink>. Dec2008, Vol. 46 Issue 1, p101-114. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+commerce%22">Electronic commerce</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Economists%22">Economists</searchLink><br /><searchLink fieldCode="DE" term="%22Strategic+planning%22">Strategic planning</searchLink><br /><searchLink fieldCode="DE" term="%22Bidding+strategies%22">Bidding strategies</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The role of automated agents for decision support in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. In this paper, we study the efficacy of using automated agents for learning bidding strategies in contexts of strategic interaction involving multiple sellers in reverse auctions. Standard game-theoretic analysis of the problem assumes completely rational and omniscient agents to derive Nash equilibrium seller policy. Most of the literature on use of learning agents uses convergence to Nash equilibrium as the validating criterion. In this paper, we consider a problem where the Nash equilibrium is unstable and hence not useful as an evaluation criterion. Instead, we propose that agents should be able to learn the optimal or best response strategies when they exist (rational behavior) and should demonstrate low variance in profits (convergence). We present rationally bounded, evolutionary and reinforcement learning agents that learn these desirable properties of rational behavior and convergence. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Decision Support Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.dss.2008.05.005 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 101 Subjects: – SubjectFull: Automation Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Electronic commerce Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Economists Type: general – SubjectFull: Strategic planning Type: general – SubjectFull: Bidding strategies Type: general Titles: – TitleFull: Learning bidding strategies with autonomous agents in environments with unstable equilibrium Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sikora, Riyaz T. – PersonEntity: Name: NameFull: Sachdev, Vishal IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2008 Type: published Y: 2008 Identifiers: – Type: issn-print Value: 01679236 Numbering: – Type: volume Value: 46 – Type: issue Value: 1 Titles: – TitleFull: Decision Support Systems Type: main |
| ResultId | 1 |
