Persistent Link:
http://hdl.handle.net/10150/185843
Title:
Stability and learning in dynamic market systems.
Author:
Yen, Jerome Chih-Hung.
Issue Date:
1992
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An Oligopoly is a special type of market system in which the number of producers is small and the interactions among them are significant. Since the interactions are significant, in order to reach higher profit it is very important for a producer to set up a prediction scheme to predict the decisions of the competitors and a good long-term strategy to occupy greater market share. To model and solve such problems, theoretical studies, field studies, and laboratory experiments (which include computer simulations), are the three major approaches. In this study, theoretical approach was used to develop four prediction schemes and study their stability conditions. Then laboratory experiments were conducted to study the decisions of the human subjects to identify the uses of these prediction schemes. The results of the experiments provided many important messages. In these experiments, not only the prediction schemes that developed by the theoretical approach have been actually used, but also from the strategies that developed by the experiment participants I saw the competitions have moved from the technical level to the psychological level. Based on the findings, I proposed some guidelines to develop a good decision model.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Dissertations, Academic.; Economics.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Systems and Industrial Engineering; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Szidarovszky, Ferenc

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleStability and learning in dynamic market systems.en_US
dc.creatorYen, Jerome Chih-Hung.en_US
dc.contributor.authorYen, Jerome Chih-Hung.en_US
dc.date.issued1992en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAn Oligopoly is a special type of market system in which the number of producers is small and the interactions among them are significant. Since the interactions are significant, in order to reach higher profit it is very important for a producer to set up a prediction scheme to predict the decisions of the competitors and a good long-term strategy to occupy greater market share. To model and solve such problems, theoretical studies, field studies, and laboratory experiments (which include computer simulations), are the three major approaches. In this study, theoretical approach was used to develop four prediction schemes and study their stability conditions. Then laboratory experiments were conducted to study the decisions of the human subjects to identify the uses of these prediction schemes. The results of the experiments provided many important messages. In these experiments, not only the prediction schemes that developed by the theoretical approach have been actually used, but also from the strategies that developed by the experiment participants I saw the competitions have moved from the technical level to the psychological level. Based on the findings, I proposed some guidelines to develop a good decision model.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectDissertations, Academic.en_US
dc.subjectEconomics.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineSystems and Industrial Engineeringen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSzidarovszky, Ferencen_US
dc.contributor.committeememberRassenti, Stephenen_US
dc.contributor.committeememberBahill, Terry A.en_US
dc.contributor.committeememberFernandez, Emmanuelen_US
dc.contributor.committeememberChang, Ai-Meien_US
dc.identifier.proquest9229839en_US
dc.identifier.oclc712674820en_US
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