The Experts below are selected from a list of 33111 Experts worldwide ranked by ideXlab platform
Keith W. Hipel - One of the best experts on this subject based on the ideXlab platform.
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Policy stable states in the Graph Model for conflict resolution
Theory and Decision, 2020Co-Authors: Dao-zhi Zeng, Liping Fang, Keith W. Hipel, D. Marc KilgourAbstract:A new approach to policy analysis is formulated within the framework of the Graph Model for conflict resolution. A policy is defined as a plan of action for a decision maker (DM) that specifies the DM’s intended action starting at every possible state in a Graph Model of a conflict. Given a profile of policies, a Policy Stable State (PSS) is a state that no DM moves away from (according to its policy), and such that no DM would prefer to change its policy given the policies of the other DMs. The profile of policies associated to a PSS is called a Policy Equilibrium. Properties of PSSs are developed, and a refinement is suggested that restricts DMs to policies that are credible in that they are in the DM’s immediate interest. Relationships with existing stability definitions in the Graph Model for conflict resolution are then explored. Copyright Springer 2004
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A hierarchical Graph Model of a two-level carbon emission conflict in China
2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016Co-Authors: Shawei He, Keith W. Hipel, Marc D. KilgourAbstract:A two-level carbon emission conflict is investigated using Graph Model for Conflict Resolution. Chinese central government has conflict with local authorities at provincial levels regarding the implementation of new carbon emission reduction policy. As the conflict takes place in multiple regions, the governments at a higher level should form strategies to interact with governments at the lower level. The resolution of this conflict obtained by calculating the stabilities in the corresponding Graph Model can be used as guidance of actions for each decision maker to follow. The stabilities indicate that the central government should form distinct strategies to deal with provinces with different levels of economic development. Hierarchical Graph Model can provide decision makers a comprehensive understanding of the carbon emission conflict taking place at different locations.
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Modeling misperception of options and preferences in the Graph Model for conflict resolution
2014 IEEE International Conference on Systems Man and Cybernetics (SMC), 2014Co-Authors: Yasir M. Aljefri, Liping Fang, Keith W. HipelAbstract:The standard Graph Model for conflict resolution is enhanced to Model misperception of options and preferences. In particular, misperception caused by unknown options by one or more decision makers (DMs) is formally defined in the modified Graph Model framework. Moreover, DMs' preferences are expressed in a relative fashion by pairwise comparisons among any pair of states. That is, the modified Graph Model can handle both transitive and intransitive preference structures. Furthermore, three sets of equilibria are defined within the modified Graph Model, namely, steady, unsteady, and stealthy equilibria. The aim of identifying equilibria sets is to ascertain the first level hypergame equilibria for the modified Graph Model. The 2011 conflict between North and South Sudan over South Sudanese oil exports is studied using the modified Graph Model to illustrate the applicability of the Model and to gain better insight into and understanding of the dispute.
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fuzzy preferences in the Graph Model for conflict resolution
IEEE Transactions on Fuzzy Systems, 2012Co-Authors: M A Bashar, D.m. Kilgour, Keith W. HipelAbstract:A new framework for the Graph Model for conflict resolution is developed so that decision makers (DMs) with fuzzy preferences can be included in conflict Models. A Graph Model is both a formal representation for multiple participant-multiple objective decision problems and a set of analysis procedures that add insights into them. Within the new framework, Graph Models can include-and integrate into the analysis-both certain and uncertain information about DMs' preferences. One key contribution of this study is to extend the four basic stability definitions for two or more DMs to Models with fuzzy preferences. Together, fuzzy Nash stability, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability provide anuanced description of human behavior. A state is fuzzy stable for a DM if a move to any other state is not sufficiently likely to yield an outcome which the DM prefers, where sufficiency is measured according to a fuzzy satisficing threshold that is the characteristic of the DM. A fuzzy equilibrium, which is an outcome that is fuzzy stable for all DMs, therefore represents a possible resolution of the strategic conflict. The practical application and interpretation of these new stability definitions are illustrated with an example.
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Using matrices to link conflict evolution and resolution in a Graph Model
European Journal of Operational Research, 2010Co-Authors: Haiyan Xu, D. Marc Kilgour, Keith W. Hipel, Graeme KemkesAbstract:The Graph Model for conflict resolution provides a convenient and effective means to Model and analyze a strategic conflict. Standard practice is to carry out a stability analysis of a Graph Model, and then to follow up with a post-stability analysis, an important component of which is status quo analysis. A Graph Model can be viewed as an edge-colored Graph, but the fundamental problem of status quo analysis - to find a shortest colored path from the status quo node to a desired equilibrium - is different from the well-known network analysis problem of finding the shortest path between two nodes. The only matrix method that has been proposed cannot track all aspects of the evolution of a conflict from the status quo state. Our explicit algebraic approach is convenient for computer implementation and, as demonstrated with a real world case study, easy to use. It provides new insights into a Graph Model, not only identifying all equilibria reachable from the status quo, but also how to reach them. Moreover, this approach bridges the gap between stability analysis and status quo analysis in the Graph Model for conflict resolution.
Rohit Negi - One of the best experts on this subject based on the ideXlab platform.
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VTC Fall - PHY-Graph Model for Ad Hoc Wireless MAC
IEEE Vehicular Technology Conference, 2006Co-Authors: Arjunan Rajeswaran, Rohit NegiAbstract:The ad hoc wireless MAC problem is based on a Model of interference between links. Prior disk Graph Models consider pair-wise interference and have been analyzed to provide bounds on MAC performance, through the application of Graph theoretic coloring results. The recently developed PHY Graph Model is based on the physical layer and so is a more realistic Model than disk Graph Models. An initial MAC performance bound based on the PHY Graph Model has been shown. Here, the Model is analyzed in a novel proof resulting in an improved bound on the MAC performance. The accuracy of the PHY Graph Model, in Modelling the ad hoc wireless problem, is explored through detailed simulations. Salient features of the Model are utilized to provide a performance improvement.
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PHY-Graph Model for Ad Hoc Wireless MAC
IEEE Vehicular Technology Conference, 2006Co-Authors: Arjunan Rajeswaran, Rohit NegiAbstract:The ad hoc wireless MAC problem is based on a Model of interference between links. Prior disk Graph Models consider pair-wise interference and have been analyzed to provide bounds on MAC performance, through the application of Graph theoretic coloring results. The recently developed PHY Graph Model is based on the physical layer and so is a more realistic Model than disk Graph Models. An initial MAC performance bound based on the PHY Graph Model has been shown. Here, the Model is analyzed in a novel proof resulting in an improved bound on the MAC performance. The accuracy of the PHY Graph Model, in Modelling the ad hoc wireless problem, is explored through detailed simulations. Salient features of the Model are utilized to provide a performance improvement.
Arjunan Rajeswaran - One of the best experts on this subject based on the ideXlab platform.
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VTC Fall - PHY-Graph Model for Ad Hoc Wireless MAC
IEEE Vehicular Technology Conference, 2006Co-Authors: Arjunan Rajeswaran, Rohit NegiAbstract:The ad hoc wireless MAC problem is based on a Model of interference between links. Prior disk Graph Models consider pair-wise interference and have been analyzed to provide bounds on MAC performance, through the application of Graph theoretic coloring results. The recently developed PHY Graph Model is based on the physical layer and so is a more realistic Model than disk Graph Models. An initial MAC performance bound based on the PHY Graph Model has been shown. Here, the Model is analyzed in a novel proof resulting in an improved bound on the MAC performance. The accuracy of the PHY Graph Model, in Modelling the ad hoc wireless problem, is explored through detailed simulations. Salient features of the Model are utilized to provide a performance improvement.
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PHY-Graph Model for Ad Hoc Wireless MAC
IEEE Vehicular Technology Conference, 2006Co-Authors: Arjunan Rajeswaran, Rohit NegiAbstract:The ad hoc wireless MAC problem is based on a Model of interference between links. Prior disk Graph Models consider pair-wise interference and have been analyzed to provide bounds on MAC performance, through the application of Graph theoretic coloring results. The recently developed PHY Graph Model is based on the physical layer and so is a more realistic Model than disk Graph Models. An initial MAC performance bound based on the PHY Graph Model has been shown. Here, the Model is analyzed in a novel proof resulting in an improved bound on the MAC performance. The accuracy of the PHY Graph Model, in Modelling the ad hoc wireless problem, is explored through detailed simulations. Salient features of the Model are utilized to provide a performance improvement.
Marc D. Kilgour - One of the best experts on this subject based on the ideXlab platform.
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A hierarchical Graph Model of a two-level carbon emission conflict in China
2016 IEEE International Conference on Systems Man and Cybernetics (SMC), 2016Co-Authors: Shawei He, Keith W. Hipel, Marc D. KilgourAbstract:A two-level carbon emission conflict is investigated using Graph Model for Conflict Resolution. Chinese central government has conflict with local authorities at provincial levels regarding the implementation of new carbon emission reduction policy. As the conflict takes place in multiple regions, the governments at a higher level should form strategies to interact with governments at the lower level. The resolution of this conflict obtained by calculating the stabilities in the corresponding Graph Model can be used as guidance of actions for each decision maker to follow. The stabilities indicate that the central government should form distinct strategies to deal with provinces with different levels of economic development. Hierarchical Graph Model can provide decision makers a comprehensive understanding of the carbon emission conflict taking place at different locations.
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Fuzzy preferences in a two-decision maker Graph Model
2010 IEEE International Conference on Systems Man and Cybernetics, 2010Co-Authors: Abul M. Bashar, Keith W. Hipel, Marc D. KilgourAbstract:A fuzzy preference framework is developed within the paradigm of the Graph Model for conflict resolution. This framework takes into account both certain and uncertain information about the preferences of decision makers (DMs) involved in a strategic conflict. The Graph Model is a solution methodology for conflict decision making that begins with a Model of the problem and suggests possible resolutions through a number of stability definitions. Four basic fuzzy stability definitions are introduced for a two-DM Graph Model to analyze conflict behavior and identify possible resolutions even when preferences are fuzzy. Fuzzy stability definitions describe varied human behavior in a conflict Model; a state is fuzzy stable for a DM according to a specific fuzzy stability definition if a move to any other state, evaluated according to that definition, does not meet the DM's fuzzy satisficing threshold. A state that is fuzzy stable for all DMs under a specific fuzzy stability definition constitutes a fuzzy equilibrium under that definition, and is interpreted as a possible resolution of the conflict. Fuzzy stability definitions include fuzzy Nash stability, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability.
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Perceptual Stability Analysis of a Graph Model System
IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans, 2009Co-Authors: Amer Obeidi, Marc D. Kilgour, Keith W. HipelAbstract:Perceptual Graph Model systems are designed to be employed when there are discrepancies in decision makers' (DMs) perceptions of a conflict, which may be caused, for instance, by the presence of negative emotion or asymmetric information among DMs. In this case, conventional stability analysis cannot be used; perceptual stability analysis is proposed as a new theoretical procedure that extends existing stability algorithms to situations when DMs have independent perceptions or awarenesses of a conflict. The overriding objective of perceptual stability analysis is to predict possible resolutions, and unveil the dependence of these predictions on variability in a DM's awareness. Perceptual stability analysis takes a two-phase approach. In Phase 1, individual stability analysis is applied to each DM's Graph Model (a perceptual Graph Model) from the point of view of the owner of the Model, for each DM in the Model, using standard or perceptual solution concepts, depending on the owner's awareness of others' perceptions. Then, in Phase 2, metastability analysis is employed to consolidate the stability assessments of a state in all perceptual Graph Models and across all variants of awareness-i.e., in all possible Graph Model systems. The distinctive modes of equilibria thus defined reflect the incompatibilities in DMs' perceptions and viewpoints, but nonetheless provide important insights into possible resolutions of the conflict. To demonstrate the practical application of these new developments, a Model of the conflict in Chechnya is presented and analyzed.
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the Graph Model for conflict resolution past present and future
Group Decision and Negotiation, 2005Co-Authors: Marc D. Kilgour, Keith W. HipelAbstract:The Graph Model for Conflict Resolution is a methodology for the Modeling and analysis of strategic conflicts. An historical overview of the Graph Model is presented, including the basic Modeling and analysis components of the methodology, the decision support system GMCR II that is now used to apply it, and the recent initiatives that are currently in various stages of development. The capacity of this simple, flexible system to provide advice to decision-makers facing strategic conflicts is emphasized throughout, and illustrated using a real-life groundwater contamination dispute.
Rahul Santhanam - One of the best experts on this subject based on the ideXlab platform.
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Graph Model selection using maximum likelihood
International Conference on Machine Learning, 2006Co-Authors: Ivona Bezakova, Adam Tauman Kalai, Rahul SanthanamAbstract:In recent years, there has been a proliferation of theoretical Graph Models, e.g., preferential attachment and small-world Models, motivated by real-world Graphs such as the Internet topology. To address the natural question of which Model is best for a particular data set, we propose a Model selection criterion for Graph Models. Since each Model is in fact a probability distribution over Graphs, we suggest using Maximum Likelihood to compare Graph Models and select their parameters. Interestingly, for the case of Graph Models, computing likelihoods is a difficult algorithmic task. However, we design and implement MCMC algorithms for computing the maximum likelihood for four popular Models: a power-law random Graph Model, a preferential attachment Model, a small-world Model, and a uniform random Graph Model. We hope that this novel use of ML will objectify comparisons between Graph Models.
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ICML - Graph Model selection using maximum likelihood
Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006Co-Authors: Ivona Bezakova, Adam Tauman Kalai, Rahul SanthanamAbstract:In recent years, there has been a proliferation of theoretical Graph Models, e.g., preferential attachment and small-world Models, motivated by real-world Graphs such as the Internet topology. To address the natural question of which Model is best for a particular data set, we propose a Model selection criterion for Graph Models. Since each Model is in fact a probability distribution over Graphs, we suggest using Maximum Likelihood to compare Graph Models and select their parameters. Interestingly, for the case of Graph Models, computing likelihoods is a difficult algorithmic task. However, we design and implement MCMC algorithms for computing the maximum likelihood for four popular Models: a power-law random Graph Model, a preferential attachment Model, a small-world Model, and a uniform random Graph Model. We hope that this novel use of ML will objectify comparisons between Graph Models.
