Preference Representation and the Limits of Reform: The 1976 Democratic Convention

1982 ◽  
Vol 44 (1) ◽  
pp. 201-211 ◽  
Author(s):  
David C. Paris ◽  
Richard D. Shingles
Keyword(s):  
Preference Representation

Compact Preference Representation for Boolean Games

2006 ◽  
pp. 41-50 ◽  
Author(s):  
Elise Bonzon ◽  
Marie-Christine Lagasquie-Schiex ◽  
Jérôme Lang
Keyword(s):  
Preference Representation ◽  
Boolean Games

Personalisation in Highly Dynamic Grid Services Environments

2009 ◽  
pp. 284-313
Author(s):  
Edgar Jembere ◽  
Matthew O. Adigun ◽  
Sibusiso S. Xulu
Keyword(s):  
User Preferences ◽  
User Preference ◽  
Grid Services ◽  
Context Aware Computing ◽  
Preference Representation ◽  
Use Of Services ◽  
Order Preference ◽  
Computing Environments ◽  
Preference Models ◽  
Intuitive Interpretation

Human Computer Interaction (HCI) challenges in highly dynamic computing environments can be solved by tailoring the access and use of services to user preferences. In this era of emerging standards for open and collaborative computing environments, the major challenge that is being addressed in this chapter is how personalisation information can be managed in order to support cross-service personalisation. The authors’ investigation of state of the art work in personalisation and context-aware computing found that user preferences are assumed to be static across different context descriptions whilst in reality some user preferences are transient and vary with changes in context. Further more, the assumed preference models do not give an intuitive interpretation of a preference and lack user expressiveness. This chapter presents a user preference model for dynamic computing environments, based on an intuitive quantitative preference measure and a strict partial order preference representation, to address these issues. The authors present an approach for mining context-based user preferences and its evaluation in a synthetic m-commerce environment. This chapter also shows how the data needed for mining context-based preferences is gathered and managed in a Grid infrastructure for mobile devices.

Learning-Based Preference Modeling in Engineering Design Decision-Making

1999 ◽  
Vol 123 (2) ◽  
pp. 191-198 ◽  
Author(s):  
Jie Wan ◽  
Sundar Krishnamurty
Keyword(s):  
Engineering Design ◽  
Deductive Reasoning ◽  
Integrated Approach ◽  
Design Decision ◽  
Learning Method ◽  
Preference Modeling ◽  
Modeling Process ◽  
Preference Representation ◽  
Elicitation Process ◽  
Effective Integration

Focusing on the efforts towards a consistent preference representation in decision based engineering design, this paper presents a learning-based comparison and preference modeling process. Through effective integration of a deductive reasoning-based on designer’s outcome ranking in a lottery questions-based elicitation process, this work offers a reliable framework for formulating utility functions that reflect designer’s priorities accurately and consistently. It is expected that this integrated approach will reduce designer’s cognitive burden, and lead to accurate and consistent preference representation. Salient features of this approach include a linear programming based dynamic preference learning method and a logical analysis of preference inconsistencies. The development of this method and its utilization in engineering design are presented in the context of a mechanism design problem and the results are discussed.

Rule-based formalism and preference representation: An extension of negoplan

1990 ◽  
Vol 45 (2-3) ◽  
pp. 309-323 ◽  
Author(s):  
Gregory E. Kersten ◽  
Stan Szpakowicz
Keyword(s):  
Rule Based ◽  
Preference Representation

AN ALGORITHMIC APPROACH TO PREFERENCE REPRESENTATION

2008 ◽  
Vol 16 (supp02) ◽  
pp. 1-18 ◽  
Author(s):  
J. YÁÑEZ ◽  
J. MONTERO ◽  
D. GÓMEZ
Keyword(s):  
Decision Makers ◽  
Dimension Theory ◽  
Preference Structure ◽  
General Representation ◽  
Algorithmic Approach ◽  
Strong Assumption ◽  
Preference Representation ◽  
Alternative Approach ◽  
Order Sets ◽  
The One

In a previous paper, the authors proposed an alternative approach to classical dimension theory, based upon a general representation of strict preferences not being restricted to partial order sets. Without any relevant restriction, the proposed approach was conceived as a potential powerful tool for decision making problems where basic information has been modeled by means of valued binary preference relations. In fact, assuming that each decision maker is able to consistently manage intensity values for preferences is a strong assumption even when there are few alternatives being involved (if the number of alternatives is large, the same criticism applies to crisp preferences). Any representation tool, as the one proposed by the authors, will in principle play a key role in order to help decision makers to understand their preference structure. In this paper we introduce an alternative approach in order to avoid certain complexity issues of the initial proposal, allowing a close representation easier to be obtained in practice.

scholarly journals Compact Preference Representation in Stable Marriage Problems

2009 ◽  
pp. 390-401 ◽  
Author(s):  
Enrico Pilotto ◽  
Francesca Rossi ◽  
Kristen Brent Venable ◽  
Toby Walsh
Keyword(s):  
Stable Marriage ◽  
Preference Representation ◽  
Marriage Problems

Preferences, Machine Learning, and Decision Support With Cyber-Physical Systems

2021 ◽  
pp. 938-968
Author(s):  
Yuri P. Pavlov ◽  
Evgeniy Ivanov Marinov
Keyword(s):  
Complex System ◽  
Decision Support ◽  
Cyber Physical Systems ◽  
Decision Support Tools ◽  
Physical Systems ◽  
Complex Processes ◽  
Preference Representation ◽  
Decision Control ◽  
Support Tools ◽  
Qualitative Nature

Modeling of complex processes with human participations causes difficulties due to the lack of precise measurement coming from the qualitative nature of the human notions. This provokes the need of utilization of empirical knowledge expressed cardinally. An approach for solution of these problems is utility theory. As cyber-physical systems are integrations of computation, networking, and physical processes in interaction with the user is needed feedback loops, the aim of the chapter is to demonstrate the possibility to describe quantitatively complex processes with human participation. This approach permits analytical representations of the users' preferences as objective utility functions and modeling of the complex system “human-process.” The mathematical technique allows CPS users dialog and is demonstrated by two case studies, portfolio allocation, and modeling of a competitive trade by a finite game and utility preference representation of the trader. The presented formulations could serve as foundation of development of decision support tools and decision control.

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