Algorithmic Decision Theory

Table of Contents

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   Book Overview
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   Chapter 1 How Hard Is it to Bribe the Judges? A Study of the Complexity of Bribery in Judgment Aggregation
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   Chapter 2 A Translation Based Approach to Probabilistic Conformant Planning
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   Chapter 3 Committee Selection with a Weight Constraint Based on a Pairwise Dominance Relation
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   Chapter 4 Algorithmic Decision Theory
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   Chapter 5 A Bi-objective Optimization Model to Eliciting Decision Maker’s Preferences for the PROMETHEE II Method
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   Chapter 6 Strategy-Proof Mechanisms for Facility Location Games with Many Facilities
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   Chapter 7 Making Decisions in Multi Partitioning
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   Chapter 8 Efficiently Eliciting Preferences from a Group of Users
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    Chapter 9 Risk-Averse Production Planning
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    Chapter 10 Minimal and Complete Explanations for Critical Multi-attribute Decisions
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    Chapter 11 Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs
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    Chapter 12 Efficient Approximation Algorithms for Multi-objective Constraint Optimization
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    Chapter 13 Empirical Evaluation of Voting Rules with Strictly Ordered Preference Data
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    Chapter 14 A Reduction of the Complexity of Inconsistencies Test in the MACBETH 2-Additive Methodology
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    Chapter 15 On Minimizing Ordered Weighted Regrets in Multiobjective Markov Decision Processes
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    Chapter 16 Algorithmic Decision Theory
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    Chapter 17 Learning the Parameters of a Multiple Criteria Sorting Method
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    Chapter 18 Handling Preferences in the ”Pre-conflicting” Phase of Decision Making Processes under Multiple Criteria
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    Chapter 19 Bribery in Path-Disruption Games
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    Chapter 20 The Machine Learning and Traveling Repairman Problem
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    Chapter 21 Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach
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    Chapter 22 Algorithmic Decision Theory
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    Chapter 23 Influence Diagrams with Memory States: Representation and Algorithms
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    Chapter 24 Game Theory and Human Behavior: Challenges in Security and Sustainability
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    Chapter 25 Constrained Multicriteria Sorting Method Applied to Portfolio Selection