Algorithmic Decision Making with Python Resources : From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs

Preface
	Acknowledgements
Introduction
	The Editing Strategy
	Organisation of the Book
	Highlights
Contents
List of Figures
List of Tables
Listings
Part I Introduction to the Digraph3 Python Resources
	1 Working with the Digraph3 Python Resources
		Contents
		1.1 Installing the Digraph3 Resources
		1.2 Organisation of the Digraph3 Python Modules
		1.3 Starting a Digraph3 Terminal Session
		1.4 Inspecting a Digraph Object
		References
	2 Working with Bipolar-Valued Digraphs
		Contents
		2.1 Random Bipolar-Valued Digraphs
		2.2 Graphviz Drawings
		2.3 Asymmetric and Symmetric Parts
		2.4 Border and Inner Parts
		2.5 Fusion by Epistemic Disjunction
		2.6 Dual, Converse, and Codual Digraphs
		2.7 Symmetric and Transitive Closures
		2.8 Strong Components
		2.9 CSV Storage
		2.10 Complete, Empty, and Indeterminate Digraphs
		Notes
		Notes
		References
	3 Working with Outranking Digraphs
		Contents
		3.1 The Hybrid Outranking Digraph Model
		3.2 The Bipolar-Valued Outranking Digraph
		3.3 Pairwise Comparisons
		3.4 Recoding the Characteristic Valuation Domain
		3.5 The Strict Outranking Digraph
		Notes
		Notes
		References
Part II Evaluation Models and Decision Algorithms
	4 Building a Best Choice Recommendation
		Contents
		4.1 What Office Location to Choose?
		4.2 The Given Performance Tableau
		4.3 Computing the Outranking Digraph
		4.4 Designing a Best Choice Recommender System
		4.5 Computing the Rubis Best Choice Recommendation
		4.6 Weakly Ordering the Outranking Digraph
		Notes
		Notes
		References
	5 How to Create a New Multiple-Criteria Performance Tableau
		Contents
		5.1 Editing a Template File
		5.2 Editing the Decision Alternatives
		5.3 Editing the Decision Objectives
		5.4 Editing the Family of Performance Criteria
		5.5 Editing the Performance Evaluations
		5.6 Inspecting the Template Outranking Relation
		References
	6 Generating Random Performance Tableaux
		Contents
		6.1 Introduction
		6.2 Random Standard Performance Tableaux
		6.3 Random Cost-Benefit Performance Tableaux
		6.4 Random Three Objectives Performance Tableaux
		6.5 Random Academic Performance Tableaux
		Reference
	7 Who Wins the Election?
		Contents
		7.1 Linear Voting Profiles
		7.2 Computing the Winner
		7.3 The Majority Margins Digraph
		7.4 Cyclic Social Preferences
		7.5 On Generating Realistic Random Linear Voting Profiles
		References
	8 Ranking with Multiple Incommensurable Criteria
		Contents
		8.1 The Ranking Problem
		8.2 The Copeland Ranking
		8.3 The NetFlows Ranking
		8.4 Kemeny Rankings
		8.5 Slater Rankings
		8.6 The Kohler Ranking-by-Choosing Rule
		8.7 The RankedPairs Ranking Rule
		References
	9 Rating by Sorting into Relative Performance Quantiles
		Contents
		9.1 Quantile Sorting on a Single Performance Criterion
		9.2 Sorting into Quantiles with Multiple Performance Criteria
		9.3 The Sparse Pre-ranked Outranking Digraph Model
		9.4 Ranking Pre-ranked Sparse Outranking Digraphs
		References
	10 Rating-by-Ranking with Learned Performance Quantile Norms
		Contents
		10.1 The Absolute Rating Problem
		10.2 Incremental Learning of Historical Performance Quantiles
		10.3 Rating-by-Ranking New Performances with Quantile Norms
		References
	11 HPC Ranking of Big Performance Tableaux
		Contents
		11.1 C-compiled Python Modules
		11.2 Big Data Performance Tableaux
		11.3 C-implemented Integer-Valued Outranking Digraphs
		11.4 The Sparse Implementation of Big Outranking Digraphs
		11.5 Quantiles Ranking of Big Performance Tableaux
		11.6 HPC Quantiles Ranking Records
		References
Part III Evaluation and Decision Case Studies
	12 Alice\'s Best Choice: A Selection Case Study
		Contents
		12.1 The Decision Problem
		12.2 The Performance Tableau
		12.3 Building a Best Choice Recommendation
		12.4 Robustness Analysis
		References
	13 The Best Academic Computer Science Depts: A Ranking Case Study
		Contents
		13.1 The THE Performance Tableau
		13.2 Ranking with Multiple Criteria of Ordinal Significance
		13.3 How to Judge the Quality of a Ranking Result?
		References
	14 The Best Students, Where Do They Study? A Rating Case Study
		Contents
		14.1 The Rating Problem
		14.2 The 2004 Performance Quintiles
		14.3 Rating-by-Ranking with Lower-Closed Quintile Limits
		14.4 Rating by Quintiles Sorting
		References
	15 Exercises
		Contents
		15.1 Who Will Receive the Best Student Award? (§)
		15.2 How to Fairly Rank Movies? (§)
		15.3 What Is Your Best Choice Recommendation? (§§)
		15.4 Planning the Next Holiday Activity (§§)
		15.5 What Is the Best Public Policy? (§§)
		15.6 A Fair Diploma Validation Decision (§§§)
		References
Part IV Advanced Topics
	16 On Measuring the Fitness of a Multiple-Criteria Ranking
		Contents
		16.1 Listing Movies from Best Star-Rated to Worst
		16.2 Kendall\'s Ordinal Correlation Tau Index
		16.3 Bipolar-Valued Relational Equivalence
		16.4 Fitness of Ranking Heuristics
		16.5 Illustrating Preference Divergences
		16.6 Exploring the ``better rated\'\' and the ``as well as rated\'\' Opinions
		References
	17 On Computing Digraph Kernels
		Contents
		17.1 What Is a Graph Kernel?
		17.2 Initial and Terminal Kernels
		17.3 Kernels in Lateralized Digraphs
		17.4 Computing First and Last Choice Recommendations
		17.5 Tractability of Kernel Computation
		17.6 Solving Kernel Equation Systems
		Notes
		Notes
		References
	18 On Confident Outrankings with Uncertain Criteria Significance Weights
		Contents
		18.1 Modelling Uncertain Criteria Significance Weights
		18.2 Bipolar-Valued Likelihood of Outranking Situations
		18.3 Confidence level Of Outranking Digraphs
		References
	19 Robustness Analysis of Outranking Digraphs
		Contents
		19.1 Cardinal or Ordinal Criteria Significance Weights?
		19.2 Qualifying the Stability of Outranking Situations
		19.3 Computing the Stability Denotation of Outranking Situations
		19.4 Robust Bipolar-Valued Outranking Digraphs
		19.5 Characterising Unopposed Multiobjective Outranking Situations
		19.6 Computing Pareto Efficient Multiobjective Choices
		References
	20 Tempering Plurality Tyranny Effects in Social Choice
		Contents
		20.1 Two-Stage Elections with Multipartisan Primary Selection
		20.2 Bipolar Approval–Disapproval Voting Systems
		20.3 Pairwise Comparison of Approval–Disapproval Votes
		20.4 Three-Valued Evaluative Voting Systems
		20.5 Favouring Multipartisan Candidates
		References
Part V Working with Undirected Graphs
	21 Bipolar-Valued Undirected Graphs
		Contents
		21.1 Implementing Simple Graphs
		21.2 Q-Coloring of a Graph
		21.3 MIS and Clique Enumeration
		21.4 Line Graphs and Maximal Matchings
		21.5 Grids and the Ising Model
		21.6 Simulating Metropolis Random Walks
		21.7 Computing the Non-isomorphic MISs of the n-Cycle Graph
		References
	22 On Tree Graphs and Graph Forests
		Contents
		22.1 Generating Random Tree Graphs
		22.2 Recognising Tree Graphs
		22.3 Spanning Trees and Forests
		22.4 Maximum Determined Spanning Forests
		References
	23 About Split, Comparability, Interval, and Permutation Graphs
		Contents
		23.1 A `multiply\' Perfect Graph
		23.2 Who Is the Liar?
		23.3 Generating Permutation Graphs
		23.4 Recognising Permutation Graphs
		References
Index