Cover image for Soft Computing Evaluation Logic

Soft Computing Evaluation Logic

Author Jozo Dujmović
Release Date: 2018/10/01
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Book Description

A novel approach to decision engineering, with a verified framework for modeling human reasoning

Soft Computing Evaluation Logic provides an in-depth examination of evaluation decision problems and presents comprehensive guidance toward the use of the Logic Scoring of Preference (LSP) method in modeling complex decision criteria. Fully aligned with current developments in computational intelligence, the discussion covers the design and use of LSP criteria for evaluation and comparison in diverse areas, such as search engines, medical conditions, real estate, space management, habitat mitigation projects in ecology, and land use and residential development suitability maps, with versatile transfer to other similar decision-modeling contexts.

Human decision making is rife with fuzziness, imprecision, uncertainty, and half-truths—yet humans make evaluation decisions every day. In this book, such decision processes are observed, analyzed, and modeled. The result is graded logic, a soft computing mathematical infrastructure that provides both formal logic and semantic generalizations of classical Boolean logic. Graded logic is used for logic aggregation in the context of evaluation models consistent with observable properties of human reasoning.  The LSP method, based on graded logic and logic aggregation, is a vital component of an industrial-strength decision engineering framework. Thus, the book:

  • Provides detailed theoretical background for graded logic
  • Provides a theory of logic aggregators
  • Explains the LSP method for designing complex evaluation criteria and their use
  • Shows techniques for evaluation, comparison, and selection of complex systems, as well as the cost/suitability analysis, optimization, sensitivity analysis, tradeoff analysis, and missingness-tolerant aggregation
  • Includes a survey of available LSP software tools, including ISEE, ANSY and LSP.NT.

With quantitative modeling of human reasoning, novel approaches to modeling decision criteria, and a verified decision engineering framework applicable to a broad array of applications, this book is an invaluable resource for graduate students, researchers, and practitioners working within the decision engineering realm.

Table of Contents

  1. Cover
  2. Preface
  3. PART ONE: EVALUATION DECISION PROBLEMS
    1. 1.1 Intuitive Evaluation as a Logic Decision Process
      1. 1.1.1 Main Observable Steps of the Intuitive Evaluation Process
      2. 1.1.2 Subjective and Objective Components in Evaluation
    2. 1.2 Quantitative Evaluation—An Introductory Example
      1. 1.2.1 Stakeholders and Their Goals
      2. 1.2.2 Attributes
      3. 1.2.3 Attribute Criteria
      4. 1.2.4 Simple Direct Ranking
      5. 1.2.5 Aggregation of Attribute Suitability Degrees
      6. 1.2.6 Using Cost and Suitability to Compute the Overall Value
    3. 1.3 Drawbacks of Simple Additive and Multiplicative Scoring and Utility Models
      1. 1.3.1 Simple Additive Scoring: The Irresistible Attractiveness of Simplicity
      2. 1.3.2 Simple Multiplicative Scoring
      3. 1.3.3 Logic Unsuitability of Scoring and Utility Theory Models in Professional Evaluation
    4. 1.4 Introduction to Professional Quantitative Evaluation
      1. 1.4.1 Five Fundamental Types of Professional Evaluation Problems
      2. 1.4.2 A Survey of Typical Professional Evaluation Problems
      3. 1.4.3 Components of Methodology for Professional Quantitative Evaluation
  4. PART TWO: GRADED LOGIC AND AGGREGATION
    1. 2.1 Graded Logic as a Generalization of Classical Boolean Logic
      1. 2.1.1 Aggregators and Their Classification
      2. 2.1.2 How Do Human Beings Aggregate Subjective Categories?
      3. 2.1.3 Definition and Classification of Logic Aggregators
      4. 2.1.4 Logic Bisection, Trisection, and Quadrisection of the Unit Hypercube
      5. 2.1.5 Propositions, Value Statements, Graded Logic, and Fuzzy Logic
      6. 2.1.6 Classical Bivalent Boolean Logic
      7. 2.1.7 Six Generalizations of Bivalent Boolean Logic
      8. 2.1.8 GL Conjecture: Ten Necessary and Sufficient GL Functions
      9. 2.1.9 Basic Idempotent GL Aggregators
      10. 2.1.10 A Summary of Differences between Graded Logic and Bivalent Boolean Logic
      11. 2.1.11 Relationships between Graded Logic, Perceptual Computing, and Fuzzy Logic
      12. 2.1.12 A Brief History of Graded Logic
    2. 2.2 Observable Properties of Human Evaluation Logic
      1. 2.2.1 Perceptual Computer and Its Basic Properties
      2. 2.2.2 Simultaneity and Substitutability in Evaluation Models
      3. 2.2.3 Basic Semantic Aspects of Evaluation Logic Reasoning
      4. 2.2.4 Multipolarity: Grouping and Aggregation of Semantically Heterogeneous Inputs
      5. 2.2.5 Grouping and Aggregation of Semantically Homogeneous Inputs
      6. 2.2.6 Imprecision, Incompleteness, Logic Inconsistency, and Errors
    3. 2.3 Andness and Orness
      1. 2.3.1 A General Definition of Andness/Orness
      2. 2.3.2 Local Andness and Orness in the Simplest Case of Two Variables
      3. 2.3.3 Variability of Local Andness
      4. 2.3.4 Mean Local Andness and Orness in the Case of Two Variables
      5. 2.3.5 Local and Mean Local Andness and Orness in the Case of n Variables
      6. 2.3.6 Global Andness and Orness
      7. 2.3.7 Mean Global Andness/Orness Theorems and Their Applications
      8. 2.3.8 Geometric Interpretations of Andness and Orness
    4. 2.4 Graded Conjunction/Disjunction and Logic Modeling of Simultaneity and Substitutability
      1. 2.4.1 Definitions and Basic Mathematical Properties of Logic Aggregators
      2. 2.4.2 Classification of Conjunctive and Disjunctive Logic Aggregators
      3. 2.4.3 Properties of Means Used in Logic Aggregation
      4. 2.4.4 Algebraic Properties of Aggregators Based on Weighted Power Means
      5. 2.4.5 Logic Aggregators Based on Weighted Means with Adjustable Andness/Orness
      6. 2.4.6 Selection and Use of the Threshold Andness Aggregator
      7. 2.4.7 Andness‐Directed Interpolative GCD Aggregators
      8. 2.4.8 Uniform and Nonuniform Interpolative GCD Aggregators
      9. 2.4.9 Extending GCD to Include Hyperconjunction and Hyperdisjunction
      10. 2.4.10 From Drastic Conjunction to Drastic Disjunction: A General GCD Aggregator
      11. 2.4.11 Gamma Aggregators versus Extended GCD Aggregators
      12. 2.4.12 Four Main Families of GCD Aggregators and Sixteen Conditions They Must Satisfy
    5. 2.5 The Percept of Importance and the Use of Weights
      1. 2.5.1 Multiplicative, Implicative, and Exponential Weights as Importance Quantifiers
      2. 2.5.2 Impact of Weights on Aggregation Results
      3. 2.5.3 Semantic Components in Logic Aggregation Models
      4. 2.5.4 Seven Techniques for Weight Adjustment
      5. 2.5.5 Multivariate Weighted Aggregation Based on Binary Aggregation Trees
    6. 2.6 Partial Absorption: A Fundamental Asymmetric Aggregator
      1. 2.6.1 Conjunctive Partial Absorption
      2. 2.6.2 Disjunctive Partial Absorption
      3. 2.6.3 Visualizing the Partial Absorption Function, Penalty, and Reward
      4. 2.6.4 Mathematical Models of Penalty and Reward
      5. 2.6.5 Selecting Parameters of Partial Absorption
    7. 2.7 Logic Functions That Use Negation
      1. 2.7.1 Negation and De Morgan’s Duality
      2. 2.7.2 De Morgan’s Laws for Weighted Aggregators and Dualized Weighted Aggregators
      3. 2.7.3 De Morgan’s Duals of Compound Functions
      4. 2.7.4 Nonidempotent Logic Functions
    8. 2.8 Penalty‐Controlled Missingness‐Tolerant Aggregation
      1. 2.8.1 Missing Data in Evaluation Problems
      2. 2.8.2 Penalty‐Controlled Numerical Coding of Missing Data
      3. 2.8.3 A Penalty‐Controlled Missingness‐Tolerant Aggregation Algorithm
      4. 2.8.4 The Impact of Penalty on Missingness‐Tolerant Aggregation
    9. 2.9 Rating Scales and Verbalization
      1. 2.9.1 Design of Rating Scales
      2. 2.9.2 Stepwise Refinement of Rating Scales for Andness and Orness
      3. 2.9.3 Scaling and Verbalizing Degrees of Importance
      4. 2.9.4 Scaling and Verbalizing Degrees of Suitability/Preference
  5. PART THREE: LSP METHOD
    1. 3.1 An Overview of the LSP Method
      1. 3.1.1 Characterization of Stakeholder and Organization of an Evaluation Project
      2. 3.1.2 Development of the Suitability Attribute Tree
      3. 3.1.3 Elementary Attribute Criteria
      4. 3.1.4 Logic Aggregation of Suitability
      5. 3.1.5 Cost/Suitability Analysis and Comparison of Evaluated Objects Using Their Overall Value
      6. 3.1.6 Summary of Properties of the LSP Method
    2. 3.2 LSP Decision Engineering Framework for Professional Evaluation Projects
      1. 3.2.1 Participants in a Professional Evaluation Process Based on LSP DEF
      2. 3.2.2 Relationships between Evaluators and Domain Experts
      3. 3.2.3 The Structure of LSP DEF and the Corresponding Professional Evaluation Process
      4. 3.2.4 Predictive Nature of Evaluation Models
      5. 3.2.5 Interpretation of Evaluation Results
      6. 3.2.6 Complexity, Completeness, and Accuracy of Evaluation Models
      7. 3.2.7 Combining Opinions of n Experts
    3. 3.3 Elementary Attribute Criteria
      1. 3.3.1 Notation of Elementary Criteria
      2. 3.3.2 Verbalization of Elementary Criteria
      3. 3.3.3 Continuous Nonlinear Elementary Criteria
      4. 3.3.4 Classification of Twelve Characteristic Types of Elementary Criteria
    4. 3.4 Aggregation Techniques and Tools
      1. 3.4.1 Selecting GCD Aggregators for an LSP Project
      2. 3.4.2 Selecting GCD Aggregators by Training Preferential Neurons
      3. 3.4.3 Analytic Techniques for Selecting Partial Absorption Aggregators
      4. 3.4.4 Boundary Penalty/Reward Tables for Selecting Partial Absorption Aggregators
      5. 3.4.5 Selecting Partial Absorption Aggregators by Training Preferential Neurons
      6. 3.4.6 Nonstationary LSP Criteria
      7. 3.4.7 Graphic Notation of Aggregation Structures
    5. 3.5 Canonical Aggregation Structures
      1. 3.5.1 Conjunctive CAS with Increasing Andness
      2. 3.5.2 Disjunctive CAS with Increasing Orness
      3. 3.5.3 Aggregated Mandatory/Optional and Sufficient/Optional CAS
      4. 3.5.4 Design of a Simple LSP Evaluator Tool
      5. 3.5.5 Distributed Mandatory/Optional and Sufficient/Optional CAS
      6. 3.5.6 Nested Mandatory/Desired/Optional and Sufficient/Desired/Optional CAS
      7. 3.5.7 Decreasing Andness and Decreasing Orness CAS
    6. 3.6 Cost/Suitability Analysis as a Graded Logic Problem
      1. 3.6.1 Cost Analysis
      2. 3.6.2 Cost/Suitability Analysis Based on Linear Equi‐Value Model
      3. 3.6.3 Using Cost/Suitability Analysis in Competitive Bidding
      4. 3.6.4 Conjunctive Suitability‐Affordability Method
    7. 3.7 Sensitivity Analysis and Tradeoff Analysis
      1. 3.7.1 Sensitivity Analysis
      2. 3.7.2 Tradeoff Analysis
    8. 3.8 Reliability Analysis
      1. 3.8.1 Sources of Errors in LSP Criteria and Their Empirical Analysis
      2. 3.8.2 The Problem of Confidence in Evaluation Results
      3. 3.8.3 Case Study of Reliability Analysis for a Computer Evaluation Project
    9. 3.9 System Optimization
      1. 3.9.1 Three Fundamental Constrained Optimization Problems
      2. 3.9.2 The Cloud Diagram and the Set of Optimum Configurations
      3. 3.9.3 A Case Study of Computer Configuration Optimization
    10. 3.10 LSP Software Technology
  6. PART FOUR: APPLICATIONS
    1. 4.1 Job Selection
      1. 4.1.1 Job Selection Attribute Tree
      2. 4.1.2 Elementary Attribute Criteria for Job Selection
      3. 4.1.3 Logic Aggregation of Suitability for the Job Selection Criterion
      4. 4.1.4 A Job Selection Example
    2. 4.2 Home Selection
      1. 4.2.1 Home Selection Using ORE Websites and LSPhome
      2. 4.2.2 Home Attribute Tree and Elementary Criteria
      3. 4.2.3 Home Suitability Aggregation Structure as a Shade Diagram
      4. 4.2.4 Using Missingness‐Tolerant LSP Criteria
      5. 4.2.5 The Optimum Home Pricing Problem
      6. 4.2.6 A Personalized Home Selection Criterion
    3. 4.3 Evaluation of Medical Conditions
      1. 4.3.1 Evaluation of Disease Severity and Patient Disability
      2. 4.3.2 Limitations of Medical Rating Scales
      3. 4.3.3 LSP Models for Computing OSD, ODD, and PDD
      4. 4.3.4 Evaluation of PDD for Peripheral Neuropathy
      5. 4.3.5 The Risky Therapy Decision Problem
      6. 4.3.6 A Case Study of Anti‐MAG Neuropathy
      7. 4.3.7 LSPmed—An Internet Tool for Medical Evaluation
    4. 4.4 LSP Criteria in Ecology: Selecting Multi‐Species Habitat Mitigation Projects
      1. 4.4.1 Multi‐Species Compensatory Mitigation Projects
      2. 4.4.2 A Generic LSP Attribute Tree for Evaluation of Habitat Mitigation Projects
      3. 4.4.3 Attribute Criteria and the Logic Aggregation Structure
      4. 4.4.4 Sensitivity Analysis
      5. 4.4.5 Logic Refining of the Aggregation Structure
      6. 4.4.6 Cost/Suitability Analysis
      7. 4.4.7 MSHCP Software Support
    5. 4.5 Space Management Decision Problems
      1. 4.5.1 A Decision Model for School Location
      2. 4.5.2 Suitability of Locations for Residential Development
    6. 4.6 LSP Suitability Maps
      1. 4.6.1 The Concept of Map Logic and LSP Suitability Maps
      2. 4.6.2 Suitability Maps Based on Points of Interest
      3. 4.6.3 The Problem of Optimum Location of City Objects
      4. 4.6.4 Suitability Analysis of Urban Locations Using the LSPmap Tool
      5. 4.6.5 GIS‐LSP Suitability Maps Based on TerrSet/Idrisi
      6. 4.6.6 GIS‐LSP Suitability Maps Based on ArcGIS
    7. 4.7 Evaluation and Comparison of Search Engines
      1. 4.7.1 Search Engine User and Workload Models
      2. 4.7.2 SEben—A Search Engine Benchmarking Tool
      3. 4.7.3 LSP Criterion for Evaluation of Search Engines
      4. 4.7.4 Search Engine Evaluation Results
  7. References
  8. Index
  9. End User License Agreement
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