patterns of problem solving
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1月 11, 2021
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Patterns of Problem Solving: An Outline
1. PROBLEM SOLVING
1-1 Culture Values and Problem Solving
1-2 Schools of Thought in Problem Solving
1-3 Models of the Problem-Solving Process
1-4 Artificial Intelligence and Expert Systems
1-5 Guides to Problem Solving
1-7 Introduction of Unnecessary Constraints: Difficulty
1-8 General Precepts as Guides to Problem Solving
1-9 Paths to a Solution
1-10 Discussing Your Problem
1-11 Summary
2. LANGUAGE AND COMMUNICATION
2-1 Introduction
2-2 Knowing a Language
2-3 Languages of the World
2-4 Language and Thought
2-5 Evolution of Written Language
2-6 The Language of Numbers
2-7 Language of Statements: Symbolic Logic
2-8 Truth Tables
2-9 Tautologies and Valid Arguments
2-10 Symbols Operations and Fundamental Laws of Symbolic Logic
2-11 The Language of Sets: Sets Subsets and Operations on Sets
2-12 Fuzzy Logic
2-13 Modern Communication Systems
2-14 Use of Redundancy in Communication
2-15 Bar Codes: Product Code Labels
2-16 Summary
3. EFFECTIVE USE OF HUMAN MEMORY
3-1 Introduction
3-2 Memory Myths
3-3 A Model of Memory
3-4 Forgetting
3-5 Storage Versus Retrieval
3-6 Memory Control Processes
3-7 Memory and Age
3-8 Memory and Diet
3-9 Summary
4. PROBABILITY AND THE WILL TO DOUBT
4-1 Introduction
4-2 Probability and Doubt
4-3 Is Mr. X Honest?
4-4 Laws of Probability
4-6 Application of Bayes’ Theorem
4-7 Mr. X Revisited
4-8 Probability and Credibility
4-10 Summary
5. MODELS AND MODELING
5-1 Introduction
5-2 Purpose of Models
5-3 Nature of Models
5-4 Validation of Models
5-5 Classification of Models
5-6 Models of History
5-7 Models of the Universe
5-8 Models of the Atom
5-9 Model of the Brain
5-10 Models in Engineering
5-11 Models in Physical Science and in Human Affairs
5-12 More on Mathematical Models
5-13 Summary
6. PROBABILISTIC MODELS
6-1 Introduction: An Exposure
6-2 Populations and Samples
6-3 Probability Distribution Models
6-4 Normal Distribution Model
6-5 Expected Values of Random Variables and Their Aggregates
6-6 Central Limit Theorem and Its Application
6-7 Random walk
6-8 From Sample to Population: Estimation of Parameters
6-9 Testing Hypotheses: Errors of Omission and Commission
6-10 Simulation of Probabilistic Models: Monte Carlo Method
6-11 Summary
7. DECISION-MAKING MODELS
7-1 Introduction
7-2 Decision Models
7-3 Decision Making Under Certainty
7-4 Decision Making Under Risk
7-5 Decision Making Under Uncertainty
7-6 Utility Theory
7-7 Utility Assignments and the Decision Models
7-8 Decision Making Under Conflict: Game Theory
7-9 Group Decision Making
7-10 Summary
8. OPTIMIZATION MODELS: “SELECTING THE BEST POSSIBLE”
8-1 Introduction
8-2 Linear Functions
8-3 Linear Programming: Exposure
8-4 Linear Programming: An Application to Dental Practice
8-5 Linear Programming: Generalization of Method
8-6 Nonlinear Programming
8-7 Dynamic Programming
8-8 Sequential Decisions with Random Outcomes
8-9 Sequential Decisions with Normal Distribution Outcomes
8-10 Summary
9. DYNAMIC SYSTEM MODELS
9-1 Introduction: An Exposure
9-2 Building Blocks in Dynamic System Models
9-3 More Recent Models of Dynamic Systems
9-4 Homeostasis: Control in Living Organisms
9-5 Controllability and Open- Versus Closed-Loop Control
9-6 Amplitude and Phase in the Response of Dynamic Systems
9-7 Characteristics of Feedback Systems
9-8 Simulation of Dynamic Systems
9-9 A Novel Application of a Control System
9-10 Dynamic Systems and Chaos
9-11 Summary
10. VALUES AND MODELS OF BEHAVIOR
10-1 Introduction
10-2 Role of Values in Problem Solving
10-3 Value Classification
10-4 Value Judgment
10-5 Knowledge and Values
10-6 A Model of Ethical Behavior
10-7 A Study of Values
10-8 Values and the Future
10-9 Dynamic Change in Values and Value Subscription
10-10 Cost-Benefit Assessment of Values
10-11 Social Preferences
10-12 The Delphi Method
10-13 Use of the Delphi Method to Develop an Interdisciplinary Course
10-14 Consensus on Values and Consensus on What to Do
10-15 Summary
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