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Tables of Contents for Motivated Metamodels
Chapter/Section Title
Page #
Page Count
Preface
iii
Figures
vii
Tables
ix
Summary
xi
Acknowledgments
xvii
Acronyms and Abbreviations
xix
Introduction
1
14
Objective
1
1
Study Context
2
6
Background on Multiresolution, Multiperspective Modeling
2
2
Why Models at Different Levels of Detail Are Needed
4
3
The Problem: We Frequently Do Not Have Good Low-Resolution Models
7
1
Metamodels
8
6
Definition
8
3
Why Metamodels May Actually Be Rather Good
11
1
Typical Methods of Metamodeling
12
1
Concerns About Metamodeling
13
1
Approach
14
1
Hypotheses, Experimentation, and Iteration
15
4
Hypotheses
15
1
An Overview of the Experimentation
16
1
Criteria: What Makes a Metamodel Good?
17
2
Results of Experimentation
19
9
Defining a Baseline for Comparison: Pure ``Statistical Metamodels''
19
2
The Approach Taken
19
2
A Path Not Taken
21
1
Types of Theoretical Knowledge
21
4
General Observations
21
2
Illustrating the Ideas in 4, 5, and 7
23
2
How Exploiting Increasing Amounts of Knowledge Improves Metamodels
25
3
Summary and Lessons Learned
28
33
Shortcomings of Our Statistical Metamodels and Conclusions from the Experimentation
29
2
Failure to Tell a Story
29
1
Critical Components
30
1
Implications for Resource Allocation
30
1
Shortcomings in the Presence of an Adversary
31
1
Benefits of Motivated Metamodels
31
1
A Significant Organizational Benefit of Motivated Metamodeling: Easy Validation
32
1
Suggested Elements of Motivated Metamodeling
32
1
Implications for Model Validation and Documentation
33
1
Possible Resistance to Motivated Metamodels
34
1
Next Steps
35
2
Appendix:
A. Implementing MRM with Switches
37
2
B. Details of the Experiment
39
18
C. Exact Solution of Equations (14) and (16)
57
2
D. Measuring Goodness of Fit
59
2
Bibliography
61