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E-grāmata: Intelligence Analysis as Discovery of Evidence, Hypotheses, and Arguments: Connecting the Dots

4.50/5 (4 ratings by Goodreads)
(George Mason University, Virginia), (George Mason University, Virginia), (George Mason University, Virginia), (George Mason University, Virginia)
  • Formāts: PDF+DRM
  • Izdošanas datums: 30-Aug-2016
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316655597
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Intelligence Analysis as Discovery of Evidence, Hypotheses, and Arguments: Connecting the DotsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 30-Aug-2016
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316655597
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This unique book on intelligence analysis covers several vital but often overlooked topics. It teaches the evidential and inferential issues involved in 'connecting the dots' to draw defensible and persuasive conclusions from masses of evidence: from observations we make, or questions we ask, we generate alternative hypotheses as explanations or answers; we make use of our hypotheses to generate new lines of inquiry and discover new evidence; and we test the hypotheses with the discovered evidence. To facilitate understanding of these issues and enable the performance of complex analyses, the book introduces an intelligent analytical tool, called Disciple-CD. Readers will practice with Disciple-CD and learn how to formulate hypotheses; develop arguments that reduce complex hypotheses to simpler ones; collect evidence to evaluate the simplest hypotheses; and assess the relevance and the believability of evidence, which combine in complex ways to determine its inferential force and the probabilities of the hypotheses.

Recenzijas

'Intelligence Analysis as Discovery of Evidence, Hypotheses, and Arguments is a valuable resource for those interested in improving intelligence analysis. It provides both prospective and current intelligence analysts with an easy to read and understand explanation of a complex process, along with suggestions for how to more effectively implement that process. The examples from intelligence analysis and comparison to similar practices in other fields such as medicine, law, and law enforcement help the reader better understand how the interplay of evidence, hypotheses, and arguments can develop in different contexts. In that sense, this book provides a wonderful platform for improving intelligence analysis by learning and employing best scientific reasoning practices.' Stephen Marrin, James Madison University, Virginia 'This is an important work in several ways. The authors both help readers learn the basics and give advanced training in the craft of analytical reasoning by providing sophisticated tools to guide understanding of its strengths, its probabilistic nature, and its limitations. 'Deep' learning is what distinguishes experts from gifted amateurs; this book will help amateurs develop expert habits through guided learning and practice. I would not only recommend this book for students of intelligence, but also for students of law, journalism, and national security. The first several chapters should be mandatory reading for 'customers' and critics of intelligence, including policymakers, legislators, and professional journalists who are quick to ask 'why didn't you connect the dots?' while they themselves probably have no idea how difficult that may be.' Mark T. Clark, Director, National Security Studies, California State University, San Bernardino, and President, Association for the Study of Middle East and Africa (ASMEA)

Papildus informācija

Using a flexible software system, this book teaches evidential and inferential issues used in drawing conclusions from masses of evidence.
Preface xiii
Acknowledgments xix
About the Authors xxi
1 Intelligence Analysis: "Connecting the Dots" 1(27)
1.1 How Easy Is It to Connect the Dots?
1(11)
1.1.1 How Many Kinds of Dots Are There?
2(1)
1.1.2 Which Evidential Dots Can Be Believed?
3(2)
1.1.3 Which Evidential Dots Should Be Considered?
5(1)
1.1.4 Which Evidential Dots Should We Try to Connect?
5(2)
1.1.5 How to Connect Evidential Dots to Hypotheses?
7(2)
1.1.6 What Do Our Dot Connections Mean?
9(2)
1.1.7 Whose Evidential Dots Should Be Connected?
11(1)
1.2 Imaginative Reasoning in Intelligence Analysis
12(5)
1.2.1 Imaginative Reasoning
12(1)
1.2.2 What Ingredients of Analysis Are to Be Generated by Imaginative Thought?
13(1)
1.2.3 Generating Main Hypotheses to Be Defended by Evidence and Argument
14(1)
1.2.4 Generating the Evidential Grounds for Arguments
15(1)
1.2.5 Generating Arguments Linking Evidence and Hypotheses
16(1)
1.3 Intelligence Analysis as Discovery of Evidence, Hypotheses, and Arguments
17(9)
1.3.1 Intelligence Analysis in the Framework of the Scientific Method
17(1)
1.3.2 Evidence in Search of Hypotheses
18(1)
1.3.3 Hypotheses in Search of Evidence
19(3)
1.3.4 Evidentiary Testing of Hypotheses
22(1)
1.3.5 Completing the Analysis
23(3)
1.4 Review Questions
26(2)
2 Marshaling Thoughts and Evidence for Imaginative Analysis 28(17)
2.1 Sherlock Holmes and Investigation or Discovery
28(1)
2.2 Mycroft Holmes and Evidence Marshaling
29(1)
2.3 Marshaling "Magnets" or Attractors
30(2)
2.4 Types of Marshaling Magnets
32(9)
2.4.1 Believability Magnet
32(1)
2.4.2 Chronology Magnet
33(2)
2.4.3 Question Magnet
35(1)
2.4.4 Hypothesis Magnet
36(1)
2.4.5 Argument Magnet
37(1)
2.4.6 Eliminative Magnet
38(1)
2.4.7 Scenario Magnet
39(2)
2.5 Use of the Marshaling Magnets
41(1)
2.6 Review Questions
42(3)
3 Disciple-CD: A Cognitive Assistant for Connecting the Dots 45(14)
3.1 System Overview
45(3)
3.2 Obtaining Disciple-CD
48(1)
3.3 Hands On: Working with Knowledge Bases
49(3)
3.3.1 Overview
49(1)
3.3.2 Basic Operations
50(2)
3.4 Knowledge Base Guidelines
52(1)
3.5 Hands On: Browsing an Argumentation
53(6)
3.5.1 Overview
53(4)
3.5.2 Practice
57(1)
3.5.3 Basic Operations
58(1)
4 Evidence 59(23)
4.1 What Is Evidence?
59(3)
4.2 The Credentials of All Evidence
62(6)
4.2.1 Relevance
62(3)
4.2.2 Believability or Credibility
65(2)
4.2.3 Force or Weight of Evidence
67(1)
4.3 Assessing the Relevance, Believability, and Inferential Force of Evidence
68(5)
4.4 Basic Operations with Disciple-CD
73(6)
4.4.1 Hands On: Define and Evaluate Evidence
73(6)
4.4.1.1 Overview
73(3)
4.4.1.2 Practice
76(1)
4.4.1.3 Basic Operations
76(3)
4.5 Advanced Operations with Disciple-CD
79(1)
4.5.1 Hands On: From Information to Evidence
79(8)
4.5.1.1 Overview
79(1)
4.5.1.2 Practice
80(1)
4.5.1.3 Advanced Operations
80(1)
4.6 Review Questions
80(2)
5 Divide and Conquer: A Necessary Approach to Complex Analysis 82(36)
5.1 Holistic Approach to Analysis
82(1)
5.2 Divide and Conquer
83(1)
5.3 Assessing Complex Hypotheses through Analysis and Synthesis
84(2)
5.4 Inquiry-driven Analysis and Synthesis
86(1)
5.5 Types of Reductions and Corresponding Syntheses
87(3)
5.5.1 Necessary and Sufficient Conditions
87(1)
5.5.2 Sufficient Conditions and Scenarios
88(1)
5.5.3 Indicators
88(2)
5.6 Problems with Argument Construction
90(3)
5.7 Basic Operations with Disciple-CD
93(9)
5.7.1 Hands On: Was the Cesium Canister Stolen?
93(5)
5.7.1.1 Hypothesis in Search of Evidence: Illustration
93(2)
5.7.1.2 Hands-On Overview
95(2)
5.7.1.3 Practice
97(1)
5.7.1.4 Basic Operations
97(1)
5.7.2 Hands On: Development and Evaluation of an Argument
98(3)
5.7.2.1 Overview
98(1)
5.7.2.2 Practice
99(1)
5.7.2.3 Basic Operations
99(2)
5.7.3 Hands On: Analysis Based on Previously Learned Patterns and Synthesis Functions
101(1)
5.7.3.1 Overview
101(1)
5.7.3.2 Practice
101(1)
5.7.3.3 Basic Operations
101(1)
5.8 Advanced Operations with Disciple-CD
102(12)
5.8.1 Hands On: Abstraction of Analysis
102(2)
5.8.1.1 Overview
102(1)
5.8.1.2 Practice
103(1)
5.8.1.3 Advanced Operations
103(1)
5.8.2 Hands On: Hypothesis Analysis and Evidence Search
104(4)
5.8.2.1 Overview
104(3)
5.8.2.2 Practice
107(1)
5.8.2.3 Advanced Operation
107(1)
5.8.3 Hands On: Justifications of Assumptions
108(1)
5.8.3.1 Overview
108(1)
5.8.3.2 Practice
108(1)
5.8.3.3 Advanced Operation
108(1)
5.8.4 Hands On: Top-down and Bottom-up Argument Development
109(10)
5.8.4.1 Overview
109(3)
5.8.4.2 Practice
112(1)
5.8.4.3 Advanced Operations
112(2)
5.9 Analysis Guidelines
114(2)
5.10 Review Questions
116(2)
6 Assessing the Believability of Evidence 118(21)
6.1 Believability: The Foundation of All Arguments from Evidence
118(1)
6.2 Classification of Evidence Based on Believability
119(1)
6.3 Tangible Evidence
119(3)
6.3.1 Real Tangible Evidence: Authenticity
120(1)
6.3.2 Demonstrative Tangible Evidence: Authenticity, Accuracy, and Reliability
120(1)
6.3.3 Examples of Tangible Evidence
121(1)
6.4 Testimonial Evidence
122(4)
6.4.1 Competence
122(1)
6.4.1.1 Access
122(1)
6.4.1.2 Understandability
122(1)
6.4.2 Credibility
123(2)
6.4.2.1 Veracity or Truthfulness
123(1)
6.4.2.2 Objectivity
123(1)
6.4.2.3 Observational Sensitivity
124(1)
6.4.3 Types of Testimonial Evidence
125(1)
6.4.4 Examples of Testimonial Evidence
126(1)
6.5 Missing Evidence
126(2)
6.5.1 Uncertainties Associated with Missing Evidence
126(1)
6.5.2 Example of Missing Evidence
127(1)
6.6 Authoritative Records
128(1)
6.7 Mixed Evidence
128(2)
6.7.1 Analysis of Mixed Evidence
128(1)
6.7.2 Examples of Mixed Evidence
128(2)
6.8 Deep Believability Analysis
130(3)
6.9 Advanced Operations with Disciple-CD
133(4)
6.9.1 Hands On: Believability Analysis
133(10)
6.9.1.1 Overview
133(2)
6.9.1.2 Practice
135(1)
6.9.1.3 Advanced Operation
136(1)
6.10 Review Questions
137(2)
7 Chains of Custody 139(9)
7.1 What Is a Chain of Custody?
139(1)
7.2 A Case Involving Chains of Custody
140(1)
7.3 A Chain of Custody for Testimonial Evidence
141(2)
7.4 A Chain of Custody for Demonstrative Tangible Evidence
143(3)
7.4.1 Chain of Custody for a Photo Given Directly to the Analyst
144(1)
7.4.2 Chain of Custody for a Written Description of a Photo Given to the Analyst
145(1)
7.5 Analyzing a Chain of Custody
146(1)
7.6 Drill-Down Analysis of Chains of Custody
147(1)
7.7 Review Questions
147(1)
8 Recurrent Substance-Blind Combinations of Evidence 148(11)
8.1 Harmonious Evidence
148(2)
8.1.1 Basic Forms of Harmonious Evidence
148(1)
8.1.2 Patterns of Evidential Harmony
149(1)
8.2 Dissonant Evidence
150(2)
8.2.1 Basic Forms of Dissonant Evidence
150(2)
8.2.2 Patterns of Evidential Dissonance
152(1)
8.3 Redundant Evidence
152(2)
8.3.1 Basic Forms of Redundant Evidence
152(1)
8.3.2 Patterns of Evidential Redundance
153(1)
8.4 Why Considering Evidence Combinations Is Important
154(1)
8.5 Basic Operations with Disciple-CD
154(2)
8.5.1 Hands On: Who Has Stolen the Cesium Canister?
154(5)
8.5.1.1 Overview
154(2)
8.5.1.2 Practice
156(1)
8.6 Review Questions
156(3)
9 Major Sources of Uncertainty in Masses of Evidence 159(14)
9.1 Incompleteness
159(3)
9.1.1 What Is Incompleteness of Evidence?
159(1)
9.1.2 Examples of Incompleteness
160(2)
9.2 Inconclusiveness
162(1)
9.2.1 What Is Inconclusiveness of Evidence?
162(1)
9.2.2 Examples of Inconclusiveness
163(1)
9.3 Ambiguity
163(2)
9.3.1 What Is Ambiguity of Evidence?
163(1)
9.3.2 Examples of Ambiguity
164(1)
9.4 Dissonance
165(1)
9.4.1 What Is the Dissonance of Evidence?
165(1)
9.4.2 Examples of Dissonance
166(1)
9.5 Imperfect Believability
166(1)
9.5.1 What Is Imperfect Believability of Evidence?
166(1)
9.5.2 Examples of Imperfect Believability
166(1)
9.6 Basic Operations with Disciple-CD
167(4)
9.6.1 Hands On: Does a Terrorist Organization Have the Cesium Canister?
167(10)
9.6.1.1 Overview
167(1)
9.6.1.2 Extracting Evidence from Information
168(2)
9.6.1.3 Practice
170(1)
9.7 Review Questions
171(2)
10 Assessing and Reporting Uncertainty: Some Alternative Methods 173(40)
10.1 Introduction
173(1)
10.2 General Classes of Probability and Uncertainty
174(1)
10.3 Enumerative Probabilities: Obtained by Counting
174(3)
10.4 Nonenumerative Probabilities: Nothing to Count
177(1)
10.5 Epistemic Probability (1): The Subjective Bayesian View
177(12)
10.5.1 Likelihood Ratios
178(5)
10.5.1.1 Analysis Using Likelihood Ratios
178(1)
10.5.1.2 Examples
179(4)
10.5.2 Bayesian Networks
183(6)
10.5.2.1 Constructing the Argument Structure
183(1)
10.5.2.2 Forming the Key List
183(1)
10.5.2.3 Identifying the Likelihoods and Prior Probabilities
184(3)
10.5.2.4 Using the Bayesian Network
187(2)
10.5.2.5 Utility and Feasibility of Bayesian Network Analyses
189(1)
10.6 Epistemic Probability (2): Belief Functions
189(8)
10.6.1 Belief Functions and Evidential Support
189(3)
10.6.2 Examples of Assigning Evidential Support
192(1)
10.6.3 Dempster's Rule for Combining Partial Beliefs
193(4)
10.7 Baconian Probability and the Importance of Evidential Completeness
197(5)
10.7.1 Variative and Eliminative Inferences
197(1)
10.7.2 Importance of Evidential Completeness
197(4)
10.7.3 Baconian Probability of Boolean Expressions
201(1)
10.8 Imprecision and Fuzzy Probability
202(3)
10.8.1 Fuzzy Force of Evidence
202(2)
10.8.2 Fuzzy Probability of Boolean Expressions
204(1)
10.8.3 On Verbal Assessments of Probabilities
204(1)
10.9 A Summary of Uncertainty Methods and What They Best Capture
205(3)
10.10 Basic Operations with Disciple-CD
208(3)
10.10.1 Hands On: Will a Bomb Be Set Off in Washington, D.C.?
208(6)
10.10.1.1 Overview
208(1)
10.10.1.2 Practice
209(2)
10.11 Review Questions
211(2)
11 Analytic Bias 213(12)
11.1 Basic Interpretations of the Term "Bias"
213(1)
11.2 Biases of the Analyst
214(5)
11.2.1 Biases in the Evaluation of Evidence
215(1)
11.2.2 Biases in the Perception of Cause and Effect
216(1)
11.2.3 Biases in Estimating Probabilities
217(1)
11.2.4 Hindsight Biases in Evaluating Intelligence Reporting
218(1)
11.3 Some Frequently Overlooked Origins of Bias
219(3)
11.3.1 HUMINT Sources
219(1)
11.3.2 Persons in Chains of Custody of Evidence
220(1)
11.3.3 Consumers of Intelligence Analyses
221(1)
11.4 Biases and the Evaluation of Analysts
222(1)
11.5 Recognizing and Countering Biases with Disciple-CD
223(1)
11.6 Review Questions
223(2)
12 Learning and Reusing Analytic Expertise: Beyond Disciple-CD 225(14)
12.1 Introduction
225(1)
12.2 Learning Agent Shell
225(3)
12.3 Learning Agent Shell for Evidence-based Reasoning
228(3)
12.3.1 Disciple-EBR
228(1)
12.3.2 Disciple-CD
229(2)
12.4 Development of a Cognitive Assistant
231(5)
12.5 Evidence-Based Reasoning Everywhere
236(3)
Glossary of Terms 239(10)
References 249(6)
Appendixes 255(2)
1 Methodological Guidelines
255(1)
2 Hands-On Exercises
255(1)
3 Operations with Disciple-CD
256(1)
Index 257
Gheorghe Tecuci (PhD, University of Paris-South and Polytechnic Institute of Bucharest) is Professor of Computer Science and Director of the Learning Agents Center at George Mason University, Virginia, Member of the Romanian Academy, and former Chair of Artificial Intelligence at the US Army War College. He has published eleven books and more than 190 papers. David A. Schum (PhD, Ohio State University) is Emeritus Professor of Systems Engineering, Operations Research, and Law, as well as Chief Scientist of the Learning Agents Center at George Mason University, Virginia. He has published more than one hundred research papers and six books on evidence and probabilistic inference, and is recognized as one of the founding fathers of the emerging Science of Evidence. Dorin Marcu (PhD, George Mason University) is Research Assistant Professor in the Learning Agents Center at George Mason University, Virginia. He collaborated in the development of the Disciple Learning Agent Shell and a series of cognitive assistants based on it for different application domains, such as Disciple-COA (course of action critiquing), Disciple-COG (strategic center of gravity analysis), Disciple-LTA (learning, tutoring, and assistant), and Disciple-EBR (evidence-based reasoning). Mihai Boicu (PhD, George Mason University) is Associate Professor of Information Sciences and Technology and Associate Director of the Learning Agents Center at George Mason University, Virginia. He is the main software architect of the Disciple agent development platform and coordinated the software development of Disciple-EBR. He has received the IAAI Innovative Application Award.
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