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E-grāmata: Theory and Practice in Policy Analysis: Including Applications in Science and Technology

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(Carnegie Mellon University, Pennsylvania)
  • Formāts: PDF+DRM
  • Izdošanas datums: 12-Oct-2017
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316886991
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Theory and Practice in Policy Analysis: Including Applications in Science and TechnologyPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 12-Oct-2017
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316886991
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Many books instruct readers on how to use the tools of policy analysis. This book is different. Its primary focus is on helping readers to look critically at the strengths, limitations, and the underlying assumptions analysts make when they use standard tools or problem framings. Using examples, many of which involve issues in science and technology, the book exposes readers to some of the critical issues of taste, professional responsibility, ethics, and values that are associated with policy analysis and research. Topics covered include policy problems formulated in terms of utility maximization such as benefit-cost, decision, and multi-attribute analysis, issues in the valuation of intangibles, uncertainty in policy analysis, selected topics in risk analysis and communication, limitations and alternatives to the paradigm of utility maximization, issues in behavioral decision theory, issues related to organizations and multiple agents, and selected topics in policy advice and policy analysis for government.

Performing good policy analysis requires more than picking up well-established tools like benefit-cost analysis and 'turning the crank'. It requires an understanding of the strengths and limitations of those tools and the broader contexts in which analysis contributes. This book will help students and practitioners develop and apply that understanding.

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Practitioners of policy analysis will better understand the tools of their trade, and the broader contexts in which analysis contributes.
Preface xiii
Acknowledgments xv
1 Policy Analysis: An Overview
1(14)
1.1 What Is Public Policy?
1(2)
1.2 What Is Policy Analysis?
3(2)
1.3 What Is Good Policy Analysis and What Should Be Its Objective?
5(3)
1.4 How Is Doing Policy Analysis Different from Doing Science?
8(3)
1.5 What Role Does Analysis Play in Making and Implementing Policy?
11(4)
Part I: Making Decisions That Maximize Utility 15(192)
2 Preferences and the Idea of Utility
17(34)
2.1 Historical Development of the Idea of Utility
18(1)
2.2 Utility in Modern Microeconomics
19(5)
2.3 Is Utility the Same Thing as Happiness?
24(6)
2.4 Measurement Scales for Utility (and Other Things)
30(5)
2.5 The Utility of Chance Outcomes
35(5)
2.6 Can Different People's Utilities Be Compared?
40(2)
2.7 Combining Individual Utilities and the Concept of a Social Welfare Function
42(2)
2.8 Preferences that Are Not Well Defined, Change over Time, or Are Inconsistent
44(2)
2.9 Back to the Basic Question: "What Is Utility?"
46(1)
2.10 Limits to the Strategy of Utility Maximization
47(4)
3 Benefit-Cost Analysis
51(42)
3.1 B-C Basics
52(6)
3.2 Pareto Optimality
58(2)
3.3 B-C versus B/C
60(1)
3.4 Simple in Theory, but Often Complicated in Practice
60(1)
3.5 The Rise of B-C Analysis in Government Decision Making
61(6)
3.6 Examples of B-C Analysis Applied to Public Decision Making
67(6)
3.7 Limitations of B-C
73(3)
3.8 Efficiency versus Equity
76(1)
3.9 Going off the Deep End with B-C Analysis
77(9)
3.10 B-C versus Precaution
86(2)
3.11 Final Thoughts on B-C
88(5)
4 Decision Analysis
93(25)
4.1 DA Basics
93(3)
4.2 A Simple Worked Example
96(1)
4.3 Stages in a Decision Analysis
96(2)
4.4 The Axioms of Decision Analysis
98(2)
4.5 A More Detailed Worked Example
100(8)
4.6 Other Examples of Decision Analysis
108(3)
4.7 Influence Diagrams and Decision Trees
111(1)
4.8 Strengths and Limitations of Decision Analysis
112(2)
4.9 A Note on the History of Decision Analysis
114(4)
5 Valuing Intangibles and Other Non-Market Outcomes
118(37)
5.1 Inferring People's Values from the Choices They Make
119(1)
5.2 The "Value of a Statistical Life" or VSL
120(2)
5.3 A Decision-Analytic Approach to Valuing One's Own Life
122(2)
5.4 Evolution of Approaches to the Economic Valuation of Lost Lives
124(2)
5.5 Use of VSL and Similar Measures in Public Policy
126(5)
5.6 Contingent Valuation (CV)
131(3)
5.7 Computing the Costs of Externalities
134(1)
5.8 Ecosystem Services
135(6)
5.9 What If People Don't Have Well-Articulated Utility Functions for Everything?
141(2)
5.10 Variations in Basic Values across Different Cultures
143(2)
5.11 Are There Some Values that Should Not Be Quantified?
145(10)
6 Multi-Attribute Utility Theory and Multi-Criteria Decision Making
155(30)
6.1 MA UT Basics
156(2)
6.2 Constructing MAU Functions Using Independence Assumptions
158(2)
6.3 Do People Have Multi-Attribute Utility Functions in Their Heads?
160(2)
6.4 Other Multiple Criteria Decision Making (MCDM) Methods
162(3)
6.5 Figuring out What You Care About
165(2)
6.6 Example Applications of MAUT and MCDM
167(4)
6.7 Limitations to the Use of MAUT and MCDM
171(2)
6.8 Multiobjective Programming
173(12)
7 Preferences over Time and across Space
185(22)
7.1 A Simple Example of When Time Differences Do and Do Not Matter
185(4)
7.2 Exponential Discounting in the Evaluation of Projects and Investment Opportunities
189(3)
7.3 The Orthodoxy of Exponential Discounting
192(1)
7.4 The Use of Real Options as an Alternative to Net Present Value
192(2)
7.5 The Pure Rate of Time Preference (PRTP) and the Consumption Discount Rate (CDR)
194(1)
7.6 Discount Rates that Decline over Time
195(3)
7.7 Empirical Studies of the Time Preferences that People Display: A Look Ahead to Part III
198(2)
7.8 Hyperbolic Discounting
200(1)
7.9 Preferences that Change over Time
201(1)
7.10 How Different Are Space and Time?
202(5)
Part II: Some Widely Used Analysis Tools And Topics 207(136)
8 Characterizing, Analyzing, and Communicating Uncertainty
209(35)
8.1 Describing Uncertainty
210(7)
8.2 The Importance of Quantifying Uncertainty
217(5)
8.3 Cognitive Challenges in Estimating Uncertainty
222(1)
8.4 Methods and Tools for Propagating and Analyzing Uncertainty
222(2)
8.5 Making Decisions in the Face of Uncertainty
224(4)
8.6 Scenario Analysis
228(2)
8.7 Precaution
230(1)
8.8 Communicating Uncertainty
231(4)
8.9 Some Simple Guidance on Characterizing and Dealing with Uncertainty
235(9)
9 Expert Elicitation
244(30)
9.1 Are There Any Experts?
245(1)
9.2 The Interpretation of Probability
245(1)
9.3 Qualitative Uncertainty Words Are Not Sufficient
246(1)
9.4 Cognitive Heuristics and Bias
247(1)
9.5 Ubiquitous Overconfidence
248(3)
9.6 Developing a Protocol
251(3)
9.7 Computer Tools to Support or Perform Elicitation
254(1)
9.8 Uncertainty about Model Functional Form
255(1)
9.9 Confidence, Second-Order Uncertainty, and Pedigree
256(1)
9.10 Diversity in Expert Opinion
257(3)
9.11 Combining Expert Judgments
260(3)
9.12 Concluding Thoughts and Advice
263(11)
10 Risk Analysis
274(35)
10.1 A Framework for Thinking about Risk
275(1)
10.2 Risk Is Inherently Uncertain
276(3)
10.3 Risk Is a Multi-Attribute Concept
279(2)
10.4 Models of Exposure and Effects Process
281(8)
10.5 Causes of Death
289(2)
10.6 Managing Risk
291(12)
10.7 The Risk of Worrying (Too Much) about Risk
303(6)
11 The Use of Models in Policy Analysis
309(34)
11.1 Types of Models Commonly Used in Technically Focused Policy Analysis
311(1)
11.2 Simple Engineering, Economic, and Policy Models
312(2)
11.3 Models for Environmental Impact Assessment
314(1)
11.4 Life Cycle Methods
315(1)
11.5 Models of the Economy
316(3)
11.6 Models of Energy Supply and Use
319(4)
11.7 Integrated Assessment Models
323(8)
11.8 Limits of Standard Analytical Tools
331(1)
11.9 Using Large Research and Scientific Models in Policy Applications
332(3)
11.10 Some Thoughts on "Large" and "Complex" Models
335(2)
11.11 Model Complexity Should Match the Analyst's Level of Understanding
337(6)
Part III: How Individuals And Organizations Actually Make Decisions 343(98)
12 Human Mental Processes for Perception, Memory, and Decision Making
345(28)
12.1 Two Kinds of Thinking
346(1)
12.2 Framing Effects and Prospect Theory
347(4)
12.3 Ubiquitous Overconfidence
351(2)
12.4 Cognitive Heuristics and Biases
353(5)
12.5 Hindsight Bias
358(2)
12.6 Scenarios and Scenario Thinking
360(3)
12.7 (Not) Honoring Sunk Costs
363(2)
12.8 Order Effects in Search
365(2)
12.9 The Power of Simple Linear Models
367(1)
12.10 Individual and Social Dilemmas
368(1)
12.11 Wrapping Up
369(4)
13 Risk Perception and Risk Ranking
373(16)
13.1 Starr on Acceptable Risk
374(2)
13.2 Public Assessment of Causes of Death
376(1)
13.3 Factors that Shape Risk Judgments
377(6)
13.4 Comparing and Ranking Risks
383(3)
13.5 Recent Summaries of Work on Risk Perception
386(3)
14 Risk Communication
389(20)
14.1 What Information Do People Need to Know about a Risk?
391(3)
14.2 Mental Models Interviews
394(6)
14.3 Structured Interviews Followed by Closed-Form Surveys
400(1)
14.4 Development and Evaluation of Communication Materials
401(1)
14.5 Are the Results Any Better?
402(3)
14.6 Communication to What End?
405(4)
15 Organizational Behavior and Decision Making
409(32)
15.1 Different Views through Different Windows
411(5)
15.2 The Carnegie School of Organizational Decision Making
416(6)
15.3 Garbage Can Models of Organizational Decision Making
422(2)
15.4 The Importance of Negotiation
424(1)
15.5 Exit, Voice, and Loyalty
424(4)
15.6 Normal Accidents versus High-Reliability Organizations
428(2)
15.7 Agent-Based Models of Social Processes and Organizations
430(3)
15.8 Studies of the Behavior of Individuals within Commercial Organizations
433(4)
15.9 Wrapping Up
437(4)
Part IV: The Policy Process And S&T Policy (Mainly) In The United States 441(126)
16 Analysis and the Policy Process
443(26)
16.1 Policy Windows
443(3)
16.2 Policy Making as a Process of Punctured Equilibrium
446(4)
16.3 Adaptive Policy and Learning
450(1)
16.4 Diversification as a Policy Strategy
451(2)
16.5 Social Control through Norms, Legal Prohibitions, Command, and Markets
453(4)
16.6 The Science of "Muddling Through"
457(3)
16.7 We Can't Always Just Muddle Through
460(1)
16.8 The Technology of Foolishness
461(1)
16.9 Implementation
462(7)
17 The Period Prior to World War II
469(28)
17.1 Thomas Jefferson and the Lewis and Clark Expedition
472(2)
17.2 Creation of the Coastal Survey
474(2)
17.3 The Smithson Will and the Creation of the Smithsonian Institution
476(2)
17.4 Appropriation of Federal Funds for Technology Demonstration
478(1)
17.5 The Extended Saga of Regulations to Prevent Steam Boiler Explosions
479(3)
17.6 The United States Exploring Expedition, 1838-1842
482(1)
17.7 The Establishment of the U.S. National Academy of Sciences
483(3)
17.8 The Great Western Exploring Expeditions
486(2)
17.9 The Creation of the U.S. Geological Survey
488(1)
17.10 World War I and the Creation of the National Research Council
489(2)
17.11 Herbert Hoover as Secretary of Commerce
491(6)
18 U.S. Science and Technology Policy from World War II to 1960
497(24)
18.1 Vannevar Bush and U.S. Defense Research and Development during World War II
498(10)
18.2 Science the Endless Frontier and the Creation of the National Science Foundation
508(6)
18.3 The Office of Naval Research: Filling the Gap between OSRD and NSF
514(2)
18.4 Civilian Control of Atomic Energy and Weapons
516(1)
18.5 IGY, Sputnik, the Space Race, and the (Phantom) Missile Gap
517(4)
19 Science and Technology Advice to Government
521(46)
19.1 Science and Technology Advice to the President
521(5)
19.2 The Administrative Procedure Act
526(3)
19.3 Examples of Science and Technology Advice to Executive Branch Agencies
529(3)
19.4 The NRC and the National Academies
532(3)
19.5 Think Tanks and Consulting Firms
535(1)
19.6 The Congressional Office of Technology Assessment
536(7)
19.7 Science and Technology Advice to the Judiciary
543(2)
19.8 Science and Technology Advice in the U.S. States and Regional Governments
545(2)
19.9 Science and Technology Advice to European Governments and to the European Union, with Ines Azevedo
547(9)
19.10 Science and Technology Advice to Government in Japan Jun Suzuki
556(3)
19.11 Science and Technology Advice to Government in China Xue Lan
559(3)
19.12 Science and Technology Advice to Government in India Anshu Bharadwaj and V.S. Arunachalam
562(5)
Appendices 567(14)
A1 A Few Key Ideas from the History and Philosophy of Science
567(10)
A1.1 Francis Bacon and the Empirical or Scientific Method
567(2)
A1.2 Karl Popper: "Falsifiability," and Deduction versus Induction
569(2)
A1.3 Hypothesis
571(1)
A1.4 Thomas Kuhn: Paradigms and Scientific Revolutions
572(5)
A2 Some Readings in Technology and Innovation
577(2)
A3 Some Readings in Science and Technology Studies
579(2)
Index 581
M. Granger Morgan is the Hamerschlag Professor of Engineering at Carnegie Mellon University, Pennsylvania, where he was the founding Head of the Department of Engineering and Public Policy. He also holds appointments in Electrical and Computer Engineering and in the H. John Heinz III College of Public Policy and Management. He has worked extensively on policy problems that involve issues in science and technology. Much of his work has focused on the characterization and treatment of uncertainty, especially as applied to environmental issues, involving energy and electric power, and many aspects of the problem of climate change. Morgan's formal academic training is in applied physics. He is a member of the US National Academy of Sciences. He is the author of many papers and five books including Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis (Cambridge, 1990) and Risk Communication: A Mental Models Approach (Cambridge, 2001).
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