Theory and practice in policy analysis : including applications in science and technology

목차 (Table of Contents)

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