ISBN:
9781461416654
,
1280996463
,
9781280996467
Language:
English
Pages:
Online-Ressource (XV, 229 p. 46 illus., 35 illus. in color, digital)
Series Statement:
SpringerLink
Series Statement:
Bücher
Parallel Title:
Buchausg. u.d.T.
Keywords:
Social sciences
;
Statistics
;
Social Sciences
;
Social sciences
;
Statistics
Abstract:
Public policy and management problems have been described as poorly defined, messy, squishy, unstructured, intractable, and wicked. In a word, they are complex. This book illustrates the development and use of simulation models designed to capture some of the complexity inherent in the formulation, management, and implementation of policies aimed at addressing such problems. Simulation models have long existed at the fringes of policy inquiry but are not yet considered an essential component of the policy analyst's toolkit. However, this situation is likely to change because with improvements in computational power and software, simulation is now easier to include in the standard repertoire of research tools available for discovery and decision support. This volume provides both a conceptual rationale for using simulations to inform public policy and a practical introduction to how such models might be constructed and employed. The focus of these papers is on the uses of simulation to gain understanding and inform policy decisions and action. Techniques represented in this volume include Monte Carlo simulation, system dynamics and agent based modeling
Abstract:
Public policy and management problems have been described as poorly defined, messy, squishy, unstructured, intractable, and wicked. In a word, they are complex. This book illustrates the development and use of simulation models designed to capture some of the complexity inherent in the formulation, management, and implementation of policies aimed at addressing such problems. Simulation models have long existed at the fringes of policy inquiry but are not yet considered an essential component of the policy analysts toolkit. However, this situation is likely to change because with improvements in computational power and software, simulation is now easier to include in the standard repertoire of research tools available for discovery and decision support. This volume provides both a conceptual rationale for using simulations to inform public policy and a practical introduction to how such models might be constructed and employed. The focus of these papers is on the uses of simulation to gain understanding and inform policy decisions and action. Techniques represented in this volume include Monte Carlo simulation, system dynamics and agent based modeling.
Description / Table of Contents:
Simulation for Policy Inquiry; Preface; Acknowledgments; Contents; Contributors; Chapter 1: Public Policy Inquiry and Simulations; 1.1 Introduction; 1.2 Characteristics of Public Policy Concerns; 1.2.1 Dynamic; 1.2.2 Adaptive; 1.2.3 Emergent; 1.2.4 Purposeful; 1.2.5 Re fl exive; 1.3 Why Simulations?; 1.3.1 Imitation; 1.3.2 Con fi dence; 1.3.3 Flexibility; 1.3.4 Multilevel; 1.4 Conclusion; References; Part I: Monte Carlo Simulation; Chapter 2: Simulation Design for Policy Audiences: Informing Decision in the Face of Uncertainty; 2.1 Introduction; 2.2 A Forecasting Example; 2.3 Synthetic Data
Description / Table of Contents:
2.4 Estimating Equity Returns in Employer-Sponsored De fi ned Contribution Programs2.5 A Digression on Monte Carlo Simulation; 2.5.1 Probability Distribution; 2.5.2 Time; 2.5.3 Worker Quality and Gender; 2.5.4 Quasi-Randomization; 2.5.5 General Rules for Guiding Assumptions; 2.5.6 Implementation Issues; 2.5.7 Developing Scenarios; 2.5.8 Stopping Rules; 2.6 So What; References; Chapter 3: Using Simulation as a Scenario Builder for Decision Making: A Case Study Using the Nurse-Family Partnership Program in Louisiana; 3.1 Introduction; 3.2 Scope of Work for the NFP Project in Louisiana
Description / Table of Contents:
3.3 Tests and Results in Elmira, Denver, and Memphis Relating to NFP Program3.4 The Workings of the Monte Carlo Model; 3.4.1 Costs; 3.4.2 Bene fi ts (Savings) 9; 3.4.3 Savings-Cost Comparison; 3.5 Non-Monetized Bene fi ts; 3.6 Summary and Comments; References; Part II: Agent-Based Models; Chapter 4: The Utility of Multilevel Modeling vs. Agent-Based Modeling in Examining Spatial Disparities in Diet and Health: The Case of Food Deserts; 4.1 Introduction; 4.2 The Reality of Food Deserts; 4.3 Conceptualization of Policy Problem; 4.4 Multilevel Modeling; 4.5 Agent-Based Modeling
Description / Table of Contents:
4.5.1 Spatial Context4.5.2 Agent Types: Parameters; 4.5.3 Interactions Among Agents: Rules; 4.5.4 Time; 4.5.5 Scenarios; 4.6 Multilevel Modeling vs. Agent-Based Modeling: Conceptual and Methodological Advantages and Challenges; 4.7 Conclusion; References; Chapter 5: Urban Renaissance or Invasion: Planning the Development of a Simulation Model of Gentri fi cation; 5.1 Introduction; 5.2 Simulating Public Policy; 5.3 Gentri fi cation and Displacement; 5.4 Agent-Based Models; 5.5 Model Planning; 5.5.1 The Region; 5.5.2 Plots; 5.5.3 Agents; 5.5.4 Rules; 5.6 Conclusion; References
Description / Table of Contents:
Chapter 6: Simulating Life Cycle Costs for Nuclear Facilities6.1 Introduction; 6.2 The Problem of Managing US Nuclear Facilities; 6.2.1 Risk for Facilities; 6.2.2 The Facility Life Cycle; 6.3 Agent-Based Facility Life Cycle Model; 6.4 Risk and Alternative Cost Scenarios; 6.5 Failures and Costs; 6.6 Discussion; 6.7 Con fi dence Through Collaboration; 6.8 Conclusion; References; Chapter 7: Simulating a Fraud Mechanism in Public Service Delivery; 7.1 Introduction; 7.1.1 Research Context; 7.2 A Systemic Perspective on White-Collar Crime; 7.3 An Arti fi cial Public Service Delivery System
Description / Table of Contents:
7.3.1 The "Simulation" Landscape
Note:
Description based upon print version of record
DOI:
10.1007/978-1-4614-1665-4
URL:
Volltext
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