Text with CD-ROM

Irwin/McGraw-Hill (2000) ISBN 0-07-238915X







Go to the Business Dynamics website

From the Preface

Accelerating economic, technological, social, and environmental change challenge managers and policy makers to learn at increasing rates, while at the same time the complexity of the systems in which we live is growing. Many of the problems we now face arise as unanticipated side effects of our own past actions. All too often the policies we implement to solve important problems fail, make the problem worse, or create new problems.

Effective decision making and learning in a world of growing dynamic complexity requires us to become systems thinkers–to expand the boundaries of our mental models and develop tools to understand how the structure of complex systems creates their behavior.

This book introduces you to system dynamics modeling for the analysis of policy and strategy, with a focus on business and public policy applications. System dynamics is a perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex systems. System dynamics is also a rigorous modeling method that enables us to build formal computer simulations of complex systems and use them to design more effective policies and organizations. Together, these tools allow us to create management flight simulators–microworlds where space and time can be compressed and slowed so we can experience the long-term side effects of decisions, speed learning, develop our understanding of complex systems, and design structures and strategies for greater success.

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Features and Content

University and graduate-level texts, particularly those focused on business and public policy applications, have not kept pace with the growth of the field. This book is designed to provide thorough coverage of the field of system dynamics today, by examining

• Systems thinking and the system dynamics worldview;

• Tools for systems thinking, including methods to elicit and map the structure of complex systems and relate those structures to their dynamics;

• Tools for modeling and simulation of complex systems;

• Procedures for testing and improving models;

• Guidelines for working with client teams and successful implementation.

You will learn about the dynamics of complex systems, including the structures that create growth, goal-seeking behavior, oscillation and instability, S-shaped growth, overshoot and collapse, path dependence, and other nonlinear dynamics. Examples and applications include

• Corporate growth and stagnation,

• The diffusion of new technologies,

• The dynamics of infectious disease such as HIV/AIDS,

• Business cycles,

• Speculative bubbles,

• The use and reliability of forecasts,

• The design of supply chains in business and other organizations,

• Service quality management,

• Transportation policy and traffic congestion,

• Project management and product development,

and many others.

The goal of systems thinking and system dynamics modeling is to improve our understanding of the ways in which an organization’s performance is related to its internal structure and operating policies, including those of customers, competitors, and suppliers and then to use that understanding to design high leverage policies for success. To do so this book utilizes

Process Points that provide practical advice for the successful application of the tools in real organizations.

• Case studies of System Dynamics in Action that present successful applications ranging from global warming and the war on drugs to reengineering the supply chain of a major computer firm, marketing strategy in the automobile industry, and process improvement in the petrochemicals industry.

System dynamics is not a spectator sport. Developing systems thinking and modeling skills requires the active participation of you, the reader, via

Challenges. The challenges, scattered throughout the text, give you practice with the tools and techniques presented in the book and stimulate your original thinking about important real world issues. The challenges range from simple thought experiments to full-scale modeling projects.

Simulation software and models. The accompanying CD-ROM and website (http://www.mhhe.com/sterman) include all the models developed in the text along with state-of-the-art simulation software to run them. There are several excellent software packages designed to support system dynamics modeling. These include ithink, Powersim, and Vensim. The CD and website include the models for the text in all three software formats. The disk also includes fully functional versions of the ithink, Powersim, and Vensim software so you can run the models using any of these packages without having to purchase any additional software.

• Additionally, the Instructor’s Manual and instructor’s section of the Website include suggested solutions for the challenges, additional assignments, Powerpoint files with the diagrams and figures from the text suitable for transparencies, suggested course sequences and syllabi, and other materials.

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Intended Audience

The book can be used as a text in courses on systems thinking, simulation modeling, complexity, strategic thinking, operations, and industrial engineering, among others. It can be used in full or half-semester courses, executive education, and self-study. The book also serves as a reference for managers, engineers, consultants, and others interested in developing their systems thinking skills or using system dynamics in their organizations.

A Note on Mathematics

System dynamics is grounded in control theory and the modern theory of nonlinear dynamics. There is an elegant and rigorous mathematical foundation for the theory and models we develop. System dynamics is also designed to be a practical tool that policy makers can use to help them solve the pressing problems they confront in their organizations. Most managers have not studied nonlinear differential equations or even calculus, or have forgotten it if they did. To be useful, system dynamics modeling must be accessible to the widest range of students and practicing managers without becoming a vague set of qualitative tools and unreliable generalizations. That tension is compounded by the diversity of backgrounds within the community of managers, students, and scholars interested in system dynamics, backgrounds ranging from people with no mathematics education beyond high school to those with doctorates in physics.

If You Don’t Have a Strong Mathematics Background, Fear Not

This book presents system dynamics with a minimum of mathematical formalism. The goal is to develop your intuition and conceptual understanding, without sacrificing the rigor of the scientific method. You do not need calculus or differential equations to understand the material. Indeed, the concepts are presented using only text, graphs, and basic algebra. Mathematical details and references to more advanced material are set aside in separate sections and footnotes. Higher mathematics, though useful, is not as important as the critical thinking skills developed here.

If You Have a Strong Mathematics Background, Fear Not

Realistic and useful models are almost always of such complexity and nonlinearity that there are no known analytic solutions, and many of the mathematical tools you have studied have limited applicability. This book will help you use your strong technical background to develop your intuition and conceptual understanding of complexity and dynamics. Modeling human behavior differs from modeling physical systems in engineering and the sciences. We cannot put managers up on the lab bench and run experiments to determine their transfer function or frequency response. We believe all electrons follow the same laws of physics, but we cannot assume all people behave in the same way. Besides a solid grounding in the mathematics of dynamic systems, modeling human systems requires us to develop our knowledge of psychology, decision making, and organizational behavior. Finally, mathematical analysis, while necessary, is far from sufficient for successful systems thinking and modeling. For your work to have impact in the real world you must learn how to develop and implement models of human behavior in organizations, with all their ambiguity, time pressure, personalities, and politics. Throughout the book I have sought to illustrate how the technical tools and mathematical concepts you may have studied in the sciences or engineering can be applied to the messy world of the policy maker.


I welcome your comments, criticisms, and suggestions. Suggestions for additional examples, cases, theory, models, flight simulators, and so on, to make the book more relevant and useful to you are especially invited. Email comments to <BusDyn@mit.edu>.

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Table of Contents

Preface 1. Learning in and about Complex Systems

1.1 Introduction

1.1.1 Policy Resistance, the Law of Unintended Consequences, and the Counterintuitive Behavior of Social Systems

1.1.2 Causes of Policy Resistance

1.1.3 Feedback

1.1.4 Process Point: The Meaning of Feedback

Challenge: Dynamics of Multiple-Loop Systems

1.2 Learning is a Feedback Process

1.3 Barriers to Learning

1.3.1 Dynamic Complexity

1.3.2 Limited Information

1.3.3 Confounding Variables and Ambiguity

1.3.4 Bounded Rationality and the Misperceptions of Feedback

1.3.5 Flawed Cognitive Maps

1.3.6 Erroneous Inferences about Dynamics

1.3.7 Unscientific Reasoning: Judgmental Errors and Biases

Challenge: Hypothesis Testing

1.3.8 Defensive Routines and Interpersonal Impediments to Learning

1.3.9 Implementation Failure

1.4 Requirements for Successful Learning in Complex Systems

1.4.1 Improving the Learning Process: Virtues of Virtual Worlds

1.4.2 Pitfalls of Virtual Worlds

1.4.3 Why Simulation is Essential

1.5 Summary

2. System Dynamics in Action

2.1 Applications of System Dynamics

2.2 Automobile Leasing Strategy: Gone Today, Here Tomorrow

2.2.1 Dynamic Hypothesis

2.2.2 Elaborating the Model

2.2.3 Policy Analysis

2.2.4 Impact and Follow-up

2.3 On Time and Under Budget: The Dynamics of Project Management

2.3.1 The Claim

2.3.2 Initial Model Development

2.3.3 Dynamic Hypothesis

2.3.4 The Modeling Process

2.3.5 Continuing Impact

2.4 Playing the Maintenance Game

2.4.1 Dynamic Hypothesis

2.4.2 The Implementation Challenge

2.4.3 Results

2.4.4 Transferring the Learning: The Lima Experience

2.5 Summary: Principles for Successful Use of System Dynamics

3. The Modeling Process

3.1 The Purpose of Modeling: Managers as Organization Designers

3.2 The Client and the Modeler

3.3 Steps of the Modeling Process

3.4 Modeling is Iterative

3.5 Overview of the Modeling Process

3.5.1 Problem Articulation: The Importance of Purpose

3.5.2 Formulating a Dynamic Hypothesis

3.5.3 Formulating a Simulation Model

3.5.4 Testing

3.5.5 Policy Design and Evaluation

3.6 Summary

4. Structure and Behavior of Dynamic Systems

4.1 Fundamental Modes of Dynamic Behavior

4.1.1 Exponential Growth

4.1.2 Goal Seeking

4.1.3 Oscillation

4.1.4 Process Point

Challenge: Identifying Feedback Structure from System Behavior

4.2 Interactions of the Fundamental Modes

4.2.1 S-shaped Growth

4.2.2 S-Shaped Growth with Overshoot

Challenge: Identifying the Limits to Growth

4.2.3 Overshoot and Collapse

4.3 Other Modes of Behavior

4.3.1 Stasis, or Equilibrium

4.3.2 Randomness

4.3.3 Chaos

4.4 Summary

5. Causal Loop Diagrams

5.1 Causal Diagram Notation

5.2 Guidelines for Causal Loop Diagrams

5.2.1 Causation versus Correlation

5.2.2 Labeling Link Polarity

Challenge: Assigning Link Polarities

5.2.3 Determining Loop Polarity

Challenge: Employee Motivation

5.2.4 Name Your Loops

5.2.5 Indicate Important Delays in Causal Links

5.2.6 Variable Names

5.2.7 Tips for Causal Loop Diagram Layout

5.2.8 Choose the Right Level of Aggregation

5.2.9 Don’t Put All the Loops into One Large Diagram

5.2.10 Make the Goals of Negative Loops Explicit

5.2.11 Distinguish between Actual and Perceived Conditions

5.3 Process Point: Developing Causal Diagrams from Interview Data

Challenge: Process Improvement

5.4 Conceptualization Case Study: Managing Your Workload

5.4.1 Problem Definition

5.4.2 Identifying Key Variables

5.4.3 Developing the Reference Mode

5.4.4 Developing the Causal Diagrams

5.4.5 Limitations of the Causal Diagram

Challenge: Policy Analysis with Causal Diagrams

5.5 Adam Smith’s Invisible Hand and the Feedback Structure of Markets

Challenge: The Oil Crises of the 1970s

Challenge: Speculative Bubbles

Challenge: The Thoroughbred Horse Market

5.4.1 Market Failure, Adverse Selection, and the Death Spiral

5.6 Explaining Policy Resistance: Traffic Congestion

5.6.1 Mental Models of the Traffic Problem

5.6.2 Compensating Feedback: The Response to Decreased Congestion

5.6.3 The Mass Transit Death Spiral

5.6.4 Policy Analysis: The Impact of Technology

5.6.5 Compensating Feedback: The Source of Policy Resistance

Challenge: Identifying the Feedback Structure of Policy Resistance

5.7 Summary

6. Stocks and Flows

6.1 Stocks, Flows, and Accumulation

6.1.1 Diagramming Notation for Stocks and Flows

6.1.2 Mathematical Representation of Stocks and Flows

6.1.3 The Contribution of Stocks to Dynamics

6.2 Identifying Stocks and Flows

6.2.1 Units of Measure in Stock and Flow Networks

6.2.2 The Snapshot Test

Challenge: Identifying Stocks and Flows

6.2.3 Conservation of Material in Stock and Flow Networks

6.2.4 State-Determined Systems

6.2.5 Auxiliary Variables

6.2.6 Stocks Change only Through their Rates

6.2.7 Continuous Time and Instantaneous Flows

6.2.8 Continuously Divisible versus Quantized Flows

6.2.9 Which Modeling Approach Should You Use?

6.2.10 Process Point: Portraying Stocks and Flows in Practice

6.3 Mapping Stocks and Flows

6.3.1 When Should Causal Loop Diagrams Show Stock and Flow Structure?

Challenge: Adding Stock and Flow Structure to Causal Diagrams

Challenge: Linking Stock and Flow Structure with Feedback

6.3.2 Aggregation in Stock and Flow Mapping

Challenge: Modifying Stock and Flow Maps

Challenge: Disaggregation

6.3.3 Guidelines for Aggregation

6.3.4 System Dynamics in Action: Modeling Large-Scale Construction Projects

6.3.5 Setting the Model Boundary: "Challenging the Clouds"

6.3.6 System Dynamics in Action: Automobile Recycling

6.4 Summary

7. Dynamics of Stocks and Flows

7.1 Relationship between Stocks and Flows

7.1.1 Static and Dynamic Equilibrium

7.1.2 Calculus without Mathematics

7.1.3 Graphical Integration

7.1.4 Graphical Differentiation

Challenge: Graphical Differentiation

7.2 System Dynamics in Action: Global Warming

7.3 System Dynamics in Action: The War on Drugs

7.3.1 The Cocaine Epidemic after 1990

7.4 Summary

8. Closing the Loop: Dynamics of Simple Structures

8.1 First-order Systems

8.2 Positive Feedback and Exponential Growth

8.2.1 Analytic Solution for the Linear First-Order System

8.2.2 Graphical Solution of the Linear First-Order Positive Feedback System

8.2.3 The Power of Positive Feedback: Doubling Times

Challenge: Paper Folding

8.2.4 Misperceptions of Exponential Growth

8.2.5 Process Point: Overcoming Overconfidence

8.3 Negative Feedback and Exponential Decay

8.3.1 Time constants and half lives

Challenge: Goal-seeking behavior

8.4 Multiple-Loop Systems

8.5 Nonlinear First-Order Systems: S-Shaped Growth

Challenge: Nonlinear Birth and Death Rates

8.5.1 Formal Definition of Loop Dominance

8.5.2 First-Order Systems Cannot Oscillate

8.6 Summary

9. S-Shaped Growth: Epidemics, Innovation Diffusion, and the Growth of New Products

9.1 Modeling S-Shaped Growth

9.1.1 Logistic Growth

9.1.2 Analytic Solution of the Logistic Equation

9.1.3 Other Common Growth Models

9.1.4 Testing the Logistic Model

9.2 Dynamics of Disease: Modeling Epidemics

9.2.1 A Simple Model of Infectious Disease

9.2.2 Modeling Acute Infection: The SIR Model

9.2.3 Model Behavior: The Tipping Point

Challenge: Exploring the SIR Model

9.2.4 Immunization and the Eradication of Smallpox

Challenge: The Efficacy Of Immunization Programs

9.2.5 Herd Immunity

9.2.6 Moving Past The Tipping Point: Mad Cow Disease

Challenge: Extending the SIR Model

9.2.7 Modeling the HIV/AIDS Epidemic

Challenge: Modeling HIV/AIDS

9.3 Innovation Diffusion as Infection: Modeling New Ideas and New Products

9.3.1 The Logistic Model of Innovation Diffusion: Examples

9.3.2 Process Point: Historical Fit and Model Validity

9.3.3 The Bass Diffusion Model

Challenge: Phase Space of the Bass Diffusion Model

9.3.4 Behavior of the Bass Model

Challenge: Critiquing the Bass Diffusion Model

Challenge: Extending the Bass Model

9.3.5 Fad and Fashion: Modeling the Abandonment of an Innovation

Challenge: Modeling Fads

9.3.6 Replacement Purchases

Challenge: Modeling the Life Cycle of Durable Products

9.4 Summary

10. Path Dependence and Positive Feedback

10.1 Path Dependence

Challenge: Identifying Path Dependence

10.2 A Simple Model of Path Dependence: The Polya Process

10.2.1 Generalizing the Model: Nonlinear Polya Processes

10.3 Path Dependence in the Economy: VHS vs. Betamax

Challenge: Formulating a Dynamic Hypothesis for the VCR Industry

10.4 Positive Feedback: The Engine of Corporate Growth

10.4.1 Product Awareness

10.4.2 Unit Development Costs

10.4.3 Price and Production Cost

10.4.4 Network Effects and Complementary Goods

10.4.5 Product Differentiation

10.4.6 New Product Development

10.4.7 Market Power

10.4.8 Mergers and Acquisitions

10.4.9 Workforce Quality and Loyalty

10.4.10 The Cost of Capital

10.4.11 The Rules of the Game

10.4.12 Ambition and Aspirations

10.4.13 Creating Synergy for Corporate Growth

10.5 Positive Feedback, Increasing Returns, and Economic Growth

10.6 Does the Economy Lock in to Inferior Technologies?

10.7 Limits to Lock In

10.8 Modeling Path Dependence and Standards Formation

10.8.1 Model Structure

10.8.2 Model Behavior

10.8.3 Policy Implications

Challenge: Policy Analysis

Challenge: Extending the Model

10.9 Summary

11. Delays

11.1 Delays: An Introduction

Challenge: Duration and Dynamics of Delays

11.1.1 Defining Delays

11.2 Material Delays: Structure and Behavior

11.2.1 What is the Average Length of the Delay?

11.2.2 What is the Distribution of the Output around the Average Delay Time?

11.2.3 Pipeline Delay

11.2.4 First-Order Material Delay

11.2.5 Higher-Order Material Delays

11.2.6 How Much is in the Delay? Little’s Law

11.3 Information Delays: Structure and Behavior

11.3.1 Modeling Perceptions: Adaptive Expectations and Exponential Smoothing

11.3.2 Higher-Order Information Delays

11.4 Response to Variable Delay Times

Challenge: Response of Delays to Changing Delay Times

11.4.1 Nonlinear Adjustment Times: Modeling Ratchet Effects

11.5 Estimating the Duration and Distribution of Delays

11.5.1 Estimating Delays when Numerical Data are Available

11.5.2 Estimating Delays when Numerical Data are not Available

11.5.3 Process Point: Walk the Line

11.6 System Dynamics in Action: Forecasting Semiconductor Demand

11.7 Mathematics of Delays: Koyck Lags and Erlang Distributions

11.7.1 General Formulation for Delays

11.7.2 First-Order Delay

11.7.3 Higher-Order Delays

11.7.4 Relation of Material and Information Delays

11.8 Summary

12. Coflows and Aging Chains

12.1 Aging Chains

12.1.1 General Structure of Aging Chains

12.1.2 Example: Population and Infrastructure in Urban Dynamics

12.1.3 Example: The Population Pyramid and the Demographic Transition

12.1.4 Aging Chains and Population Inertia

12.1.5 System Dynamics in Action: World Population and Economic Development

12.1.6 Case Study: Growth and the Age Structure of Organizations

12.1.7 Promotion Chains and the Learning Curve

12.1.8 Mentoring and On-The-Job Training

Challenge: The Interactions of Training Delays and Growth

12.2 Coflows: Modeling the Attributes of a Stock

Challenge: Coflows

12.2.1 Coflows with Nonconserved Flows

Challenge: The Dynamics of Experience and Learning

12.2.2 Integrating Coflows and Aging Chains

Challenge: Modeling Design Wins in the Semiconductor Industry

12.3 Summary

13. Modeling Decision Making

13.1 Principles for Modeling Decision Making

13.1.1 Decisions and Decision Rules

13.1.2 Five Formulation Fundamentals

Challenge: Finding Formulation Flaws

13.2 Formulating Rate Equations

13.2.1 Fractional Increase Rate

13.2.2 Fractional Decrease Rate

13.2.3 Adjustment to a Goal

13.2.4 The Stock Management Structure: Rate = Normal Rate + Adjustments

13.2.5 Flow = Resource * Productivity

13.2.6 Y = Y* * Effect of X1 on Y * Effect of X2 on Y * … * Effect of Xn on Y

13.2.7 Y = Y* + Effect of X1 on Y + Effect of X2 on Y + … + Effect of Xn on Y

13.2.8 Fuzzy MIN Function

13.2.9 Fuzzy MAX Function

13.2.10 Floating Goals

Challenge: Floating Goals

Challenge: Goal Formation with Internal and External Inputs

13.2.11 Nonlinear Weighted Average

13.2.12 Modeling Search: Hill-Climbing Optimization

Challenge: Finding the Optimal Mix of Capital and Labor

13.2.13 Resource Allocation

13.3 Common Pitfalls

13.3.1 All Outflows Require First-Order Control

Challenge: Preventing Negative Stocks

13.3.2 Avoid IF…THEN…ELSE Formulations

13.3.3 Disaggregate Net Flows

13.4 Summary

14. Formulating Nonlinear Relationships

14.1 Table Functions

14.1.1 Specifying Table Functions

14.1.2 Example: Building a Nonlinear Function

14.1.3 Process Point: Table Functions Versus Analytic Functions

14.2 Case Study: Cutting Corners Versus Overtime

Challenge: Formulating Nonlinear Functions

14.2.1 Working Overtime: The Effect of Schedule Pressure on Workweek

14.2.2 Cutting Corners: The Effect of Schedule Pressure on Time per Task

14.3 Case Study: Estimating Nonlinear Functions With Qualitative and Numerical Data

Challenge: Refining Table Functions with Qualitative Data

14.4 Common Pitfalls

14.4.1 Using the Wrong Input

Challenge: Critiquing Nonlinear Functions

14.4.2 Improper Normalization

14.4.3 Avoid Hump-shaped Functions

Challenge: Formulating the Error Rate

Challenge: Testing the Full Model

14.5 Eliciting Model Relationships Interactively

14.5.1 Case Study: Estimating Precedence Relationships in Product Development

14.6 Summary

15. Modeling Human Behavior: Bounded Rationality or Rational Expectations?

15.1 Human Decision Making: Bounded Rationality or Rational Expectations?

15.2 Cognitive Limitations

15.3 Individual and Organizational Responses to Bounded Rationality

15.3.1 Habit, Routines, and Rules of Thumb

15.3.2 Managing Attention

15.3.3 Goal Formation and Satisficing

15.3.4 Problem Decomposition and Decentralized Decision Making

15.4 Intended Rationality

15.4.1 Testing for Intended Rationality: Partial Model Tests

15.5 Case Study: Modeling High-Tech Growth Firms

15.5.1 Model Structure: Overview

15.5.2 Order Fulfillment

15.5.3 Capacity Acquisition

Challenge: Hill Climbing

15.5.4 The Sales Force

15.5.5 The Market

15.5.6 Behavior of the Full System

Challenge: Policy Design in the Market Growth Model

15.6 Summary

16. Forecasts and Fudge Factors: Modeling Expectation Formation

16.1 Modeling Expectation Formation

16.1.1 Modeling Growth Expectations: The TREND Function

16.1.2 Behavior of the TREND Function

16.2 Case Study: Energy Consumption

16.3 Case Study: Commodity Prices

16.4 Case Study: Inflation

16.5 Implications for Forecast Consumers

Challenge: Extrapolation and Stability

16.6 Initialization and Steady State Response of the TREND Function

16.7 Summary

17. Supply Chains and the Origin of Oscillations

17.1 Supply Chains in Business and Beyond

17.1.1 Oscillation, Amplification, and Phase Lag

17.2 The Stock Management Problem

17.2.1 Managing a Stock: Structure

17.2.2 Steady State Error

17.2.3 Managing a Stock: Behavior

17.3 The Stock Management Structure

17.3.1 Behavior of the Stock Management Structure

17.4 The Origin of Oscillations

17.4.1 Mismanaging the Supply Line: The Beer Distribution Game

17.4.2 Why Do We Ignore the Supply Line?

17.4.3 Case Study: Boom and Bust in Real Estate Markets

17.5 Summary

18. The Manufacturing Supply Chain

18.1 The Policy Structure of Inventory and Production

18.1.1 Order Fulfillment

18.1.2 Production

18.1.3 Production Starts

18.1.4 Demand Forecasting

18.1.5 Process Point: Initializing a Model in Equilibrium

Challenge: Simultaneous Initial Conditions

18.1.6 Behavior of the Production Model

18.1.7 Enriching the Model: Adding Order Backlogs

18.1.8 Behavior of the Firm with Order Backlogs

18.1.9 Adding Raw Materials Inventory

18.2 Interactions among Supply Chain Partners

18.2.1 Instability and Trust in Supply Chains

18.2.2 From Functional Silos to Integrated Supply Chain Management

Challenge: Reengineering the Supply Chain

18.3 System Dynamics in Action: Reengineering the Supply Chain in a High-Velocity Industry

18.3.1 Initial Problem Definition

18.3.2 Reference Mode and Dynamic Hypothesis

18.3.3 Model Formulation

18.3.4 Testing the Model

18.3.5 Policy Analysis

18.3.6 Implementation: Sequential Debottlenecking

18.3.7 Results

18.4 Summary

19. The Labor Supply Chain and the Origin of Business Cycles

19.1 The Labor Supply Chain

19.1.1 Structure of Labor and Hiring

19.1.2 Behavior of the Labor Supply Chain

19.2 Interactions of Labor and Inventory Management

Challenge: Mental Simulation of Inventory Management with Labor

19.2.1 Inventory—Workforce Interactions: Behavior

19.2.2 Process Point: Explaining Model Behavior

Challenge: Explaining Oscillations

19.2.3 Understanding the Sources of Oscillation

Challenge: Policy Design to Enhance Stability

19.2.4 Adding Overtime

19.2.5 Response to Flexible Workweeks

Challenge: Reengineering a Manufacturing Firm for Enhanced Stability

19.2.6 The Costs of Instability

Challenge: The Costs of Instability

Challenge: Adding Training and Experience

19.3 Inventory—Workforce Interactions and the Business Cycle

19.3.1 Is the Business Cycle Dead?

19.4 Summary

20. The Invisible Hand Sometimes Shakes: Commodity Cycles

20.1 Commodity Cycles: From Aircraft to Zinc

20.2 A Generic Commodity Market Model

20.2.1 Production and Inventory

20.2.2 Capacity Utilization

20.2.3 Production Capacity

20.2.4 Desired Capacity

Challenge: Intended Rationality of the Investment Process

20.2.5 Demand

20.2.6 The Price-Setting Process

20.3 Application: Cycles in the Pulp and Paper Industry

Challenge: Sensitivity to Uncertainty in Parameters

Challenge: Sensitivity to Structural Changes

Challenge: Implementing Structural Changes–Modeling Livestock Markets

Challenge: Policy Analysis

20.4 Summary

21. Truth and Beauty: Validation and Model Testing

21.1 Validation and Verification are Impossible

21.2 Questions Model Users Should Ask–But Usually Don’t

21.3 Pragmatics and Politics of Model Use

21.3.1 Types of Data

21.3.2 Documentation

21.3.3 Replicability

21.3.4 Protective versus Reflective Modeling

21.4 Model Testing in Practice

21.4.1 Boundary Adequacy Tests

21.4.2 Structure Assessment Tests

21.4.3 Dimensional Consistency

21.4.4 Parameter Assessment

21.4.5 Extreme Condition Tests

Challenge: Extreme Condition Tests

21.4.6 Integration Error Tests

21.4.7 Behavior Reproduction Tests

21.4.8 Behavior Anomaly Tests

21.4.9 Family Member Tests

21.4.10 Surprise Behavior Tests

21.4.11 Sensitivity Analysis

21.4.12 System Improvement Tests

Challenge: Model Testing

21.5 Summary

22. Challenges for the Future

22.1 Theory

22.2 Technology

22.3 Implementation

22.4 Education

22.5 Applications

Challenge: Putting System Dynamics Into Action

Appendix A Numerical Integration

Challenge: Choosing a Time Step

Appendix B Noise

Challenge: Exploring Noise



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