{"id":212,"date":"2017-11-21T20:01:07","date_gmt":"2017-11-21T20:01:07","guid":{"rendered":"http:\/\/mitmgmtfaculty.mit.edu\/jsterman\/?page_id=212"},"modified":"2023-07-21T13:11:01","modified_gmt":"2023-07-21T13:11:01","slug":"business-dynamics","status":"publish","type":"page","link":"https:\/\/mitmgmtfaculty.mit.edu\/jsterman\/business-dynamics\/","title":{"rendered":"Business Dynamics"},"content":{"rendered":"<div id=\"pl-212\"  class=\"panel-layout\" ><div id=\"pg-212-0\"  class=\"panel-grid panel-no-style\" ><div id=\"pgc-212-0-0\"  class=\"panel-grid-cell\" ><div id=\"panel-212-0-0-0\" class=\"so-panel widget widget_mit-pf-wysiwyg widget_mit_pf_wysiwyg panel-first-child panel-last-child\" data-index=\"0\" ><div class=\"textwidget\"><h4><a href=\"http:\/\/mitmgmtfaculty.mit.edu\/jsterman\/publications\/\"><span style=\"text-decoration: underline;\">Back to All Publications<\/span><\/a><\/h4>\n<p><span style=\"text-decoration: underline;\"><a name=\"top\"><\/a><\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-350 alignleft\" src=\"https:\/\/s3.amazonaws.com\/mitsloan-php\/wp-faculty\/sites\/92\/2017\/11\/28203343\/Business_Dynamics_Cover.gif\" alt=\"\" width=\"241\" height=\"309\" \/><\/td>\n<td width=\"20px\"><\/td>\n<td><a href=\"#from-the-preface\">From Business Dynamics Preface<\/a><\/p>\n<p><a href=\"#features-and-content\">Features and Content from Business Dynamics<\/a><\/p>\n<p><a href=\"#intended-audience\">Intended Audience for Business Dynamics\u00a0<\/a><\/p>\n<p><a href=\"#a-note-on-mathematics\">A Note on Mathematics in Business Dynamics<\/a><\/p>\n<p><a href=\"#feedback\">Feedback on Business Dynamics<\/a><\/p>\n<p><a href=\"#table-of-contents\">Table of Contents for Business Dynamics<\/a><\/p>\n<p><a href=\"http:\/\/www.mhhe.com\/sterman\" target=\"_blank\" rel=\"noopener noreferrer\">Go to the Business Dynamics website<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Irwin\/McGraw-Hill (2000)<br \/>\nISBN 0-07-238915X<\/p>\n<p><a name=\"from-the-preface\"><\/a><strong>From the Preface<\/strong><\/p>\n<p>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.<\/p>\n<p>Effective decision making and learning in a world of growing dynamic complexity requires us to become systems thinkers\u2013to expand the boundaries of our mental models and develop tools to understand how the structure of complex systems creates their behavior.<\/p>\n<p>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\u2013microworlds 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.<\/p>\n<p><a href=\"#top\">Top of Page<\/a><\/p>\n<p><a name=\"features-and-content\"><\/a><strong>Features and Content<\/strong><\/p>\n<p>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<\/p>\n<ul>\n<li>Systems thinking and the system dynamics worldview;<\/li>\n<li>Tools for systems thinking, including methods to elicit and map the structure of complex systems and relate those structures to their dynamics;<\/li>\n<li>Tools for modeling and simulation of complex systems;<\/li>\n<li>Procedures for testing and improving models;<\/li>\n<li>Guidelines for working with client teams and successful implementation<\/li>\n<\/ul>\n<p>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<\/p>\n<ul>\n<li>Corporate growth and stagnation,<\/li>\n<li>The diffusion of new technologies,<\/li>\n<li>The dynamics of infectious disease such as HIV\/AIDS,<\/li>\n<li>Business cycles,<\/li>\n<li>Speculative bubbles,<\/li>\n<li>The use and reliability of forecasts,<\/li>\n<li>The design of supply chains in business and other organizations,<\/li>\n<li>Service quality management,<\/li>\n<li>Transportation policy and traffic congestion,<\/li>\n<li>Project management and product development,<\/li>\n<li>and many others.<\/li>\n<\/ul>\n<p>The goal of systems thinking and system dynamics modeling is to improve our understanding of the ways in which an organization\u2019s 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<\/p>\n<ul>\n<li><strong>Process Points<\/strong> that provide practical advice for the successful application of the tools in real organizations.<\/li>\n<\/ul>\n<ul>\n<li>Case studies of <strong>System Dynamics in Action<\/strong> 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.<\/li>\n<\/ul>\n<p>System dynamics is not a spectator sport. Developing systems thinking and modeling skills requires the active participation of you, the reader, via<\/p>\n<ul>\n<li><strong>Challenges.<\/strong> 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.<\/li>\n<\/ul>\n<ul>\n<li><strong>Simulation software and models. <\/strong>The accompanying CD-ROM and <a href=\"http:\/\/(http:\/\/www.mhhe.com\/sterman\">website include all the models developed<\/a> 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.<\/li>\n<\/ul>\n<ul>\n<li>Additionally, the <strong>Instructor\u2019s Manual <\/strong>and instructor\u2019s section of the <strong>Website<\/strong> 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.<\/li>\n<\/ul>\n<p><a href=\"#top\">Top of Page<\/a><\/p>\n<p><a name=\"intended-audience\"><\/a><strong>Intended Audience<\/strong><\/p>\n<p>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.<\/p>\n<p><a href=\"#top\">Top of Page<\/a><\/p>\n<p><a name=\"a-note-on-mathematics\"><\/a><strong>A Note on Mathematics<\/strong><\/p>\n<p>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.<\/p>\n<p><strong>If You Don\u2019t Have a Strong Mathematics Background, Fear Not<\/strong><\/p>\n<p>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.<\/p>\n<p><strong>If You Have a Strong Mathematics Background, Fear Not<\/strong><\/p>\n<p>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.<\/p>\n<p><a href=\"#top\">Top of Page<\/a><\/p>\n<p><a name=\"feedback\"><\/a><strong>Feedback<\/strong><\/p>\n<p>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 <a href=\"mailto:busdyn@mit.edu\" target=\"_blank\" rel=\"noopener\">busdyn@mit.edu<\/a>.<\/p>\n<p><a href=\"#top\">Top of Page<\/a><\/p>\n<p><a name=\"table-of-contents\"><\/a><strong>Table of Contents<\/strong><\/p>\n<p><strong>Preface 1. Learning in and about Complex Systems<\/strong><\/p>\n<p>1.1 Introduction<\/p>\n<p>1.1.1 Policy Resistance, the Law of Unintended Consequences, and the Counterintuitive Behavior of Social Systems<\/p>\n<p>1.1.2 Causes of Policy Resistance<\/p>\n<p>1.1.3 Feedback<\/p>\n<p>1.1.4 Process Point: The Meaning of Feedback<\/p>\n<p>Challenge: Dynamics of Multiple-Loop Systems<\/p>\n<p>1.2 Learning is a Feedback Process<\/p>\n<p>1.3 Barriers to Learning<\/p>\n<p>1.3.1 Dynamic Complexity<\/p>\n<p>1.3.2 Limited Information<\/p>\n<p>1.3.3 Confounding Variables and Ambiguity<\/p>\n<p>1.3.4 Bounded Rationality and the Misperceptions of Feedback<\/p>\n<p>1.3.5 Flawed Cognitive Maps<\/p>\n<p>1.3.6 Erroneous Inferences about Dynamics<\/p>\n<p>1.3.7 Unscientific Reasoning: Judgmental Errors and Biases<\/p>\n<p>Challenge: Hypothesis Testing<\/p>\n<p>1.3.8 Defensive Routines and Interpersonal Impediments to Learning<\/p>\n<p>1.3.9 Implementation Failure<\/p>\n<p>1.4 Requirements for Successful Learning in Complex Systems<\/p>\n<p>1.4.1 Improving the Learning Process: Virtues of Virtual Worlds<\/p>\n<p>1.4.2 Pitfalls of Virtual Worlds<\/p>\n<p>1.4.3 Why Simulation is Essential<\/p>\n<p>1.5 Summary<\/p>\n<p><strong>2. System Dynamics in Action<\/strong><\/p>\n<p>2.1 Applications of System Dynamics<\/p>\n<p>2.2 Automobile Leasing Strategy: Gone Today, Here Tomorrow<\/p>\n<p>2.2.1 Dynamic Hypothesis<\/p>\n<p>2.2.2 Elaborating the Model<\/p>\n<p>2.2.3 Policy Analysis<\/p>\n<p>2.2.4 Impact and Follow-up<\/p>\n<p>2.3 On Time and Under Budget: The Dynamics of Project Management<\/p>\n<p>2.3.1 The Claim<\/p>\n<p>2.3.2 Initial Model Development<\/p>\n<p>2.3.3 Dynamic Hypothesis<\/p>\n<p>2.3.4 The Modeling Process<\/p>\n<p>2.3.5 Continuing Impact<\/p>\n<p>2.4 Playing the Maintenance Game<\/p>\n<p>2.4.1 Dynamic Hypothesis<\/p>\n<p>2.4.2 The Implementation Challenge<\/p>\n<p>2.4.3 Results<\/p>\n<p>2.4.4 Transferring the Learning: The Lima Experience<\/p>\n<p>2.5 Summary: Principles for Successful Use of System Dynamics<\/p>\n<p><strong>3. The Modeling Process<\/strong><\/p>\n<p>3.1 The Purpose of Modeling: Managers as Organization Designers<\/p>\n<p>3.2 The Client and the Modeler<\/p>\n<p>3.3 Steps of the Modeling Process<\/p>\n<p>3.4 Modeling is Iterative<\/p>\n<p>3.5 Overview of the Modeling Process<\/p>\n<p>3.5.1 Problem Articulation: The Importance of Purpose<\/p>\n<p>3.5.2 Formulating a Dynamic Hypothesis<\/p>\n<p>3.5.3 Formulating a Simulation Model<\/p>\n<p>3.5.4 Testing<\/p>\n<p>3.5.5 Policy Design and Evaluation<\/p>\n<p>3.6 Summary<\/p>\n<p><strong>4. Structure and Behavior of Dynamic Systems<\/strong><\/p>\n<p>4.1 Fundamental Modes of Dynamic Behavior<\/p>\n<p>4.1.1 Exponential Growth<\/p>\n<p>4.1.2 Goal Seeking<\/p>\n<p>4.1.3 Oscillation<\/p>\n<p>4.1.4 Process Point<\/p>\n<p>Challenge: Identifying Feedback Structure from System Behavior<\/p>\n<p>4.2 Interactions of the Fundamental Modes<\/p>\n<p>4.2.1 S-shaped Growth<\/p>\n<p>4.2.2 S-Shaped Growth with Overshoot<\/p>\n<p>Challenge: Identifying the Limits to Growth<\/p>\n<p>4.2.3 Overshoot and Collapse<\/p>\n<p>4.3 Other Modes of Behavior<\/p>\n<p>4.3.1 Stasis, or Equilibrium<\/p>\n<p>4.3.2 Randomness<\/p>\n<p>4.3.3 Chaos<\/p>\n<p>4.4 Summary<\/p>\n<p><strong>5. Causal Loop Diagrams<\/strong><\/p>\n<p>5.1 Causal Diagram Notation<\/p>\n<p>5.2 Guidelines for Causal Loop Diagrams<\/p>\n<p>5.2.1 Causation versus Correlation<\/p>\n<p>5.2.2 Labeling Link Polarity<\/p>\n<p>Challenge: Assigning Link Polarities<\/p>\n<p>5.2.3 Determining Loop Polarity<\/p>\n<p>Challenge: Employee Motivation<\/p>\n<p>5.2.4 Name Your Loops<\/p>\n<p>5.2.5 Indicate Important Delays in Causal Links<\/p>\n<p>5.2.6 Variable Names<\/p>\n<p>5.2.7 Tips for Causal Loop Diagram Layout<\/p>\n<p>5.2.8 Choose the Right Level of Aggregation<\/p>\n<p>5.2.9 Don\u2019t Put All the Loops into One Large Diagram<\/p>\n<p>5.2.10 Make the Goals of Negative Loops Explicit<\/p>\n<p>5.2.11 Distinguish between Actual and Perceived Conditions<\/p>\n<p>5.3 Process Point: Developing Causal Diagrams from Interview Data<\/p>\n<p>Challenge: Process Improvement<\/p>\n<p>5.4 Conceptualization Case Study: Managing Your Workload<\/p>\n<p>5.4.1 Problem Definition<\/p>\n<p>5.4.2 Identifying Key Variables<\/p>\n<p>5.4.3 Developing the Reference Mode<\/p>\n<p>5.4.4 Developing the Causal Diagrams<\/p>\n<p>5.4.5 Limitations of the Causal Diagram<\/p>\n<p>Challenge: Policy Analysis with Causal Diagrams<\/p>\n<p>5.5 Adam Smith\u2019s Invisible Hand and the Feedback Structure of Markets<\/p>\n<p>Challenge: The Oil Crises of the 1970s<\/p>\n<p>Challenge: Speculative Bubbles<\/p>\n<p>Challenge: The Thoroughbred Horse Market<\/p>\n<p>5.4.1 Market Failure, Adverse Selection, and the Death Spiral<\/p>\n<p>5.6 Explaining Policy Resistance: Traffic Congestion<\/p>\n<p>5.6.1 Mental Models of the Traffic Problem<\/p>\n<p>5.6.2 Compensating Feedback: The Response to Decreased Congestion<\/p>\n<p>5.6.3 The Mass Transit Death Spiral<\/p>\n<p>5.6.4 Policy Analysis: The Impact of Technology<\/p>\n<p>5.6.5 Compensating Feedback: The Source of Policy Resistance<\/p>\n<p>Challenge: Identifying the Feedback Structure of Policy Resistance<\/p>\n<p>5.7 Summary<\/p>\n<p><strong>6. Stocks and Flows<\/strong><\/p>\n<p>6.1 Stocks, Flows, and Accumulation<\/p>\n<p>6.1.1 Diagramming Notation for Stocks and Flows<\/p>\n<p>6.1.2 Mathematical Representation of Stocks and Flows<\/p>\n<p>6.1.3 The Contribution of Stocks to Dynamics<\/p>\n<p>6.2 Identifying Stocks and Flows<\/p>\n<p>6.2.1 Units of Measure in Stock and Flow Networks<\/p>\n<p>6.2.2 The Snapshot Test<\/p>\n<p>Challenge: Identifying Stocks and Flows<\/p>\n<p>6.2.3 Conservation of Material in Stock and Flow Networks<\/p>\n<p>6.2.4 State-Determined Systems<\/p>\n<p>6.2.5 Auxiliary Variables<\/p>\n<p>6.2.6 Stocks Change only Through their Rates<\/p>\n<p>6.2.7 Continuous Time and Instantaneous Flows<\/p>\n<p>6.2.8 Continuously Divisible versus Quantized Flows<\/p>\n<p>6.2.9 Which Modeling Approach Should You Use?<\/p>\n<p>6.2.10 Process Point: Portraying Stocks and Flows in Practice<\/p>\n<p>6.3 Mapping Stocks and Flows<\/p>\n<p>6.3.1 When Should Causal Loop Diagrams Show Stock and Flow Structure?<\/p>\n<p>Challenge: Adding Stock and Flow Structure to Causal Diagrams<\/p>\n<p>Challenge: Linking Stock and Flow Structure with Feedback<\/p>\n<p>6.3.2 Aggregation in Stock and Flow Mapping<\/p>\n<p>Challenge: Modifying Stock and Flow Maps<\/p>\n<p>Challenge: Disaggregation<\/p>\n<p>6.3.3 Guidelines for Aggregation<\/p>\n<p>6.3.4 System Dynamics in Action: Modeling Large-Scale Construction Projects<\/p>\n<p>6.3.5 Setting the Model Boundary: \"Challenging the Clouds\"<\/p>\n<p>6.3.6 System Dynamics in Action: Automobile Recycling<\/p>\n<p>6.4 Summary<\/p>\n<p><strong>7. Dynamics of Stocks and Flows<\/strong><\/p>\n<p>7.1 Relationship between Stocks and Flows<\/p>\n<p>7.1.1 Static and Dynamic Equilibrium<\/p>\n<p>7.1.2 Calculus without Mathematics<\/p>\n<p>7.1.3 Graphical Integration<\/p>\n<p>7.1.4 Graphical Differentiation<\/p>\n<p>Challenge: Graphical Differentiation<\/p>\n<p>7.2 System Dynamics in Action: Global Warming<\/p>\n<p>7.3 System Dynamics in Action: The War on Drugs<\/p>\n<p>7.3.1 The Cocaine Epidemic after 1990<\/p>\n<p>7.4 Summary<\/p>\n<p><strong>8. Closing the Loop: Dynamics of Simple Structures<\/strong><\/p>\n<p>8.1 First-order Systems<\/p>\n<p>8.2 Positive Feedback and Exponential Growth<\/p>\n<p>8.2.1 Analytic Solution for the Linear First-Order System<\/p>\n<p>8.2.2 Graphical Solution of the Linear First-Order Positive Feedback System<\/p>\n<p>8.2.3 The Power of Positive Feedback: Doubling Times<\/p>\n<p>Challenge: Paper Folding<\/p>\n<p>8.2.4 Misperceptions of Exponential Growth<\/p>\n<p>8.2.5 Process Point: Overcoming Overconfidence<\/p>\n<p>8.3 Negative Feedback and Exponential Decay<\/p>\n<p>8.3.1 Time constants and half lives<\/p>\n<p>Challenge: Goal-seeking behavior<\/p>\n<p>8.4 Multiple-Loop Systems<\/p>\n<p>8.5 Nonlinear First-Order Systems: S-Shaped Growth<\/p>\n<p>Challenge: Nonlinear Birth and Death Rates<\/p>\n<p>8.5.1 Formal Definition of Loop Dominance<\/p>\n<p>8.5.2 First-Order Systems Cannot Oscillate<\/p>\n<p>8.6 Summary<\/p>\n<p><strong>9. S-Shaped Growth: Epidemics, Innovation Diffusion, and the Growth of New Products<\/strong><\/p>\n<p>9.1 Modeling S-Shaped Growth<\/p>\n<p>9.1.1 Logistic Growth<\/p>\n<p>9.1.2 Analytic Solution of the Logistic Equation<\/p>\n<p>9.1.3 Other Common Growth Models<\/p>\n<p>9.1.4 Testing the Logistic Model<\/p>\n<p>9.2 Dynamics of Disease: Modeling Epidemics<\/p>\n<p>9.2.1 A Simple Model of Infectious Disease<\/p>\n<p>9.2.2 Modeling Acute Infection: The SIR Model<\/p>\n<p>9.2.3 Model Behavior: The Tipping Point<\/p>\n<p>Challenge: Exploring the SIR Model<\/p>\n<p>9.2.4 Immunization and the Eradication of Smallpox<\/p>\n<p>Challenge: The Efficacy Of Immunization Programs<\/p>\n<p>9.2.5 Herd Immunity<\/p>\n<p>9.2.6 Moving Past The Tipping Point: Mad Cow Disease<\/p>\n<p>Challenge: Extending the SIR Model<\/p>\n<p>9.2.7 Modeling the HIV\/AIDS Epidemic<\/p>\n<p>Challenge: Modeling HIV\/AIDS<\/p>\n<p>9.3 Innovation Diffusion as Infection: Modeling New Ideas and New Products<\/p>\n<p>9.3.1 The Logistic Model of Innovation Diffusion: Examples<\/p>\n<p>9.3.2 Process Point: Historical Fit and Model Validity<\/p>\n<p>9.3.3 The Bass Diffusion Model<\/p>\n<p>Challenge: Phase Space of the Bass Diffusion Model<\/p>\n<p>9.3.4 Behavior of the Bass Model<\/p>\n<p>Challenge: Critiquing the Bass Diffusion Model<\/p>\n<p>Challenge: Extending the Bass Model<\/p>\n<p>9.3.5 Fad and Fashion: Modeling the Abandonment of an Innovation<\/p>\n<p>Challenge: Modeling Fads<\/p>\n<p>9.3.6 Replacement Purchases<\/p>\n<p>Challenge: Modeling the Life Cycle of Durable Products<\/p>\n<p>9.4 Summary<\/p>\n<p><strong>10. Path Dependence and Positive Feedback<\/strong><\/p>\n<p>10.1 Path Dependence<\/p>\n<p>Challenge: Identifying Path Dependence<\/p>\n<p>10.2 A Simple Model of Path Dependence: The Polya Process<\/p>\n<p>10.2.1 Generalizing the Model: Nonlinear Polya Processes<\/p>\n<p>10.3 Path Dependence in the Economy: VHS vs. Betamax<\/p>\n<p>Challenge: Formulating a Dynamic Hypothesis for the VCR Industry<\/p>\n<p>10.4 Positive Feedback: The Engine of Corporate Growth<\/p>\n<p>10.4.1 Product Awareness<\/p>\n<p>10.4.2 Unit Development Costs<\/p>\n<p>10.4.3 Price and Production Cost<\/p>\n<p>10.4.4 Network Effects and Complementary Goods<\/p>\n<p>10.4.5 Product Differentiation<\/p>\n<p>10.4.6 New Product Development<\/p>\n<p>10.4.7 Market Power<\/p>\n<p>10.4.8 Mergers and Acquisitions<\/p>\n<p>10.4.9 Workforce Quality and Loyalty<\/p>\n<p>10.4.10 The Cost of Capital<\/p>\n<p>10.4.11 The Rules of the Game<\/p>\n<p>10.4.12 Ambition and Aspirations<\/p>\n<p>10.4.13 Creating Synergy for Corporate Growth<\/p>\n<p>10.5 Positive Feedback, Increasing Returns, and Economic Growth<\/p>\n<p>10.6 Does the Economy Lock in to Inferior Technologies?<\/p>\n<p>10.7 Limits to Lock In<\/p>\n<p>10.8 Modeling Path Dependence and Standards Formation<\/p>\n<p>10.8.1 Model Structure<\/p>\n<p>10.8.2 Model Behavior<\/p>\n<p>10.8.3 Policy Implications<\/p>\n<p>Challenge: Policy Analysis<\/p>\n<p>Challenge: Extending the Model<\/p>\n<p>10.9 Summary<\/p>\n<p><strong>11. Delays<\/strong><\/p>\n<p>11.1 Delays: An Introduction<\/p>\n<p>Challenge: Duration and Dynamics of Delays<\/p>\n<p>11.1.1 Defining Delays<\/p>\n<p>11.2 Material Delays: Structure and Behavior<\/p>\n<p>11.2.1 What is the Average Length of the Delay?<\/p>\n<p>11.2.2 What is the Distribution of the Output around the Average Delay Time?<\/p>\n<p>11.2.3 Pipeline Delay<\/p>\n<p>11.2.4 First-Order Material Delay<\/p>\n<p>11.2.5 Higher-Order Material Delays<\/p>\n<p>11.2.6 How Much is in the Delay? Little\u2019s Law<\/p>\n<p>11.3 Information Delays: Structure and Behavior<\/p>\n<p>11.3.1 Modeling Perceptions: Adaptive Expectations and Exponential Smoothing<\/p>\n<p>11.3.2 Higher-Order Information Delays<\/p>\n<p>11.4 Response to Variable Delay Times<\/p>\n<p>Challenge: Response of Delays to Changing Delay Times<\/p>\n<p>11.4.1 Nonlinear Adjustment Times: Modeling Ratchet Effects<\/p>\n<p>11.5 Estimating the Duration and Distribution of Delays<\/p>\n<p>11.5.1 Estimating Delays when Numerical Data are Available<\/p>\n<p>11.5.2 Estimating Delays when Numerical Data are not Available<\/p>\n<p>11.5.3 Process Point: Walk the Line<\/p>\n<p>11.6 System Dynamics in Action: Forecasting Semiconductor Demand<\/p>\n<p>11.7 Mathematics of Delays: Koyck Lags and Erlang Distributions<\/p>\n<p>11.7.1 General Formulation for Delays<\/p>\n<p>11.7.2 First-Order Delay<\/p>\n<p>11.7.3 Higher-Order Delays<\/p>\n<p>11.7.4 Relation of Material and Information Delays<\/p>\n<p>11.8 Summary<\/p>\n<p><strong>12. Coflows and Aging Chains<\/strong><\/p>\n<p>12.1 Aging Chains<\/p>\n<p>12.1.1 General Structure of Aging Chains<\/p>\n<p>12.1.2 Example: Population and Infrastructure in Urban Dynamics<\/p>\n<p>12.1.3 Example: The Population Pyramid and the Demographic Transition<\/p>\n<p>12.1.4 Aging Chains and Population Inertia<\/p>\n<p>12.1.5 System Dynamics in Action: World Population and Economic Development<\/p>\n<p>12.1.6 Case Study: Growth and the Age Structure of Organizations<\/p>\n<p>12.1.7 Promotion Chains and the Learning Curve<\/p>\n<p>12.1.8 Mentoring and On-The-Job Training<\/p>\n<p>Challenge: The Interactions of Training Delays and Growth<\/p>\n<p>12.2 Coflows: Modeling the Attributes of a Stock<\/p>\n<p>Challenge: Coflows<\/p>\n<p>12.2.1 Coflows with Nonconserved Flows<\/p>\n<p>Challenge: The Dynamics of Experience and Learning<\/p>\n<p>12.2.2 Integrating Coflows and Aging Chains<\/p>\n<p>Challenge: Modeling Design Wins in the Semiconductor Industry<\/p>\n<p>12.3 Summary<\/p>\n<p><strong>13. Modeling Decision Making<\/strong><\/p>\n<p>13.1 Principles for Modeling Decision Making<\/p>\n<p>13.1.1 Decisions and Decision Rules<\/p>\n<p>13.1.2 Five Formulation Fundamentals<\/p>\n<p>Challenge: Finding Formulation Flaws<\/p>\n<p>13.2 Formulating Rate Equations<\/p>\n<p>13.2.1 Fractional Increase Rate<\/p>\n<p>13.2.2 Fractional Decrease Rate<\/p>\n<p>13.2.3 Adjustment to a Goal<\/p>\n<p>13.2.4 The Stock Management Structure: Rate = Normal Rate + Adjustments<\/p>\n<p>13.2.5 Flow = Resource * Productivity<\/p>\n<p>13.2.6 Y = Y<sup>*<\/sup> * Effect of X<sub>1<\/sub> on Y * Effect of X<sub>2<\/sub> on Y * \u2026 * Effect of X<sub>n<\/sub> on Y<\/p>\n<p>13.2.7 Y = Y<sup>*<\/sup> + Effect of X<sub>1<\/sub> on Y + Effect of X<sub>2<\/sub> on Y + \u2026 + Effect of X<sub>n<\/sub> on Y<\/p>\n<p>13.2.8 Fuzzy MIN Function<\/p>\n<p>13.2.9 Fuzzy MAX Function<\/p>\n<p>13.2.10 Floating Goals<\/p>\n<p>Challenge: Floating Goals<\/p>\n<p>Challenge: Goal Formation with Internal and External Inputs<\/p>\n<p>13.2.11 Nonlinear Weighted Average<\/p>\n<p>13.2.12 Modeling Search: Hill-Climbing Optimization<\/p>\n<p>Challenge: Finding the Optimal Mix of Capital and Labor<\/p>\n<p>13.2.13 Resource Allocation<\/p>\n<p>13.3 Common Pitfalls<\/p>\n<p>13.3.1 All Outflows Require First-Order Control<\/p>\n<p>Challenge: Preventing Negative Stocks<\/p>\n<p>13.3.2 Avoid IF\u2026THEN\u2026ELSE Formulations<\/p>\n<p>13.3.3 Disaggregate Net Flows<\/p>\n<p>13.4 Summary<\/p>\n<p><strong>14. Formulating Nonlinear Relationships<\/strong><\/p>\n<p>14.1 Table Functions<\/p>\n<p>14.1.1 Specifying Table Functions<\/p>\n<p>14.1.2 Example: Building a Nonlinear Function<\/p>\n<p>14.1.3 Process Point: Table Functions Versus Analytic Functions<\/p>\n<p>14.2 Case Study: Cutting Corners Versus Overtime<\/p>\n<p>Challenge: Formulating Nonlinear Functions<\/p>\n<p>14.2.1 Working Overtime: The Effect of Schedule Pressure on Workweek<\/p>\n<p>14.2.2 Cutting Corners: The Effect of Schedule Pressure on Time per Task<\/p>\n<p>14.3 Case Study: Estimating Nonlinear Functions With Qualitative and Numerical Data<\/p>\n<p>Challenge: Refining Table Functions with Qualitative Data<\/p>\n<p>14.4 Common Pitfalls<\/p>\n<p>14.4.1 Using the Wrong Input<\/p>\n<p>Challenge: Critiquing Nonlinear Functions<\/p>\n<p>14.4.2 Improper Normalization<\/p>\n<p>14.4.3 Avoid Hump-shaped Functions<\/p>\n<p>Challenge: Formulating the Error Rate<\/p>\n<p>Challenge: Testing the Full Model<\/p>\n<p>14.5 Eliciting Model Relationships Interactively<\/p>\n<p>14.5.1 Case Study: Estimating Precedence Relationships in Product Development<\/p>\n<p>14.6 Summary<\/p>\n<p><strong>15. Modeling Human Behavior: Bounded Rationality or Rational Expectations?<\/strong><\/p>\n<p>15.1 Human Decision Making: Bounded Rationality or Rational Expectations?<\/p>\n<p>15.2 Cognitive Limitations<\/p>\n<p>15.3 Individual and Organizational Responses to Bounded Rationality<\/p>\n<p>15.3.1 Habit, Routines, and Rules of Thumb<\/p>\n<p>15.3.2 Managing Attention<\/p>\n<p>15.3.3 Goal Formation and Satisficing<\/p>\n<p>15.3.4 Problem Decomposition and Decentralized Decision Making<\/p>\n<p>15.4 Intended Rationality<\/p>\n<p>15.4.1 Testing for Intended Rationality: Partial Model Tests<\/p>\n<p>15.5 Case Study: Modeling High-Tech Growth Firms<\/p>\n<p>15.5.1 Model Structure: Overview<\/p>\n<p>15.5.2 Order Fulfillment<\/p>\n<p>15.5.3 Capacity Acquisition<\/p>\n<p>Challenge: Hill Climbing<\/p>\n<p>15.5.4 The Sales Force<\/p>\n<p>15.5.5 The Market<\/p>\n<p>15.5.6 Behavior of the Full System<\/p>\n<p>Challenge: Policy Design in the Market Growth Model<\/p>\n<p>15.6 Summary<\/p>\n<p><strong>16. Forecasts and Fudge Factors: Modeling Expectation Formation<\/strong><\/p>\n<p>16.1 Modeling Expectation Formation<\/p>\n<p>16.1.1 Modeling Growth Expectations: The TREND Function<\/p>\n<p>16.1.2 Behavior of the TREND Function<\/p>\n<p>16.2 Case Study: Energy Consumption<\/p>\n<p>16.3 Case Study: Commodity Prices<\/p>\n<p>16.4 Case Study: Inflation<\/p>\n<p>16.5 Implications for Forecast Consumers<\/p>\n<p>Challenge: Extrapolation and Stability<\/p>\n<p>16.6 Initialization and Steady State Response of the TREND Function<\/p>\n<p>16.7 Summary<\/p>\n<p><strong>17. Supply Chains and the Origin of Oscillations<\/strong><\/p>\n<p>17.1 Supply Chains in Business and Beyond<\/p>\n<p>17.1.1 Oscillation, Amplification, and Phase Lag<\/p>\n<p>17.2 The Stock Management Problem<\/p>\n<p>17.2.1 Managing a Stock: Structure<\/p>\n<p>17.2.2 Steady State Error<\/p>\n<p>17.2.3 Managing a Stock: Behavior<\/p>\n<p>17.3 The Stock Management Structure<\/p>\n<p>17.3.1 Behavior of the Stock Management Structure<\/p>\n<p>17.4 The Origin of Oscillations<\/p>\n<p>17.4.1 Mismanaging the Supply Line: The Beer Distribution Game<\/p>\n<p>17.4.2 Why Do We Ignore the Supply Line?<\/p>\n<p>17.4.3 Case Study: Boom and Bust in Real Estate Markets<\/p>\n<p>17.5 Summary<\/p>\n<p><strong>18. The Manufacturing Supply Chain<\/strong><\/p>\n<p>18.1 The Policy Structure of Inventory and Production<\/p>\n<p>18.1.1 Order Fulfillment<\/p>\n<p>18.1.2 Production<\/p>\n<p>18.1.3 Production Starts<\/p>\n<p>18.1.4 Demand Forecasting<\/p>\n<p>18.1.5 Process Point: Initializing a Model in Equilibrium<\/p>\n<p>Challenge: Simultaneous Initial Conditions<\/p>\n<p>18.1.6 Behavior of the Production Model<\/p>\n<p>18.1.7 Enriching the Model: Adding Order Backlogs<\/p>\n<p>18.1.8 Behavior of the Firm with Order Backlogs<\/p>\n<p>18.1.9 Adding Raw Materials Inventory<\/p>\n<p>18.2 Interactions among Supply Chain Partners<\/p>\n<p>18.2.1 Instability and Trust in Supply Chains<\/p>\n<p>18.2.2 From Functional Silos to Integrated Supply Chain Management<\/p>\n<p>Challenge: Reengineering the Supply Chain<\/p>\n<p>18.3 System Dynamics in Action: Reengineering the Supply Chain in a High-Velocity Industry<\/p>\n<p>18.3.1 Initial Problem Definition<\/p>\n<p>18.3.2 Reference Mode and Dynamic Hypothesis<\/p>\n<p>18.3.3 Model Formulation<\/p>\n<p>18.3.4 Testing the Model<\/p>\n<p>18.3.5 Policy Analysis<\/p>\n<p>18.3.6 Implementation: Sequential Debottlenecking<\/p>\n<p>18.3.7 Results<\/p>\n<p>18.4 Summary<\/p>\n<p><strong>19. The Labor Supply Chain and the Origin of Business Cycles<\/strong><\/p>\n<p>19.1 The Labor Supply Chain<\/p>\n<p>19.1.1 Structure of Labor and Hiring<\/p>\n<p>19.1.2 Behavior of the Labor Supply Chain<\/p>\n<p>19.2 Interactions of Labor and Inventory Management<\/p>\n<p>Challenge: Mental Simulation of Inventory Management with Labor<\/p>\n<p>19.2.1 Inventory\u2014Workforce Interactions: Behavior<\/p>\n<p>19.2.2 Process Point: Explaining Model Behavior<\/p>\n<p>Challenge: Explaining Oscillations<\/p>\n<p>19.2.3 Understanding the Sources of Oscillation<\/p>\n<p>Challenge: Policy Design to Enhance Stability<\/p>\n<p>19.2.4 Adding Overtime<\/p>\n<p>19.2.5 Response to Flexible Workweeks<\/p>\n<p>Challenge: Reengineering a Manufacturing Firm for Enhanced Stability<\/p>\n<p>19.2.6 The Costs of Instability<\/p>\n<p>Challenge: The Costs of Instability<\/p>\n<p>Challenge: Adding Training and Experience<\/p>\n<p>19.3 Inventory\u2014Workforce Interactions and the Business Cycle<\/p>\n<p>19.3.1 Is the Business Cycle Dead?<\/p>\n<p>19.4 Summary<\/p>\n<p><strong>20. The Invisible Hand Sometimes Shakes: Commodity Cycles<\/strong><\/p>\n<p>20.1 Commodity Cycles: From Aircraft to Zinc<\/p>\n<p>20.2 A Generic Commodity Market Model<\/p>\n<p>20.2.1 Production and Inventory<\/p>\n<p>20.2.2 Capacity Utilization<\/p>\n<p>20.2.3 Production Capacity<\/p>\n<p>20.2.4 Desired Capacity<\/p>\n<p>Challenge: Intended Rationality of the Investment Process<\/p>\n<p>20.2.5 Demand<\/p>\n<p>20.2.6 The Price-Setting Process<\/p>\n<p>20.3 Application: Cycles in the Pulp and Paper Industry<\/p>\n<p>Challenge: Sensitivity to Uncertainty in Parameters<\/p>\n<p>Challenge: Sensitivity to Structural Changes<\/p>\n<p>Challenge: Implementing Structural Changes\u2013Modeling Livestock Markets<\/p>\n<p>Challenge: Policy Analysis<\/p>\n<p>20.4 Summary<\/p>\n<p><strong>21. Truth and Beauty: Validation and Model Testing<\/strong><\/p>\n<p>21.1 Validation and Verification are Impossible<\/p>\n<p>21.2 Questions Model Users Should Ask\u2013But Usually Don\u2019t<\/p>\n<p>21.3 Pragmatics and Politics of Model Use<\/p>\n<p>21.3.1 Types of Data<\/p>\n<p>21.3.2 Documentation<\/p>\n<p>21.3.3 Replicability<\/p>\n<p>21.3.4 Protective versus Reflective Modeling<\/p>\n<p>21.4 Model Testing in Practice<\/p>\n<p>21.4.1 Boundary Adequacy Tests<\/p>\n<p>21.4.2 Structure Assessment Tests<\/p>\n<p>21.4.3 Dimensional Consistency<\/p>\n<p>21.4.4 Parameter Assessment<\/p>\n<p>21.4.5 Extreme Condition Tests<\/p>\n<p>Challenge: Extreme Condition Tests<\/p>\n<p>21.4.6 Integration Error Tests<\/p>\n<p>21.4.7 Behavior Reproduction Tests<\/p>\n<p>21.4.8 Behavior Anomaly Tests<\/p>\n<p>21.4.9 Family Member Tests<\/p>\n<p>21.4.10 Surprise Behavior Tests<\/p>\n<p>21.4.11 Sensitivity Analysis<\/p>\n<p>21.4.12 System Improvement Tests<\/p>\n<p>Challenge: Model Testing<\/p>\n<p>21.5 Summary<\/p>\n<p><strong>22. Challenges for the Future<\/strong><\/p>\n<p>22.1 Theory<\/p>\n<p>22.2 Technology<\/p>\n<p>22.3 Implementation<\/p>\n<p>22.4 Education<\/p>\n<p>22.5 Applications<\/p>\n<p>Challenge: Putting System Dynamics Into Action<\/p>\n<p><strong>Appendix A Numerical Integration<\/strong><\/p>\n<p>Challenge: Choosing a Time Step<\/p>\n<p><strong>Appendix B Noise<\/strong><\/p>\n<p>Challenge: Exploring Noise<\/p>\n<p><strong>References<\/strong><\/p>\n<p><strong>Index<\/strong><\/p>\n<p><a href=\"#top\">Top of Page<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>","protected":false},"excerpt":{"rendered":"<p>Back to All Publications From Business Dynamics Preface Features and Content from Business Dynamics Intended Audience for Business Dynamics\u00a0 A Note on Mathematics in Business Dynamics Feedback on Business Dynamics Table of Contents for Business Dynamics Go to the Business Dynamics website Irwin\/McGraw-Hill (2000) ISBN 0-07-238915X From the Preface Accelerating economic, technological, social, and environmental [&hellip;]<\/p>\n","protected":false},"author":152,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-two-column.php","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"class_list":["post-212","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.0 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>MIT Sloan Faculty: John Sterman| Business Dynamics<\/title>\n<meta name=\"description\" content=\"Sterman is the author of the book, Modeling for Organizational Learning, and the award-winning textbook, Business Dynamics.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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