System Dynamics An Introduction Shahram Shadrokh.

System DynamicsAn Introduction

Shahram Shadrokh

Objectives and Scope

Why do so many business strategies fail?

Why do so many others fail to produce lasting results?

Why do many businesses suffer from periodic crises,fluctuating sales, earnings, and morale?

Why do some firms grow while others not?

How do once-dominant firms lose their competitiveedge?


Accelerating economic, technological, social, and environmental change challenge managers to learn at increasing rates. And we must increasingly learn how to design and manage complex systems with multiple feedback effects, long time delays, and nonlinear responses to our decisions. Yet learning in such environments is difficult precisely because we never confront many of the consequences of our most important decisions.Effective learning in such environments requires methods to develop systems thinking, to represent and assess such dynamic complexity – and tools managers can use to accelerate learning throughout an organization.


This course introduces you to system dynamics modeling for the analysis of business policy and strategy. You will learn to visualize a business organization in terms of the structures and policies that create dynamics and regulate performance.

System dynamics allows us to create ‘microworlds,’ management flight simulators where space and time can be compressed, slowed, and stopped so we can experience the long-term side effects of decisions, systematically explore new strategies, and develop our understanding of complex systems.

Text and Software

The primary text is Sterman, J. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin/McGraw Hill. ISBN 0-07-231135-5.

Several excellent packages for system dynamics simulation are now available commercially, including:

iThink, from High Performance Systems, Powersim, from Powersim Corporation, Professional DYNAMO, from Pugh-Roberts Associates, and Vensim, from Ventana Systems.

Text and Software

iThink: See the High Performance Systems web site at http://www.hps-inc.com/hps.mhtml Powersim: See the Powersim web site at http://www.powersim.no Professional DYNAMO: Contact Pugh-Roberts Associates, 41 William Linskey Way, Cambridge, MA 02142, 617/864-8880 Vensim: See the Ventana Systems web site at http://www.vensim.com/

In this course, we will be using the Vensim Personal Learning Edition (Vensim PLE), a FREE package offered by Ventana Systems. Vensim PLE is available for both Windows and Macintosh; models created with it are fully convertible across platforms.

http://www.hps-inc.com/hps.mhtml

http://www.powersim.no/

http://www.vensim.com/

Outline

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.

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.

Outline

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.

Higher mathematics, though useful, is not as important as the critical thinking skills developed here.

Outline

The greatest constant of modern times is change.

• Most important, most of the changes we now struggle to comprehend arise as consequences, intended and unintended, of humanity itself.

• All too often, well-intentioned efforts to solve pressing problems lead to policy resistance, where our policies are delayed, diluted, or defeated by the unforeseen reactions of other people or of nature.

• Many times our best efforts to solve a problem actually make it worse.

A Systems Tale• New CEO of noodle-producer discovers: Too much spaghetti on

stock!• CEO calls Marketing Director: “Make your Job!“• Marketing Director creates a big campaign: “Take two, pay one“.• Campaign extremely successful – 3 months later store is almost

empty.• CEO to Marketing Director: “Well done!“• At the same time: Production Accountant reports to Production

Director: “Our sales increased dramatically!“• “The forecast says that in six months our sales will have doubled

from 200 tons to 400 tons per month! We have to increase production dramatically to fulfill the increased demand!“

• After seeing the figures the Production Director is shocked: “We are already running two shifts. Now we have to introduce a night-shift, too...“

How did the Production Director react?

• He recognized the considerable gap between sales and production.

• He concluded that due to the forecast this gap apparently will become even bigger.

• He decided to expand capacities dramatically by introducing a third (night) shift. All holidays had been cancelled and new personnel was recruited immediately.

• He was absolutely determined to increase production by one-third within six weeks.

What actually happened with Spaghetti Sales?

• Customers ate as much spaghetti as usual. • The extra spaghetti packs bought at the “2 for 1“ campaign

were stored at home in their kitchens and pantries.• Thus after the “Get 2 for 1“ campaign spaghetti sales

actually decreased dramatically below the 200 tons p.m. average.

• The non-systemic forecast led the production completely into the wrong direction. At the end of June the stock was even higher than before the “2 for 1“ campaign.

In June the CEO fired the Marketing Director who apparently had completely neglected his duties in the months after the great “2 for 1“ – campaign.

Shifting the Burden

• A “quick and dirty“ solution is applied for a short-term benefit, avoiding a painful (but necessary) “fundamental solution“

• Aspirin against tooth-ache instead of consulting a dentist• Debt: “Get the benefit now, pay later“• “Spaghetti Production“ story• „Pyramid Games“:

1. Pay me € 1.000,- and I give you the right to look for 3 people who

2. pay you € 1,000,- each for the same right to look for 3 other people each ...

3. So yours (and their) net gain will be € 2.000,- each – and all will be happy... ???

Shifting the Burden

• Human beings are quick problem solvers.

• From an evolutionary standpoint,this makes sense if a tiger is bounding toward you, you need to quickly decide on a course of action, or you won't be around for long.

• Thus,quick problem solvers were the ones who survived.

Policy resistence

• Poeple seeking to solve a problem often make it worse

• Our policies may create unanticipated side effects

• Our attemps to stabilize the system may destabilize it

• Low tar and nicotine cigarettes actually increase intake of carcinogens, CO, etc. as smokers compensate for the low nicotine content by smoking more cigarettes per day, by taking longer, more frequent drags, and by holding the smoke in their lungs longer.

• Antilock brakes and other automotive safety devices cause some people to drive more aggressively, offsetting some of their benefits.

• Information technology has not enabled the "paperless office"-paper consumption is up.

• Road building programs designed to reduce congestion have increased traffic, delays, and pollution.

• The US policy of fire suppression has increased the size and severity of forest fires.

• Flood control efforts such as levee and dam construction have led to more severe floods by preventing the natural dissipation of excess water in flood plains.

• Antibiotics have stimulated the evolution of drug-resistant pathogens

Policy resistence

•But how can one come to understand the whole system? •How does policy resistance arise? •How can we learn to avoid it, to find the high leverage policies that can produce sustainable benefit?

One cause of policy resistance is our tendency to interpret experience as a series of events

We are taught from an early age that every event has a cause, which in turn is an effect of some still earlier cause:

"Inventory is too high because sales unexpectedly fell. Sales fell because the competitors lowered their price. The competitors lowered their price because. . ."

Causes of Policy Resistance

The event-oriented worldview leads to an event-oriented approach to problem solving.

Here shows how we often try to solve problems.


Situation

Goals

Problem Decision Results

But Yesterday's solution becomes today's problem.

Since we are embedded in the system.

Policy resistance arises because we often do not understand the full range of feedbacks operating in the system


•We frequently talk about side effects as if they were a feature of reality.

•Actually in reality, there are no side effects, there are just effects.

•The effects we thought of in advance, or were beneficial, we call the main, or intended effects.

• The effects we didn't anticipate, the effects which fed back to undercut our policy, the effects which harmed the system-these are the ones we claim to be side effects.

Causes of Policy Resistance•Unanticipated side effects arise because we too often act as if cause and effect were always closely linked in time and space.

•But in complex systems such as an urban center , a business, society, or ecosystem cause and effect are often distant in time and space.

A story about electric cars

Feedback•Much of the art of system dynamics modeling is discovering and representing the feedback processes

•You might imagine that there is an immense range of different feedback processes and other structures to be mastered before one can understand the dynamics of complex systems.

•In fact, the most complex behaviors usually arise from the interactions (feedbacks) among the components of the system, not from the complexity of the components themselves.

•All dynamics arise from the interaction of just two types of feedback loops, positive (or self-reinforcing) and negative (or self-correcting) loops

•Positive loops tend to reinforce or amplify whatever is happening in the system:

- The more nuclear weapons NATO deployed during the Cold War, the more the Soviet Union built, leading NATO to build still more.

-The larger the installed base of Microsoft software and lntel machines, the more attractive the "Wintel" architecture became…

Feedback

•Negative loops counteract and oppose change.

- The more attractive a neighborhood or city, the greater the immigration from surrounding areas will be, increasing unemployment, housing prices, crowding in the schools, and traffic congestion until it is no more attractive than other places people might live.

- The higher the price of a commodity, the lower the demand leading to inventory accumulation and pressure for lower prices to eliminate the excess stock.

Systemic Escalations: A Couple in Trouble (Paul Watzlawick)

HE: "I go out, because you are always complaining!"SHE: "I‘m just complaining, because you always leave me alone!“

HE: goes out SHE complains

+

+

Both sides try to solve the „problem“ (which is seen in the other person!)

Systemic Structure: Escalating Feedback Loop!

The Couple in Trouble: Behind the Scenes

• The systemic view reveals the escalation feedback loop.

• More of the same – or “When the ‚solution is the problem“

• The systemic solutions are exactly the opposite of the non-systemic solutions: – he stays home (ignoring her quarreling) or– she stops quarrelling although he is going out.

Feedback•Positive feedback: Positive loops are self-reinforcing. In this case, more chickens lay more eggs, which hatch and add to the chicken population, leading to still more eggs, and so on.

A Causal Loop Diagram or CLD (chapter 5) captures the feedback dependency of chickens and eggs.

R ChickensEggs

+

+

A system’s feedback structure

generates its dynamics

Time

Eggs

Chickens

Feedback•Negative feedback: Negative loops are self-correcting. They counteract change. As the chicken population grows, various negative loops will act to balance the chicken population with its carrying capacity.

The more chickens, the more road crossings they will attempt. If there is any traffic, more road crossings will lead to fewer chickens

BChickens RoadCrossings

+

-

Structure:

Behavior:

Time

RoadCrossings

Chickens

Dynamics of Multiple-Loop Systems

BR ChickensEggs RoadCrossings

+

+

+

-

Dynamical Thinking: “The Disappearing Multiplier“

HouseholdsProduction

Savings S Investments I

Y = C+I

IC = Y - S= c*YS = s*Y;

s=1-c

Gross National Income Y

Consumer Expenses C

c: marginalconsumption quota

s: marginalSavings quota

Equilibrium: Y = C+I; C = Y-S; I = S;Multiplier effect: We

increase I by a. Then Ygl

increases by 1/s * a

I and Y in Equilibrium (Yeq):I = S and S = s*Y Yeq = 1/s*I

Basic Keynesian Economy: Example

• Y = 1000; s=0,1 S = 100, C= 900 Equilibrium at I = 100• I will be increased by 10 up to 110:• New Equilibrium at: Y=1100, S=110, C=990

• Multiplier effect: an Increase of I by 10 increases Yeq by 10*10=100!

HouseholdsProduction

Savings S Investments I

Y = C+I

IC = Y - S= c*YS = s*Y;

s=1-c

Gross National Income Y

Consumer Expenses C

c: marginaleconsum quote:

s: marginalsavings quota:

Equilibrium: Y = C+I; C = Y-S; => I = S;Multiplier Effect: We

increase I by a. Then Yeq increases 1/s * a

I and Y in Equilibrium (Yeq):I = S und S = s*Y =>Yeq = 1/s*I

The Multiplier Effect in practical Politics

• Key question: Must the higher (additional) investment be made only once or permanently every period (month)?

• Background: in a statical model this question does not even appear, because the model contains no dynamics over time.

• The result of the dynamical modelling is shocking: The higher investment must be done permanently each month in order to approach the higher level of Equilibrium after about 3 years!

My Definition of “Systems Thinking“

• Dynamical Dimension - Thinking in dynamical processes over time: beyond snapshot-thinking; understanding stocks vs. flows, delays, oscillations...

• Model Dimension - Thinking in Models /Awareness of Systems: mental models, modeling assumptions, model-reality; qualitative vs. quantitative models

• Feedback Dimension - Thinking in loops and networks: interrelated structures, indirect effects, feedback loops, networks of interrelations

• Pragmatical Dimension - Steering of Systems: finding the "leverage point", counterintuitive behavior, proper intensity and timing of actions. Dealing with conflicts and „impossible“ situations.

Systems Thinking has four interrelated dimensions:

This Definition integrates different views of Systems Thinking!

Intersection: Dec 27-Dec 28

Dynamical Thinking: Discerning stocks and flows

Correct: < 20%

When was the number of Hotel guests a maximum? Give your answer just by a quick glimpse at the diagram without any tedious calculations!

Arrivals and Departures in a Hotel: Dec 18 - Jan 10

ArrDep

How can we learn Systems Thinking?

1. The first step is Awareness of Systems Principles!

2. Systems Thinking needs systemic denotations, models and tools! (CLD‘s, Stock-Flow-Diagrams, simulation models...)

3. Quantitative Systems Modelling expands Systems Thinking capabilities drastically!

LEARNING Is A FEEDBACK PROCESS

Just as dynamics arise from feedback, so too all learning depends on feedback.

-We make decisions that alter the real world

-We gather information feedback about the real world

-Using the new information we revise our understanding of the world and the decisions we make to bring our perception of the state of the system closer to our goals

RealWorld

DecisionsInformationFeedback

LEARNING Is A FEEDBACK PROCESS

But information feedback about the real world is not the only input to our decisions.

Decisions are the result of applying a decision rule or policy to information about the world as we perceive it.

The policies are themselves conditioned by institutional structures, organizational strategies, and cultural norms.

RealWorld

Strategy, Structure,Decision Rules

Mental Modelsof Real World


As long as the mental models remain unchanged, the feedback loop shown in the figure represents what calls single-loop learning

LEARNING Is A FEEDBACK PROCESSThe concept of the mental model has been central to system dynamics from the beginning of the field.

All decisions are based on models, usually mental models.

Most of us do not appreciate the ubiquity and invisibility of mental models, instead believing naively that our senses reveal the world as it is Our world is actively constructed (modeled) by our senses and brain.

LEARNING Is A FEEDBACK PROCESSinformation feed back about the real world not only alters our decisions within the context of existing frames and decision rules but also feeds back to alter our mental models. Real

World


Mental Modelsof Real World


In system dynamics, the term "mental model" includes our beliefs about the networks of causes and effects that describe how a system operates, along with the boundary of the model (which variables are included and which are excluded) and the time horizon we consider relevant our framing or articulation of a problem.

BARRIERS TO LEARNINGReal World

Decisions• Implementation failure• Game playing• Inconsistency• Performance is goal

• Unknown structure• Dynamic complexity• Time delays• Inability to conduct controlled experiments


• Inability to infer dynamics from mental models

Mental Models• Misperceptions of feedback• Unscientific reasoning• Judgmental biases• Defensive routines

• Selective perception• Missing feedback• Delay• Bias, distortion, error• Ambiguity

Information Feedback

BARRIERS TO LEARNINGFor learning to occur each link in the two feedback loops shown must work effectively

Dynamic Complexity

Natural and human systems have high levels of dynamic complexity.

Most people think of complexity in terms of the number of components in a system or the number of combinations one must consider in making a decision.

The problem of optimally scheduling an airline's flights and crews is highly complex, but the complexity lies in finding the best solution out of an astronomical number of possibilities.

Such needle-in-a-haystack problems have high levels of combinatorial complexity

Dynamic complexity, in contrast, can arise even in simple systems with low combinatorial complexity.

Time delays between taking a decision and its effects on the state of the system are common and particularly troublesome.

In many cases controlled experiments are prohibitively costly or unethical.

BARRIERS TO LEARNINGLimited Information

We experience the real world through filters.

No one knows the current sales rate of their company, the current rate of production, or the true value of the order back log at any given time.

The act of measurement introduces distortions, delays, biases, errors, and other imperfections

Above all, measurement is an act of selection.

- Some of the selection is hard wired (we cannot see in the infrared or hear ultrasound).

-Some results from our own decisions. We define gross domestic product (GDP) so that extraction of non renewable resources counts as production rather than depletion of natural capital stocks and so

that medical care and funeral expenses caused by pollution-induced disease add to the GDP while the production of the pollution itself

does not reduce it.

BARRIERS TO LEARNING

Confounding Variables and Ambiguity

To learn we must use the limited and imperfect information available to us to understand the effects of our own decisions

Ambiguity arises because changes in the state of the system resulting from our own decisions are confounded with simultaneous changes in a host of other variables.

The number of variables that might affect the system vastly overwhelms the data available to rule out alternative theories and competing interpretations.


Bounded Rationality and the Misperceptions of Feedback

Dynamic complexity and limited information reduce the potential for learning and performance by limiting our knowledge of the real world.

But how wisely do we use the knowledge we do have?

Do we process the information we do get in the best way and make the best decisions we can?

Unfortunately, the answer is no.


Flawed Cognitive Maps

Causal attributions are a central feature of mental models.

Studies of cognitive maps show that few incorporate any feedback loops.

rather, people tended to formulate intuitive decision trees relating possible actions to probable consequences

The heuristics we use to judge causal relations lead systematically to cognitive maps that ignore feedbacks, multiple interconnections, nonlinearities, time delays, and the other elements of dynamic complexity.


Erroneous Inferences about Dynamics

To use a mental model to design a new strategy or organization we must make inferences about the consequences of decision rules that have never been tried and for which we have no data.

People cannot simulate mentally even the simplest possible feedback system, the first-order linear positive feedback loop.


Unscientific Reasoning: Judgmental Errors and Biases

To learn effectively in a world of dynamic complexity and imperfect information people must develop "insight skills“ the skills that help people learn when feedback is ambiguous.

Unfortunately, people are poor intuitive scientists, generally failing to reason in accordance with the principles of scientific method.

Hypothesis TestingYou are shown these four cards. Each card has a letter on one side and a number on the other. What is the smallest number of cards you should turn over to test the rule that cards with vowels on one side have even numbers on the reverse? Which are they?

E 74K


Defensive Routines and Interpersonal Impediments to Learning

Implementation Failure

BARRIERS TO LEARNINGImproving the Learning Process: Virtues of Virtual Worlds

Virtual World• Known structure• Variable level of complexity• Controlled experiments


• Simulation used to infer dynamics of mental models correctly

Real World• Unknown structure• Dynamic complexity• Time delays• Inability to conduct controlled experiments

Mental Models• Mapping of feedback structure• Disciplined application of scientific reasoning• Discussability of group process, defensive behavior

Virtual World• Perfect Implementation• Consistent incentives• Consistent application of decision rules• Learning can be goal

Real WorldDecisions

Real World• Selective perception• Missing feedback• Delay• Bias, distortion, error• Ambiguity

Information Feedback

• Implementation failure• Game playing• Inconsistency• Performance is goal

• Complete, accurate, immediate feedback

Virtual World

System Dynamics An Introduction Shahram Shadrokh.

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Transcript of System Dynamics An Introduction Shahram Shadrokh.