Why systems thinking?
 Because our logical deduction
mechanisms are trained to induct linearly,
not cyclically
 We don’t see the feedback loops
 Consequently, we don’t comprehend the
opportunities for reinforcement or the
consequences of limitations/constraints
 Forrester: every decision, every action is
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embedded in an information feedback loop
More motivation
 We are immersed in and victims of
structures that we have little awareness of
 Causes and their effects are often spatially
and temporally separated
 Today’s problems are yesterday’s solutions
 To make good decisions we need to
understand dynamic complexity, not detail
complexity
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Still more motivation
 The integration that comes from the
application of information technology is
creating complexity at a frenetic pace
 Out of the complexity comes the potential
for chaos and catastrophe
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Key Benefits of the ST
 A deeper level of learning
• Far better than a mere verbal description
 A clear structural representation of the
problem or process
 A way to extract the behavioral
implications from the structure and data
 A “hands on” tool on which to conduct WHAT
IF
Senge’s Five Disciplines
 Personal Mastery
– because we need to be the very best we can be
 Mental Models
– because these are the basis of all decision-making
 Shared Vision
– because this galvanizes workers to pursue a
common goal
 Team Learning
– because companies are organized into teams
 Systems Thinking
Reinforcement
 What were those experiments with rats???
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The Saga of Peoples Air
 A totally different airline
 Founded in 1980 to provide low-cost, high-
quality airline service to travelers in the
Eastern U.S.
 Grew to nation’s 5th largest carrier
 Brought in a host of innovative human
resource policies
 In 1986 lost $133M in first six months, and
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was taken over by Texas Air
What brought PEOPLES down??
 Many explanations
 Some blame Burr’s “soft” people-oriented
management policies
 Some blamed the unions
 Others blamed the use of Americans’ Sabre
Reservation system
• Load management could offer a limited
number of low-cost seats while others were
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“full coach”
What variables to blame?
 Fleet variables
 Human resource variables
 Competitive factors
 Financial variables
 Policy Levers
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Fleet variables
 Planes
 Capacity of aircraft
 Routes
 Scheduled flights
 Competitor routes
 Service hours per plane per day
 Fuel efficiency
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Human resources
Service personnel
 Aircraft personnel
 Maintenance
personnel
 Hiring
 Training
 Turnover
 Morale

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 Productivity
 Experience
Team management
 Job rotation
 Stock ownership
 Temporaries

Competitive Factors
 Market size
 Market segments
 Reputation
 Service quality
 Competitor service quality
 Fares “load Management”
 Competitor fares
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Financial variables
Revenues
 Profit
 Cost of plane
operations
 Cost of service
operations
 Cost of marketing
 Wages

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 Stock price
 Growth rate
 Debt
 Interest Rate
Policy Levers
 Buying planes
 Hiring people
 Pricing
 Marketing expenditures
 Service scope
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Enormous detail complexity
 We could build a model that contained all
of this detail
 Or we could use the systems archetypes to
disentangle this parable of complexity
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Systems Thinking basics
 Peruse relevant literature
 Talk to people knowledgeable about the
problem
 List relevant variables
 Describe causal interactions between
variables
 Fully delineate the causal diagram
 Draw behavior over time graphs
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Examples
 Itch--scratch
 population and growth rate of population
 revenues, sales force size, sales
 inventory, order rate, desired inventory,
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A single-sector Exponential
growth Model
Consider a simple population with
infinite resources--food, water, air,
etc. Given, mortality information in
terms of birth and death rates, what
is this population likely to grow to by
a certain time?
Exponentially growing
population model
 In 1900 there were just 1.65 billion people
on the planet. Today, there are more than 6
billion people on the planet. Every year
there are .04 births per capita and .028
deaths per capita.
 The .04 births p er capita shall be referred
to as a parameter called BIRTH RATE
NORMAL
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Experiments with growth models
 Models with only one rate and one state
 Average lifetime death rates
 cohorts
 Models in which the exiting rate is not a
function of its adjacent state
 Including effects from other variables
• ratios and table functions
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What do we have in terms of
loops?
 A growth loop certainly (reinforcing)
• The airline, unlike WonderTech was investing
in its capital equipment infrastructure
• It was buying planes to accommodate the
growth
 A balancing loop
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What archetypes?
 LIMITS TO GROWTH
 SHIFTING THE BURDEN
• Erodiing goals (standards)
 The combination of these produces a third
archetype
• The Growth and Underinvestment Archetype
• This was first seen in the WonderTech
Scenario
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The Simplified Structure--p. 133
Reputation
Fleet size & flights
R
No. of pass. carried
B
Revenues
Service quality
Delay
Service quality standa
Service capacity
B
Perceived need to improve quality
Delay2
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Additions to service capacity
The Simulation Structure-Reinforcing Loop
Lifetime
Deprec
Flights/yr
Flights/$-yr
Fleet capital
Avg. load factor
Addns to fleet capital
R
No. of pass carried/yr
Rev. to fleet fract
Revenues/yr
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Avg. Revenue/pass
The Simulation
Lifetime
Deprec
Flights/yr
Flights/$-yr
Fleet capital
Addns to fleet capital
No. of pass carried/yr
Rev. to fleet fract
Revenues/yr
Avg.
Revenue/pass
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Avg. load factor
From Causal Diagram to
Schematic (Stock & Flow)
Diagram
 Some simple causal models
 Some associated schematic models
 Some rules
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Can you construct the schematic
model for this Causal model?
desired level
adjustment rate
adjustment time
actual level
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We know what that is
Dessired level
Adjustment time
Adjustment rate
Actual level
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How about this one?
birth rate norm
birth rate
Population
death rate norm
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death rate
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We know what it is
Birth rate norm
Birth rate
Population
Death rate norm
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Death rate
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Some rules
 There are two types of causal links in
causal models
• Information
• Flow
 Information proceeds from stocks and
parameters toward rates where it is used to
control flows
 Flow edges proceed from rates to states
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(stocks) in theSystems
causal
diagram always
Loops
 In any loop involving a pair of
quantities/edges,
 one quantity must be a rate
 the other a state or stock,
 one edge must be a flow edge
 the other an information edge
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CONSISTENCY
 All of the edges directed toward a quantity
are of the same type
 All of the edges directed away from a
quantity are of the same type
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Rates and their edges
q4
q1
q2
Information
edges
RATES Flow edges
q3
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q5
q6
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Parameters and their edges
q1
PARAMETER
Information
edges
q2
q3
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Stocks and their edges
q4
q1
STOCK
q2
Flow edges
q5
Information edges
q3
q6
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Auxiliaries and their edges
q1
q4
q2
Information
edges
AUXILIARY
q5
Information
edges
q3
q6
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Outputs and their edges
q1
q2
Information
edges
OUTPUT
q3
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STEP 1: Identify parameters
 Parameters have no edges directed toward
them
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STEP 2: Identify the edges
directed from parameters
 These are information edges always
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STEP 3: By consistency identify
as many other edge types as you
can
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STEP 4: Look for loops involving a
pair of quantities only
 Use the rules identified above
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System Dynamics Software
 STELLA and I think
• High Performance Systems, Inc.
• best fit for K-12 education
 Vensim
• Ventana systems, Inc.
• Free from downloading off their web site:
www.vensim.com
• Robust--including parametric data fitting and
optimization
What is system dynamics
 A way to characterize systems as stocks
and flows between stocks
 Stocks are variables that accumulate the
affects of other variables
 Rates are variables the control the flows of
material into andout of stocks
 Auxiliaries are variables the modify
information as it is passed from stocks to
rates
A DEMO
Nature’s Templates: the
Archetypes
 Structures of which we are unaware hold
us prisoner
– The swimmer scenario
 Certain patterns of structure occur again
and again: called ARCHETYPES
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We are creating a “language”
 reinforcing feedback and balancing
feedback are like the nouns and verbs
 systems archetypes are the basic
sentences
 Behavior patterns appear again in all
disciplines--biology, psychology, family
therapy, economics, political science,
ecology and management
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 Can result in the unification of knowledge
Recurring behavior patterns
 Do we know how to recognize them?
 Do we know how to describe them?
 Do we know how to prescribe cures for
them?
 The ARCHETYPES describe these recurring
behavior patterns
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The ARCHETYPES
 provide leverage points, intervention
junctures at which substantial change can
be brought about
 put the systems perspective into practice
 About a dozen systems ARCHETYPES have
been identified
 All ARCHETYPES are made up of the systems
building blocks: reinforcing processes,
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balancing processes, delays
Before attacking the ARCHETYPES
we need to understand simple
structures
 the reinforcing feedback loop
 the balancing feedback loop
 THE DEMO
 Pages 520-525 in Austin/Burns--your
handout
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ARCHETYPE 1: LIMITS TO GROWTH
 A reinforcing process is set in motion to
produce a desired result. It creates a
spiral of success but also creates
inadvertent secondary effects (manifested
in a alancing process) that eventually slow
down the success.
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Management Principle relative
to ARCHETYPE 1
 Don’t push growth or success; remove the
factors limiting growth
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ARCHETYPE 1: LIMITS TO GROWTH
 Useful in all situations where growth
bumps up against limits
 Firms grow for a while, then plateau
 Individuals get better for a while, then their
personal growth slows.
 Falling in love is kind of like this
– The love begins to plateau as the couple get to
know each other better
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Structure
growing action
Reinforcing
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state of stock
slowing action
Balancing
Understanding the Structure
 High-tech orgs grow rapidly because of
ability to introduce new products
 This growth plateaus as lead times become
too long
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How to achieve Leverage
 Most managers react to the slowing growth
by puching harder on the reinforcing loop
 Unfortunately, the more vigorously you
push the familiar levels, the more strongly
the balancing proces resists, and the more
futile your efforts become.
 Instead, concentrate on the balancing
loop--changing the limiting factor
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– This is akin to Goldratt’s Theory of Constraints--
Applications to Quality Circles
and JIT
 Quality circles work best when there is
even-handed emphasis on both balancing
and reinforcing loops
 JIT has had to focus on recalcitrant
suppliers
 THERE WILL ALWAYS BE MORE LIMITING
PROCESSES
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– When once source of limitatiin is removed, another
will surface
Create your own LIMITS TO
GROWTH story
 Identify a limits to growth pattern in your
own experience
 Diagram it
• What is growing
• What might be limitations
• Example--the COBA and University capital
campaigns
• NOW, LOOK FOR LEVERAGE
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Test your LIMITS TO GROWTH
model
 Talk to others about your perception
 Test your ideas about leverage in small
real-life experiments
 Run and re-run the simulation model
 Approach possible resistance and seek
WIN-WIN strategies with them
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ARCHETYPE 2: shifting the burden
 An underlying problem generates
symptoms that demand attention. But the
underlying problem is difficult for people to
address, either because it is obscure or
costly to confront. So people “shift the
burden” of their problem to other solutions-well-intentioned, easy fixes that seem
extremely efficient. Unfortunately the
easier solutions only ameliorate the
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The Stereotype Structure
Symptiom-Correcting
Process
Symptomatic Solution
Addictioin Loop
BALANCING
REINFORCING
Problem
BALANCING
Problem-Correcting
Process
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Fundamental Solution
Side effect
Special Case: Eroding Goals
 Full employment meant 4% unemployment
in the 60%, but 6 to 7% unemployment in the
early 1980’s
 Gramm-Rudman bill called for reaching a
balanced budget by 1991, but this was
shifted to 1993 and from 1993 to 1996 and
from 1996 to 1998
 “If all else fails, lower your goals..”
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EXAMPLE
Alcohol
Alcohol
BALANCING
Stress/Depression
BALANCING
Health
BALANCING
Stress/Depression
Reduce workload
BALANCING
Reduce workload
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Health
Another Example
Raise tuition, add course fees, etc.
Costs of Higher Ed not funded by State
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Lower enrollments
Perceived cost to the stude
Still Another Example
Symptom-correcting
process
Heroics and Overtime
Addiction Loop
Project Delayed
Reward for heroic behavior
Improvement of processes/practices
Efectiveness of PM practices
Problem-correcting
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Process
“Shifting the Burden” is an
insidious problem
 Is has a subtle reinforcing cycle
 This increases dependence on the
symptomatic solution
 But eventually, the system loses the ability
to apply the fundamental solution
 The system collapses
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Senge Says
 Today’s problems are yesterday’s solutions
 We tend to look for solutions where they
are easiest to find
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HOW TO ACHIEVE LEVERAGE
 Must strengthen the fundamental response
• Requires a long-term orientation and a shared
vision
 Must weaken the symptomatic response
• Requires a willingness to tell the truth about
these “solutions”
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Create your own “Shifting the
Burden” Story
 Is there a problem that is getting gradually
worse over the long term?
 Is the overall health of the system
gradually worsening?
 Is there a growing feeling of helplessness?
 Have short-term fixes been applied?
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– The Casa Olay problem of using cupouns to
generate business and then can’t get away from
using the coupons because their customer base is
hucked on coupons
To structure your problem
 Identify the problem
 Next, identify a fundamental solution
 Then, identify one or several symptomatic
solutions
 Finally, identify the possible negative “side
effects” of the symptomatic solution
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Review
 We have now seen two of the basic systems
archetypes.
• The Limits to Growth Archetype
• The Shifting the Burden Archetype
 As the archetypes are mastered, they
become combined into more elaborate
systemic descriptions.
 The basic “sentences” become parts of
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paragraphs
Seeing Structures, not just Trees
 Helps us focus on what is important and
what is not
 Helps us determine what variables to focus
on and which to play less attention to
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WonderTech: The Chapter 7
Scenario
 A lesson in Growth and Underinvestment
 What Senge gets out of this is the Growth
and Underinvestment Archetype
• A combination of variants of the Limits to
Growth Archetype and the Shifting the Burden
Archetype
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The WonderTech Scenario
 WonderTech continues to invest in the growth side
of the process. Sales grow but then plateau.
Management puts more sales people into the field.
Offers more incentives to sales force. But because
of long lead times, customers wane. “Yes you have
a great product, but you can’t deliver on your lead
time promise of eight weeks. We know; we’ve
heard from your other customers.” In fact, the
company relaxed its lead-time standard out to
twelve to sixteen weeks because of insufficient
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capacity.
The Reinforcing Loop
Size of Sales Force
REINFOR
CING
Revenues
Number of Orders
The Balancing Loop: Following
the LTG Archetype
Sales Difficulty
Size of Sales Force
Number of Orders
Revenues
Size of Backlog
Delay
Delivery Time
The Growth Curve: Page 117
What’s happened?
 WT’s management did not pay much
attention to their delivery service. They
mainly tracked sales, profits, market share
and return on investment. WT’s managers
waited until demand fell off before getting
concerned about delivery times. But this is
too late. The slow delivery time has already
begun to correct itself. The management
was not very concerned about the relaxed
The WonderTech Scenario
 The firm decides to build a new
manufacturing facility. But the facility
comes on line at a time when sales are
declining and lead times are coming back
to the eight-week standard.
 Of every 10 startup companies, 5 will
disappear with five years, only 4 survive
into their tenth year and only 3 into their
fifteenth year.
The Shifting the Burden Component
Sales Difficulty
Number of Orders
Delay
Size of Backlog
Delivery Time
Delivery time standard
Production Capacity
Perceived need to improve delivery time
Delay2
Planned additions to capacity
Put the whole thing together
Comments on The Senge
Methodology
 Sees problems as conforming to a finite
number of “archetypes”
 Formulates models based on combinations
of the archetypes
 Addresses problem-driven situations
• What about situations and systems that are
technology-driven, dynamics-driven,
exogenously-driven, anything but problemdriven
More Comments on the Senge
Methodology
 But does this become sufficiently general
to accommodate all dynamical “scenarios
and situations”?
 It is difficult to translate his archetypes
and causal models into running system
dynamics simulations
• A lot of variables (RATE VARIABLES, specifically)
get left out in terms of connections
More Comments on the Senge
Methodology
 The focus is on characterizing the
dynamics, not on how to capture that in
terms of stocks, flows and information
paths
 He doesn’t label his edges with “+” or “-”
signs
Another methodology: The
Sector Approach to SD model
formulation
 Begin by identifying the sectors
• A “sector” is all the structure associated with a
single flow
• There could be several states in a single
sector
 Determine the within-sector structure
• Reuse existing “molecules” where possible
 Determine the between-sector information
infrastructure
A Single-sector Exponential
goal-seeking Model
 Sonya Magnova is a television retailer
who wishes to maintain a desired
inventory of DI television sets so that
she doesn’t have to sell her
demonstrator and show models.
Sonya’s ordering policy is quite simple-adjust actual inventory I toward
desired inventory DI so as to force
these to conform as closely as
possible. The initial inventory is Io. The
time required for ordered inventory to
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be received is AT.
A Two-sector
Housing/population Model
 A resort community in Colorado has
determined that population growth in the
area depends on the availability of
hoousing as well as the persistent natural
attractiveness of the area. Abundant
housing attracts people at a greater rate
than under normal conditions. The
opposite is true when housing is tight. Area
Residents also leave the community at a
certain rate due primarily to the availability
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of housing.
Two-sector Population/housing
Model, Continued
 The housing construction iindustry, on the
other hand, fluctuates depending on the
land availability and housing desires.
Abundant housing cuts back the
construction of houses while the opposite
is true when the housing situation is tight.
Also, as land for residential development
fills up (in this mountain valley), the
construction rate decreases to the level of
the demolition rate of houses.
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What are the main sectors and
how do these interact?
 Population
 Housing
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What is the structure within each
sector?
 Determine state/rate interactions first
 Determine necessary supportng
infrastructure
• PARAMETERS
• AUXILIARIES
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What does the structure within
the population sector look like?
 RATES:
in-migration, out-migration, net
death rate
 STATES: population
 PARAMETERS: in-migration normal, outmigration normal, net death-rate normal
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What does the structure within
the housing sector look like?
 RATES:
construction rate, demolition rate
 STATES: housing
 AUXILIARIES: Land availability multiplier,
land fraction occupied
 PARAMETERS: normal housing construction,
average lifetime of housing
 PARAMETERS: land occupied by each unit,
total residential land
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What is the structure between
sectors?
 There are only AUXILIARIES, PARAMETERS,
INPUTS and OUTPUTS
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What are the between-sector
auxiliaries?
 Housing desired
 Housing ratio
 Housing construction multiplier
 Attractiveness for in-migration multiplier
 PARAMETER:
person
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Housing units required per
dimensions
1
2
3
4
5
6
7
8
9 10 11 12 13 4 15 16
17
1 AA
2 AA/DD
1
1
4 dimless
1
5 CC/AA
7
1
1
3 I/DD
6 AA/DD
-1
1
1
-1
AA/(BB.DD)
1
8 AA/ZZ
1
9 BB
1
10 CC
1
-1
11 CC\DD
1
1
12 DD
-1
13 CC/DD
-1
14 CC/(AA.DD)
1
15 ZZ
16 CC/AA
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17 CC
-1
-1
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