Presented by Karl M. Rich and Kanar Dizyee at a training course on systems thinking and spatial group model building, May 2020.

1. An introduction to systems thinking:
concepts and simple models (part 1)
Better lives through livestock
O
K
A
PiS
Training course on Systems Thinking and Spatial Group Model Building
Materials prepared and presented by Karl M. Rich and Kanar Dizyee
Foresight Modeling & Policy Team, Policies, Institutions, and Livelihoods
International Livestock Research Institute (ILRI), Dakar, Senegal
Version May 2020 (draft)

2. 2
What this course is and is not
It is an introduction to systems thinking concepts and approaches
It is a course that will provide you skills and exposure to the development of
systems models with stakeholders, and applied in various fields
It is not a modeling course per se – this course will not give expertise on
building quantitative system dynamics models but will give insights on such
approaches (and a teaser on how to do it)
Specific training on system dynamics modeling can accompany this course
Delivered through either traditional classroom or online means (!!)

3. 3
What’s in it for me?
1. Develop an understanding of systems thinking and
how to put causal relationships together qualitatively
using system dynamics language
2. Gain facility in the use of participatory GIS and
facilitation tools to develop system model structure
with stakeholders (including online tools)
3. Tools and methods to organize and administer a group
model building session

4. 4
Online facilitation
For online delivery, our course will entail the use of a suite of
excellent online and web-based tools:
• Microsoft Teams: main platform for verbal communication
and visualization of web pages in one place
• Google Jamboard: a web-based platform for real-time
collaboration/brainstorming
• Vecta.io: an online editor for collaborative graphics editing
(and with a useful layers feature!)
• InsightMaker: a web-based system dynamics software
package for initial concept models
• Stella Architect: our workhorse SD modeling software, with
ability to host models and interfaces online.

5. 5
Who we are
O
K
A
PiS

6. 6
Outline
Motivation
Principles of systems thinking/system dynamics
Stocks, flows, feedbacks, delays, causal loop diagrams
Practice with CLDs
Putting a simple SD model together – product
adoption

7. 7
Overview
Agricultural value chains present a number of
challenges in their analysis
• Complexity
• Data collection (primary and secondary data)
• Dynamics and evolution over time
• Impact assessment (qualitative vs.
quantitative)
• Engagement with policy processes
• Resolution of analysis
Over-reliance on qualitative, descriptive methods

8. 8
Overview
Systems approaches hold promise as a
means to overcome some of these
challenges.
System dynamics (SD) – a platform for
modelling and simulating agricultural value
chains (Rich et al. 2011)
Provides a way to visualize/understand a
host of prospective outcomes to inform
development interventions and investment
options Picture credit: Jared Berends 2019 (Palaw, Myanmar) Source: Dizyee et al. (2017)

9. 9
What is system dynamics?
System dynamics is a computer-aided approach
to policy analysis and design. It applies to
dynamic problems arising in complex social,
managerial, economic, or ecological systems —
literally any dynamic systems characterized by
interdependence, mutual interaction, information
feedback, and circular causality. (System
Dynamics Society)
A methodology for studying complex dynamic
systems that include nonlinearities, delays, and
feedback loops.
Source:
https://commons.wikimedia.org/wiki/File:Feedback_Loops_in_a_System_Dynamics_
Model.jpg#/media/File:Feedback_Loops_in_a_System_Dynamics_Model.jpg; Public
Domain

10. 10
Origins
Founded by Jay W. Forrester of MIT in 1950s
Industrial Dynamics (Forrester 1961)
Urban Dynamics (Forrester 1969)
World Dynamics (Forrester 1971)
Graphical Simulators: Dynamo, iThink, Powersim, Vensim, etc. (1985 –
later)
System dynamics is currently applied in economics, public policy,
environmental studies, defense, commodity cycles, management, etc.

11. 11
Concepts
Stocks (accumulation)
Flows (change overtime – rate/time unit)
Feedback loops (circular causality)
Delays

12. 12
Building blocks: stocks
A stock symbolizes anything that accumulates (builds
stock) or drains (loses stock) over time.
At time t, the value of any given stock will be what has
entered the stock minus anything that has left it.
Example: drinking a glass of water
Represented in system dynamics as a “box” shape
Water in glass

13. 13
Building blocks: flows
This leads us directly to our second
building block, flows.
A flow is the rate of change in a stock.
In the previous example, we would
have two flows: water entering the
glass and water leaving it.
Water in glass
Water enteringglass Water leavingglass

14. 14
Building blocks: converters
Flows cannot change by themselves – there are various
factors that influence these rates
In system dynamics, we define converters (or parameters)
to define variable that take an input and translate them
into a relationship to calibrate flows and other variables
These converters can be numbers, equations, or defined
by tables or graphs
What kinds of parameters might be defined in our water
example?
Water in glass
Water enteringglass
Converter

15. 15
Building blocks: converters
Water in glass
Water enteringglass Water leavingglass
Angle holdingglass
S
peed of drinking
S
peed of pouring

16. 16
Feedback
Feedback is “the process wherein one component of the
model initiates changes in other components, and those
modifications lead to further changes in the component that
set the process in motion” (McGarvey and Hannon 2004: 6).
More simply, feedback determines the dynamic process of a
system and how things evolve over time.
Feedback can be positive or negative.
Positive feedback is self-reinforcing (R). It amplifies what
happens in the system. Negative feedback is self-correcting (B).
It counteracts and opposes change the system. (examples
coming!)

17. 17
Causal loop diagrams
Causal loop diagrams (CLDs) are a qualitative way of
developing intuition about complex systems
They highlight cause and effects relationships within
systems and help us visualize feedback effects
Provide insights on leverage points that could improve
desired system behavior
Combinations of these loops specify specific behavior of a
system over time.

18. 18
Causal loop diagrams
Links in a CLD can go in only one of two
directions
They can move in the same direction
(denoted by “s” or +)
They can move in opposite directions
(denoted by “o” or -)
Sales Profits
s
Incidence of
disease
o
Vaccination
”More sales leads to more profits”
”More vaccination leads to less incidence of disease”

19. 19
Online facilitation of CLD development
We can easily facilitate the collaborative
development of CLDs in real-time online via
Jamboard (http://jamboard.google.com).
Once a Google Jamboard link is set up, those
with the link address can anonymously
contribute to its development.
Sticky notes can be used for each node of the
CLD. Arrows (and polarity, + or -) can be drawn
with the freehand writing icon.
Icon for freehand writing (icon below is to erase)
Icon for sticky notes

20. 20
Feedback loops
A reinforcing loop can have
only “s” links or an even
number of “o” links
Funds for
investment
Customers
Sales revenue
Profits
Source: Sherwood (2002)
s
s
s
s

21. 21
Feedback loops
A reinforcing loop can have
only “s” links or an even
number of “o” links
The even number of “o”
links “cancel” out (like a
double negative).
Source: Sherwood (2002)
Strain on
management
Workload
Coping ability
Incidence of errors
o
s
o
s

22. 22
Reinforcing loops – system behavior
Time
System
state
Time
System
state
OR
System
state
Time
Question: how might this system behavior happen?
21
Reinforcing loops – system behavior
Time
System
state
Time
System
state
System
state
Time

23. 23
Reinforcing loops – system behavior
Funds for
investment
Customers
Sales revenue
Profits
Source: Sherwood (2002)
s
s
s
s

24. 24
Funds for
investment
Customers
Sales revenue
Profits
Source: Sherwood (2002)
s
o
s
s
External shock
s
External shock
o
External shock
o
External shock o
What external shocks
might we consider?
Reinforcing loops - dangles

25. 25
Reinforcing loops – dangles
These external influences on
the system as in the previous
slide are called dangles.
They can either provide input
into the system (as in the
diagram) or being a
consequence of system
behavior (output dangles).
Dangles can be purely
externally driven (a shock) or
can be a policy choice of
decision maker – a lever for
decision making.
Funds for
investment
Customers
Sales revenue
Profits
Source: Sherwood (2002)
s
o
s
s
External shock
s
External shock
o
External shock
o
Returns to investors
s

26. 26
Balancing loops
Balancing loops, by
contrast, counteract
and resist change.
In a balancing loop,
the system converges
on a goal or target.
Example: pouring a
cup of coffee
Source: Sherwood (2002)
Actual level of coffee
in the cup
Gap between target
and actual levels
Physical action
Source: Sherwood (2002)
s
s
o
s
Target level
of coffee
Gap = TARGET-ACTUAL

27. 27
Balancing loops – system behavior
Time
Time
System
state

28. 28
Delays
Delays are “the process whose
output lags behind input.”
In any system, it takes time to
measure, evaluate, and report
information.
It takes time for decisions to affect
the state of the system.
Question – what happens if there’s
a delay in the system?
Time
System
state

29. 29
Delays - example
Actual temperature
Gap between target
and actual temperature
Adjusting the tap
Source: Sherwood (2002)
s
s
o
s
Target
temperature
//
Indicates a delay

30. 30
Impacts of feedback loops
Real-life systems: a combination of positive and
negative feedback loops.
Specific behaviors induced by different
combinations
These behaviors can be gleaned qualitatively
(systems archetypes) and/or more directly
(precisely) through quantitative modeling
We’ll focus on the latter, but I’ll provide a
couple of examples of the former in a moment
Source:
https://en.wikipedia.org/wiki/System_archetype#/media/
File:SD_Archetypes.png, CC-BY-SA 4.0

31. 31
Limits to growth
Feedback Structure: Switching of the
dominance of reinforcing and balancing
feedback loops.
Causes S-shaped growth
Strategies:
• Figure out ways to reduce/mitigate causes of
limits to growth
• How balance growth and balancing processes?
Time
System
state

32. Funds for
investment
Customers
Sales revenue
Profits
s
s
s
s
Market size
Market
share
o
s
s
o
Source: Sherwood (2002)

33. Funds for
investment
Customers
Sales revenue
Profits
s
s
s
s
Market size
Market
share
o
s
s
o
Regulation
What are
impacts?
Source: Sherwood (2002)

34. 34
Fixes that fail
Application of short term fix create long-
term consequences, requiring more short-
term fixes in the future
Root cause of the problem not addressed.
Strategies:
• Focus on root causes of the problem
• Increase awareness of unintended
consequences
• Focus on long-term, including metrics for
performance
Source: https://commons.wikimedia.org/wiki/File:Fixes_that_fail.PNG, CC-
BY-SA 3.0
https://en.wikipedia.org/wiki/Fixes_that_fail#/media/File:Ftfgraph.png, CC-BY 3.0

35. 35
A's success
A's activity with B
B's success
B's activity with A
+
+
+
+
R1
A's actions to
improve A's
results
B's actions to
improve B's
results
-
+
+
-
B1
B2
A's unintended
obstruction of
B's success
B's unintended
obstruction of
A's success
+
-
+
- R2
Source: Lie et al. (2016), based on Kemeny (1994)
Leverage points:
• Review conditions that were
basis for coordination – were
they appropriate?
• What internal incentives drive
external system behavior?
• Directly demostrate effects of
each other’s behavior’s
(simulation)
• Develop broader goals and
metrics that align behavior
• Communication strategies
Accidental adversaries

36. 36
Accidental adversaries
Source: Lie et al. (2016)
+
Cooperative strength
relative to smallholders'
strength
Cooperative influence
over smallholders
Degree of
smallholders'
autonomy
Smallholders'
non-cooperative
activities
Smallholders'
asset base
-
+
-
+
-
+
+
Dairy strength relative
to cooperative strength
Dairy influence
over cooperative
Degree of
cooperative
autonomy
Ability to achieve
non-partnership
cooperative-specific
objectives
Health of the
cooperative
+
-
+
+
-
+
-
Leverage points
e.g. Level of trust
-
-
Market strength relative
to processor strength
Market influence
over processor
Degree of
processor
autonomy
Ability to achieve
processor and partnership
obligations
Health of the
processor
+
+
-
-
+
+
+
Individual
smallholders'
success
+
Cooperative
success
+
-
+
R
R
R
R
R
R
B
B
B

37. 37
Causal loop diagrams – some pointers (1)
Know your boundaries – try to stay relevant!
Start somewhere interesting: ask “What are the key
external drivers?/What are the key results?
What are the key items related to the problem wanted to
be solved?”
Ask “What does this drive/cause?” and “What is this driven
by?”
Don’t get cluttered
Use nouns not verbs
Don’t use words like “increase in” or “decrease in”
Source:
https://en.wikipedia.org/wiki/System_archetype#/media/File:Tragedy_of_the_com
mons.PNG, CC-SA-BY 3.0

38. 38
Causal loop diagrams – some pointers (2)
Don’t be afraid of unusual terms
Define s and o (or + and -) as you go
Keep going!!
Ensure reality in your diagram by users
Don’t fall in love with your diagrams – they can
(will) change!
Note that no diagram is ever finished!
Picture credit: K.M. Rich 2019 (Hanoi, Vietnam)

39. 39
Recap
The utility of systems thinking – what it is, why it matters
The language of systems thinking: stocks, flows, converters,
feedback loops.
The roles of feedback loops and CLDs in revealing patterns of
dynamic behavior.
Source:
https://commons.wikimedia.org/wiki/File:Augustus_Edwin_Mulready_A_London_
Newsboy.jpg; public domain

40. 40
Exercises (offline or online)
In plenary:
Competition for resources (ch. 5 of
Sherwood)
In small groups:
Group applications based on group
interest/background (to be given by
facilitator)
Source: https://commons.wikimedia.org/wiki/File:Man_Lifting_Barbell_Cartoon.svg
CC-BY-SA 4.0"

41. 41
A model of new product adoption
Let’s put our systems thinking knowledge to use to co-
develop a simple model using our new vocabulary.
Main idea: process by which new products or ideas diffuse
in a population (think technology, extension, etc.); see Bass
(1969)
Transitions between two populations:
• Potential adopters, who have not adopted a
technology
• Actual adopters that have
What would influence the adoption decision?
Source: https://commons.wikimedia.org/wiki/File:Bass_diffusion_model.svg,
public domain

42. 42
A model of new product adoption: drivers
• http://www.nzdl.org/gsdlmod?e=d-00000-00---off-0hdl--00-0----
0-10-0---0---0direct-10---4-------0-1l--11-en-50---20-about---00-0-
1-00-0--4----0-0-11-10-0utfZz-8-
00&a=d&cl=CL1.1&d=HASHf4ce87f94c88c31e4a2d70.7.fc
Source:
https://commons.wikimedia.org/wiki/File:Goat_detail,_Bock,_stock_beer_advertising_poster,_1
889_(cropped).jpg, public domain
PEER EFFECTS (OR WORD OF
MOUTH) – what drives this?
ADVERTISING – what drives this?

43. 43
Translating the product adoption model into SD
Let’s put this in a systems thinking framework using STELLA
Architect icons for visualization
Some leading questions:
1) We have two populations (potential adopters and actual
adopters) – how would we represent this in system
dynamics (what would they be: a stock, a flow, or a
converter?)
2) How do we consider the transition of populations i.e.
from potential adopters to actual adopters? Is this a
stock, flow, or converter?
3) How do we represent the drivers of adoption – are they
stocks, flows, or converters?

44. 44
Translating the product adoption model into SD
Actual adopters
Potential adopters
1) Our two populations are stocks (number
of people at a given period in time). In
STELLA, we represent as a rectangle.
2) Moving from potential to actual
adopters is a flow: a change in a stock
over time. Here, it is a biflow to
consider disadoption.
3) Drivers of adoption (advertising, peer
effects) are converters: they contain
data or equations that influence the
rate of change of our flow.
Actual adopters
Potential adopters
Adoption rate
Actual adopters
Potential adopters
Adoption rate
Adoption from peer effects
Adoption from advertising

45. 45
Translating the product adoption model into SD
How do we define “adoption from
advertising”?
This will depend on how good advertising is
and the number of those that have not
adopted.
We define this as a multiplicative relationship,
defining a new converter, “Effectiveness of
advertising” and linking it and the stock
“Potential adopters” to it.
Actual adopters
Potential adopters
Adoption rate
Adoption from peer effects
Adoption from advertising
Effectiveness of advertising

46. 46
Translating the product adoption model into SD
Now, how do we define adoption by
word of mouth?
This will depend on:
• The mixing of adopters and
potential adopters
• The rate of contact between them
(stock, flow, or converter?)
• The percentage of those contacted
that adopt (stock, flow, or
converter)
• The total population in the system
(stock, flow, or converter?)
Actual adopters
Total population
Potential adopters
Adoption rate
Contact rate
Adoption fraction
Adoption from peer effects
Adoption from advertising
Effectiveness of advertising
Change in population
Population growth rate

47. 47
Results and extensions
Let’s show some results from STELLA Architect
based on this model.
What happens if we change the parameters for
contact rate, advertising effectiveness, and
fraction adopted. Why?
Some extensions:
What if products do not last forever? How might
this model changes if consumers have to discard
old products to get new ones?
What about learning or price effects?
What about links to other systems?
Weeks
0
500000
1000000
1.00 25.75 50.50 75.25 100.00
Potential adopters Actual adopters

48. 48
Wrap-up: modeling considerations
Taking a modular approach – consider
interfaces between system phenomena:
biophysical relationships, trade, adoption,
cashflow (credit), social dynamics, etc.
Simplicity vs. modeling “everything”
Role of participatory approaches (means
of bringing diverse stakeholders together)
for data and validation. This is the topic
for the next two sessions!
Animal
disease
dynamics
Animal
production
Market
dynamics
Investment
dynamics
Environmental
impact
biosecurity
Resource
constraints
sales

49. 49
References
Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215-227.
Lie, H., Brønn, C., & Rich, K.M. (2016). A systems perspective on partnership governance in smallholder
agricultural value chains. Paper presented at the International Agribusiness and Food Management Association
Annual Meetings, Aarhus, Denmark, June 2016.
Sherwood, D. (2002). Seeing the forest for the trees: a manager's guide to applying systems thinking. London,
Nicholas Brealey International.
Wanyoike, F. & Rich, K.M., 2007. Socio-Economic Impacts of the 2007 Rift Valley Fever Outbreak in Kenya: A
Case Study of the North Eastern Province Livestock Marketing Chain. Unpublished report for the USAID project,
“Learning the Lessons of Rift Valley Fever: Improved Detection and Mitigation of Outbreaks,” Nairobi, Kenya:
ILRI.

50. THANK YOU
Picture credit: K.M. Rich 2019 (Niamey, Niger)