1.
H O W C A N W E F O S T E R C I T I Z E N PA RT I C I PAT I O N
A N D W H O L E S Y S T E M S T H I N K I N G
@ A N S E L M @ M A K E R L A B

2.
A B O U T M E
• Anselm Hook (@anselm)
• Software Developer
• Worked on Augmented
Reality interfaces for the
last 5 years.

3.
1 . T H E L AY O F T H E L A N D

4.
O U R W O R L D

5.
TA K E A D E E P B R E AT H A N D C O N S I D E R
O U R H O M E

6.
A D I V E R S E B I RT H R I G H T T O P R O T E C T

7.
W E E A C H G R E W U P C O N N E C T E D T O T H E
W O R L D . H O W A R E Y O U C O N N E C T E D ?

8.
T H E M E D I A P O RT R AY S A C R I S I S

9.
C L I M AT E C H A N G E
S E A L E V E L R I S E
O C E A N A C I D I F I C AT I O N
R E S O U R C E E X H A U S T I O N
I N VA S I V E S P E C I E S
A N T I B I O T I C C O L L A P S E
H A B I TAT A N D D I V E R S I T Y L O S S
M O N O C U LT U R E FA R M I N G
D R O U G H T
L A R G E S C A L E R E F U G E E M I G R AT I O N
V I O L E N C E A N D WA R
N U C L E A R WA S T E

10.
“ I T W I L L B E D I F F I C U LT
E N O U G H T O AV O I D
D I S A S T E R O N P L A N E T
E A RT H I N T H E N E X T
H U N D R E D Y E A R S ”
Stephen Hawking

11.
4 0 % D I V E R S I T Y D I E O F F I N T H E L A S T 2 0 0 Y E A R S
I S T H I S T H E S I X T H E X T I N C T I O N ?

12.
T H E C L AT H R AT E G U N -
A N T H R O P O C E N E T I P P I N G P O I N T S ?

13.
I T I S N ’ T A L L D O O M A N D G L O O M . F O R E X A M P L E
S E E J E S S E A U S U B E L’ S C O M M E N T S A B O U T H O W
M A N Y L O C A L S Y S T E M S A R E R E B O U N D I N G :
H T T P : / / L O N G N O W. O R G / S E M I N A R S / 0 2 0 1 5 / J A N /
1 3 / N AT U R E - R E B O U N D I N G - L A N D - A N D - O C E A N -
S PA R I N G - T H R O U G H - C O N C E N T R AT I N G - H U M A N -
A C T I V I T I E S /

14.
T H E S E I M A G E S C R E AT E C O N F L I C T I N G
R E A C T I O N S I N O U R C O M M U N I T I E S
• ambivalence
• powerlessness
• uncertainty
• discord
• grief
• denial
I T ’ S W O R T H E X A M I N I N G T H O S E E M O T I O N S A N D H O W
T H E Y C O N T R O L U S . I T ’ S A L M O S T A S I F O U R W H O L E
C U LT U R E N E E D S T H E R A P Y.

15.
W E N E E D T O F I N D WAY S T O A C T I N
C O N C E R T W I T H E A C H O T H E R A N D
W I T H N AT U R E

16.
W E A R E A L L S TA K E H O L D E R S I N
M A N Y B I O R E G I O N A L I S S U E S .

17.
F O R E X A M P L E - P O L I C I E S T O M A N A G E F O R E S T
F I R E S I N C A L I F O R N I A M A D E T H E M W O R S E
Y E T O U R D E C I S I O N M A K I N G T E C H N I Q U E S
A R E S P E C TA C U L A R LY I N E F F E C T I V E .

18.
T H E C A L I F O R N I A D R O U G H T I S T H E P R O D U C T
O F 5 0 Y E A R S O F WAT E R M I S M A N A G E M E N T

19.
C H A I R M A N M A O ’ S F O U R P E S T S C A M PA I G N
H E L P E D K I L L 2 0 M I L L I O N P E O P L E

20.
T H E VA L U E O F M A N G R O V E S WA M P S
I T T U R N S O U T I S VA S T LY I N E X C E S S
O F T H E VA L U E O F C U T T I N G T H E M .

21.
T H E M A C A N D O P R O S P E C T H A D 5 0 M I L L I O N B A R R E L S
E S T I M AT E D . AT $ 1 0 0 P E R B A R R E L T H I S W O U L D B E $ 5
B I L L I O N I N C O M E . T O TA L C O S T O F T H E D E E P WAT E R
H O R I Z O N S P I L L I S E S T I M AT E D AT $ 5 0 B I L L I O N .

22.
M O S T O F U S D O N O T D I R E C T LY PA RT I C I PAT E I N
D E C I S I O N S T H AT A F F E C T O U R Q U A L I T Y O F L I F E .
W E A R E S T E E R E D B Y M E D I A R H E T O R I C A N D
U LT E R I O R M O T I V E S A R E D I F F I C U LT T O S E E .
T H E F E W W H O A R E O R G A N I Z E D A R E A B L E T O
D I S P R O P O RT I O N AT E LY B E N E F I T T H E M S E LV E S .
S I D E - E F F E C T S O F L AW S W E PA S S O F T E N
D O M I N AT E O V E R I N T E N D E D C O N S E Q U E N C E S .
T H E W O R L D I S T O O C O M P L E X F O R H U M A N S T O
U N D E R S TA N D . W E H AV E T O C H A N G E .

23.
W E H AV E T O H A C K T H E S TAT U S Q U O

24.
2 . W H O L E S Y S T E M S M O D E L S

25.
T H E R E I N T R O D U C T I O N O F W O LV E S I N T O
Y E L L O W S T O N E M A D E T H E R I V E R S R U N D E E P E R

26.
D I G G I N G H O L E S I N R A J A S T H A N ’ S A LWA R
D I S T R I C T R E V I TA L I Z E D A C O M M U N I T Y

27.
R E C O N N E C T I N G L A K E M E R R I T T T O T H E B AY
H A S H A D A C A S C A D E O F P O S I T I V E I M PA C T S

28.
W H AT I F Y O U C O U L D M A K E Y O U R W O R L D
A V I D E O G A M E T H AT Y O U C O U L D P L AY
• Imagine being able to pull up a model of your own local
watershed, your community, your local politics.
• Having it show the intersection of land-use, law and
policy.
• Predicting economic, environmental and social costs
and benefits of local agriculture changes, new bylaws,
climate and population changes.
• Adding your own data and drawing your own
conclusions and sharing them with other people.

29.
T H E B A L I N E S E WAT E R T E M P L E S I M U L AT I O N S T O P P E D
A B I L L I O N D O L L A R G R E E N R E V O L U T I O N P L A N

30.
N E W C A L I F O R N I A WAT E R AT L A S

31.
M O D E L I N G C A L I F O R N I A D R O U G H T

32.
B E N E F I T S O F M O D E L B A S E D T H I N K I N G
• Even a bad model is better than rhetoric and even a bad
model at least has to have internal consistency.
• Encouraging the use of models by ordinary citizens is
educational; help people become systems thinkers and
world makers. It’s a right.
• These are a class of tools that could help all of us as
stakeholders think better and produce better outcomes.
• Computers provide brute force computation. Humans
provide intuition.

33.
3 . H O W D O W E G E T T H E R E ?

34.
1 ) W E N E E D C O M M U N I T Y
• Who is solving these kinds of problems using whole systems
models? Examples like the re-introduction of wolves into
Yellowstone are likely informed by people with an understanding of
the trophic cascades.
• Notably what civic participation exists; not just participation by
scientists and academics?
• How can we encourage others to become world makers? What
tools do they need to participate?

35.
2 ) W E N E E D M O D E L S A N D D ATA
• What and where are examples of models? California is one of the
most observed and modeled places in the world. What kinds of
models of this bioregion exist?
• The USGS, NOAA and many government agencies have a mandate to
produce open source data. But do ordinary citizens contribute data?
• Beyond just data, do we have models of the relationships between
things (say between salmon populations and water turbidity)?

36.
3 ) W E N E E D T O T RY I T
• Are there examples similar to the Balinese Water Temple
Simulation where models could be used to affect decisions in
local government?
• Are there historical topics that can be modeled using computer
simulations to validate computer models (such as say the four
pests campaign in China)?
• Can we measure the benefits / issues of using models?

37.
I N A W O R L D T H AT S E E M S T O B E
S U C C U M B I N G T O E N T R O P Y
• We can empower ourselves for better solutions; moving
towards a net positive rather than just negative.
• We can make this world as we wish; we can thrive, we
can probably do it on the same time frames that we
caused the damage within.
• But we need to change and become something new:
how we think, see and make decisions. We basically
need to become a new kind of organism.

38.
T H A N K Y O U
• http://simulate.world collects articles and citations
• https://www.facebook.com/groups/world.modelers
• If you have input, suggestions or data please feel
free to contact me at anselm@gmail.com or
anselm@makerlab.com