1. Managing
uncertainty
in
resilient
organizations
P.
H.
Longstaff
Syracuse
University
KNOW4DRR
Bolzano
2013
P.H.
Longstaff

2. Planning
Options
• Resistance
(The
Citadel)
• In
baLle,
surprise
or
superior
force
reduces
ability
to
resist
• Tendency
to
fail
catastrophically
• Trust
high
unPl
failure
• Resilience
(Surviving
to
operate
another
day)
• StaPc:
Bouncing
back
–
return
to
“normal”
• AdapPve:
Bouncing
forward
• Trust
built
and
reinforced
oYen
KNOW4DRR
Bolzano
2013
P.H.
Longstaff

3. • For
predictable
systems:
• Development
of
facts,
reproducibility,
risk
eliminaPon
(resistance)
• For
known
unknowns:
• Cyclical
systems
and
unpredictable
emergence
(power
laws)
• Development
of
“odds”
and
risk
miPgaPon
(sta8c
resilience)
•
For
unpredictable
systems:
• Black
Swans,
new
surprises
• Development
of
acceptable
parameters;
nudging
and
learning
(adap8ve
resilience)
Goals
for
managing
uncertainty

4. “Normal”
distribution
Normal
Frequency
75604530150-15-30
60
50
40
30
20
10
0
A Typical Normal Distribution
Mean~20 ; Std Dev ~20
Normal

5. Gamma
(Power
Law)
distribution
Gamma
Frequency
1251007550250-25
100
80
60
40
20
0
A Typical Gamma Distribution
Mean~20 ; Std Dev ~20

6. Power
Laws
and
Hollywood:
Typical
Revenue
pattern
REVENUE
400.0
360.0
320.0
280.0
240.0
200.0
160.0
120.0
80.0
40.0
0.0
50
40
30
20
10
0
Std. Dev = 70.38
Mean = 57.0
N = 189.00

7. Resilience
usually
increases
with
• Diversity
–
many
opPons
for
resources
• Interoperability,
cross-­‐training
• Access
to
other
networks
(bridgers)
• IntervenPon
at
the
right
scale
• Right
balance
of
Tight/Loose
Coupling
• Adap8ve
capacity
–
mechanisms
for
• Ability
to
change
• Knowledge
management
(knowing
and
remembering)
KNOW4DRR
Bolzano
2013
P.H.
Longstaff

8. Resilience
requires
trustworthy
information
• Accurate
sensing
of
environment
• Watch
out
for
Local
adaptaPon
and
PracPcal
DriY
(ScoL
Snook,
USAF,
ret.)
• CounPng
the
right
stuff
(not
what’s
handy,
what
proves
it’s
working)
• What
is
NOT
working
(hide
it
or
suffer?)
• InsPtuPonal
memory
• ConnecPon
to
other
info
and
ideas
• Unexpected
events
“audit”
our
ability
to
adapt
–
how
do
we
learn
from
that?
KNOW4DRR
Bolzano
2013
P.H.
Longstaff

9. Learning
and
Adaptation
are
Lowered
by
• Hindsight
bias
• ConfirmaPon
Bias/MoPvated
ScepPcism
• Overconfidence
in
knowledge
–
“planning
fallacy”
• No
ba6le
plan
ever
survives
first
contact
with
the
enemy
Helmuth
von
Moltke,
A
19th-­‐century
head
of
the
Prussian
army
• Plans
can
decrease
mindful
an=cipa=on
of
the
unexpected
Weick
and
Sutcliffe,
Managing
the
Unexpected
•
Clinging
to
Cogworld
(Microscope
v.
Kaleidoscope
-­‐
NSF)
• Demands
a
“fix”
–
constrain
system,
new
complexity,
more
uncertainty
• The
Blame
Game
• The
Buck
doesn’t
stop
anywhere
KNOW4DRR
Bolzano
2013
P.H.
Longstaff

10. Changing
the
Game:
building
adaptability
in
environments
with
high
uncertainty
• Acknowledge
unpredictability
and
create
new
ways
to
learn
and
plan
• Create
a
sub-­‐system
for
Pmes
of
crisis
and
plan
how
you
will
learn
in
that
sub-­‐system
• Decide
when
improvisaPon
is
going
to
be
OK
and
how
you
can
learn
from
it
• Set
up
sensors
that
indicate
when
• adapPve
mechanisms
are
failing
(e.g.
challenges
cascade)
• Ppping
points
are
near
• buffers/reserves
are
near
exhausPon
KNOW4DRR
Bolzano
2013
P.H.
Longstaff

11. Heroes
of
Uncertainty
• Combine
an
awareness
of
common
paLerns
with
an
acute
aLenPon
to
the
specific
circumstances
of
a
unique
situaPon.
• David
Brooks
NYT
28
May
2013
• Understand
that
they
don’t
know
it
all
–
humility.
• Know
that
they
may
fail
and
accept
it
as
a
temporary
set-­‐
back.
KNOW4DRR
Bolzano
2013
P.H.
Longstaff