k4 d ws_p_longstaff_bolzano_2013
TRANSCRIPT
Managing uncertainty in resilient organizations P. H. Longstaff Syracuse University
KNOW4D
RR Bo
lzano
2013
P.H. Lon
gstaff
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
KNOW4D
RR Bo
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gstaff
• 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
“Normal” distribution
Normal
Freq
uenc
y
75604530150-15-30
60
50
40
30
20
10
0
A Typical Normal Distribution
Mean~20 ; Std Dev ~20
Normal
Gamma (Power Law) distribution
Gamma
Freq
uenc
y
1251007550250-25
100
80
60
40
20
0
A Typical Gamma Distribution
Mean~20 ; Std Dev ~20
Power Laws and Hollywood: Typical Revenue pattern
REVENUE
400.0360.0
320.0280.0
240.0200.0
160.0120.0
80.040.0
0.0
50
40
30
20
10
0
Std. Dev = 70.38 Mean = 57.0
N = 189.00
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)
KNOW4D
RR Bo
lzano
2013
P.H. Lon
gstaff
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?
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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
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RR Bo
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gstaff
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
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gstaff
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.
KNOW4D
RR Bo
lzano
2013
P.H. Lon
gstaff