Main takeaways
LEARNING FROM COMPLEX SYSTEMS
Andrew Hatch
@hatchman76
whoami
20+ years
Linear Models of Delivery
Evolution to autonomous teams
Nodes in a network
Independent, self-organizing
....mostly
Mindset shift
Centralized,
command and control
Distributed,
localized
How can we learn?
As complexity increases,
and the knowledge gap grows?
Traditional Incident Management
Incidents are always preventable!
People aren't following processes!
Management is not in control!
The Post-mortem
IN
OUT
Root Cause
Action Items!
"How could you not have noticed that?"
"But isn't that the way it should work?"
Diagnosis?
Is there a better way?
Maybe learning and adapting is a better strategy?
Management of work
People are the unpredictable parts of the system
variance and local adaptation must be neutralised
Management should enforce rules and workers should obey
All functions and associated training are defined in intimate detail, people only act as directed
Frederick Winslow Taylor
The Principles of Scientific Management (1911)
=
"unpredictable"
"inefficient"
"untrustworthy"
WORK-AS-IMAGINED = WORK-AS-DONE
rooted in linear thinking, which is understandable due to the mass industrilaisation of the 20th century.
Mechanisation of animals from the late 18th century reaching it's zenith in the early 20th. Mechanisation of thought, electrons replacing gears and cogs
>Incidents followed this pattern. Problems are thought of a chain of events, needing isolation down
More of than not this is the human, the unpredictable, flawed component of the system
Breakdown follows reductionism principles
Linear Thinking
Chain of events
1:1 Cause and Effect
Breakdown in WAI vs WAD!
Causal Determinism
Reductionism
Anything can be known,
by following reducible analysis....
and controlling the environment
Knowledge and Understanding
(Linear Analysis and Systems Thinking)
Understanding the affects of environments is crucial!
What are the influencing factors, internal and external?
How does the system respond and adapt its behaviors?
Where linear analysis breaks down
ROOT CAUSE!
5 Whys
???
???
???
Characteristics of Complex Systems
Philosophy and sciences can agree that:
They have lots of components, that interact locally, not globally
Small changes done locally, can have unintented effects globally
Embed in their environments, adapt, grow and sensitive to changes
Require constant energy, entropy is constant, equilibrium is impossible
Hierarchy imposes constraints, added layers become more abstract
They have a history, which is crucial to their growth
How can we learn better?
Contributing factors
Signals received
Event timeline
Scribe
Facilitator
Patterns
Themes
Focus areas
Support & assistance
We will build more resilient systems
And improve our Knowledge and Understanding
Resiliency in nature
Australian Eucalypts
We've engineered resilience for millenia
Conditions for Resiliency
Learn from incidents as much as possible
They are
part of normal complex system behavior. Use them.
You can't wait for resilience to evolve naturally.
It must
become an on-going practice
Create conditions and environments where teams can sustain adaptive capacity - wherever the work-is-done
Understand the interactions between
people and
technology.
Don't isolate them as separate
challenges
Focus and promote what you
do well.
Sustain and grow the learning culture
Thank you
https://lfi.hatchman76.com
@hatchman76