Presentation for the Consortium for Service Innovation's team meeting on Knowledge as a Service.

1. Expanding our Understanding:
Complex Adaptive Systems
Jon Stevens-Hall, Principal Product Manager (ITSM)
Consortium for Service Innovation Team Meeting: “Knowledge as a Service”
November 2020
November 2020

2. https://www.washingtonpost.com/graphics/national/maps-of-american-infrastrucure/
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3. https://www.washingtonpost.com/graphics/national/power-plants/
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4. 4
Defining Complexity
“The property of a real-world system that is
manifest in the in ability of any one formalism
being adequate to capture all its properties”
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“The emergence of complexity: science coming of age or science growing old?”
– Donald Mikulecky, Virginia Commonwealth University 2001
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5. 5
Complex Adaptive Systems
“A group of semi-autonomous agents who interact in
interdependent ways to produce system-wide patterns,
such that those patterns influence behavior of the agents.”
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“Complex Adaptive Systems – A Nominal Definition”
– Kevin Dooley, Departments of Management and Industrial & Management Systems Engineering, Arizona State University, 1996
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6. 6
Key Characteristics of Complex Adaptive Systems
Self Organisation
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Emergence
Resilience
Observer Dependency
Path Dependency
Chaos
Irreducibility
Intractability
The overall order is one of constant fluctuations and
feedback loops in the interactions between individual parts.
Individual parts continually combine and uncombine to form
new system characteristics.
The system exhibits an ability to react to failures and
negative events by reorganizing to maintain its functions.
The nature of the system will appear different to different
observers, depending on their specific viewpoint.
Transformations to the system can not always be reverted
back to their previous state.
The present nature of the system is heavily influenced by
decisions and events in the past.
Small variations in input can lead to dramatic changes in
output, and vice versa.
It is impossible to predict all future behaviours and states of
the system.
Photo by David Berry on Flickr, used under Creative Commons license

7. Complexity in Digital Systems
• Essential Complexity
• “Adding new features to software (or safety properties
like availability and security) requires the addition of
complexity”
• Driven by addition of new features and capabilities
• Accidental Complexity
• “The consequence of writing software within a
resource-limited setting”
• Code is a compromise
• Accrues as a by-product of work
• Suboptimal system components accumulate beyond
intuitive understanding
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8. Complexity in Digital Systems
Digital Services
Service
Subscribers
Tools
Processes
Services
Vulnerabilities3rd Party
Providers
Compliance
Environments
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9. Complexity in Digital Systems
Compliance
Environments
Private
Cloud
Mainframe
Hybrid
Cloud
Data
Centers
Public Cloud
Digital Services
Service
Subscribers
3rd Party
Providers
Tools
Processes
Services
Vulnerabilities
Waterfall
Agile
Lean
SLAs
DevOps
DevSecOps
ITIL
End Users
Agents
IT Ops
Security
Applications
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10. 10
Failure in Complex Systems
• Complex systems contain mixtures of latent issues
• Most flaws are insufficient to cause significant issues
• The flaws change constantly, due to evolving technology and organisational
factors
• Complex systems run as broken but the system continues to function
sometimes in degraded mode.
• Issues have multiple causes, not a single root-cause
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“How Complex Systems Fail” – Richard Cook, Cognitive Technologies Library, University of Chicago, 1990.
“Distributed systems have an infinite list of almost impossible failure scenarios”
– Charity Majors, Honeycomb.io, at Configuration Management Camp, Ghent, 2019
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11. 11
• Desired State
• No corrective actions are
necessary
• Allowed State
• Behaviours have diverged from
what is desired,
• No drastic measures yet required
• Possible State
• System has moved into realm of
what is possible but not allowed
• Immediate resolution action
needed
Rethinking system management for Emergence
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“Concepts in Complexity Engineering”
– Regina Frei, Imperial College, 2011.
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12. • Obvious and Complicated domains:
• Repeating relationship between cause and effect
• With Complicated you need to do analysis to find
that relationship
• Complex domain:
• Understanding the problem requires
experimentation and analysis.
• May, over time, be able to move to Complicated
• Chaotic domain:
• Dramatic and unconstrained
• Focus on damage limitation, try to move to
another domain
Cynefin – A framework approach to complexity
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13. 13
Some further talking points
• Plenty of scope for discussion on the implications of complexity on KCS
• KCS has some clear strengths
• E.g. it is designed to enable content to evolve and adapt
• But… is Knowledge Management too deterministic for supporting
complexity?
• For example, assumption of relationship between cause and effect
within a specified environment?
• New roles for Knowledge Management?
• E.g. as a means to enable understanding of what is Desired,
Possible and Allowed in an emergent system
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