Cynefin iscram summer_school

Cynefin, Knowledge
Management and
Research

Simon French
simon.french@warwick.ac.uk

Research projects differ
(Social)
• Some seek to explore issues Sciences
– field studies vs laboratory studies
• Some seek to confirm or disprove
hypotheses
– field studies vs laboratory studies
• Some seek to critically evaluate an area
• Some seek to solve a problem and
implement a solution
• Some design and implement new systems
• Some seek to develop new theory or Engineering &
algorithms mathematics

2

Cynefin: a Welsh habitat
Cynefin:
Complex
• physical environment
• cultural environment
The realm of Social Systems Knowable
Cause and effect may be
• social environment determined after the event The realm of
• historical environment Scientific Inquiry
• ….. Cause and effect can
be determined with
Chaotic sufficient data
Cause and effect
not discernable

D. Snowden (2002). Known
"Complex acts of knowing - The realm of Scientific
paradox and descriptive self- Knowledge
awareness." Journal of Cause and effect understood
Knowledge Management 6 and predicable
pp. 100-11.

A conventional view of
emergency management
Complex
The realm of
Scientific Inquiry

Chaotic

Known
The realm of Scientific
Knowledge

What can we learn from the
past?

Chernobyl
Complex
The realm of
Scientific Inquiry

Chaotic

Known
Knowledge

Three Mile Island
Complex
The realm of
Scientific Inquiry

Chaotic

Known
Knowledge

More generally
Complex
Recovery The realm of
Scientific Inquiry Response
Chaotic

Known
Knowledge
Threat

Knowledge Management and
Nonaka’s SECI
Externalisation

tacit explicit
knowledge knowledge

Socialisation Combination
Internalisation

Cynefin and Knowledge Management
Complex
determined after the event The realm of
Scientific Inquiry

Combination
Cause and effect can
Socialisation

be determined with
Cause and effect
not discernable

Known
Knowledge
Cause and effect understood
and predicable

Explicit Knowledge
Data/Information
Tacit Knowledge Management
Collaboration tools

Applications of Cynefin
• Emergency Management
• Categorisation of DSS and OR/DA
techniques
• Human Reliability Analysis
• High Reliability Organisations
• Knowledge Management
• Sensemaking
• Research Methodology

Cynefin: learning, repeatability
Complex
Scientific Inquiry
be determined with
Cause and effect
not discernable

Known
Knowledge
Cause and effect understood Repeatability
and predicable
and
increasing
familiarity

Cynefin and data collection
Complex
Scientific Inquiry
be determined with
Cause and effect
not discernable

Known
Knowledge
and predicable

Cynefin and statistics
Complex
The realm of Social Systems
Unique Knowable
events Scientific Inquiry
be determined with
Cause and effect
not discernable
Events?
Known
Knowledge
and predicable

So ...
... why do we see so many articles in
social science and management journals
dealing with issues that are clearly in the
complex space but using confirmatory
methods:
– Analysis of variance/covariance
– Structural equation modelling
– Precise p-values
– ...

Cynefin and Values
Complex
Unique
Scientific Inquiry
events Cause and effect can
be determined with
Cause and effect
not discernable

Known
Events? The realm of Scientific
Knowledge
and predicable

Complexity vs Complicated
• Confusingly complexity science does not
relate primarily to the complex space!
• It is about computationally intensive
models: i.e. complicated models
Models understanding cause and effect
• So complexity science applies to the
knowable space much more than the
complex space.

Human Reliability Modelling
Complex
The realm of
Scientific Inquiry

Chaotic

Known
Knowledge

High reliability organisations
Complex
The realm of
Scientific Inquiry

Chaotic

Known
Knowledge

Do preferences exist?

• DeFinetti famously said
– “Probabilities do not exist”
• Do preferences exist?
• or better
– When do preferences come into
existence?

Cynefin and decision making
Complex
probe, The realm of Social Systems Knowable
sense, determined after the event The realm of
respond Scientific Inquiry
Cause and effect can Sense,
be determined with analyse
and
Cause and effect
act not discernable respond
sense
respond Known
Knowledge
and predicable

categorise and respond

Complex
Level 0: soft OR, PSM, EDA, EIS, data-
mining
Level 1: expert judgement
Level 2: scenario planning
Level 3: simpler MCDM models, Knowable
simpler decision trees,
influence diagrams Level 0: databases, MIS
Level 1: statistical inference, Level 0: Acquisition, checking and
forecasting presentation of data,
Chaotic Level 2: OR models e.g. LP directly or with minimal
simulation analysis, to DMs
Level 0: exploration,

}{
Level 3: decision trees,
Level 1: trial and error, Level 1: Analysis and forecasting of
influence diagrams
Level 2: building the current and future
Level 3: hypotheses. environment.
Known Level 2: Simulation and analysis of
the consequences of
Level 0: database systems
potential strategies;
Level 1: forecasting
determination of their
Level 2: simulation
feasibility and quantification
Level 3: AI, e.g. expert systems,
neural nets
of their benefits and
disadvantages.
Data and model based systems Level 3: Evaluation and ranking of
(cf. combination cycle of SECI) alternative strategies in the
Collaboration face of uncertainty by
tools, GDSS, decision balancing their respective
conferencing, etc. benefits and
disadvantages.
(cf. socialisation cycle of SECI)

Cynefin iscram summer_school

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