Complex Knowledge

1. Complex Knowledge Dave Snowden Cynefin Centre for Organisational Complexity

2. The three rules of Knowledge Management • Knowledge can only be volunteered, it cannot be conscripted • Camouflage – motivated by fear of abuse • Conformity – motivated by time • I only know what I know when I need to know it • Human knowledge requires contextual stimulation • The way that we know things is not the way that we say we know them, and that goes for decision making too • We always know more than we can say, and we will always say more than we can write down • Context Management • Narrative Management • Content Management

3. Knowledge is the means by which we inform, not a higher order of information Wisdom Knowledge Information Data

4. Knowledge is the means by which we inform, not a higher order of information Wisdom Knowledge Information Data

5. Knowledge is the means by which we inform, not a higher order of information Context Data Information Knowledge Wisdom

6. The model that launched a thousand failed KM initiatives: Nonaka’s SECI • Originally a paper on knowledge transfer in the consumer goods manufacturing industry in Japan • Rapidly generalised as a model for knowledge transfer in all sectors • Closely linked with Business Process Re-engineering • Used way beyond Nonaka’s original intent Tacit Explicit S E Tacit Externalisation Socialisation I C Explicit Internalisation Combination

7. ASHEN Modela meaningful question • ArtefactsAny thing made by people, processes, documents, tools in which knowledge is imbedded • SkillsAbilities that can be trained and measured without ambiguity, but remember the time issue • HeuristicsRules of thumb, the outcome of experience, the main repository of knowledge mostly unarticulated • ExperienceAccumulated experience of failure and success which allows the right pattern to be triggered in the right context • Natural TalentSome people are just better at doing things than other people – and they are often not the people you expect

8. Knowledge Disclosure Pointsa meaningful context • Any event or activity which reveals knowledge through use • Decisions • Problems resolution • Solution creation • Judgement • Learning points • Used to create the context in which the ASHEN question can be asked • Can be achieved through virtual and physical means • Virtual allows anonymity and multi-persona techniques to be used • Physical provides a different dynamic of interaction to vitual (not better or worse but virtual) • DO NOT judge or value or move to solution

9. First Exercise Knowledge Disclosure Points

10. Discovering KDP & ASHEN • Anecdote Circles • A group with cohesive experience • May use a structured task (e.g. future history) • Stimulate Ditting • Use of Actors, Cartoonists etc • Use of Proxies • Mass Capture Techniques • Naïve Interviews • The Set up • Participative techniques • Field Interviews • Participative Observation • Deep Emersion

11. From KDP to Knowledge Objects KDP CLUSTERS KNOWLEDGE OBJECTS ASK ASHEN S A E A S A N H A S A H A ASHEN CLUSTERS

12. From KDP to Knowledge Objects KDP CLUSTERS KNOWLEDGE OBJECTS ASK ASHEN S A E A S A N H A S A H A ASHEN CLUSTERS

13. Communities and JIT Knowledge management Using the natural flow patterns of an organisation

14. Acceptable levels of Abstraction Abstraction ULAA LLAA Cost of Codification

15. The Cynefin sense making framework applied to knowledge management High Abstraction Communities of Practice The Domain 150 The Shadow Organisation Domain of 15 Learning Cultures Teaching Cultures Chaos the threat of the new Crisis management & opportunity Committees & Review Bodies Bureaucracy Low Abstraction

16. The Cynefin sense making framework applied to knowledge management High Abstraction Communities of Practice The Domain 150 The Shadow Organisation Domain of 15 Learning Cultures Teaching Cultures Chaos the threat of the new Crisis management & opportunity Committees & Review Bodies Bureaucracy Low Abstraction

17. The Cynefin sense making framework applied to knowledge management High Abstraction Good Practice Emergent Practice Learning Cultures Teaching Cultures Decisive Action Best Practice Low Abstraction

18. Communitiesan emergent model

19. Communitiesan emergent model

20. Social Network Analysis

21. Knowledge Project Matrix THE THINGS DECISION MAKERS VALUE 5 2 4 1 3 7 6 !!!! !! !!!! KNOWLEDGE OBJECTS !! !!!!!!

22. Knowledge Project Matrixeasy to sell, hard to implement THE THINGS DECISION MAKERS VALUE 5 2 4 1 3 7 6 !!!! !! !!!! KNOWLEDGE OBJECTS !! !!!!!!

23. Knowledge Project Matrixhard to sell, easy to implement THE THINGS DECISION MAKERS VALUE 5 2 4 1 3 7 6 !!!! !! !!!! KNOWLEDGE OBJECTS !! !!!!!!

24. Knowledge Project Matrixa portfolio of knowledge projects THE THINGS DECISION MAKERS VALUE 5 2 4 1 3 7 6 !!!! !! !!!! KNOWLEDGE OBJECTS !! !!!!!!

25. Creating a Knowledge Strategy“what” related to value, where and flow

26. The history and context of knowledge management • The first generation before the SECI model • Information Management • Technology supports human decision making • The second generation the SECI period (corruption of Nonaka) • Knowledge is tacit or explicit • KM practice focuses on the conversion of tacit to explicit knowledge • Closely linked to Business Process Re-engineering (BPR) • Focus on maximising efficiency • The third generation post SECIthe impact of complexity science • Knowledge is never wholly tacit or explicit • Just in Time Knowledge Management • A focus on effectiveness rather than efficiency

27. To improve the effectiveness (not the efficiency) of decision making and to create the conditions for innovation The purpose of Knowledge Management

28. 3rd Generation or Post SECI KM • Content Management • Narrative Management • Narrative representation, neither qualitative, quantitative but emergent • Archetypes & themes & & & & • Narrative databases • Indexed on emergent, NPML and Demographic data • Enable Serendipitous encounter • Myth Management • Fable Construction • Story Virus • Monitoring • Context Management • Social network stimulation • Expertise location • Narrative (metaphor) • JIT KM

29. Technology is a tool: if we pick it up and it fits our hand it is useful, if we have to bio-reengineer our hand to fit the tool something is going badly wrong!

30. Knowledge Managementthe final metaphor How do you learn to ride a bicycle?

31. The Cynefin Centre Nasrudin found a weary falconsitting one day on his window-sill. He had never seen a bird like this before. ‘You poor thing’, he said, ‘how ever were you to allowed to get into this state?’ He clipped the falcon’s talons and cut its beak straight, and trimmed its feathers. ‘Now you look more like a bird,’ said Nasrudin. From IdriesShah, The exploits of the Incomparable Mulla Nasrudin & The subtleties of the Inimitable Mulla Nasrudin. Octagon Press, London 1985

32. The Cynefin Centre A new paradigm in research and method development

33. The presumptions of management science and consultant practice • The presumption of rational choice • human actors make a “rational” decision based on self interest • individual and collective behavior can be managed by manipulation of pain/pleasure • education to make those consequences evident will change behavior • The presumption of intentional capability • the acquisition of capability indicates an intention to use that capability • actions from competitors are the result of intentional behaviour: every “blink” we see is in effect a “wink” and act accordingly. • hypocrisy of intent, we have accidents, they don’t • The presumption of unitary and stable identity • organisations are aggregations of discrete individuals • categorisation and stereotyping • scalability – the rule of 15 & 150 • The presumption of order • relationships between cause and effect capable of discovery and empirical verification and in consequence, prescriptive and predictive models are possible

34. Contextual Complexitythe landscape of management Complex The domain of many possibilities Cause and effect coherent in retrospect, repeat accidentally Empirically Knowable The domain ofthe probable Cause and effect separatedover time & space but repeat ChaosThe domain of the inconceivable No cause and effect relationships generally perceivable Empirically Known The domain ofthe actual Cause and effect relationsrepeating & predictable

35. Contextual Complexitydecision models The domain ofthe probable Sense Analyse Respond The domain of many possibilities ProbeSenseRespond The domain of the inconceivable ActSenseRespond The domain ofthe actual SenseCategorise Respond

36. Contextual ComplexityIntervention types Complex The domain of many possibilities Pattern Management PERSPECTIVE FILTERS COMPLEX ADAPTIVE SYSTEMS Empirically Knowable The domain ofthe probable Analytical/Reductionist SCENARIO PLANNING SYSTEMS THINKING ChaosThe domain of the inconceivable Stability focused intervention ENACTMENT TOOLS CRISIS MANAGEMENT Empirically Known The domain ofthe actual Legitimate best practice STANDARD PROCEEDURES PROCESS RE-ENGINEERING

37. Interpretation of state determined by preference for action Complex Knowable DISORDER Known Chaos

38. Crisis managementmulti-point attractors • Asymmetric Collapseexcessive order leads to the collapse of order – chaos • Single point attractorsthe charismatic dictator stabilise to order, with the danger of asymmetric collapse • Multi point attractorscreate several points of attraction (chaos to complexity) to seed possibilities • Select favourable patterns to exploit (Complex to Knowable) • Kill unfavourable patterns (but you don’t know which they will be X X

39. Discovery and InterventionNetwork linkages Domain of Possibilities Domain of the Probable Weak central strong distributed Strong central strong distributed Inconceivable Domain Domain of the Actual Weak Central weak distributed Strong central weak distributed

40. Diagnosis - principles Domain of Possibilities Every time we think we have solved the problem it returns in a different form Minor, troublesome events occur but are easily rationalised Diagnostics are interventions – they influence the patterns Domain of the Probable We have learnt how to solve this problem and are now in a stable situation We have been here before and any deviations are readily understood Diagnostics can be separatedfrom intervention Inconceivable Domain This is a new situation in which we no previous experience and have no reflective time Something completely unexpected at this time There is no time or need for any diagnostic Domain of the Actual We can teach people how to do things very quickly and ensure compliance We control the space and can determine behaviour Any diagnostic is a simple check against predetermined criteria

41. Intervention Domain of Possibilities Multiple small and diverse interventions to create options Always have an exit strategy to the ordered domains for exploitation PROBE – SENSE - RESPOND Domain of the Probable Analytical techniques to determine facts and option range Create ritual/cyclical process to prevent entrainment of expertise SENSE – ANALYSE - RESPOND Domain of the Actual Standard process with reviewcycle & clear measures Establish indicators to prevent catastrophic failure SENSE – CATEGORISE - RESPOND Inconceivable Domain Single or multi point attractor(s) to stabilise situation Avoid creating long term dependency on single attractor ACT – SENSE - RESPOND

42. Licensed Methods Comprise Components Models and Tools Which have Attributes and Uses And combine into Assemblies and Recipes Interface with Other bounded techniques The Cynefin Life Cycle Discovery Programs Address • Intractable Problems • Re-vitalising other approaches Comprise • Catalytic and Patterning Events • Focused Participatory Learning Streams • Emergent Research Typically • Run over 3-6 months • Are Multi-client Emergent Methods Are • Experimental • Innovative and • Challenging They are • Sold at cost • Using key staff and • Aim to become Licensed They can • Use the elements of licensed method • Create new elements