Please see below selected recent complexity-related change.
Complexity, The Emerging Science at the Edge of Order and Chaos, by: M. Mitchel Waldrop, claims that in the rarified world of scientific research, a revolution has been brewing. Its activists are not anarchists, but rather Nobel Laureates in physics and economics and graduates, mathematicians, and computer scientists from all over the world. Their radical idea is to create a new science: complexity. They want to know how a primordial soup of simple molecules managed to turn itself into the first living cell - and what the origin of life some four billion years ago can tell us about the process of technological innovation today.
- Complexity science can enable those thinking about and working on these issues to better understand and adapt to the complexities of the real world. A series drawn directly from the Overseas Development Institute Working Paper Exploring the science of complexity: Ideas and implications for development and humanitarian efforts, while written in the context of development and humanitarian work, is also broadly relevant, including to knowledge management and change management.
- Notes from a Cognitive Edge/Dave Snowden presentation in 2009:
- We always know more than we can say, and we will always say more than we can write down
- Knowledge is volunteered, it cannot be conscripted
- We only know what we know when we need to know it
- In the context of real need few people withhold their knowledge
- Tolerated failure imprints learning better than success
- The way we know things is not the way we report we know things
- Everything is fragmented
- A system is any network that has coherence - it may be fuzzy, it may or may not have purpose
- An agent is anything which acts within the system - individual, group, idea etc.
- Three types of system: Ordered: system constrains agents, reductionism & rules, deterministic, observer independence; Chaotic: agents unconstrained & independent of each other ,studied through statistics & probability; Complex: system lightly constrains agents, agents modify system by their interaction wit hit and each other, they co-evolve (irreversibility).
- Highly sensitive to small changes
- Proximity & connectivity of agents has high impact
- Meaning emerges through interaction
- Hindsight does not lead to foresight
- Shift from fail-safe design to safe-fail experimentation
- Use of distributed cognition = wisdom but not foolishness of crowds
- Work with finely granulated objects information and organisational
- Putting decision makers in direct contact with raw data
- Strategy needs to be distributed
- From prediction to anticipatory awareness
- From scenario planning to dynamic micro scenarios
- New organisational forms from “the matrix” to crews
- Knowledge Management needs to get messy
- Lessons learnt, to lessons learning
- The voluntary nature of knowledge exchange & emergent trust
- Worst practice systems
- Social computing, paradox of publishing, blog storms & wikis
- From CoPs to self-organising networks of meaning
- The use of micro-narratives (not story telling)
- Fragments = transcripts, audio, video clips, URLs etc
- Signifiers = semi-constrained indexes, serendipitous discovery of fragmented material
- Allows for impact measurement as well as knowledge, distribution and research
- Sensemaking (the ability to "situate" a network)
- Continuous monitoring allows for weak signal detection; quarterly surveys etc. don't
- Continuous capture of weakly-constrained micro-narratives provides qualitative human intelligence that can then be analysed (clustered, mapped etc.) quantitatively and almost in real time, as the flow of intelligence is continuous - if you want the detail you can drill down.