Please see below selected recent complexity-related change.
See also:
November 2024
- Sara Imari Walker, author of the 2024 book Life as No One Knows It, is at the forefront of the “physics of life,” a new field investigating what makes life different from non-life. While physics has long excelled at describing inanimate objects, life poses a unique puzzle: Living systems create, adapt, and evolve in ways that non-living matter does not. What is different about the organisation of living systems that makes this possible? To tackle this, Walker and her collaborators developed Assembly Theory, a framework that reimagines the role of information and complexity in explaining life’s unique properties. Assembly Theory explores what enables complex structures to emerge and persist, showing how certain molecular arrangements hold functional or evolutionary significance and offers a new lens for measuring life’s complexity, one rooted in fundamental physical principles.
June 2024
- Complexity science shifts scientific inquiry from predictable laws to studying dynamic, emergent systems. By taking a transdisciplinary approach to studying complex systems, complexity science could help us tackle some fundamental questions: What is life? How do minds work? How does a biosphere co-evolve with the rest of a planet etc??
September 2023
- In Notes on Complexity, Neil Theise intertwined the discoveries of Western science — from particle physics to neuroscience to chaos theory — with Eastern metaphysical traditions and his own long-time Zen Buddhist practice.
June 2022
- Further reading:
November 2021
- One of the paradoxes of the modern world of work is that the more complex, skilled, and well-paid some jobs are, the harder it is for others to tell if someone is performing well or not. In other words, greater complexity can make performance harder to judge.
July 2021
-
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.
November 2018
- 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.
Pre 2018
- 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
- Disintermediation
- 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.
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