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Liz Rykert's insight:
I just posted an article from this great site CultureUniversity.com thinking it was the full resource and it was not. So I am reposting it.
The most effective approach to change does not start or end in the C-suite. It happens at the heart of the organization, where mid-level managers and their teams build the momentum to implement and lead change. Executives initiate and support change, the rest of the organization lead change.
Learn why it's important to embrace complexity when working in the social sector. Professor Brenda Zimmerman of York University, shared her perspective in th...
Liz Rykert's insight:
This video of Brenda Zimmerman was taken at the Next Generation Evaluation Conference at Stanford in November 2013. She is doing a summary of the day and as she does she hits important highlights of embracing complexity - the title of her talk. It is about 45 mins.
To solve society's most pressing problems requires a system leader who can catalyze collective leadership. Includes magazine extras.
Liz Rykert's insight:
System leaders are leaders who can see and understand the larger underlying dynamics feeding the culture one finds oneself in. This is a good read but misses all the great material from people like June Holley https://twitter.com/juneholley and the Leadership Learning Community http://leadershiplearning.org/ who have been thinking and working in this space for some time.
Love the nine propositions outlined in this paper. Well worth the download and read through. They really are words to live by. I know they relate to evaluation of complex systems. But isn't evaluation really a chance to reflect and learn? And don't we all spend every day deeply engaged and interconnected in these complex networks all around us?
Love that someone has really thought this through:
Characteristics of Complex Systems
Propositions for Evaluation
A complex system is always changing, often in unpredictable ways; it is never static
1 Design and implement evaluations to be adaptive, flexible, and iterative
Everything is connected; events in one part of the system affect all other parts
2 Seek to understand and describe the whole system, including components and connections
Information is the fuel that drives learning and helps the system thrive
3 Support the learning capacity of the system by strengthening feedback loops and improving access to information
Context matters; it can often make or break an initiative
4 Pay particular attention to context and be responsive to changes as they occur
Each situation is unique; best principles are more likely to be seen than best practices
5 Look for effective principles of practice in action, rather than assessing adherence to a predetermined set of activities
Different sources of energy and convergence can be observed at different times
6 Identify points of energy and influence, as well as ways in which momentum and power flow within the system
Relationships between entities are equally if not more important than the entities themselves
7 Focus on the nature of relationships and interdependencies within the system
Cause and effect is not a linear, predicable, or one-directional process; it is much more iterative
8 Explain the non-linear and multi-directional relationships between the initiative and its intended and unintended outcomes
Patterns emerge from several semi-independent and diverse agents who are free to act in autonomous ways
9 Watch for patterns, both one-off and repeating, at different levels of the system
Ashby's law of requisite variety states that a controller must have at least as much variety (complexity) as the controlled. Maturana and Varela proposed autopoiesis (self-production) to define living systems. Living systems also require to fulfill the law of requisite variety. A measure of autopoiesis has been proposed as the ratio between the complexity of a system and the complexity of its environment. Self-organization can be used as a concept to guide the design of systems towards higher values of autopoiesis, with the potential of making technology more "living", i.e. adaptive and robust.
Requisite Variety, Autopoiesis, and Self-organization Carlos Gershenson
This animation distils hundreds of years of culture into just five minutes. A team of historians and scientists wanted to map cultural mobility, so they tracked the births and deaths of notable individuals like David, King of Israel, and Leonardo da Vinci, from 600 BC to the present day. Using them as a proxy for skills and ideas, their map reveals intellectual hotspots and tracks how empires rise and crumble .The information comes from Freebase, a Google-owned database of well-known people and places, and other catalogues of notable individuals. The visualization was created by Maximilian Schich (University of Texas at Dallas) and Mauro Martino (IBM). https://www.youtube.com/watch?v=4gIhRkCcD4U&index=1&list=PL7yuGPz_odjMW3YfSRkFRjoDdGsTrZDyD Read Nature's news story: http://www.nature.com/news/1.15650See Also: http://sco.lt/8by75F
Via Complexity Digest
At the Collective Impact Summit held Oct 6-10 in Toronto, keynote speaker Brenda Zimmerman discussed 'Preventing Snap Back.' Brenda Zimmerman is the unpreced...
Liz Rykert's insight:
What is Snap Back? you might ask...
Snap back is the thing that happens when you have made a change effort and you find yourself returning to where you started rather than sustaining the change. This talk - given at the Collective Impact Summit in November 2014 is a helpful resource to gain insight on adaptive resilience and to understand how relational patterns of interaction are fractal in nature, meaning they can repeat at different levels. If you are working on large systems change this resource will be particularly useful. 15 mins.
Check out this great Case Study on how one of Great Britain's Intelligence Agencies worked to introduce a culture of innovation. For me this is not just the notion of spurring a culture of failing fast and being creative but one that also encourages people to explore and then encourage each other rather than leaping to the conclusion "that won't work around here".
1. Tame the Gator
"Part of this involved thinking about how people communicated and collaborated together. Worried by how often ideas got shot down at an early stage, one of the developers, Lambert, handed everyone on the team a little toy alligator. ‘We all have a “gater brain”’ he explained, ‘the flight or fight response that is triggered whenever we come across something new and which makes us dismiss or attack ideas. We needed to “tame the gater”. Now, if someone starts knocking an idea down rather than building on it – we chuck a toy ‘gater across the room. It’s fun, but it’s a serious reminder about how we communicate.’
2. Paradox of Intelligence and Secrecy - Go to External Sources!
One of the most unexpected was to launch a public call for bids. Initially run jointly with MI5, it invited companies and start-ups to bid on several broad problems – including how to verify people’s online identity; how to use open-source data to predict events and how to work securely in an insecure environment. As an experiment it has proved remarkably successful, bringing in a raft of new ideas and new relationships."
The challenges of transforming distressed communities are heightened by the complexity of the problems community change actors address and the complexity of the environments in which they work. Confr
Liz Rykert's insight:
This new publication looks very helpful for bringing the insights of complexity to community groups and organizations. Written by Pat Auspos and Mark Cabaj for the Aspen Institute Roundtable for Community Change. Free download.
The Matthew effect describes the phenomenon that in societies the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimisation, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.
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