A Conversation with Otto Scharmer – Academy for Systems Change


Source: A Conversation with Otto Scharmer – Academy for Systems Change


A Conversation with Otto Scharmer

A Conversation with Otto Scharmer

In this 60 minute recorded webinar, the Academy hosts Otto Scharmer in discussion about his work on Theory U, focusing on core principles and applications. Academy Founders, Peter Senge and Darcy Winslow, along with Academy Fellow and Strategic Design Manager, Katie Stubley, talk about turning theory into practice and take questions from participants as to how we can implement this work in our daily lives.

Otto Scharmer is a Senior Lecturer in the MIT Sloan School of Management and founder of the Presencing Institute. He chairs the MIT IDEAS program for cross-sector innovation, which helps leaders in business, government, and civil society to innovate at the level of the whole system. Scharmer introduced the concept of “presencing” – learning from the emerging future – in his bestselling books Theory U and Presence (the latter co-authored with Peter Senge, Joseph Jaworski, and Betty Sue Flowers). His new book, The Essentials of Theory U (2018), is a powerful pocket guide for practitioners that distills all of the research and materials found in his seminal texts Theory U and Leading from the Emerging Future. This book enables leaders and organizations in all industries and sectors to shift awareness, connect with the highest future possibilities, and strengthen the capacity to co-shape the future.

A System Leader’s Fieldbook from the Academy for Systems Change


Source: A System Leader’s Fieldbook


A System Leader’s Fieldbook

Gaining traction on today’s ever-more complex challenges requires collective leadership. That means practicing new ways of operating at the levels of Self, Team, Organization, and System. This online Fieldbook provides tools and resources for system leaders to use in supporting people and groups as they develop the skills to accelerate progress on intractable problems together.

To make real and lasting change, we need to:

Recognize that we are part of the systems we seek to change: Self
Interact productively with—and learn from—others: Team
Collaborate across internal stakeholder groups: Organization
Work across boundaries to co-create the future: System

Questions for Getting Started

Hover over the different segments of the circle, to the left, to identify the modules that will help you build your capacity to become a system leader.

Developing Systems-wise People

“Change must start from within—with deep self-awareness. We start by identifying and discussing what each individual already has—their roots—and we work with them as they learn from a mentor and practice by doing.”

– Udom Hongchatikul, Consultant

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Created by the Academy for Systems Change

Large teams develop and small teams disrupt science and technology

Complexity Digest

One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence1,2,3. Increases in team size have been attributed to the specialization of scientific activities3, improvements in communication technology4,5, or the complexity of modern problems that require interdisciplinary solutions6,7,8. This shift in team size raises the question of whether and how the character of the science and technology produced by large teams differs from that of small teams. Here we analyse more than 65 million papers, patents and software products that span the period 1954–2014, and demonstrate that across this period smaller teams have tended to disrupt science and technology with new ideas and opportunities, whereas larger teams have tended to develop existing ones. Work from larger teams builds on more-recent and popular developments, and attention to their work comes immediately. By contrast…

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Systems Literacy | A global action to create a systems literate world

Another initiative from the ISSS


Source: Systems Literacy | A global action to create a systems literate world



“Grand Vision” for Systems Sciences on Vimeo – Peter Tuddenham, President of ISSS

link: https://vimeo.com/317104695


Complex Networks: Theory, Methods, and Applications – Lake Como School of Advanced Studies – May 13-17, 2019

Complexity Digest

Complex networks: theory, methods, and applications (5th edition)
Villa del Grumello, Como, Italy, May 13-17, 2019

Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline —including sociology, transportation, economics and finance, biology, and myriad others — and the study of “network science” has thus become a crucial component of modern scientific education.

The school “Complex Networks: Theory, Methods, and Applications” offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind…

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Detecting sequences of system states in temporal networks

…continuing my discussion of ‘complexity’ methods – this looks like a truly fascinating attempt to infer something essential or internal to a system from description of observable characteristics, using hard maths.
There’s something about a focus on the material here, and perhaps an equivalent to informed brute-force decryption attacks?

Complexity Digest

Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system’s states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein…

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