Focus 2014

The 2014 Dynamical Systems Innovation Lab is structured to explore both research and practice in four thematic areas and then to link this work in practice with local partners in Hawaii.  The four areas are often associated with the phases of a Third Party Systems Change Model.

Click DST Innovation Lab Design for a PDF of the design document containing the following and provided to participants on January 28, 2014.

Four Thematic Areas

The agenda for the DST Innovation Lab 2014 will dive more deeply into four thematic  areas integral to systemic change.  Work groups will focus on the leading-edge science and practice of four phases/modes of third-party systems change:

  1. Complexity mapping and visualization
  2. Resonance identification and utilization
  3. Institutionalization of DST attitudes, behaviors and structures
  4. Learning and non-linear impact assessment

Listing of members of each of the four workgroups can be found here.  Groups will survey literature, training and practice in each of these areas, outline key issues, and suggest possibilities for innovation.

A goal of the Lab is to author new content as products of the Lab – a paper, edited book, presentations, video tutorials,, etc – allowing the group to share innovations with a broader audience.

1.  Complexity Mapping and Visualization

The analysis of a problem begins with the ability to see it, understand it, and be able to communicate that understanding to others.  Often, this begins by being able to formulate diverse data in a coherent way.  This theme will show how different methods can be used to generate visual representations of complex situations.  These methods are story telling in a visual way and provide a richer way (qualitatively different, more complete and helpful) to grapple effectively with complexity and complex systems that can be more comprehensive, clarify interconnections and patterns and show the dynamism of the system possibly suggesting helpful interventions.

2.  Resonance Identification and Utilization

shutterstock water ripples_18248563This process typically involves identifying, fostering and marshaling motivation and energy in networks of people in service of change. It may spring from a variety of sources including from an increased awareness of basic human needs or injustice, from the emergence of crises and opportunities, from top-down, middle-out or bottom-up leadership and mobilization, or from external actors or events. Resonance also can be mercurial; ebbing and flowing and taking different forms at different stages of systemic change. Ultimately, resonance is the energy necessary to drive systemic change.

3.  Institutionalization of DST Attitudes, Behaviors and Structures

Danny's systems mapIn order to make sustainable change in complex social systems, it is necessary for people to work together as teams, organizations, and networks of organizations. However, many of the traditional ways organizations are structured and run are founded on more linear approaches that make it very difficult for these organizations to support non-linear, complex, and systemic efforts. This creates a dual challenge to a systems practitioner – both how to grapple with the complexity “out there” (in the social contexts in which they work) and to grapple with the complexity “in here” (in the complex organizations they work within). This thematic strand looks at good practice in the area of building organizations that can operate in non-linear and systemic ways.  What are the needed attitudinal, structural, and transactional/behavioral qualities of a “systems-enabled” organization and how can we transition more linear organizations into ones that think and act in non-linear/systemic ways?

4.  Learning and Non-linear Impact Assessment

Learning and non-linear impact assessment is a fundamental issue to be addressed in the implementation of innovation that employs dynamical systems theory (DST). Complex social and social-ecological systems change in non-linear and unpredictable ways, and the knowledge about their dynamics and how to best affect outcomes emerges over time. All too often, monitoring and evaluation is based on pre-determined indicators (typically output metrics) that serve only to measure attainment of and/or compliance with project goals but provide little value for learning about the workings of the system in a way that can facilitate understanding of the effects of the work on the system and inform adaptive management to improve outcomes.

This thematic strand will look at good practice and innovation in tools and processes for measuring (evaluating) programmatic impacts in complex systems and for learning-enabled monitoring systems that can both “learn fast” but to still learn systemically.  Illustrative issues in this area include:

  • how to assess latent attractor changes as well as effects of work that are separated in time, space or level from outputs
  • how to use DST approaches to develop indicators that capture the critical connectivity and feedback processes at work in the system
  • how to design monitoring and evaluation processes to gather qualitative and quantitative data at the necessary spatial and temporal scales relevant to the dynamics of the system
  • how to formulate systems maps, models and frameworks that allow for the use of recursive scenario “play” for characterizing non-linear dynamics of interventions
  • how to effectively link diverse philosophical analytical approaches from big-picture inductive (e.g. neural network pattern analysis) to emergent mechanistic (e.g. agent-based models).

Work with Local Partners

During the second half of the Lab we will restructure participants into groups that will work with local partners.  Groups might contain people from each of the thematic work groups or have a thematic group focus on applying a tool/practice to the needs of a local partner, or some combination. The Omidyar Group (TOG), our hosts for the 2014 Lab, are deeply invested in improving quality of life for the people of Hawai’i (Hawaiians refers to Native Hawaiians) and are also strong believers in the power of a systems approach to contribute to sustainable social change. They see the Lab as a great opportunity to bring those two passions together. Similar to the 2013 Lab, we will organize different subgroups, with representatives from each of the thematic areas, to work with 4-5 local initiatives. Stakeholders from each of the local partner organizations will also attend the Lab as participants.  The goals of the ‘local partners’ track are: (a) to provide these initiatives with analysis and advice from a systems thinking/complexity perspective in general, and specifically in regard to the four innovation themes and (b) to deepen the understanding and innovation of Lab participants concerning each of the four themes. More information on each of the local partners will be distributed to Lab participants well before the July Lab. To find out more about how two different TOG-related initiatives have used systems thinking in their work, visit the Ulupono Initiative or see Hawaii Quality of Life, which contains interactive systems maps

Third Party Systems Change Model

Generally speaking, these four areas are often associated with different phases of systems change, including Entry, Analysis, Contextualization and Planning (Complexity Mapping and Visualization), Ripeness and Mobilization (Resonance Identification and Utilization), Institutionalization of Change (Attitudinal, Behavioral, Narrative and Structural Change), and Learning (Tracking and Assessment of Change).

Third Party Systems Change

Documentation and Data

Documentation and information collection efforts are being designed to accomplish three goals: 1) to provide an organized source of descriptive and evaluative data, in the form of participant reflections, working group outcomes, small and large group evaluation, and more, to be studied for better understanding lab impacts, outcomes, goals and identity; 2) to create an open and shared resource containing a growing collection of lab-produced and other content that can be continually revisited, revised, critiqued and improved both during and after the lab, and 3) to generate novel online educational materials for students, researchers and practitioners who seek to understand, use or study DST within their own research and practice contexts

Open Space

Finally, we are proposing that the early morning sessions of the Lab offer blocks of time (7am-9am) to be reserved for parallel session presentations by systems’ practitioners who wish to present a specific case (Myanmar, Colombia, Thailand, Lebanon, etc.) and receive feedback, and/or by scholars who wish to vet new ideas or models (systems thinking assessments, narratives, etc.). These will follow the Open Space approach from last year.

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