Fellowship: Visual Representation of key Features of Complex Systems
Focus: The aim of this fellowship will be to develop visual representation of some of the key features of complexity that communicate these easily to policy makers, practitioners and evaluators who do not have a back ground in complexity science. Appreciation of the need for new approaches and methods for complexity sensitive evaluation approaches and methods depends, to some extent, on how the characteristics of complex systems, and the challenges these pose for policy making and policy evaluation, are understood. An effective visual depiction of these will potentially have wide application – in presentations, EPPN, Magenta book Annex, course material etc.
Activities: Production of a set of visual images representing key features of complex systems. Examples of the ideas we would like to see illustrated include:
- Tipping points or thresholds: beyond which system outcomes may change rapidly.
- Domains of relative stability: systems may have more than one relatively stable state, (called attractors in complexity science), and these may change as the context evolves.
- Levers and hubs: there may be elements or components of a system that have a disproportionate influence because of the structure of their connections.
- Time-dependent evolution: how the system unfolds in the future depends on how it got to its present state, not just where it is now. History matters..
- Feedback loops: when a result or output of a process influences the input either directly or indirectly, these can accelerate or suppress change.
- Emergence and self-organisation: new and unexpected regularities or higher level properties can arise from the interaction of components.
- Adaptation: “Components” or actors within the system are capable of learning or evolving, changing how the system behaves in response to interventions as they are applied.
Expertise: While it is not essential the applicant for this fellowship has an understanding of complex systems it would be advantageous. The applicant should have good skills in visual representation, and a willingness to work closely alongside colleagues with an understanding of complexity science, and of the target audiences with whom CECAN wishes to communicate.
Mentors: Alex Penn, Pete Barbrook-Johnson, Martha Bicket
Deadline for applications - Friday 21st July 2017